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Picard M, Scott-Boyer MP, Bodein A, Leclercq M, Prunier J, Périn O, Droit A. Target repositioning using multi-layer networks and machine learning: The case of prostate cancer. Comput Struct Biotechnol J 2024; 24:464-475. [PMID: 38983753 PMCID: PMC11231507 DOI: 10.1016/j.csbj.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Julien Prunier
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Transformation and Innovation Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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2
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Savage SR, Yi X, Lei JT, Wen B, Zhao H, Liao Y, Jaehnig EJ, Somes LK, Shafer PW, Lee TD, Fu Z, Dou Y, Shi Z, Gao D, Hoyos V, Gao Q, Zhang B. Pan-cancer proteogenomics expands the landscape of therapeutic targets. Cell 2024; 187:4389-4407.e15. [PMID: 38917788 DOI: 10.1016/j.cell.2024.05.039] [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: 02/16/2023] [Revised: 04/03/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024]
Abstract
Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.
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Affiliation(s)
- Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hongwei Zhao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion of the Ministry of China, Fudan University, 180 Fenglin Road, Shanghai 200032, China
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lauren K Somes
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Paul W Shafer
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tobie D Lee
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zile Fu
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion of the Ministry of China, Fudan University, 180 Fenglin Road, Shanghai 200032, China
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daming Gao
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Valentina Hoyos
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion of the Ministry of China, Fudan University, 180 Fenglin Road, Shanghai 200032, China.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
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3
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Digles D, Ingles-Prieto A, Dvorak V, Mocking TAM, Goldmann U, Garofoli A, Homan EJ, Di Silvio A, Azzollini L, Sassone F, Fogazza M, Bärenz F, Pommereau A, Zuschlag Y, Ooms JF, Tranberg-Jensen J, Hansen JS, Stanka J, Sijben HJ, Batoulis H, Bender E, Martini R, IJzerman AP, Sauer DB, Heitman LH, Manolova V, Reinhardt J, Ehrmann A, Leippe P, Ecker GF, Huber KVM, Licher T, Scarabottolo L, Wiedmer T, Superti-Furga G. Advancing drug discovery through assay development: a survey of tool compounds within the human solute carrier superfamily. Front Pharmacol 2024; 15:1401599. [PMID: 39050757 PMCID: PMC11267547 DOI: 10.3389/fphar.2024.1401599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024] Open
Abstract
With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our open-access resources on assays and compounds will support the development of future drug discovery campaigns for SLCs.
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Affiliation(s)
- Daniela Digles
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Alvaro Ingles-Prieto
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Vojtech Dvorak
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Tamara A. M. Mocking
- Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, Netherlands
| | - Ulrich Goldmann
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Andrea Garofoli
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Evert J. Homan
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | | | - Felix Bärenz
- Sanofi, Integrated Drug Discovery, Industriepark Hoechst, Frankfurt am Main, Hessen, Germany
| | - Antje Pommereau
- Sanofi, Integrated Drug Discovery, Industriepark Hoechst, Frankfurt am Main, Hessen, Germany
| | - Yasmin Zuschlag
- Sanofi, Integrated Drug Discovery, Industriepark Hoechst, Frankfurt am Main, Hessen, Germany
| | - Jasper F. Ooms
- Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, Netherlands
| | - Jeppe Tranberg-Jensen
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jesper S. Hansen
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Josefina Stanka
- Lead Identification and Characterization, Bayer Pharmaceuticals, Wuppertal, Germany
| | - Hubert J. Sijben
- Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, Netherlands
| | - Helena Batoulis
- Lead Identification and Characterization, Bayer Pharmaceuticals, Wuppertal, Germany
| | - Eckhard Bender
- Lead Identification and Characterization, Bayer Pharmaceuticals, Wuppertal, Germany
| | - Riccardo Martini
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, Netherlands
| | - David B. Sauer
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Laura H. Heitman
- Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, Netherlands
| | | | | | - Alexander Ehrmann
- Lead Identification and Characterization, Bayer Pharmaceuticals, Wuppertal, Germany
| | - Philipp Leippe
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Kilian V. M. Huber
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Thomas Licher
- Sanofi, Integrated Drug Discovery, Industriepark Hoechst, Frankfurt am Main, Hessen, Germany
| | | | - Tabea Wiedmer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
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4
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Desai TA, Hedman ÅK, Dimitriou M, Koprulu M, Figiel S, Yin W, Johansson M, Watts EL, Atkins JR, Sokolov AV, Schiöth HB, Gunter MJ, Tsilidis KK, Martin RM, Pietzner M, Langenberg C, Mills IG, Lamb AD, Mälarstig A, Key TJ, Travis RC, Smith-Byrne K. Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics. EBioMedicine 2024; 105:105168. [PMID: 38878676 PMCID: PMC11233900 DOI: 10.1016/j.ebiom.2024.105168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING This work was supported by Cancer Research UK (grant no. C8221/A29017).
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Affiliation(s)
- Trishna A Desai
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.
| | - Åsa K Hedman
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Marios Dimitriou
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
| | - Sandy Figiel
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Wencheng Yin
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Eleanor L Watts
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Joshua R Atkins
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden
| | - Marc J Gunter
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ian G Mills
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Alastair D Lamb
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Anders Mälarstig
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim J Key
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
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5
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Metzger VT, Cannon DC, Yang JJ, Mathias SL, Bologa CG, Waller A, Schürer SC, Vidović D, Kelleher KJ, Sheils TK, Jensen LJ, Lambert CG, Oprea TI, Edwards JS. TIN-X version 3: update with expanded dataset and modernized architecture for enhanced illumination of understudied targets. PeerJ 2024; 12:e17470. [PMID: 38948230 PMCID: PMC11212617 DOI: 10.7717/peerj.17470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 05/06/2024] [Indexed: 07/02/2024] Open
Abstract
TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.
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Affiliation(s)
- Vincent T. Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
| | | | - Jeremy J. Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
| | - Stephen L. Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
| | - Cristian G. Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
| | - Anna Waller
- Center for Molecular Discovery, University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Dušica Vidović
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Keith J. Kelleher
- National Center for Advancing Translational Science, Rockville, Maryland, United States
| | - Timothy K. Sheils
- National Center for Advancing Translational Science, Rockville, Maryland, United States
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christophe G. Lambert
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
| | - Jeremy S. Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico, United States
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6
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Munson BP, Chen M, Bogosian A, Kreisberg JF, Licon K, Abagyan R, Kuenzi BM, Ideker T. De novo generation of multi-target compounds using deep generative chemistry. Nat Commun 2024; 15:3636. [PMID: 38710699 DOI: 10.1038/s41467-024-47120-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 03/18/2024] [Indexed: 05/08/2024] Open
Abstract
Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.
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Affiliation(s)
- Brenton P Munson
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michael Chen
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Audrey Bogosian
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jason F Kreisberg
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Katherine Licon
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Brent M Kuenzi
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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Nievergelt CM, Maihofer AX, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Daskalakis NP, Duncan LE, Polimanti R, Aaronson C, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegoviç E, Babić D, Bacanu SA, Baker DG, Batzler A, Beckham JC, Belangero S, Benjet C, Bergner C, Bierer LM, Biernacka JM, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Brandolino A, Breen G, Bressan RA, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum AD, Børte S, Cahn L, Calabrese JR, Caldas-de-Almeida JM, Chatzinakos C, Cheema S, Clouston SAP, Colodro-Conde L, Coombes BJ, Cruz-Fuentes CS, Dale AM, Dalvie S, Davis LK, Deckert J, Delahanty DL, Dennis MF, Desarnaud F, DiPietro CP, Disner SG, Docherty AR, Domschke K, Dyb G, Kulenović AD, Edenberg HJ, Evans A, Fabbri C, Fani N, Farrer LA, Feder A, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Gelernter J, Geuze E, Gillespie CF, Goleva SB, Gordon SD, Goçi A, Grasser LR, Guindalini C, Haas M, Hagenaars S, Hauser MA, Heath AC, Hemmings SMJ, Hesselbrock V, Hickie IB, Hogan K, Hougaard DM, Huang H, Huckins LM, Hveem K, Jakovljević M, Javanbakht A, Jenkins GD, Johnson J, Jones I, Jovanovic T, Karstoft KI, Kaufman ML, Kennedy JL, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kotov R, Kranzler HR, Krebs K, Kremen WS, Kuan PF, Lawford BR, Lebois LAM, Lehto K, Levey DF, Lewis C, Liberzon I, Linnstaedt SD, Logue MW, Lori A, Lu Y, Luft BJ, Lupton MK, Luykx JJ, Makotkine I, Maples-Keller JL, Marchese S, Marmar C, Martin NG, Martínez-Levy GA, McAloney K, McFarlane A, McLaughlin KA, McLean SA, Medland SE, Mehta D, Meyers J, Michopoulos V, Mikita EA, Milani L, Milberg W, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Mufford MS, Nelson EC, Nordentoft M, Norman SB, Nugent NR, O'Donnell M, Orcutt HK, Pan PM, Panizzon MS, Pathak GA, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Porjesz B, Powers A, Qin XJ, Ratanatharathorn A, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero KJ, Rung A, Runz H, Rutten BPF, de Viteri SS, Salum GA, Sampson L, Sanchez SE, Santoro M, Seah C, Seedat S, Seng JS, Shabalin A, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stensland S, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Tiwari AK, Trapido E, Uddin M, Ursano RJ, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt FR, Werge T, Williams MA, Williamson DE, Winsvold BS, Winternitz S, Wolf C, Wolf EJ, Xia Y, Xiong Y, Yehuda R, Young KA, Young RM, Zai CC, Zai GC, Zervas M, Zhao H, Zoellner LA, Zwart JA, deRoon-Cassini T, van Rooij SJH, van den Heuvel LL, Stein MB, Ressler KJ, Koenen KC. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nat Genet 2024; 56:792-808. [PMID: 38637617 PMCID: PMC11396662 DOI: 10.1038/s41588-024-01707-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 03/05/2024] [Indexed: 04/20/2024]
Abstract
Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.
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Affiliation(s)
- Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA.
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA.
| | - Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Chia-Yen Chen
- Biogen Inc.,Translational Sciences, Cambridge, MA, USA
| | - Karmel W Choi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan R I Coleman
- King's College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Nikolaos P Daskalakis
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Laramie E Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Renato Polimanti
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Cindy Aaronson
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ananda B Amstadter
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Soren B Andersen
- The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark
| | - Ole A Andreassen
- Oslo University Hospital, Division of Mental Health and Addiction, Oslo, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Paul A Arbisi
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | | | - S Bryn Austin
- Boston Children's Hospital, Division of Adolescent and Young Adult Medicine, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Esmina Avdibegoviç
- Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina
| | - Dragan Babić
- Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina
| | - Silviu-Alin Bacanu
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Dewleen G Baker
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jean C Beckham
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
| | - Sintia Belangero
- Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil
- Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, Brazil
| | - Corina Benjet
- Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Center for Global Mental Health, Mexico City, Mexico
| | - Carisa Bergner
- Medical College of Wisconsin, Comprehensive Injury Center, Milwaukee, WI, USA
| | - Linda M Bierer
- Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jonathan I Bisson
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elizabeth A Bolger
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Amber Brandolino
- Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gerome Breen
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- King's College London, NIHR Maudsley BRC, London, UK
| | - Rodrigo Affonseca Bressan
- Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, Brazil
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Richard A Bryant
- University of New South Wales, School of Psychology, Sydney, New South Wales, Australia
| | - Angela C Bustamante
- Department of Internal Medicine, University of Michigan Medical School, Division of Pulmonary and Critical Care Medicine, Ann Arbor, MI, USA
| | - Jonas Bybjerg-Grauholm
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Marie Bækvad-Hansen
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
| | - Sigrid Børte
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
| | - Leah Cahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Joseph R Calabrese
- Case Western Reserve University, School of Medicine, Cleveland, OH, USA
- Department of Psychiatry, University Hospitals, Cleveland, OH, USA
| | | | - Chris Chatzinakos
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Division of Depression and Anxiety Disorders, Belmont, MA, USA
| | - Sheraz Cheema
- University of Toronto, CanPath National Coordinating Center, Toronto, Ontario, Canada
| | - Sean A P Clouston
- Stony Brook University, Family, Population, and Preventive Medicine, Stony Brook, NY, USA
- Stony Brook University, Public Health, Stony Brook, NY, USA
| | - Lucía Colodro-Conde
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Carlos S Cruz-Fuentes
- Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Anders M Dale
- Department of Radiology, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Shareefa Dalvie
- Department of Pathology, University of Cape Town, Division of Human Genetics, Cape Town, South Africa
| | - Lea K Davis
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Jürgen Deckert
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | | | - Michelle F Dennis
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
| | - Frank Desarnaud
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Christopher P DiPietro
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- McLean Hospital, Division of Depression and Anxiety Disorders, Belmont, MA, USA
| | - Seth G Disner
- Minneapolis VA Health Care System, Research Service Line, Minneapolis, MN, USA
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Anna R Docherty
- Huntsman Mental Health Institute, Salt Lake City, UT, USA
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Katharina Domschke
- University of Freiburg, Faculty of Medicine, Centre for Basics in Neuromodulation, Freiburg, Denmark
- Department of Psychiatry and Psychotherapy, University of Freiburg, Faculty of Medicine, Freiburg, Denmark
| | - Grete Dyb
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Alma Džubur Kulenović
- Department of Psychiatry, University Clinical Center of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Howard J Edenberg
- Indiana University School of Medicine, Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Medical and Molecular Genetics, Indianapolis, IN, USA
| | - Alexandra Evans
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Chiara Fabbri
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Norah C Feeny
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Janine D Flory
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David Forbes
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sandro Galea
- Boston University School of Public Health, Boston, MA, USA
| | - Melanie E Garrett
- Duke University, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joel Gelernter
- VA Connecticut Healthcare Center, Psychiatry Service, West Haven, CT, USA
- Department of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Elbert Geuze
- Netherlands Ministry of Defence, Brain Research and Innovation Centre, Utrecht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Charles F Gillespie
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Slavina B Goleva
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
- National Institutes of Health, National Human Genome Research Institute, Bethesda, MD, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Aferdita Goçi
- Department of Psychiatry, University Clinical Centre of Kosovo, Prishtina, Kosovo
| | - Lana Ruvolo Grasser
- Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciencess, Detroit, MI, USA
| | - Camila Guindalini
- Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, Queensland, Australia
| | - Magali Haas
- Cohen Veterans Bioscience, New York City, NY, USA
| | - Saskia Hagenaars
- King's College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Michael A Hauser
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Andrew C Heath
- Department of Genetics, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sian M J Hemmings
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
| | - Victor Hesselbrock
- University of Connecticut School of Medicine, Psychiatry, Farmington, CT, USA
| | - Ian B Hickie
- University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia
| | - Kelleigh Hogan
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - David Michael Hougaard
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Hailiang Huang
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Kristian Hveem
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
| | - Miro Jakovljević
- Department of Psychiatry, University Hospital Center of Zagreb, Zagreb, Croatia
| | - Arash Javanbakht
- Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciencess, Detroit, MI, USA
| | - Gregory D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jessica Johnson
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ian Jones
- Cardiff University, National Centre for Mental Health, Cardiff University Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Karen-Inge Karstoft
- The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Milissa L Kaufman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - James L Kennedy
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alaptagin Khan
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Nathan A Kimbrel
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
- Durham VA Health Care System, Mental Health Service Line, Durham, NC, USA
| | - Anthony P King
- The Ohio State University, College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA
| | - Nastassja Koen
- University of Cape Town, Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kristi Krebs
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Bruce R Lawford
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Kelli Lehto
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | - Daniel F Levey
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Catrin Lewis
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Israel Liberzon
- Department of Psychiatry and Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA
| | - Sarah D Linnstaedt
- Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
| | - Mark W Logue
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
| | - Adriana Lori
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Benjamin J Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Michelle K Lupton
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Jurjen J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Iouri Makotkine
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | | | - Shelby Marchese
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Marmar
- New York University, Grossman School of Medicine, New York City, NY, USA
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Genetics, Brisbane, Queensland, Australia
| | - Gabriela A Martínez-Levy
- Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Kerrie McAloney
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Alexander McFarlane
- University of Adelaide, Discipline of Psychiatry, Adelaide, South Australia, Australia
| | | | - Samuel A McLean
- Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
- Department of Emergency Medicine, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Divya Mehta
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, Queensland, Australia
| | - Jacquelyn Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vasiliki Michopoulos
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Elizabeth A Mikita
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Lili Milani
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | | | - Mark W Miller
- Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
| | - Rajendra A Morey
- Duke University School of Medicine, Duke Brain Imaging and Analysis Center, Durham, NC, USA
| | - Charles Phillip Morris
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University Hospital-Psychiatry, Psychosis Research Unit, Aarhus, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Aarhus University, Centre for Integrated Register-Based Research, Aarhus, Denmark
- Aarhus University, National Centre for Register-Based Research, Aarhus, Denmark
| | - Mary S Mufford
- Department of Pathology, University of Cape Town, Division of Human Genetics, Cape Town, South Africa
| | - Elliot C Nelson
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- University of Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Sonya B Norman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- National Center for Post Traumatic Stress Disorder, Executive Division, White River Junction, VT, USA
| | - Nicole R Nugent
- Department of Emergency Medicine, Alpert Brown Medical School, Providence, RI, USA
- Department of Pediatrics, Alpert Brown Medical School, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Brown Medical School, Providence, RI, USA
| | - Meaghan O'Donnell
- Department of Psychiatry, University of Melbourne, Phoenix Australia, Melbourne, Victoria, Australia
| | - Holly K Orcutt
- Department of Psychology, Northern Illinois University, DeKalb, IL, USA
| | - Pedro M Pan
- Universidade Federal de São Paulo, Psychiatry, São Paulo, Brazil
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gita A Pathak
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Edward S Peters
- University of Nebraska Medical Center, College of Public Health, Omaha, NE, USA
| | - Alan L Peterson
- South Texas Veterans Health Care System, Research and Development Service, San Antonio, TX, USA
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Matthew Peverill
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA
| | - Melissa A Polusny
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
- Center for Care Delivery and Outcomes Research (CCDOR), Minneapolis, MN, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Abigail Powers
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Xue-Jun Qin
- Duke University, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Columbia University Mailmain School of Public Health, New York City, NY, USA
| | - Victoria B Risbrough
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alex O Rothbaum
- Department of Psychological Sciences, Emory University, Atlanta, GA, USA
- Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA
| | - Barbara O Rothbaum
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Peter Roy-Byrne
- Department of Psychiatry, University of Washington, Seattle, WA, USA
| | - Kenneth J Ruggiero
- Department of Nursing, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
| | - Ariane Rung
- Department of Epidemiology, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA, USA
| | - Heiko Runz
- Biogen Inc., Research & Development, Cambridge, MA, USA
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, Maastricht Universitair Medisch Centrum, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | | | - Giovanni Abrahão Salum
- Child Mind Institute, New York City, NY, USA
- Instituto Nacional de Psiquiatria de Desenvolvimento, São Paulo, Brazil
| | - Laura Sampson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Sixto E Sanchez
- Department of Medicine, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Marcos Santoro
- Universidade Federal de São Paulo, Departamento de Bioquímica-Disciplina de Biologia Molecular, São Paulo, Brazil
| | - Carina Seah
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Stellenbosch University, SAMRC Extramural Genomics of Brain Disorders Research Unit, Cape Town, South Africa
| | - Julia S Seng
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
- Department of Women's and Gender Studies, University of Michigan, Ann Arbor, MI, USA
- University of Michigan, Institute for Research on Women and Gender, Ann Arbor, MI, USA
- University of Michigan, School of Nursing, Ann Arbor, MI, USA
| | - Andrey Shabalin
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Christina M Sheerin
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Derrick Silove
- Department of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Alicia K Smith
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Department of Gynecology and Obstetrics, Department of Psychiatry and Behavioral Sciences, Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Dan J Stein
- University of Cape Town, Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Synne Stensland
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Jennifer A Sumner
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Martin H Teicher
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Developmental Biopsychiatry Research Program, Belmont, MA, USA
| | - Wesley K Thompson
- Mental Health Centre Sct. Hans, Institute of Biological Psychiatry, Roskilde, Denmark
- University of California San Diego, Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla, CA, USA
| | - Arun K Tiwari
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Edward Trapido
- Department of Epidemiology, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA, USA
| | - Monica Uddin
- University of South Florida College of Public Health, Genomics Program, Tampa, FL, USA
| | - Robert J Ursano
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA
| | - Unnur Valdimarsdóttir
- Karolinska Institutet, Unit of Integrative Epidemiology, Institute of Environmental Medicine, Stockholm, Sweden
- University of Iceland, Faculty of Medicine, Center of Public Health Sciences, School of Health Sciences, Reykjavik, Iceland
| | - Miranda Van Hooff
- University of Adelaide, Adelaide Medical School, Adelaide, South Australia, Australia
| | - Eric Vermetten
- ARQ Nationaal Psychotrauma Centrum, Psychotrauma Research Expert Group, Diemen, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Department of Psychiatry, New York University School of Medicine, New York City, NY, USA
| | - Christiaan H Vinkers
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Joanne Voisey
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, Queensland, Australia
| | - Yunpeng Wang
- Department of Psychology, University of Oslo, Lifespan Changes in Brain and Cognition (LCBC), Oslo, Norway
| | - Zhewu Wang
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
- Department of Mental Health, Ralph H Johnson VA Medical Center, Charleston, SC, USA
| | - Monika Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Heike Weber
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | - Frank R Wendt
- Department of Anthropology, University of Toronto, Dalla Lana School of Public Health, Toronto, Ontario, Canada
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Copenhagen University Hospital, Institute of Biological Psychiatry, Mental Health Services, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- University of Copenhagen, The Globe Institute, Lundbeck Foundation Center for Geogenetics, Copenhagen, Denmark
| | - Michelle A Williams
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Douglas E Williamson
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
| | - Bendik S Winsvold
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Sherry Winternitz
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Christiane Wolf
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | - Erika J Wolf
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yan Xia
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Ying Xiong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rachel Yehuda
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Mental Health, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Keith A Young
- Central Texas Veterans Health Care System, Research Service, Temple, TX, USA
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, Bryan, TX, USA
| | - Ross McD Young
- Queensland University of Technology, School of Clinical Sciences, Kelvin Grove, Queensland, Australia
- University of the Sunshine Coast, The Chancellory, Sippy Downs, Queensland, Australia
| | - Clement C Zai
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathology, University of Toronto, Toronto, Ontario, Canada
| | - Gwyneth C Zai
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, General Adult Psychiatry and Health Systems Division, Toronto, Ontario, Canada
| | - Mark Zervas
- Cohen Veterans Bioscience, New York City, NY, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Lori A Zoellner
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - John-Anker Zwart
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
| | - Terri deRoon-Cassini
- Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Leigh L van den Heuvel
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- University of California San Diego, School of Public Health, La Jolla, CA, USA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
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8
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Vidović D, Waller A, Holmes J, Sklar LA, Schürer SC. Best practices for managing and disseminating resources and outreach and evaluating the impact of the IDG Consortium. Drug Discov Today 2024; 29:103953. [PMID: 38508231 PMCID: PMC11335350 DOI: 10.1016/j.drudis.2024.103953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/08/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
The Illuminating the Druggable Genome (IDG) consortium generated reagents, biological model systems, data, informatic databases, and computational tools. The Resource Dissemination and Outreach Center (RDOC) played a central administrative role, organized internal meetings, fostered collaboration, and coordinated consortium-wide efforts. The RDOC developed and deployed a Resource Management System (RMS) to enable efficient workflows for collecting, accessing, validating, registering, and publishing resource metadata. IDG policies for repositories and standardized representations of resources were established, adopting the FAIR (findable, accessible, interoperable, reusable) principles. The RDOC also developed metrics of IDG impact. Outreach initiatives included digital content, the Protein Illumination Timeline (representing milestones in generating data and reagents), the Target Watch publication series, the e-IDG Symposium series, and leveraging social media platforms.
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Affiliation(s)
- Dušica Vidović
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anna Waller
- Department of Pathology, Health Sciences Center, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Larry A Sklar
- Department of Pathology, Health Sciences Center, University of New Mexico, Albuquerque, NM, USA; Autophagy, Inflammation, & Metabolism (AIM) Center, University of New Mexico, Albuquerque, NM, USA
| | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA; Frost Institute for Data Science & Computing, University of Miami, Miami, FL, USA.
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9
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Offensperger F, Tin G, Duran-Frigola M, Hahn E, Dobner S, Ende CWA, Strohbach JW, Rukavina A, Brennsteiner V, Ogilvie K, Marella N, Kladnik K, Ciuffa R, Majmudar JD, Field SD, Bensimon A, Ferrari L, Ferrada E, Ng A, Zhang Z, Degliesposti G, Boeszoermenyi A, Martens S, Stanton R, Müller AC, Hannich JT, Hepworth D, Superti-Furga G, Kubicek S, Schenone M, Winter GE. Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells. Science 2024; 384:eadk5864. [PMID: 38662832 DOI: 10.1126/science.adk5864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/22/2024] [Indexed: 05/04/2024]
Abstract
Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.
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Affiliation(s)
- Fabian Offensperger
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Gary Tin
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Miquel Duran-Frigola
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
- Ersilia Open Source Initiative, Cambridge CB1 3DE, UK
| | - Elisa Hahn
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Sarah Dobner
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | | | - Andrea Rukavina
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Vincenth Brennsteiner
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Kevin Ogilvie
- Medicine Design, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Nara Marella
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Katharina Kladnik
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Rodolfo Ciuffa
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | | | - Ariel Bensimon
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Luca Ferrari
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna Biocenter 5, 1030 Vienna, Austria
- University of Vienna, Max Perutz Labs, Vienna Biocenter 5, 1030 Vienna, Austria
| | - Evandro Ferrada
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Amanda Ng
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Zhechun Zhang
- Molecular Informatics, Machine Learning and Computational Sciences, Early Clinical Development, Pfizer, Cambridge, MA 02139, USA
| | - Gianluca Degliesposti
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Andras Boeszoermenyi
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Sascha Martens
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna Biocenter 5, 1030 Vienna, Austria
- University of Vienna, Max Perutz Labs, Vienna Biocenter 5, 1030 Vienna, Austria
| | - Robert Stanton
- Molecular Informatics, Machine Learning and Computational Sciences, Early Clinical Development, Pfizer, Cambridge, MA 02139, USA
| | - André C Müller
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - J Thomas Hannich
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | - Giulio Superti-Furga
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
- Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Stefan Kubicek
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | - Georg E Winter
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
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10
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Richardson R, Tejedor Navarro H, Amaral LAN, Stoeger T. Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results. eLife 2024; 12:RP93429. [PMID: 38546716 PMCID: PMC10977968 DOI: 10.7554/elife.93429] [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] [Subscribe] [Scholar Register] [Indexed: 04/01/2024] Open
Abstract
Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.
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Affiliation(s)
- Reese Richardson
- Interdisciplinary Biological Sciences, Northwestern UniversityEvanstonUnited States
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
| | - Heliodoro Tejedor Navarro
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
- Northwestern Institute on Complex Systems, Northwestern UniversityEvanstonUnited States
| | - Luis A Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
- Northwestern Institute on Complex Systems, Northwestern UniversityEvanstonUnited States
- Department of Molecular Biosciences, Northwestern UniversityEvanstonUnited States
- Department of Physics and Astronomy, Northwestern UniversityEvanstonUnited States
| | - Thomas Stoeger
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
- The Potocsnak Longevity Institute, Northwestern UniversityChicagoUnited States
- Simpson Querrey Lung Institute for Translational Science, Northwestern UniversityChicagoUnited States
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11
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Suhre K. Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions. CELL GENOMICS 2024; 4:100506. [PMID: 38412862 PMCID: PMC10943581 DOI: 10.1016/j.xgen.2024.100506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/25/2023] [Accepted: 01/26/2024] [Indexed: 02/29/2024]
Abstract
Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development because they provide genetic evidence to support protein function, suggest relationships between cis- and trans-associated proteins, and link proteins to disease endpoints. Using Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank, we identified 4,248 associations with 2,821 ratios between protein levels (rQTLs). rQTLs were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. Conducting a GWAS on ratios increased the number of discovered genetic signals by 24.7%. The approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large-scale GWAS.
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Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
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12
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Browning JL, Wilson KA, Shandra O, Wei X, Mahmutovic D, Maharathi B, Robel S, VandeVord PJ, Olsen ML. Applying Proteomics and Computational Approaches to Identify Novel Targets in Blast-Associated Post-Traumatic Epilepsy. Int J Mol Sci 2024; 25:2880. [PMID: 38474127 DOI: 10.3390/ijms25052880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Traumatic brain injury (TBI) can lead to post-traumatic epilepsy (PTE). Blast TBI (bTBI) found in Veterans presents with several complications, including cognitive and behavioral disturbances and PTE; however, the underlying mechanisms that drive the long-term sequelae are not well understood. Using an unbiased proteomics approach in a mouse model of repeated bTBI (rbTBI), this study addresses this gap in the knowledge. After rbTBI, mice were monitored using continuous, uninterrupted video-EEG for up to four months. Following this period, we collected cortex and hippocampus tissues from three groups of mice: those with post-traumatic epilepsy (PTE+), those without epilepsy (PTE-), and the control group (sham). Hundreds of differentially expressed proteins were identified in the cortex and hippocampus of PTE+ and PTE- relative to sham. Focusing on protein pathways unique to PTE+, pathways related to mitochondrial function, post-translational modifications, and transport were disrupted. Computational metabolic modeling using dysregulated protein expression predicted mitochondrial proton pump dysregulation, suggesting electron transport chain dysregulation in the epileptic tissue relative to PTE-. Finally, data mining enabled the identification of several novel and previously validated TBI and epilepsy biomarkers in our data set, many of which were found to already be targeted by drugs in various phases of clinical testing. These findings highlight novel proteins and protein pathways that may drive the chronic PTE sequelae following rbTBI.
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Affiliation(s)
- Jack L Browning
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
- Genetics, Bioinformatics and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Kelsey A Wilson
- Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Oleksii Shandra
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA
| | - Xiaoran Wei
- Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Dzenis Mahmutovic
- Department of Cell Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Biswajit Maharathi
- Neurology & Rehabilitation, University of Illinois, Chicago, IL 60612, USA
| | - Stefanie Robel
- Department of Cell Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Pamela J VandeVord
- Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
- Salem Veteran Affairs Medical Center, Salem, VA 24153, USA
| | - Michelle L Olsen
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
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13
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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14
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Taujale R, Gravel N, Zhou Z, Yeung W, Kochut K, Kannan N. Informatic challenges and advances in illuminating the druggable proteome. Drug Discov Today 2024; 29:103894. [PMID: 38266979 DOI: 10.1016/j.drudis.2024.103894] [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: 10/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.
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Affiliation(s)
- Rahil Taujale
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | | | - Wayland Yeung
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Krystof Kochut
- School of Computing, University of Georgia, Athens, GA, USA
| | - Natarajan Kannan
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
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15
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Richardson RAK, Tejedor Navarro H, Amaral LAN, Stoeger T. Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.28.530483. [PMID: 36909550 PMCID: PMC10002660 DOI: 10.1101/2023.02.28.530483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.
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Affiliation(s)
- Reese AK Richardson
- Interdisciplinary Biological Sciences, Northwestern University
- Department of Chemical and Biological Engineering, Northwestern University
| | - Heliodoro Tejedor Navarro
- Department of Chemical and Biological Engineering, Northwestern University
- Northwestern Institute on Complex Systems, Northwestern University
| | - Luis A Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern University
- Northwestern Institute on Complex Systems, Northwestern University
- Department of Physics and Astronomy, Northwestern University
- Department of Molecular Biosciences, Northwestern University
| | - Thomas Stoeger
- Department of Chemical and Biological Engineering, Northwestern University
- The Potocsnak Longevity Institute, Northwestern University
- Simpson Querrey Lung Institute for Translational Science, Northwestern University
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16
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Khodair AI, El-Hallouty SM, Cagle-White B, Abdel Aziz MH, Hanafy MK, Mowafy S, Hamdy NM, Kassab SE. Camptothecin structure simplification elaborated new imidazo[2,1-b]quinazoline derivative as a human topoisomerase I inhibitor with efficacy against bone cancer cells and colon adenocarcinoma. Eur J Med Chem 2024; 265:116049. [PMID: 38185054 DOI: 10.1016/j.ejmech.2023.116049] [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: 10/03/2023] [Revised: 11/17/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024]
Abstract
Camptothecin is a pentacyclic natural alkaloid that inhibits the hTop1 enzyme involved in DNA transcription and cancer cell growth. Camptothecin structure pitfalls prompted us to design new congeners using a structure simplification strategy to reduce the ring extension number from pentacyclic to tetracyclic while maintaining potential stacking of the new compounds with the DNA base pairs at the Top1-mediated cleavage complex and aqueous solubility, as well as minimizing compound-liver toxicity. The principal axis of this study was the verification of hTop1 inhibiting activity as a possible mechanism of action and the elaboration of new simplified inhibitors with improved pharmacodynamic and pharmacokinetic profiling using three structure panels (A-C) of (isoquinolinoimidazoquinazoline), (imidazoquinazoline), and (imidazoisoquinoline), respectively. DNA relaxation assay identified five compounds as hTop1 inhibitors belonging to the imidazoisoquinolines 3a,b, the imidazoquinazolines 12, and the isoquinolinoimidazoquinazolines 7a,b. In an MTT cytotoxicity assay against different cancer cell lines, compound 12 was the most potent against HOS bone cancer cells (IC50 = 1.47 μM). At the same time, the other inhibitors had no detectable activity against any cancer cell type. Compound (12) demonstrated great penetrating power in the HOS cancer cells' 3D-multicellular tumor spheroid model. Bioinformatics research of the hTop1 gene revealed that the TP53 cell proliferative gene is in the network of hTop1. The finding is confirmed empirically using the gene expression assay that proved the increase in p53 expression. The impact of structure simplification on compound 12 profile, characterized by the absence of acute oral liver toxicity when compared to Doxorubicin as a standard inhibitor, the lethal dose measured on Swiss Albino female mice and reported at LD50 = 250 mg/kg, and therapeutic significance in reducing colon adenocarcinoma tumor volume by 75.36 % after five weeks of treatment with compound 12. The molecular docking solutions of the active CPT-based derivative 12 and the inactive congener 14 into the active site of hTop1 and the activity cliffing of such MMP directed us to recommend the addition of HBD and HBA variables to compound 12 imidazoquinazoline core scaffold to enhance the potency via hydrogen bond formation with the major groove amino acids (Asp533, Lys532) as well as maintaining the hydrogen bond with the minor groove amino acid Arg364.
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Affiliation(s)
- Ahmed I Khodair
- Chemistry Department, Faculty of Science, Kafrelsheikh University, 33516, Kafrelsheikh, Egypt.
| | - Salwa M El-Hallouty
- Drug Bioassay-Cell Culture Laboratory, Department of Pharmacognosy, National Research Centre, Dokki, Giza 12622, Egypt
| | - Brittnee Cagle-White
- Department of Pharmaceutical Sciences and Health Outcomes, Fisch College of Pharmacy, The University of Texas at Tyler, Tyler, TX, TX 75799, USA
| | - May H Abdel Aziz
- Department of Pharmaceutical Sciences and Health Outcomes, Fisch College of Pharmacy, The University of Texas at Tyler, Tyler, TX, TX 75799, USA
| | - Mahmoud Kh Hanafy
- Drug Bioassay-Cell Culture Laboratory, Department of Pharmacognosy, National Research Centre, Dokki, Giza 12622, Egypt; Research Centre for Idling Brain Science, Department of Biochemistry, Graduate School of Medicine and Pharmaceutical Science, University of Toyama, 930-0194, Japan
| | - Samar Mowafy
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Cairo, 11431, Egypt
| | - Nadia M Hamdy
- Biochemistry Dept., Faculty of Pharmacy, Ain Shams University, Cairo, 11566, Egypt.
| | - Shaymaa E Kassab
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Damanhour University, Damanhour, El-Buhaira, 22516, Egypt.
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17
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Koutrouli M, Nastou K, Piera Líndez P, Bouwmeester R, Rasmussen S, Martens L, Jensen LJ. FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data. Bioinformatics 2024; 40:btae010. [PMID: 38192003 PMCID: PMC10868155 DOI: 10.1093/bioinformatics/btae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/07/2023] [Accepted: 01/05/2024] [Indexed: 01/10/2024] Open
Abstract
MOTIVATION Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex. RESULTS To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source. AVAILABILITY AND IMPLEMENTATION Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.
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Affiliation(s)
- Mikaela Koutrouli
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Katerina Nastou
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Pau Piera Líndez
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
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18
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Xiong Y, Li S, Bai Y, Chen T, Sun W, Chen L, Yu J, Sun L, Li C, Wang J, Wu B. Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies. Aging (Albany NY) 2024; 16:2194-2231. [PMID: 38289616 PMCID: PMC10911347 DOI: 10.18632/aging.205478] [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: 08/21/2023] [Accepted: 12/13/2023] [Indexed: 02/22/2024]
Abstract
Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.
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Affiliation(s)
- Ying Xiong
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Sidi Li
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China
| | - Yunmeng Bai
- Department of Nephrology, Shenzhen key Laboratory of Kidney Diseases, Shenzhen People’s Hospital, The First Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Ting Chen
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Wenwen Sun
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Lijie Chen
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Jia Yu
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Liwei Sun
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Chijun Li
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
| | - Jiajian Wang
- Department of Pediatrics, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China
- Clinical Laboratory Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen and Longgang District People’s Hospital of Shenzhen, Shenzhen 518172, China
- Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen Key Laboratory of Metabolic Health, Shenzhen 518055, China
| | - Bo Wu
- Department of Dermatology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
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19
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Clifford RE, Maihofer AX, Chatzinakos C, Coleman JRI, Daskalakis NP, Gasperi M, Hogan K, Mikita EA, Stein MB, Tcheandjieu C, Telese F, Zuo Y, Ryan AF, Nievergelt CM. Genetic architecture distinguishes tinnitus from hearing loss. Nat Commun 2024; 15:614. [PMID: 38242899 PMCID: PMC10799010 DOI: 10.1038/s41467-024-44842-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024] Open
Abstract
Tinnitus is a heritable, highly prevalent auditory disorder treated by multiple medical specialties. Previous GWAS indicated high genetic correlations between tinnitus and hearing loss, with little indication of differentiating signals. We present a GWAS meta-analysis, triple previous sample sizes, and expand to non-European ancestries. GWAS in 596,905 Million Veteran Program subjects identified 39 tinnitus loci, and identified genes related to neuronal synapses and cochlear structural support. Applying state-of-the-art analytic tools, we confirm a large number of shared variants, but also a distinct genetic architecture of tinnitus, with higher polygenicity and large proportion of variants not shared with hearing difficulty. Tissue-expression analysis for tinnitus infers broad enrichment across most brain tissues, in contrast to hearing difficulty. Finally, tinnitus is not only correlated with hearing loss, but also with a spectrum of psychiatric disorders, providing potential new avenues for treatment. This study establishes tinnitus as a distinct disorder separate from hearing difficulties.
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Affiliation(s)
- Royce E Clifford
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA.
- University of California San Diego, Division of Otolaryngology - Head and Neck Surgery, La Jolla, CA, USA.
| | - Adam X Maihofer
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Chris Chatzinakos
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Jonathan R I Coleman
- King's College London, NIHR Maudsley BRC, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Nikolaos P Daskalakis
- Harvard Medical School, Department of Psychiatry, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Marianna Gasperi
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Kelleigh Hogan
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Elizabeth A Mikita
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Murray B Stein
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- University of California San Diego, School of Public Health, La Jolla, CA, USA
| | | | - Francesca Telese
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Yanning Zuo
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Allen F Ryan
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
- University of California San Diego, Division of Otolaryngology - Head and Neck Surgery, La Jolla, CA, USA
| | - Caroline M Nievergelt
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA.
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA.
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20
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Lv D, Li D, Cai Y, Guo J, Chu S, Yu J, Liu K, Jiang T, Ding N, Jin X, Li Y, Xu J. CancerProteome: a resource to functionally decipher the proteome landscape in cancer. Nucleic Acids Res 2024; 52:D1155-D1162. [PMID: 37823596 PMCID: PMC10767844 DOI: 10.1093/nar/gkad824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/07/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.
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Affiliation(s)
- Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiyu Guo
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Sen Chu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Kefan Liu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Xiyun Jin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province 150000, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
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21
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Halder A, Drummond E. Strategies for translating proteomics discoveries into drug discovery for dementia. Neural Regen Res 2024; 19:132-139. [PMID: 37488854 PMCID: PMC10479849 DOI: 10.4103/1673-5374.373681] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/25/2023] [Accepted: 04/06/2023] [Indexed: 07/26/2023] Open
Abstract
Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.
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Affiliation(s)
- Aditi Halder
- School of Medical Sciences and Brain & Mind Center, University of Sydney, NSW, Sydney, Australia
- Department of Aged Care, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Eleanor Drummond
- School of Medical Sciences and Brain & Mind Center, University of Sydney, NSW, Sydney, Australia
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22
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Aldaba-Muruato LR, Sánchez-Barbosa S, Rodríguez-Purata VH, Cabrera-Cruz G, Rosales-Domínguez E, Martínez-Valentín D, Alarcón-López YA, Aguirre-Vidal P, Hernández-Serda MA, Cárdenas-Granados LA, Vázquez-Valadez VH, Angeles E, Macías-Pérez JR. In Vivo and In Silico Studies of the Hepatoprotective Activity of Tert-Butylhydroquinone. Int J Mol Sci 2023; 25:475. [PMID: 38203648 PMCID: PMC10779046 DOI: 10.3390/ijms25010475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/14/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Tert-butylhydroquinone (TBHQ) is a synthetic food antioxidant with biological activities, but little is known about its pharmacological benefits in liver disease. Therefore, this work aimed to evaluate TBHQ during acute liver damage induced by CCl4 (24 h) or BDL (48 h) in Wistar rats. It was found that pretreatment with TBHQ prevents 50% of mortality induced by a lethal dose of CCl4 (4 g/kg, i.p.), and 80% of BDL+TBHQ rats survived, while only 50% of the BDL group survived. Serum markers of liver damage and macroscopic and microscopic (H&E staining) observations suggest that TBHQ protects from both hepatocellular necrosis caused by the sublethal dose of CCl4 (1.6 g/kg, i.p.), as well as necrosis/ductal proliferation caused by BDL. Additionally, online databases identified 49 potential protein targets for TBHQ. Finally, a biological target candidate (Keap1) was evaluated in a proof-of-concept in silico molecular docking assay, resulting in an interaction energy of -5.5491 kcal/mol, which was higher than RA839 and lower than monoethyl fumarate (compounds known to bind to Keap1). These findings suggest that TBHQ increases the survival of animals subjected to CCl4 intoxication or BDL, presumably by reducing hepatocellular damage, probably due to the interaction of TBHQ with Keap1.
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Affiliation(s)
- Liseth Rubi Aldaba-Muruato
- Biomedical Science Laboratory, Clinical Chemistry, Faculty of Professional Studies Huasteca Zone, Autonomous University of San Luis Potosi, Ciudad Valles 79060, San Luis Potosi, Mexico; (L.R.A.-M.); (S.S.-B.); (G.C.-C.); (E.R.-D.); (D.M.-V.)
| | - Sandra Sánchez-Barbosa
- Biomedical Science Laboratory, Clinical Chemistry, Faculty of Professional Studies Huasteca Zone, Autonomous University of San Luis Potosi, Ciudad Valles 79060, San Luis Potosi, Mexico; (L.R.A.-M.); (S.S.-B.); (G.C.-C.); (E.R.-D.); (D.M.-V.)
| | - Víctor Hugo Rodríguez-Purata
- Pharmacobiological Sciences, Faculty of Chemical Sciences, Autonomous University of San Luis Potosi, San Luis Potosi 78210, Mexico;
| | - Georgina Cabrera-Cruz
- Biomedical Science Laboratory, Clinical Chemistry, Faculty of Professional Studies Huasteca Zone, Autonomous University of San Luis Potosi, Ciudad Valles 79060, San Luis Potosi, Mexico; (L.R.A.-M.); (S.S.-B.); (G.C.-C.); (E.R.-D.); (D.M.-V.)
| | - Estefany Rosales-Domínguez
- Biomedical Science Laboratory, Clinical Chemistry, Faculty of Professional Studies Huasteca Zone, Autonomous University of San Luis Potosi, Ciudad Valles 79060, San Luis Potosi, Mexico; (L.R.A.-M.); (S.S.-B.); (G.C.-C.); (E.R.-D.); (D.M.-V.)
| | - Daniela Martínez-Valentín
- Biomedical Science Laboratory, Clinical Chemistry, Faculty of Professional Studies Huasteca Zone, Autonomous University of San Luis Potosi, Ciudad Valles 79060, San Luis Potosi, Mexico; (L.R.A.-M.); (S.S.-B.); (G.C.-C.); (E.R.-D.); (D.M.-V.)
| | - Yoshio Aldo Alarcón-López
- Laboratorio de Química Teórica y Medicinal, FESC, Universidad Nacional Autónoma de México, Avenida 1 de Mayo S/N, Santa María las Torre, Cuautitlán Izcalli 54750, Estado de México, Mexico; (Y.A.A.-L.); (P.A.-V.); (M.A.H.-S.); (L.A.C.-G.); (V.H.V.-V.); (E.A.)
| | - Pablo Aguirre-Vidal
- Laboratorio de Química Teórica y Medicinal, FESC, Universidad Nacional Autónoma de México, Avenida 1 de Mayo S/N, Santa María las Torre, Cuautitlán Izcalli 54750, Estado de México, Mexico; (Y.A.A.-L.); (P.A.-V.); (M.A.H.-S.); (L.A.C.-G.); (V.H.V.-V.); (E.A.)
| | - Manuel Alejandro Hernández-Serda
- Laboratorio de Química Teórica y Medicinal, FESC, Universidad Nacional Autónoma de México, Avenida 1 de Mayo S/N, Santa María las Torre, Cuautitlán Izcalli 54750, Estado de México, Mexico; (Y.A.A.-L.); (P.A.-V.); (M.A.H.-S.); (L.A.C.-G.); (V.H.V.-V.); (E.A.)
| | - Luis Alfonso Cárdenas-Granados
- Laboratorio de Química Teórica y Medicinal, FESC, Universidad Nacional Autónoma de México, Avenida 1 de Mayo S/N, Santa María las Torre, Cuautitlán Izcalli 54750, Estado de México, Mexico; (Y.A.A.-L.); (P.A.-V.); (M.A.H.-S.); (L.A.C.-G.); (V.H.V.-V.); (E.A.)
| | - Víctor Hugo Vázquez-Valadez
- Laboratorio de Química Teórica y Medicinal, FESC, Universidad Nacional Autónoma de México, Avenida 1 de Mayo S/N, Santa María las Torre, Cuautitlán Izcalli 54750, Estado de México, Mexico; (Y.A.A.-L.); (P.A.-V.); (M.A.H.-S.); (L.A.C.-G.); (V.H.V.-V.); (E.A.)
| | - Enrique Angeles
- Laboratorio de Química Teórica y Medicinal, FESC, Universidad Nacional Autónoma de México, Avenida 1 de Mayo S/N, Santa María las Torre, Cuautitlán Izcalli 54750, Estado de México, Mexico; (Y.A.A.-L.); (P.A.-V.); (M.A.H.-S.); (L.A.C.-G.); (V.H.V.-V.); (E.A.)
| | - José Roberto Macías-Pérez
- Biomedical Science Laboratory, Clinical Chemistry, Faculty of Professional Studies Huasteca Zone, Autonomous University of San Luis Potosi, Ciudad Valles 79060, San Luis Potosi, Mexico; (L.R.A.-M.); (S.S.-B.); (G.C.-C.); (E.R.-D.); (D.M.-V.)
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Sanjak J, Binder J, Yadaw AS, Zhu Q, Mathé EA. Clustering rare diseases within an ontology-enriched knowledge graph. J Am Med Inform Assoc 2023; 31:154-164. [PMID: 37759342 PMCID: PMC10746319 DOI: 10.1093/jamia/ocad186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/01/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVE Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases. MATERIALS AND METHODS Data on 3242 rare diseases were extracted from the National Center for Advancing Translational Science Genetic and Rare Diseases Information center internal data resources. The rare disease data enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data, and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were trained and clustered. We validated the disease clusters through semantic similarity and feature enrichment analysis. RESULTS Thirty-seven disease clusters were created with a mean size of 87 diseases. We validate the clusters quantitatively via semantic similarity based on the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters are highly related. DISCUSSION We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and drugs are enumerated for follow-up efforts. CONCLUSION We lay out a method for clustering rare diseases using graph node embeddings. We develop an easy-to-maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems.
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Affiliation(s)
- Jaleal Sanjak
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
- Chief Technology Office, Booz Allen Hamilton, Bethesda, MD, United States
| | - Jessica Binder
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
| | - Arjun Singh Yadaw
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
| | - Qian Zhu
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
| | - Ewy A Mathé
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
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24
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Soleymani S, Gravel N, Huang LC, Yeung W, Bozorgi E, Bendzunas NG, Kochut KJ, Kannan N. Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology. PeerJ 2023; 11:e16087. [PMID: 38077442 PMCID: PMC10704995 DOI: 10.7717/peerj.16087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/22/2023] [Indexed: 12/18/2023] Open
Abstract
The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.
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Affiliation(s)
- Saber Soleymani
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Wayland Yeung
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Elika Bozorgi
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Nathaniel G. Bendzunas
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States
| | - Krzysztof J. Kochut
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Natarajan Kannan
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States
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25
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Prator CA, Dorratt BM, O’Donnell KL, Lack J, Pinski AN, Ricklefs S, Martens CA, Messaoudi I, Marzi A. Transcriptional profiling of immune responses in NHPs after low-dose, VSV-based vaccination against Marburg virus. Emerg Microbes Infect 2023; 12:2252513. [PMID: 37616377 PMCID: PMC10498809 DOI: 10.1080/22221751.2023.2252513] [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: 03/23/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023]
Abstract
Infection with Marburg virus (MARV), the causative agent of Marburg virus disease (MVD), results in haemorrhagic disease and high case fatality rates (>40%) in humans. Despite its public health relevance, there are no licensed vaccines or therapeutics to prevent or treat MVD. A vesicular stomatitis virus (VSV)-based vaccine expressing the MARV glycoprotein (VSV-MARV) is currently in clinical development. Previously, a single 10 million PFU dose of VSV-MARV administered 1-5 weeks before lethal MARV challenge conferred uniform protection in nonhuman primates (NHPs), demonstrating fast-acting potential. Additionally, our group recently demonstrated that even a low dose VSV-MARV (1000 PFU) protected NHPs when given 7 days before MARV challenge. In this study, we longitudinally profiled the transcriptional responses of NHPs vaccinated with this low dose of VSV-MARV either 14 or 7 days before lethal MARV challenge. NHPs vaccinated 14 days before challenge presented with transcriptional changes consistent with an antiviral response before challenge. Limited gene expression changes were observed in the group vaccinated 7 days before challenge. After challenge, genes related to lymphocyte-mediated immunity were only observed in the group vaccinated 14 days before challenge, indicating that the length of time between vaccination and challenge influenced gene expression. Our results indicate that a low dose VSV-MARV elicits distinct immune responses that correlate with protection against MVD. A low dose of VSV-MARV should be evaluated in clinical rails as it may be an option to deliver beneficial public health outcomes to more people in the event of future outbreaks.
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Affiliation(s)
- Cecilia A. Prator
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
| | - Brianna M. Dorratt
- Department of Microbiology, Immunology, and Molecular Genetics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Kyle L. O’Donnell
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
| | - Justin Lack
- NIAID Collaborative Bioinformatics Resource (NCBR), National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Amanda N. Pinski
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Stacy Ricklefs
- Research Technology Branch, Division of Intramural Research, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rocky Mountain Laboratories, Hamilton, MT, USA
| | - Craig A. Martens
- Research Technology Branch, Division of Intramural Research, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rocky Mountain Laboratories, Hamilton, MT, USA
| | - Ilhem Messaoudi
- Department of Microbiology, Immunology, and Molecular Genetics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Andrea Marzi
- Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
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26
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Halip L, Avram S, Curpan R, Borota A, Bora A, Bologa C, Oprea TI. Exploring DrugCentral: from molecular structures to clinical effects. J Comput Aided Mol Des 2023; 37:681-694. [PMID: 37707619 PMCID: PMC10692006 DOI: 10.1007/s10822-023-00529-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.
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Affiliation(s)
- Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ana Borota
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
- Expert Systems Inc, San Diego, CA, USA.
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27
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Xie Z, Chen C, Ma’ayan A. Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis. PeerJ 2023; 11:e16351. [PMID: 37953774 PMCID: PMC10638921 DOI: 10.7717/peerj.16351] [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] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/04/2023] [Indexed: 11/14/2023] Open
Abstract
Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.
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Affiliation(s)
- Zhuorui Xie
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clara Chen
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avi Ma’ayan
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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28
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Salcedo MV, Gravel N, Keshavarzi A, Huang LC, Kochut KJ, Kannan N. Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. PeerJ 2023; 11:e15815. [PMID: 37868056 PMCID: PMC10590106 DOI: 10.7717/peerj.15815] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/10/2023] [Indexed: 10/24/2023] Open
Abstract
The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.
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Affiliation(s)
- Mariah V. Salcedo
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
| | - Abbas Keshavarzi
- School of Computing, University of Georgia, Athens, GA, United States of America
| | - Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
| | - Krzysztof J. Kochut
- School of Computing, University of Georgia, Athens, GA, United States of America
| | - Natarajan Kannan
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
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29
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Kjær C, Palasca O, Barzaghi G, Bak LK, Durhuus RKJ, Jakobsen E, Pedersen L, Bartels ED, Woldbye DPD, Pinborg LH, Jensen LJ. Differential Expression of the β3 Subunit of Voltage-Gated Ca 2+ Channel in Mesial Temporal Lobe Epilepsy. Mol Neurobiol 2023; 60:5755-5769. [PMID: 37341859 PMCID: PMC10471638 DOI: 10.1007/s12035-023-03426-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/05/2023] [Indexed: 06/22/2023]
Abstract
The purpose of this study was to identify and validate new putative lead drug targets in drug-resistant mesial temporal lobe epilepsy (mTLE) starting from differentially expressed genes (DEGs) previously identified in mTLE in humans by transcriptome analysis. We identified consensus DEGs among two independent mTLE transcriptome datasets and assigned them status as "lead target" if they (1) were involved in neuronal excitability, (2) were new in mTLE, and (3) were druggable. For this, we created a consensus DEG network in STRING and annotated it with information from the DISEASES database and the Target Central Resource Database (TCRD). Next, we attempted to validate lead targets using qPCR, immunohistochemistry, and Western blot on hippocampal and temporal lobe neocortical tissue from mTLE patients and non-epilepsy controls, respectively. Here we created a robust, unbiased list of 113 consensus DEGs starting from two lists of 3040 and 5523 mTLE significant DEGs, respectively, and identified five lead targets. Next, we showed that CACNB3, a voltage-gated Ca2+ channel subunit, was significantly regulated in mTLE at both mRNA and protein level. Considering the key role of Ca2+ currents in regulating neuronal excitability, this suggested a role for CACNB3 in seizure generation. This is the first time changes in CACNB3 expression have been associated with drug-resistant epilepsy in humans, and since efficient therapeutic strategies for the treatment of drug-resistant mTLE are lacking, our finding might represent a step toward designing such new treatment strategies.
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Affiliation(s)
- Christina Kjær
- Biomedical Laboratory Science, Department of Technology, Faculty of Health and Technology, University College Copenhagen, Sigurdsgade 26, 1St, 2200 Copenhagen, Denmark
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Oana Palasca
- Disease Systems Biology Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Guido Barzaghi
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
- Faculty of Biosciences, Collaboration for Joint PhD Degree Between EMBL and Heidelberg University, Heidelberg, Germany
| | - Lasse K. Bak
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
- Dept. of Clinical Biochemistry, 2600 RigshospitaletCopenhagen, Denmark
| | - Rúna K. J. Durhuus
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
- Specific Pharma A/S, Borgmester Christiansens Gade 40, 2450 Copenhagen, SV Denmark
| | - Emil Jakobsen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
- Takeda Pharma A/S, Delta Park 45, 2665 Vallensbaek Strand, Denmark
| | - Louise Pedersen
- Biomedical Laboratory Science, Department of Technology, Faculty of Health and Technology, University College Copenhagen, Sigurdsgade 26, 1St, 2200 Copenhagen, Denmark
- Dept. of Clinical Biochemistry, 2600 RigshospitaletCopenhagen, Denmark
| | - Emil D. Bartels
- Dept. of Clinical Biochemistry, 2600 RigshospitaletCopenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - David P. D. Woldbye
- Department of Neuroscience, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Lars H. Pinborg
- Epilepsy Clinic & Neurobiology Research Unit, Copenhagen University Hospital, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Lars Juhl Jensen
- Disease Systems Biology Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
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30
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Desai TA, Hedman ÅK, Dimitriou M, Koprulu M, Figiel S, Yin W, Johansson M, Watts EL, Atkins JR, Sokolov AV, Schiöth HB, Gunter MJ, Tsilidis KK, Martin RM, Pietzner M, Langenberg C, Mills IG, Lamb AD, Mälarstig A, Key TJ, Travis RC, Smith-Byrne K. Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.21.23295864. [PMID: 37790472 PMCID: PMC10543057 DOI: 10.1101/2023.09.21.23295864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Background Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.
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Affiliation(s)
- Trishna A Desai
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Åsa K Hedman
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Department of Medicine, Stockholm, Sweden
| | - Marios Dimitriou
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Department of Medicine, Stockholm, Sweden
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
| | - Sandy Figiel
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Wencheng Yin
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Eleanor L Watts
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
| | - Joshua R Atkins
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124 Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124 Uppsala, Sweden
| | - Marc J Gunter
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
- Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
- Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ian G Mills
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Alastair D Lamb
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Anders Mälarstig
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Department of Medicine, Stockholm, Sweden
| | - Tim J Key
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
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Nievergelt CM, Maihofer AX, Atkinson EG, Chen CY, Choi KW, Coleman JR, Daskalakis NP, Duncan LE, Polimanti R, Aaronson C, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegoviç E, Babic D, Bacanu SA, Baker DG, Batzler A, Beckham JC, Belangero S, Benjet C, Bergner C, Bierer LM, Biernacka JM, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Brandolino A, Breen G, Bressan RA, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum AD, Børte S, Cahn L, Calabrese JR, Caldas-de-Almeida JM, Chatzinakos C, Cheema S, Clouston SAP, Colodro-Conde L, Coombes BJ, Cruz-Fuentes CS, Dale AM, Dalvie S, Davis LK, Deckert J, Delahanty DL, Dennis MF, deRoon-Cassini T, Desarnaud F, DiPietro CP, Disner SG, Docherty AR, Domschke K, Dyb G, Kulenovic AD, Edenberg HJ, Evans A, Fabbri C, Fani N, Farrer LA, Feder A, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Gelernter J, Geuze E, Gillespie CF, Goci A, Goleva SB, Gordon SD, Grasser LR, Guindalini C, Haas M, Hagenaars S, Hauser MA, Heath AC, Hemmings SM, Hesselbrock V, Hickie IB, Hogan K, Hougaard DM, Huang H, Huckins LM, Hveem K, Jakovljevic M, Javanbakht A, Jenkins GD, Johnson J, Jones I, Jovanovic T, Karstoft KI, Kaufman ML, Kennedy JL, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kotov R, Kranzler HR, Krebs K, Kremen WS, Kuan PF, Lawford BR, Lebois LAM, Lehto K, Levey DF, Lewis C, Liberzon I, Linnstaedt SD, Logue MW, Lori A, Lu Y, Luft BJ, Lupton MK, Luykx JJ, Makotkine I, Maples-Keller JL, Marchese S, Marmar C, Martin NG, MartÍnez-Levy GA, McAloney K, McFarlane A, McLaughlin KA, McLean SA, Medland SE, Mehta D, Meyers J, Michopoulos V, Mikita EA, Milani L, Milberg W, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Mufford MS, Nelson EC, Nordentoft M, Norman SB, Nugent NR, O'Donnell M, Orcutt HK, Pan PM, Panizzon MS, Pathak GA, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Porjesz B, Powers A, Qin XJ, Ratanatharathorn A, Risbrough VB, Roberts AL, Rothbaum BO, Rothbaum AO, Roy-Byrne P, Ruggiero KJ, Rung A, Runz H, Rutten BPF, de Viteri SS, Salum GA, Sampson L, Sanchez SE, Santoro M, Seah C, Seedat S, Seng JS, Shabalin A, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stensland S, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Tiwari AK, Trapido E, Uddin M, Ursano RJ, Valdimarsdóttir U, van den Heuvel LL, Van Hooff M, van Rooij SJ, Vermetten E, Vinkers CH, Voisey J, Wang Z, Wang Y, Waszczuk M, Weber H, Wendt FR, Werge T, Williams MA, Williamson DE, Winsvold BS, Winternitz S, Wolf EJ, Wolf C, Xia Y, Xiong Y, Yehuda R, Young RM, Young KA, Zai CC, Zai GC, Zervas M, Zhao H, Zoellner LA, Zwart JA, Stein MB, Ressler KJ, Koenen KC. Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.31.23294915. [PMID: 37693460 PMCID: PMC10491375 DOI: 10.1101/2023.08.31.23294915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Posttraumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 novel). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (e.g., GRIA1, GRM8, CACNA1E ), developmental, axon guidance, and transcription factors (e.g., FOXP2, EFNA5, DCC ), synaptic structure and function genes (e.g., PCLO, NCAM1, PDE4B ), and endocrine or immune regulators (e.g., ESR1, TRAF3, TANK ). Additional top genes influence stress, immune, fear, and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.
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Lundgaard AT, Burdet F, Siggaard T, Westergaard D, Vagiaki D, Cantwell L, Röder T, Vistisen D, Sparsø T, Giordano GN, Ibberson M, Banasik K, Brunak S. BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus. PLoS Comput Biol 2023; 19:e1011403. [PMID: 37590326 PMCID: PMC10464978 DOI: 10.1371/journal.pcbi.1011403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 08/29/2023] [Accepted: 07/31/2023] [Indexed: 08/19/2023] Open
Abstract
Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.
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Affiliation(s)
- Agnete T. Lundgaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Frédéric Burdet
- Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Troels Siggaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Danai Vagiaki
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Lisa Cantwell
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Timo Röder
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Dorte Vistisen
- Clinical Epidemiological Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Sparsø
- Bioinformatics and Data Mining, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Giuseppe N. Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mark Ibberson
- Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
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33
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Xie L, Xie L. Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning. PLoS Comput Biol 2023; 19:e1010974. [PMID: 37590332 PMCID: PMC10464998 DOI: 10.1371/journal.pcbi.1010974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/29/2023] [Accepted: 07/27/2023] [Indexed: 08/19/2023] Open
Abstract
Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules that induce the degradation of target proteins by recruiting an E3 ligase. PROTACs have the potential to inactivate disease-related genes that are considered undruggable by small molecules, making them a promising therapy for the treatment of incurable diseases. However, only a few hundred proteins have been experimentally tested for their amenability to PROTACs, and it remains unclear which other proteins in the entire human genome can be targeted by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved a ROC-AUC of 0.81, an average precision of 0.84, and over 40% sensitivity at a false positive rate of 0.05. When evaluated by an external test set which comprised proteins from different structural folds than those in the training set, the performance of PrePROTAC did not drop significantly, indicating its generalizability. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method, which extends conventional SHAP analysis for original features to an embedding space through in silico mutagenesis. This method allowed us to identify key residues in the protein structure that play critical roles in PROTAC activity. The identified key residues were consistent with existing knowledge. Using PrePROTAC, we identified over 600 novel understudied proteins that are potentially degradable by CRBN and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease.
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Affiliation(s)
- Li Xie
- Department of Computer Science, Hunter College, The City University of New York, New York City, New York, United States of America
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York City, New York, United States of America
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York City, New York, United States of America
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York City, New York, United States of America
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34
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Cunningham M, Pins D, Dezső Z, Torrent M, Vasanthakumar A, Pandey A. PINNED: identifying characteristics of druggable human proteins using an interpretable neural network. J Cheminform 2023; 15:64. [PMID: 37468968 PMCID: PMC10354961 DOI: 10.1186/s13321-023-00735-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/10/2023] [Indexed: 07/21/2023] Open
Abstract
The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.
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Affiliation(s)
- Michael Cunningham
- Genomics Research Center, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA.
| | - Danielle Pins
- Information Research, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
| | - Zoltán Dezső
- Genomics Research Center, AbbVie Inc., 1000 Gateway Boulevard, South San Francisco, CA, 94080, USA
| | - Maricel Torrent
- Small Molecule Therapeutics and Platform Technologies, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
| | - Aparna Vasanthakumar
- Genomics Research Center, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
| | - Abhishek Pandey
- Information Research, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL, 60064, USA
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35
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Kornienko J, Rodríguez-Martínez M, Fenzl K, Hinze F, Schraivogel D, Grosch M, Tunaj B, Lindenhofer D, Schraft L, Kueblbeck M, Smith E, Mao C, Brown E, Owens A, Saguner AM, Meder B, Parikh V, Gotthardt M, Steinmetz LM. Mislocalization of pathogenic RBM20 variants in dilated cardiomyopathy is caused by loss-of-interaction with Transportin-3. Nat Commun 2023; 14:4312. [PMID: 37463913 DOI: 10.1038/s41467-023-39965-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
Severe forms of dilated cardiomyopathy (DCM) are associated with point mutations in the alternative splicing regulator RBM20 that are frequently located in the arginine/serine-rich domain (RS-domain). Such mutations can cause defective splicing and cytoplasmic mislocalization, which leads to the formation of detrimental cytoplasmic granules. Successful development of personalized therapies requires identifying the direct mechanisms of pathogenic RBM20 variants. Here, we decipher the molecular mechanism of RBM20 mislocalization and its specific role in DCM pathogenesis. We demonstrate that mislocalized RBM20 RS-domain variants retain their splice regulatory activity, which reveals that aberrant cellular localization is the main driver of their pathological phenotype. A genome-wide CRISPR knockout screen combined with image-enabled cell sorting identified Transportin-3 (TNPO3) as the main nuclear importer of RBM20. We show that the direct RBM20-TNPO3 interaction involves the RS-domain, and is disrupted by pathogenic variants. Relocalization of pathogenic RBM20 variants to the nucleus restores alternative splicing and dissolves cytoplasmic granules in cell culture and animal models. These findings provide proof-of-principle for developing therapeutic strategies to restore RBM20's nuclear localization in RBM20-DCM patients.
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Affiliation(s)
- Julia Kornienko
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
| | | | - Kai Fenzl
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
| | - Florian Hinze
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Schraivogel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Markus Grosch
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brigit Tunaj
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Dominik Lindenhofer
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Laura Schraft
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Moritz Kueblbeck
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Eric Smith
- University of Michigan, Ann Arbor, MI, USA
| | - Chad Mao
- Children's Healthcare of Atlanta & Emory University, Atlanta, GA, USA
| | | | - Anjali Owens
- University of Pennsylvania, Philadelphia, PA, USA
| | - Ardan M Saguner
- Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Benjamin Meder
- Cardiogenetics Center Heidelberg, Department of Cardiology, Angiology and Pulmology, University Hospital Heidelberg, Heidelberg, Germany
| | - Victoria Parikh
- Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Gotthardt
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lars M Steinmetz
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- DZHK (German Centre for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Genome Technology Center, Palo Alto, CA, USA.
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Evangelista JE, Clarke DJB, Xie Z, Marino GB, Utti V, Jenkins SL, Ahooyi TM, Bologa CG, Yang JJ, Binder JL, Kumar P, Lambert CG, Grethe JS, Wenger E, Taylor D, Oprea TI, de Bono B, Ma'ayan A. Toxicology knowledge graph for structural birth defects. COMMUNICATIONS MEDICINE 2023; 3:98. [PMID: 37460679 PMCID: PMC10352311 DOI: 10.1038/s43856-023-00329-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 06/29/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.
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Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Vivian Utti
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sherry L Jenkins
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Taha Mohseni Ahooyi
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Cristian G Bologa
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jeremy J Yang
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jessica L Binder
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Praveen Kumar
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Christophe G Lambert
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jeffrey S Grethe
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Eric Wenger
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Deanne Taylor
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Tudor I Oprea
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Bernard de Bono
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Beavers D, Brunson T, Sanati N, Matthews L, Haw R, Shorser S, Sevilla C, Viteri G, Conley P, Rothfels K, Hermjakob H, Stein L, D’Eustachio P, Wu G. Illuminate the Functions of Dark Proteins Using the Reactome-IDG Web Portal. Curr Protoc 2023; 3:e845. [PMID: 37467006 PMCID: PMC10399304 DOI: 10.1002/cpz1.845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Understudied or dark proteins have the potential to shed light on as-yet undiscovered molecular mechanisms that underlie phenotypes and suggest innovative therapeutic approaches for many diseases. The Reactome-IDG (Illuminating the Druggable Genome) project aims to place dark proteins in the context of manually curated, highly reliable pathways in Reactome, the most comprehensive, open-source biological pathway knowledgebase, facilitating the understanding functions and predicting therapeutic potentials of dark proteins. The Reactome-IDG web portal, deployed at https://idg.reactome.org, provides a simple, interactive web page for users to search pathways that may functionally interact with dark proteins, enabling the prediction of functions of dark proteins in the context of Reactome pathways. Enhanced visualization features implemented at the portal allow users to investigate the functional contexts for dark proteins based on tissue-specific gene or protein expression, drug-target interactions, or protein or gene pairwise relationships in the original Reactome's systems biology graph notation (SBGN) diagrams or the new simplified functional interaction (FI) network view of pathways. The protocols in this chapter describe step-by-step procedures to use the web portal to learn biological functions of dark proteins in the context of Reactome pathways. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Search for interacting pathways of a protein Support Protocol: Interacting pathway results for an annotated protein Alternate Protocol: Use individual pairwise relationships to predict interacting pathways of a protein Basic Protocol 2: Using the IDG pathway browser to study interacting pathways Basic Protocol 3: Overlaying tissue-specific expression data Basic Protocol 4: Overlaying protein/gene pairwise relationships in the pathway context Basic Protocol 5: Visualizing drug/target interactions.
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Affiliation(s)
- Deidre Beavers
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Nasim Sanati
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - 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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38
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Salazar GA, Luciani A, Watkins X, Kandasaamy S, Rice DL, Blum M, Bateman A, Martin M. Nightingale: web components for protein feature visualization. BIOINFORMATICS ADVANCES 2023; 3:vbad064. [PMID: 37359723 PMCID: PMC10287899 DOI: 10.1093/bioadv/vbad064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/20/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023]
Abstract
Motivation The visualization of biological data is a fundamental technique that enables researchers to understand and explain biology. Some of these visualizations have become iconic, for instance: tree views for taxonomy, cartoon rendering of 3D protein structures or tracks to represent features in a gene or protein, for instance in a genome browser. Nightingale provides visualizations in the context of proteins and protein features. Results Nightingale is a library of re-usable data visualization web components that are currently used by UniProt and InterPro, among other projects. The components can be used to display protein sequence features, variants, interaction data, 3D structure, etc. These components are flexible, allowing users to easily view multiple data sources within the same context, as well as compose these components to create a customized view. Availability and implementation Nightingale examples and documentation are freely available at https://ebi-webcomponents.github.io/nightingale/. It is distributed under the MIT license, and its source code can be found at https://github.com/ebi-webcomponents/nightingale.
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Affiliation(s)
- Gustavo A Salazar
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Aurélien Luciani
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Xavier Watkins
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Swaathi Kandasaamy
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Daniel L Rice
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Matthias Blum
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Alex Bateman
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Maria Martin
- Macromolecules, Structure, Chemistry and Bioimaging Section, European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
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Ciancone AM, Seo KW, Chen M, Borne AL, Libby AH, Bai DL, Kleiner RE, Hsu KL. Global Discovery of Covalent Modulators of Ribonucleoprotein Granules. J Am Chem Soc 2023; 145:11056-11066. [PMID: 37159397 PMCID: PMC10392812 DOI: 10.1021/jacs.3c00165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Stress granules (SGs) and processing-bodies (PBs, P-bodies) are ubiquitous and widely studied ribonucleoprotein (RNP) granules involved in cellular stress response, viral infection, and the tumor microenvironment. While proteomic and transcriptomic investigations of SGs and PBs have provided insights into molecular composition, chemical tools to probe and modulate RNP granules remain lacking. Herein, we combine an immunofluorescence (IF)-based phenotypic screen with chemoproteomics to identify sulfonyl-triazoles (SuTEx) capable of preventing or inducing SG and PB formation through liganding of tyrosine (Tyr) and lysine (Lys) sites in stressed cells. Liganded sites were enriched for RNA-binding and protein-protein interaction (PPI) domains, including several sites found in RNP granule-forming proteins. Among these, we functionally validate G3BP1 Y40, located in the NTF2 dimerization domain, as a ligandable site that can disrupt arsenite-induced SG formation in cells. In summary, we present a chemical strategy for the systematic discovery of condensate-modulating covalent small molecules.
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Affiliation(s)
- Anthony M. Ciancone
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | - Kyung W. Seo
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Miaomiao Chen
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | - Adam L. Borne
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, Virginia 22908, United States
| | - Adam H. Libby
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
- University of Virginia Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
| | - Dina L. Bai
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | - Ralph E. Kleiner
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Ku-Lung Hsu
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
- Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, Virginia 22908, United States
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22908, United States
- University of Virginia Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
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40
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Hargreaves D, Carbajo RJ, Bodnarchuk MS, Embrey K, Rawlins PB, Packer M, Degorce SL, Hird AW, Johannes JW, Chiarparin E, Schade M. Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1. Proc Natl Acad Sci U S A 2023; 120:e2221967120. [PMID: 37186857 PMCID: PMC10214187 DOI: 10.1073/pnas.2221967120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
The structure-based design of small-molecule inhibitors targeting protein-protein interactions (PPIs) remains a huge challenge as the drug must bind typically wide and shallow protein sites. A PPI target of high interest for hematological cancer therapy is myeloid cell leukemia 1 (Mcl-1), a prosurvival guardian protein from the Bcl-2 family. Despite being previously considered undruggable, seven small-molecule Mcl-1 inhibitors have recently entered clinical trials. Here, we report the crystal structure of the clinical-stage inhibitor AMG-176 bound to Mcl-1 and analyze its interaction along with clinical inhibitors AZD5991 and S64315. Our X-ray data reveal high plasticity of Mcl-1 and a remarkable ligand-induced pocket deepening. Nuclear Magnetic Resonance (NMR)-based free ligand conformer analysis demonstrates that such unprecedented induced fit is uniquely achieved by designing highly rigid inhibitors, preorganized in their bioactive conformation. By elucidating key chemistry design principles, this work provides a roadmap for targeting the largely untapped PPI class more successfully.
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Affiliation(s)
- David Hargreaves
- Discovery Sciences, AstraZeneca, CambridgeCB4 0WG, United Kingdom
| | | | | | - Kevin Embrey
- Discovery Sciences, AstraZeneca, CambridgeCB4 0WG, United Kingdom
| | | | - Martin Packer
- Chemistry, Oncology R&D, AstraZeneca, CambridgeCB4 0WG, United Kingdom
| | | | | | | | | | - Markus Schade
- Chemistry, Oncology R&D, AstraZeneca, CambridgeCB4 0WG, United Kingdom
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41
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Li S, Chen X, Chen J, Wu B, Liu J, Guo Y, Li M, Pu X. Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes. Comput Biol Med 2023; 161:106988. [PMID: 37201441 DOI: 10.1016/j.compbiomed.2023.106988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 03/30/2023] [Accepted: 04/27/2023] [Indexed: 05/20/2023]
Abstract
G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.
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Affiliation(s)
- Shiqi Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Xin Chen
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Jianfang Chen
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Binjian Wu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Jing Liu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
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Nguyen K, Boehling J, Tran MN, Cheng T, Rivera A, Collins-Burow BM, Lee SB, Drewry DH, Burow ME. NEK Family Review and Correlations with Patient Survival Outcomes in Various Cancer Types. Cancers (Basel) 2023; 15:cancers15072067. [PMID: 37046733 PMCID: PMC10093199 DOI: 10.3390/cancers15072067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/22/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
The Never in Mitosis Gene A (NIMA)–related kinases (NEKs) are a group of serine/threonine kinases that are involved in a wide array of cellular processes including cell cycle regulation, DNA damage repair response (DDR), apoptosis, and microtubule organization. Recent studies have identified the involvement of NEK family members in various diseases such as autoimmune disorders, malignancies, and developmental defects. Despite the existing literature exemplifying the importance of the NEK family of kinases, this family of protein kinases remains understudied. This report seeks to provide a foundation for investigating the role of different NEKs in malignancies. We do this by evaluating the 11 NEK family kinase gene expression associations with patients’ overall survival (OS) from various cancers using the Kaplan–Meier Online Tool (KMPlotter) to correlate the relationship between mRNA expression of NEK1-11 in various cancers and patient survival. Furthermore, we use the Catalog of Somatic Mutations in Cancer (COSMIC) database to identify NEK family mutations in cancers of different tissues. Overall, the data suggest that the NEK family has varying associations with patient survival in different cancers with tumor-suppressive and tumor-promoting effects being tissue-dependent.
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43
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Jacques F, Bolivar P, Pietras K, Hammarlund EU. Roadmap to the study of gene and protein phylogeny and evolution-A practical guide. PLoS One 2023; 18:e0279597. [PMID: 36827278 PMCID: PMC9955684 DOI: 10.1371/journal.pone.0279597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 12/12/2022] [Indexed: 02/25/2023] Open
Abstract
Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.
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Affiliation(s)
- Florian Jacques
- Lund University Cancer Centre, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Paulina Bolivar
- Lund University Cancer Centre, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Kristian Pietras
- Lund University Cancer Centre, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Emma U. Hammarlund
- Lund University Cancer Centre, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
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44
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Xie L, Xie L. Elucidation of Genome-wide Understudied Proteins targeted by PROTAC-induced degradation using Interpretable Machine Learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.23.529828. [PMID: 36865212 PMCID: PMC9980153 DOI: 10.1101/2023.02.23.529828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules. They induce the degradation of a target protein by recruiting an E3 ligase to the target. The PROTAC can inactivate disease-related genes that are considered as understudied, thus has a great potential to be a new type of therapy for the treatment of incurable diseases. However, only hundreds of proteins have been experimentally tested if they are amenable to the PROTACs. It remains elusive what other proteins can be targeted by the PROTAC in the entire human genome. For the first time, we have developed an interpretable machine learning model PrePROTAC, which is based on a transformer-based protein sequence descriptor and random forest classification to predict genome-wide PROTAC-induced targets degradable by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved ROC-AUC of 0.81, PR-AUC of 0.84, and over 40% sensitivity at a false positive rate of 0.05, respectively. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method to identify positions in the protein structure, which play key roles in the PROTAC activity. The key residues identified were consistent with our existing knowledge. We applied PrePROTAC to identify more than 600 novel understudied proteins that are potentially degradable by CRBN, and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease. Author Summary Many human diseases remain incurable because disease-causing genes cannot by selectively and effectively targeted by small molecules. Proteolysis-targeting chimera (PROTAC), an organic compound that binds to both a target and a degradation-mediating E3 ligase, has emerged as a promising approach to selectively target disease-driving genes that are not druggable by small molecules. Nevertheless, not all of proteins can be accommodated by E3 ligases, and be effectively degraded. Knowledge on the degradability of a protein will be crucial for the design of PROTACs. However, only hundreds of proteins have been experimentally tested if they are amenable to the PROTACs. It remains elusive what other proteins can be targeted by the PROTAC in the entire human genome. In this paper, we propose an intepretable machine learning model PrePROTAC that takes advantage of powerful protein language modeling. PrePROTAC achieves high accuracy when evaluated by an external dataset which comes from different gene families from the proteins in the training data, suggesting the generalizability of PrePROTAC. We apply PrePROTAC to the human genome, and identify more than 600 understudied proteins that are potentially responsive to the PROTAC. Furthermore, we design three PROTAC compounds for novel drug targets associated with Alzheimer's disease.
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Affiliation(s)
- Li Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10021, USA
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45
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Sanjak J, Zhu Q, Mathé EA. Clustering rare diseases within an ontology-enriched knowledge graph. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528673. [PMID: 36824742 PMCID: PMC9949046 DOI: 10.1101/2023.02.15.528673] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Objective Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing and/or platform based therapeutic development. Toward that aim, we utilized an integrative knowledge graph-based approach to constructing clusters of rare diseases. Materials and Methods Data on 3,242 rare diseases were extracted from the National Center for Advancing Translational Science (NCATS) Genetic and Rare Diseases Information center (GARD) internal data resources. The rare disease data was enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were used to convert nodes into vectors upon which k-means clustering was applied. We validated the disease clusters through semantic similarity and feature enrichment analysis. Results A node embedding model was trained on the ontology enriched rare disease KG and k-means clustering was applied to the embedding vectors resulting in 37 disease clusters with a mean size of 87 diseases. We validate the disease clusters quantitatively by looking at semantic similarity of clustered diseases, using the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters were shown to be highly related. Discussion We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and approved or investigational drugs are enumerated for follow-up efforts. Conclusion Our study lays out a method for clustering rare diseases using the graph node embeddings. We develop an easy to maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems. Detailed subnetwork analysis and in-depth review of individual clusters may lead to translatable findings. Future work will focus on incorporation of additional data sources, with a particular focus on common disease data.
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Affiliation(s)
- Jaleal Sanjak
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
| | - Qian Zhu
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
| | - Ewy A Mathé
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
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Doncheva NT, Morris JH, Holze H, Kirsch R, Nastou KC, Cuesta-Astroz Y, Rattei T, Szklarczyk D, von Mering C, Jensen LJ. Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks. J Proteome Res 2023; 22:637-646. [PMID: 36512705 PMCID: PMC9904289 DOI: 10.1021/acs.jproteome.2c00651] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Indexed: 12/15/2022]
Abstract
Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.
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Affiliation(s)
- Nadezhda T. Doncheva
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - John H. Morris
- Resource
on Biocomputing, Visualization, and Informatics, University of California, San
Francisco, California 94143, United States
| | - Henrietta Holze
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Rebecca Kirsch
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Katerina C. Nastou
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Yesid Cuesta-Astroz
- Instituto
Colombiano de Medicina Tropical, Universidad
CES, 055413 Sabaneta, Colombia
| | - Thomas Rattei
- Centre
for Microbiology and Environmental Systems Science, University of Vienna, 1030 Vienna, Austria
| | - Damian Szklarczyk
- Department
of Molecular Life Sciences, University of
Zurich, 8057 Zurich, Switzerland
- SIB
Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Christian von Mering
- Department
of Molecular Life Sciences, University of
Zurich, 8057 Zurich, Switzerland
- SIB
Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Lars J. Jensen
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
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47
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Coral DE, Fernandez-Tajes J, Tsereteli N, Pomares-Millan H, Fitipaldi H, Mutie PM, Atabaki-Pasdar N, Kalamajski S, Poveda A, Miller-Fleming TW, Zhong X, Giordano GN, Pearson ER, Cox NJ, Franks PW. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nat Metab 2023; 5:237-247. [PMID: 36703017 PMCID: PMC9970876 DOI: 10.1038/s42255-022-00731-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/20/2022] [Indexed: 01/27/2023]
Abstract
Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
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Affiliation(s)
- Daniel E Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
| | - Juan Fernandez-Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Neli Tsereteli
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Pomares-Millan
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Pascal M Mutie
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Naeimeh Atabaki-Pasdar
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tyne W Miller-Fleming
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ewan R Pearson
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Population Health and Genomics, University of Dundee, Dundee, UK
| | - Nancy J Cox
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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48
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Kelleher KJ, Sheils TK, Mathias SL, Yang JJ, Metzger V, Siramshetty V, Nguyen DT, Jensen LJ, Vidović D, Schürer S, Holmes J, Sharma K, Pillai A, Bologa C, Edwards J, Mathé E, Oprea T. Pharos 2023: an integrated resource for the understudied human proteome. Nucleic Acids Res 2023; 51:D1405-D1416. [PMID: 36624666 PMCID: PMC9825581 DOI: 10.1093/nar/gkac1033] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/12/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022] Open
Abstract
The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.
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Affiliation(s)
- Keith J Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Vishal B Siramshetty
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen 2200, Copenhagen, Denmark
| | - Dušica Vidović
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Stephan C Schürer
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Karlie R Sharma
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ajay Pillai
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy S Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Ewy A Mathé
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
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49
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Parolo S, Mariotti F, Bora P, Carboni L, Domenici E. Single-cell-led drug repurposing for Alzheimer's disease. Sci Rep 2023; 13:222. [PMID: 36604493 PMCID: PMC9816180 DOI: 10.1038/s41598-023-27420-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy.
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Affiliation(s)
- Silvia Parolo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068, Rovereto, Italy.
| | - Federica Mariotti
- grid.491181.4Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Pranami Bora
- grid.491181.4Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Lucia Carboni
- grid.6292.f0000 0004 1757 1758Department of Pharmacy and Biotechnology, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Enrico Domenici
- grid.491181.4Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy ,grid.11696.390000 0004 1937 0351Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123 Trento, Italy
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50
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Magariños MP, Gaulton A, Félix E, Kiziloren T, Arcila R, Oprea TI, Leach AR. Illuminating the druggable genome through patent bioactivity data. PeerJ 2023; 11:e15153. [PMID: 37151295 PMCID: PMC10162037 DOI: 10.7717/peerj.15153] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/10/2023] [Indexed: 05/09/2023] Open
Abstract
The patent literature is a potentially valuable source of bioactivity data. In this article we describe a process to prioritise 3.7 million life science relevant patents obtained from the SureChEMBL database (https://www.surechembl.org/), according to how likely they were to contain bioactivity data for potent small molecules on less-studied targets, based on the classification developed by the Illuminating the Druggable Genome (IDG) project. The overall goal was to select a smaller number of patents that could be manually curated and incorporated into the ChEMBL database. Using relatively simple annotation and filtering pipelines, we have been able to identify a substantial number of patents containing quantitative bioactivity data for understudied targets that had not previously been reported in the peer-reviewed medicinal chemistry literature. We quantify the added value of such methods in terms of the numbers of targets that are so identified, and provide some specific illustrative examples. Our work underlines the potential value in searching the patent corpus in addition to the more traditional peer-reviewed literature. The small molecules found in these patents, together with their measured activity against the targets, are now accessible via the ChEMBL database.
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Affiliation(s)
| | - Anna Gaulton
- EMBL-EBI, Hinxton, United Kingdom
- Exscientia, Oxford, United Kingdom
| | | | | | | | - Tudor I. Oprea
- Translational informatics Division, Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, United States
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