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Kliche J, Simonetti L, Krystkowiak I, Kuss H, Diallo M, Rask E, Nilsson J, Davey NE, Ivarsson Y. Proteome-scale characterisation of motif-based interactome rewiring by disease mutations. Mol Syst Biol 2024:10.1038/s44320-024-00055-4. [PMID: 39009827 DOI: 10.1038/s44320-024-00055-4] [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: 09/15/2023] [Revised: 06/14/2024] [Accepted: 06/28/2024] [Indexed: 07/17/2024] Open
Abstract
Whole genome and exome sequencing are reporting on hundreds of thousands of missense mutations. Taking a pan-disease approach, we explored how mutations in intrinsically disordered regions (IDRs) break or generate protein interactions mediated by short linear motifs. We created a peptide-phage display library tiling ~57,000 peptides from the IDRs of the human proteome overlapping 12,301 single nucleotide variants associated with diverse phenotypes including cancer, metabolic diseases and neurological diseases. By screening 80 human proteins, we identified 366 mutation-modulated interactions, with half of the mutations diminishing binding, and half enhancing binding or creating novel interaction interfaces. The effects of the mutations were confirmed by affinity measurements. In cellular assays, the effects of motif-disruptive mutations were validated, including loss of a nuclear localisation signal in the cell division control protein CDC45 by a mutation associated with Meier-Gorlin syndrome. The study provides insights into how disease-associated mutations may perturb and rewire the motif-based interactome.
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Affiliation(s)
- Johanna Kliche
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Leandro Simonetti
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Izabella Krystkowiak
- Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, SW3 6JB, Chelsea, London, UK
| | - Hanna Kuss
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, DE-48149, Münster, Germany
| | - Marcel Diallo
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Emma Rask
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Jakob Nilsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Norman E Davey
- Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, SW3 6JB, Chelsea, London, UK.
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
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2
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van Essen BJ, Tharshana GN, Ouwerkerk W, Yeo PSD, Sim D, Jaufeerally F, Ong HY, Ling LH, Soon DKN, Lee SGS, Leong G, Loh SY, San Tan R, Ramachandra CJ, Hausenloy DJ, Liew OW, Chong J, Voors AA, Lam CSP, Richards AM, Tromp J. Distinguishing heart failure with reduced ejection fraction from heart failure with preserved ejection fraction: A phenomics approach. Eur J Heart Fail 2024; 26:841-850. [PMID: 38311963 DOI: 10.1002/ejhf.3156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/11/2024] [Accepted: 01/19/2024] [Indexed: 02/06/2024] Open
Abstract
AIM Pathophysiological differences between patients with heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction (EF) remain unclear. Therefore we used a phenomics approach, integrating selected proteomics data with patient characteristics and cardiac structural and functional parameters, to get insight into differential pathophysiological mechanisms and identify potential treatment targets. METHODS AND RESULTS We report data from a representative subcohort of the prospective Singapore Heart Failure Outcomes and Phenotypes (SHOP), including patients with HFrEF (EF <40%, n = 217), HFpEF (EF ≥50%, n = 213), and age- and sex-matched controls without HF (n = 216). We measured 92 biomarkers using a proximity extension assay and assessed cardiac structure and function in all participants using echocardiography. We used multi-block projection to latent structure analysis to integrate clinical, echocardiographic, and biomarker variables. Candidate biomarker targets were cross-referenced with small-molecule and drug databases. The total cohort had a median age of 65 years (interquartile range 60-71), and 50% were women. Protein profiles strongly discriminated patients with HFrEF (area under the curve [AUC] = 0.89) and HFpEF (AUC = 0.94) from controls. Phenomics analyses identified unique druggable inflammatory markers in HFpEF from the tumour necrosis factor receptor superfamily (TNFRSF), which were positively associated with hypertension, diabetes, and increased posterior and relative wall thickness. In HFrEF, interleukin (IL)-8 and IL-6 were possible targets related to lower EF and worsening renal function. CONCLUSION We identified pathophysiological mechanisms related to increased cardiac wall thickness parameters and potentially druggable inflammatory markers from the TNFRSF in HFpEF.
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Affiliation(s)
- Bart J van Essen
- Department of Cardiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Ganash N Tharshana
- Saw Swee Hock School of Public Health and The National University Health System, Singapore, Singapore
| | - Wouter Ouwerkerk
- Department of Dermatology, Amsterdam UMC, University of Amsterdam, Amsterdam Infection and Immunity Institute, Amsterdam, The Netherlands
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
| | | | - David Sim
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
| | - Fazlur Jaufeerally
- Duke-NUS Medical School, Singapore, Singapore
- Department of Medicine, Singapore General Hospital, Singapore, Singapore
| | | | - Lieng Hsi Ling
- National University Heart Centre Singapore, Cardiovascular Research Institute Singapore, National University of Singapore, Singapore, Singapore
| | | | - Shao Guang Sheldon Lee
- National University Heart Centre Singapore, Cardiovascular Research Institute Singapore, National University of Singapore, Singapore, Singapore
| | | | | | - Ru San Tan
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
| | - Chrishan J Ramachandra
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
- Changi General Hospital, Singapore, Singapore
| | - Derek J Hausenloy
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
- Changi General Hospital, Singapore, Singapore
- Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
- The Hatter Cardiovascular Institute, University College London, London, UK
| | - Oi Wai Liew
- National University Heart Centre Singapore, Cardiovascular Research Institute Singapore, National University of Singapore, Singapore, Singapore
| | - Jenny Chong
- National University Heart Centre Singapore, Cardiovascular Research Institute Singapore, National University of Singapore, Singapore, Singapore
| | - Adriaan A Voors
- Department of Cardiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Carolyn S P Lam
- Department of Cardiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - A Mark Richards
- Khoo Teck Puat Hospital, Singapore, Singapore
- Christchurch Heart Institute, University of Otago, Dunedin, New Zealand
| | - Jasper Tromp
- Saw Swee Hock School of Public Health and The National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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3
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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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4
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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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5
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Molinaro M, Torrente Y, Villa C, Farini A. Advancing Biomarker Discovery and Therapeutic Targets in Duchenne Muscular Dystrophy: A Comprehensive Review. Int J Mol Sci 2024; 25:631. [PMID: 38203802 PMCID: PMC10778889 DOI: 10.3390/ijms25010631] [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: 11/23/2023] [Revised: 12/25/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Mounting evidence underscores the intricate interplay between the immune system and skeletal muscles in Duchenne muscular dystrophy (DMD), as well as during regular muscle regeneration. While immune cell infiltration into skeletal muscles stands out as a prominent feature in the disease pathophysiology, a myriad of secondary defects involving metabolic and inflammatory pathways persist, with the key players yet to be fully elucidated. Steroids, currently the sole effective therapy for delaying onset and symptom control, come with adverse side effects, limiting their widespread use. Preliminary evidence spotlighting the distinctive features of T cell profiling in DMD prompts the immuno-characterization of circulating cells. A molecular analysis of their transcriptome and secretome holds the promise of identifying a subpopulation of cells suitable as disease biomarkers. Furthermore, it provides a gateway to unraveling new pathological pathways and pinpointing potential therapeutic targets. Simultaneously, the last decade has witnessed the emergence of novel approaches. The development and equilibrium of both innate and adaptive immune systems are intricately linked to the gut microbiota. Modulating microbiota-derived metabolites could potentially exacerbate muscle damage through immune system activation. Concurrently, genome sequencing has conferred clinical utility for rare disease diagnosis since innovative methodologies have been deployed to interpret the functional consequences of genomic variations. Despite numerous genes falling short as clinical targets for MD, the exploration of Tdark genes holds promise for unearthing novel and uncharted therapeutic insights. In the quest to expedite the translation of fundamental knowledge into clinical applications, the identification of novel biomarkers and disease targets is paramount. This initiative not only advances our understanding but also paves the way for the design of innovative therapeutic strategies, contributing to enhanced care for individuals grappling with these incapacitating diseases.
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Affiliation(s)
- Monica Molinaro
- Neurology Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (M.M.); (Y.T.)
| | - Yvan Torrente
- Neurology Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (M.M.); (Y.T.)
- Stem Cell Laboratory, Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, 20100 Milan, Italy;
| | - Chiara Villa
- Stem Cell Laboratory, Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, 20100 Milan, Italy;
| | - Andrea Farini
- Neurology Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (M.M.); (Y.T.)
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6
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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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7
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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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8
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Woodward DJ, Thorp JG, Akosile W, Ong JS, Gamazon ER, Derks EM, Gerring ZF. Identification of drug repurposing candidates for the treatment of anxiety: A genetic approach. Psychiatry Res 2023; 326:115343. [PMID: 37473490 PMCID: PMC10493169 DOI: 10.1016/j.psychres.2023.115343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Anxiety disorders are a group of prevalent and heritable neuropsychiatric diseases. We previously conducted a genome-wide association study (GWAS) which identified genomic loci associated with anxiety; however, the biological consequences underlying the genetic associations are largely unknown. Integrating GWAS and functional genomic data may improve our understanding of the genetic effects on intermediate molecular phenotypes such as gene expression. This can provide an opportunity for the discovery of drug targets for anxiety via drug repurposing. We used the GWAS summary statistics to determine putative causal genes for anxiety using MAGMA and colocalization analyses. A transcriptome-wide association study was conducted to identify genes with differential genetically regulated levels of gene expression in human brain tissue. The genes were integrated with a large drug-gene expression database (Connectivity Map), discovering compounds that are predicted to "normalise" anxiety-associated expression changes. The study identified 64 putative causal genes associated with anxiety (35 genes upregulated; 29 genes downregulated). Drug mechanisms adrenergic receptor agonists, sigma receptor agonists, and glutamate receptor agonists gene targets were enriched in anxiety-associated genetic signal and exhibited an opposing effect on the anxiety-associated gene expression signature. The significance of the project demonstrated genetic links for novel drug candidates to potentially advance anxiety therapeutics.
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Affiliation(s)
- Damian J Woodward
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; School of Biomedical Science, Queensland University of Technology, Kelvin Grove, QLD, Australia.
| | - Jackson G Thorp
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Wole Akosile
- School of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Jue-Sheng Ong
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Centre, Nashville, TN, USA; Clare Hall, University of Cambridge, Cambridge, UK
| | - Eske M Derks
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Zachary F Gerring
- Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
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9
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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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10
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Pergola G, Parihar M, Sportelli L, Bharadwaj R, Borcuk C, Radulescu E, Bellantuono L, Blasi G, Chen Q, Kleinman JE, Wang Y, Sripathy SR, Maher BJ, Monaco A, Rossi F, Shin JH, Hyde TM, Bertolino A, Weinberger DR. Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions. SCIENCE ADVANCES 2023; 9:eade2812. [PMID: 37058565 PMCID: PMC10104472 DOI: 10.1126/sciadv.ade2812] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.
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Affiliation(s)
- Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Sportelli
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Rahul Bharadwaj
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Christopher Borcuk
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Eugenia Radulescu
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Loredana Bellantuono
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yanhong Wang
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Srinidhi Rao Sripathy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Brady J. Maher
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Fabiana Rossi
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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11
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McNair D. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annu Rev Pharmacol Toxicol 2023; 63:77-97. [PMID: 35679624 DOI: 10.1146/annurev-pharmtox-051921-023255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
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Affiliation(s)
- Douglas McNair
- Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;
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12
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Groza T, Gomez FL, Mashhadi HH, Muñoz-Fuentes V, Gunes O, Wilson R, Cacheiro P, Frost A, Keskivali-Bond P, Vardal B, McCoy A, Cheng TK, Santos L, Wells S, Smedley D, Mallon AM, Parkinson H. The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Res 2023; 51:D1038-D1045. [PMID: 36305825 PMCID: PMC9825559 DOI: 10.1093/nar/gkac972] [Citation(s) in RCA: 120] [Impact Index Per Article: 120.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/05/2022] [Accepted: 10/14/2022] [Indexed: 01/30/2023] Open
Abstract
The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal makes available curated, integrated and analysed knockout mouse phenotyping data generated by the IMPC project consisting of 85M data points and over 95,000 statistically significant phenotype hits mapped to human diseases. The IMPC portal delivers a substantial reference dataset that supports the enrichment of various domain-specific projects and databases, as well as the wider research and clinical community, where the IMPC genotype-phenotype knowledge contributes to the molecular diagnosis of patients affected by rare disorders. Data from 9,000 mouse lines and 750 000 images provides vital resources enabling the interpretation of the ignorome, and advancing our knowledge on mammalian gene function and the mechanisms underlying phenotypes associated with human diseases. The resource is widely integrated and the lines have been used in over 4,600 publications indicating the value of the data and the materials.
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Affiliation(s)
- Tudor Groza
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
| | - Federico Lopez Gomez
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
| | - Hamed Haseli Mashhadi
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
| | - Violeta Muñoz-Fuentes
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
| | - Osman Gunes
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
| | - Robert Wilson
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
| | - Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Anthony Frost
- Mary Lyon Centre at MRC Harwell, Harwell Campus OX11 7UE, UK
| | | | - Bora Vardal
- Mary Lyon Centre at MRC Harwell, Harwell Campus OX11 7UE, UK
| | - Aaron McCoy
- Mary Lyon Centre at MRC Harwell, Harwell Campus OX11 7UE, UK
| | - Tsz Kwan Cheng
- Mary Lyon Centre at MRC Harwell, Harwell Campus OX11 7UE, UK
| | - Luis Santos
- Research Data Team, The Turing Institute, 96 Euston Rd, London NW1 2DB, UK
| | - Sara Wells
- Mary Lyon Centre at MRC Harwell, Harwell Campus OX11 7UE, UK
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Ann-Marie Mallon
- Research Data Team, The Turing Institute, 96 Euston Rd, London NW1 2DB, UK
| | - Helen Parkinson
- European Bioinformatics Institute, European Molecular Biology Laboratory, Welcome Genome Campus, Hinxton CB10 1SD, UK
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13
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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 2022; 51:D1405-D1416. [PMID: 36624666 PMCID: PMC9825581 DOI: 10.1093/nar/gkac1033] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [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
- Correspondence may also be addressed to Jeremy Edwards. Tel: +1 505 277 6655;
| | - Ewy A Mathé
- To whom correspondence should be addressed. Tel: +1 301 402 8953;
| | - 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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14
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Kori M, Arga KY. Human oncogenic viruses: an overview of protein biomarkers in viral cancers and their potential use in clinics. Expert Rev Anticancer Ther 2022; 22:1211-1224. [PMID: 36270027 DOI: 10.1080/14737140.2022.2139681] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Although the idea that carcinogenesis might be caused by viruses was first voiced about 100 years ago, today's data disappointingly show that we have not made much progress in preventing and/or treating viral cancers in a century. According to recent studies, infections are responsible for approximately 13% of cancer development in the world. Today, it is accepted and proven by many authorities that Epstein-Barr virus (EBV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Human Herpesvirus 8 (HHV8), Human T-cell Lymphotropic virus 1 (HTLV1) and highly oncogenic Human Papillomaviruses (HPVs) cause or/and contribute to cancer development in humans. AREAS COVERED Considering the insufficient prevention and/or treatment strategies for viral cancers, in this review we present the current knowledge on protein biomarkers of oncogenic viruses. In addition, we aimed to decipher their potential for clinical use by evaluating whether the proposed biomarkers are expressed in body fluids, are druggable, and act as tumor suppressors or oncoproteins. EXPERT OPINION Consequently, we believe that this review will shed light on researchers and provide a guide to find remarkable solutions for the prevention and/or treatment of viral cancers.
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Affiliation(s)
- Medi Kori
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey.,Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Turkey
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15
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Ding X, Sharko AC, McDermott MSJ, Schools GP, Chumanevich A, Ji H, Li J, Zhang L, Mack ZT, Sikirzhytski V, Shtutman M, Ivers L, O’Donovan N, Crown J, Győrffy B, Chen M, Roninson IB, Broude EV. Inhibition of CDK8/19 Mediator kinase potentiates HER2-targeting drugs and bypasses resistance to these agents in vitro and in vivo. Proc Natl Acad Sci U S A 2022; 119:e2201073119. [PMID: 35914167 PMCID: PMC9371674 DOI: 10.1073/pnas.2201073119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 06/28/2022] [Indexed: 02/03/2023] Open
Abstract
Breast cancers (BrCas) that overexpress oncogenic tyrosine kinase receptor HER2 are treated with HER2-targeting antibodies (such as trastuzumab) or small-molecule kinase inhibitors (such as lapatinib). However, most patients with metastatic HER2+ BrCa have intrinsic resistance and nearly all eventually become resistant to HER2-targeting therapy. Resistance to HER2-targeting drugs frequently involves transcriptional reprogramming associated with constitutive activation of different signaling pathways. We have investigated the role of CDK8/19 Mediator kinase, a regulator of transcriptional reprogramming, in the response of HER2+ BrCa to HER2-targeting drugs. CDK8 was in the top 1% of all genes ranked by correlation with shorter relapse-free survival among treated HER2+ BrCa patients. Selective CDK8/19 inhibitors (senexin B and SNX631) showed synergistic interactions with lapatinib and trastuzumab in a panel of HER2+ BrCa cell lines, overcoming and preventing resistance to HER2-targeting drugs. The synergistic effects were mediated in part through the PI3K/AKT/mTOR pathway and reduced by PI3K inhibition. Combination of HER2- and CDK8/19-targeting agents inhibited STAT1 and STAT3 phosphorylation at S727 and up-regulated tumor suppressor BTG2. The growth of xenograft tumors formed by lapatinib-sensitive or -resistant HER2+ breast cancer cells was partially inhibited by SNX631 alone and strongly suppressed by the combination of SNX631 and lapatinib, overcoming lapatinib resistance. These effects were associated with decreased tumor cell proliferation and altered recruitment of stromal components to the xenograft tumors. These results suggest potential clinical benefit of combining HER2- and CDK8/19-targeting drugs in the treatment of metastatic HER2+ BrCa.
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Affiliation(s)
- Xiaokai Ding
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Amanda C. Sharko
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Martina S. J. McDermott
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Gary P. Schools
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Alexander Chumanevich
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Hao Ji
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Jing Li
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Li Zhang
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Zachary T. Mack
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Vitali Sikirzhytski
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Michael Shtutman
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Laura Ivers
- National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland
| | - Norma O’Donovan
- National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland
| | - John Crown
- National Institute for Cellular Biotechnology, Dublin City University, Dublin 9, Ireland
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, H-1085, Hungary
- Oncology Biomarker Research Group, Research Center for Natural Sciences, H-1117, Budapest, Hungary
| | - Mengqian Chen
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
- Senex Biotechnology, Inc., 715 Sumter St., Columbia, SC, 29208
| | - Igor B. Roninson
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
| | - Eugenia V. Broude
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, 715 Sumter St., Columbia, SC, 29208
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16
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Identification of novel γ-globin inducers among all potential erythroid druggable targets. Blood Adv 2022; 6:3280-3285. [PMID: 35240686 PMCID: PMC9198928 DOI: 10.1182/bloodadvances.2021006802] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/23/2022] [Indexed: 01/28/2023] Open
Abstract
Human γ-globin is predominantly expressed in fetal liver erythroid cells during gestation from 2 nearly identical genes, HBG1 and HBG2, that are both perinatally silenced. Reactivation of these fetal genes in adult red blood cells can ameliorate many symptoms associated with the inherited β-globinopathies, sickle cell disease, and Cooley anemia. Although promising genetic strategies to reactivate the γ-globin genes to treat these diseases have been explored, there are significant barriers to their effective implementation worldwide; alternatively, pharmacological induction of γ-globin synthesis could readily reach the majority of affected individuals. In this study, we generated a CRISPR knockout library that targeted all erythroid genes for which prospective or actual therapeutic compounds already exist. By probing this library for genes that repress fetal hemoglobin (HbF), we identified several novel, potentially druggable, γ-globin repressors, including VHL and PTEN. We demonstrate that deletion of VHL induces HbF through activation of the HIF1α pathway and that deletion of PTEN induces HbF through AKT pathway stimulation. Finally, we show that small-molecule inhibitors of PTEN and EZH induce HbF in both healthy and β-thalassemic human primary erythroid cells.
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17
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Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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18
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Hajjaji N, Aboulouard S, Cardon T, Bertin D, Robin YM, Fournier I, Salzet M. Path to Clonal Theranostics in Luminal Breast Cancers. Front Oncol 2022; 11:802177. [PMID: 35096604 PMCID: PMC8793283 DOI: 10.3389/fonc.2021.802177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/06/2021] [Indexed: 12/18/2022] Open
Abstract
Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.
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Affiliation(s)
- Nawale Hajjaji
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Soulaimane Aboulouard
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Tristan Cardon
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Delphine Bertin
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Yves-Marie Robin
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Isabelle Fournier
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Institut universitaire de France, Paris, France
| | - Michel Salzet
- Univ. Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Institut universitaire de France, Paris, France
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19
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Müller S, Ackloo S, Al Chawaf A, Al-Lazikani B, Antolin A, Baell JB, Beck H, Beedie S, Betz UAK, Bezerra GA, Brennan PE, Brown D, Brown PJ, Bullock AN, Carter AJ, Chaikuad A, Chaineau M, Ciulli A, Collins I, Dreher J, Drewry D, Edfeldt K, Edwards AM, Egner U, Frye SV, Fuchs SM, Hall MD, Hartung IV, Hillisch A, Hitchcock SH, Homan E, Kannan N, Kiefer JR, Knapp S, Kostic M, Kubicek S, Leach AR, Lindemann S, Marsden BD, Matsui H, Meier JL, Merk D, Michel M, Morgan MR, Mueller-Fahrnow A, Owen DR, Perry BG, Rosenberg SH, Saikatendu KS, Schapira M, Scholten C, Sharma S, Simeonov A, Sundström M, Superti-Furga G, Todd MH, Tredup C, Vedadi M, von Delft F, Willson TM, Winter GE, Workman P, Arrowsmith CH. Target 2035 - update on the quest for a probe for every protein. RSC Med Chem 2022; 13:13-21. [PMID: 35211674 PMCID: PMC8792830 DOI: 10.1039/d1md00228g] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/21/2021] [Indexed: 01/11/2023] Open
Abstract
Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.
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Affiliation(s)
- Susanne Müller
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt Frankfurt 60438 Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt Frankfurt 60438 Germany
| | - Suzanne Ackloo
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
| | | | - Bissan Al-Lazikani
- Department of Data Science, The Institute of Cancer Research London SM2 5NG UK
- CRUK ICR/Imperial Convergence Science Centre London SM2 5NG UK
| | - Albert Antolin
- Department of Data Science, The Institute of Cancer Research London SM2 5NG UK
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research London SM2 5NG UK
| | - Jonathan B Baell
- Monash Institute of Pharmaceutical Sciences, Monash University Parkville Victoria 3052 Australia
- School of Pharmaceutical Sciences, Nanjing Tech University No. 30 South Puzhu Road Nanjing 211816 People's Republic of China
| | - Hartmut Beck
- Research and Development, Bayer AG, Pharmaceuticals 42103 Wuppertal Germany
| | - Shaunna Beedie
- Centre for Medicines Discovery, University of Oxford Old Road Campus Research Building, Roosevelt Drive Oxford OX3 7DQ UK
| | | | - Gustavo Arruda Bezerra
- Centre for Medicines Discovery, University of Oxford Old Road Campus Research Building, Roosevelt Drive Oxford OX3 7DQ UK
| | - Paul E Brennan
- Alzheimer's Research UK Oxford Drug Discovery Institute, Centre for Medicines Discovery, University of Oxford Oxford OX3 7FZ UK
| | - David Brown
- Institut Recherches de Servier 125 Chemin de Ronde 78290 Croissy France
| | - Peter J Brown
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
| | - Alex N Bullock
- Centre for Medicines Discovery, University of Oxford Old Road Campus Research Building, Roosevelt Drive Oxford OX3 7DQ UK
| | - Adrian J Carter
- Discovery Research, Boehringer Ingelheim 55216 Ingelheim am Rhein Germany
| | - Apirat Chaikuad
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt Frankfurt 60438 Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt Frankfurt 60438 Germany
| | - Mathilde Chaineau
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital, McGill University Montreal QC Canada
| | - Alessio Ciulli
- School of Life Sciences, Division of Biological Chemistry and Drug Discovery, University of Dundee James Black Centre Dundee UK
| | - Ian Collins
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research London SM2 5NG UK
| | - Jan Dreher
- Research and Development, Bayer AG, Pharmaceuticals 42103 Wuppertal Germany
| | - David Drewry
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy Chapel Hill NC USA
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill Chapel Hill NC 27599 USA
| | - Kristina Edfeldt
- Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet Stockholm Sweden
| | - Aled M Edwards
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
| | - Ursula Egner
- Nuvisan Innovation Campus Berlin GmbH Müllerstraße 178 13353 Berlin Germany
| | - Stephen V Frye
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill Chapel Hill NC 27599 USA
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill Chapel Hill NC 27599 USA
| | | | - Matthew D Hall
- National Center for Advancing Translational Sciences, National Institutes of Health Rockville Maryland 20850 USA
| | - Ingo V Hartung
- Medicinal Chemistry, Global R&D, Merck Healthcare KGaA Frankfurter Straße 250 64293 Darmstadt Germany
| | - Alexander Hillisch
- Research and Development, Bayer AG, Pharmaceuticals 42103 Wuppertal Germany
| | | | - Evert Homan
- Science for Life Laboratory, Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden
| | - Natarajan Kannan
- Institute of Bioinformatics and Department of Biochemistry and Molecular Biology, University of Georgia Athens GA USA
| | - James R Kiefer
- Genentech, Inc. 1 DNA Way South San Francisco California 94080 USA
| | - Stefan Knapp
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt Frankfurt 60438 Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt Frankfurt 60438 Germany
| | - Milka Kostic
- Department of Cancer Biology and Chemical Biology Program, Dana-Farber Cancer Institute 450 Brookline Ave Boston MA 02215 USA
| | - Stefan Kubicek
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute Wellcome Genome Campus, Hinxton Cambridgeshire CB10 1SD UK
| | - Sven Lindemann
- Strategic Innovation, Global R&D, Merck Healthcare KGaA Frankfurter Straße 250 64293 Darmstadt Germany
| | - Brian D Marsden
- Centre for Medicines Discovery, University of Oxford Old Road Campus Research Building, Roosevelt Drive Oxford OX3 7DQ UK
- Kennedy Institute of Rheumatology, NDORMS, University of Oxford UK
| | - Hisanori Matsui
- Neuroscience Drug Discovery Unit, Research, Takeda Pharmaceutical Company Limited Fujisawa Kanagawa Japan
| | - Jordan L Meier
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health Frederick MD USA
| | - Daniel Merk
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt Frankfurt 60438 Germany
- LMU Munich, Department of Pharmacy, Chair of Pharmaceutical and Medicinal Chemistry 81377 Munich Germany
| | - Maurice Michel
- Science for Life Laboratory, Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden
| | - Maxwell R Morgan
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
| | | | - Dafydd R Owen
- Discovery Network Group, Pfizer Medicine Design Cambridge MA 02139 USA
| | - Benjamin G Perry
- Drugs for Neglected Diseases initiative 15 Chemin Camille Vidart Geneva 1202 Switzerland
| | | | - Kumar Singh Saikatendu
- Global Research Externalization, Takeda California, Inc. 9625 Towne Center Drive San Diego CA 92121 USA
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
- Department of Pharmacology & Toxicology, University of Toronto Toronto Ontario M5S 1A8 Canada
| | - Cora Scholten
- Research and Development, Bayer AG, Pharmaceuticals 13353 Berlin Germany
| | - Sujata Sharma
- Structural & Protein Sciences, Discovery Sciences, Janssen Research & Development 1400 McKean Rd Spring House PA 19477 USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences, National Institutes of Health Rockville Maryland 20850 USA
| | - Michael Sundström
- Division of Rheumatology, Department of Medicine Solna, Karolinska University Hospital and Karolinska Institutet Stockholm Sweden
| | - 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
| | - Matthew H Todd
- School of Pharmacy, University College London London WC1N 1AX UK
| | - Claudia Tredup
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt Frankfurt 60438 Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt Frankfurt 60438 Germany
| | - Masoud Vedadi
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
- Department of Pharmacology & Toxicology, University of Toronto Toronto Ontario M5S 1A8 Canada
| | - Frank von Delft
- Centre for Medicines Discovery, University of Oxford Old Road Campus Research Building, Roosevelt Drive Oxford OX3 7DQ UK
- Diamond Light Source Ltd Harwell Science and Innovation Campus Didcot OX11 0QX UK
- Department of Biochemistry, University of Johannesburg Auckland Park 2006 South Africa
- Research Complex at Harwell Harwell Science and Innovation Campus Didcot OX11 0FA UK
| | - Timothy M Willson
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill Chapel Hill NC 27599 USA
| | - Georg E Winter
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria
| | - Paul Workman
- CRUK ICR/Imperial Convergence Science Centre London SM2 5NG UK
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research London SM2 5NG UK
| | - Cheryl H Arrowsmith
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
- Princess Margaret Cancer Centre Toronto Ontario M5G 1L7 Canada
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20
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Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, McEntire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC Bioinformatics 2022; 23:37. [PMID: 35021991 PMCID: PMC8756622 DOI: 10.1186/s12859-021-04530-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 12/13/2021] [Indexed: 11/12/2022] Open
Abstract
Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04530-9.
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Kropiwnicki E, Binder J, Yang J, Holmes J, Lachmann A, Clarke DJB, Sheils T, Kelleher K, Metzger V, Bologa CG, Oprea TI, Ma’ayan A. Getting Started with the IDG KMC Datasets and Tools. Curr Protoc 2022; 2:e355. [PMID: 35085427 PMCID: PMC10789444 DOI: 10.1002/cpz1.355] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.
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Affiliation(s)
- Eryk Kropiwnicki
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Jessica Binder
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, 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, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Timothy Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Keith Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Vincent Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian G. Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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22
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Awale M, Hert J, Guasch L, Riniker S, Kramer C. The Playbooks of Medicinal Chemistry Design Moves. J Chem Inf Model 2021; 61:729-742. [PMID: 33522806 DOI: 10.1021/acs.jcim.0c01143] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Large databases of biologically relevant molecules, such as ChEMBL, SureChEMBL, or compound collections of pharmaceutical or agrochemical companies, are invaluable sources of medicinal chemistry information, albeit implicit. We developed a modified matched molecular pair approach to systematically and exhaustively extract the transformations in these databases and distill them into snippets of explicit design knowledge that are easily interpretable and directly applicable. The resulting "playbooks of medicinal chemistry design moves" capture the collective pharmaceutical and agrochemical research expertise across multiple chemists, companies, targets, and projects. They can be queried in an automated fashion for systematic prospective design and compound generation. The ChEMBL playbook and an application to exploit it are available at https://github.com/mahendra-awale/medchem_moves.
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Affiliation(s)
- Mahendra Awale
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Jérôme Hert
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Laura Guasch
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Christian Kramer
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
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23
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Sheils TK, Mathias SL, Kelleher KJ, Siramshetty VB, Nguyen DT, Bologa CG, Jensen LJ, Vidović D, Koleti A, Schürer SC, Waller A, Yang JJ, Holmes J, Bocci G, Southall N, Dharkar P, Mathé E, Simeonov A, Oprea TI. TCRD and Pharos 2021: mining the human proteome for disease biology. Nucleic Acids Res 2021; 49:D1334-D1346. [PMID: 33156327 PMCID: PMC7778974 DOI: 10.1093/nar/gkaa993] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/13/2022] Open
Abstract
In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.
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Affiliation(s)
- 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
| | - Keith J Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, 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
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, 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
| | - Amar Koleti
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, 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
| | - Anna Waller
- UNM Center for Molecular Discovery, 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
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Noel Southall
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Poorva Dharkar
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ewy Mathé
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Anton Simeonov
- 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
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- UNM Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 40530 Gothenburg, Sweden
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