1
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Siminea N, Czeizler E, Popescu VB, Petre I, Păun A. Connecting the dots: Computational network analysis for disease insight and drug repurposing. Curr Opin Struct Biol 2024; 88:102881. [PMID: 38991238 DOI: 10.1016/j.sbi.2024.102881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/22/2024] [Accepted: 06/19/2024] [Indexed: 07/13/2024]
Abstract
Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.
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Affiliation(s)
- Nicoleta Siminea
- Faculty of Mathematics and Computer Science, University of Bucharest, Romania; National Institute of Research and Development for Biological Sciences, Romania
| | - Eugen Czeizler
- Faculty of Medicine, University of Helsinki, Finland; National Institute of Research and Development for Biological Sciences, Romania
| | | | - Ion Petre
- Department of Mathematics and Statistics, University of Turku, Finland; National Institute of Research and Development for Biological Sciences, Romania.
| | - Andrei Păun
- Faculty of Mathematics and Computer Science, University of Bucharest, Romania; National Institute of Research and Development for Biological Sciences, Romania.
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2
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Massacci G, Venafra V, Zwiebel M, Wahle M, Cerroni R, Bissacco J, Perfetto L, Michienzi V, Stefani A, Mercuri NB, Schirinzi T, Sacco F. Stage-dependent phosphoproteome remodeling of Parkinson's disease blood cells. Neurobiol Dis 2024; 200:106622. [PMID: 39097034 DOI: 10.1016/j.nbd.2024.106622] [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: 06/07/2024] [Revised: 07/29/2024] [Accepted: 07/29/2024] [Indexed: 08/05/2024] Open
Abstract
The complexity and heterogeneity of PD necessitate advanced diagnostic and prognostic tools to elucidate its molecular mechanisms accurately. In this study, we addressed this challenge by conducting a pilot phospho-proteomic analysis of peripheral blood mononuclear cells (PBMCs) from idiopathic PD patients at varying disease stages to delineate the functional alterations occurring in these cells throughout the disease course and identify key molecules and pathways contributing to PD progression. By integrating clinical data with phospho-proteomic profiles across various PD stages, we identify potential stage-specific molecular signatures indicative of disease progression. This integrative approach allows for the discernment of distinct disease states and enhances our understanding of PD heterogeneity.
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Affiliation(s)
- Giorgia Massacci
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Veronica Venafra
- PhD Program in Cellular and Molecular Biology, Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Maximilian Zwiebel
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maria Wahle
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Rocco Cerroni
- Neurology Unit - Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Jacopo Bissacco
- Neurology Unit - Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Livia Perfetto
- Department of Biology and Biotechnologies "Charles Darwin", University of Rome La Sapienza, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Vito Michienzi
- Neurology Unit - Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Alessandro Stefani
- Neurology Unit - Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Nicola Biagio Mercuri
- Neurology Unit - Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Tommaso Schirinzi
- Neurology Unit - Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy.
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy; Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Naples, Italy
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3
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Najm M, Martignetti L, Cornet M, Kelly-Aubert M, Sermet I, Calzone L, Stoven V. From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis. BMC Genomics 2024; 25:892. [PMID: 39342081 PMCID: PMC11438383 DOI: 10.1186/s12864-024-10752-x] [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: 11/21/2023] [Accepted: 08/30/2024] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Cystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways. METHODS We propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes. RESULTS Based on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology. CONCLUSION Analysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.
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Affiliation(s)
- Matthieu Najm
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France.
- Institut Curie, Université PSL, 75005, Paris, France.
- INSERM U900, 75005, Paris, France.
| | - Loredana Martignetti
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France
- Institut Curie, Université PSL, 75005, Paris, France
- INSERM U900, 75005, Paris, France
| | - Matthieu Cornet
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France
- Institut Curie, Université PSL, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- Institut Necker Enfants Malades, INSERM U1151, 75015, Paris, France
| | - Mairead Kelly-Aubert
- Institut Necker Enfants Malades, INSERM U1151, 75015, Paris, France
- Université Paris Cité, 75015, Paris, France
| | - Isabelle Sermet
- Institut Necker Enfants Malades, INSERM U1151, 75015, Paris, France
- Université Paris Cité, 75015, Paris, France
- Centre de Référence Maladies Rares, Mucoviscidose et Maladies Apparentées, Hôpital Necker Enfants Malades AP-HP Centre Paris Cité, 75015, Paris, France
| | - Laurence Calzone
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France.
- Institut Curie, Université PSL, 75005, Paris, France.
- INSERM U900, 75005, Paris, France.
| | - Véronique Stoven
- Center for Computational Biology (CBIO), Mines Paris-PSL, 75006, Paris, France.
- Institut Curie, Université PSL, 75005, Paris, France.
- INSERM U900, 75005, Paris, France.
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4
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Bourdakou MM, Melliou E, Magiatis P, Spyrou GM. Computational investigation of the functional landscape of the protective role that extra virgin olive oil consumption may have on chronic lymphocytic leukemia. J Transl Med 2024; 22:869. [PMID: 39334178 PMCID: PMC11428436 DOI: 10.1186/s12967-024-05672-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 09/04/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND The health benefits of the Mediterranean diet are partially attributed to the polyphenols present in extra virgin olive oil (EVOO), which have been shown to have anti-cancer properties. However, the possible effect that EVOO could have on Chronic Lymphocytic Leukemia (CLL) has not been fully explored. METHODS This study investigates the anti-CLL activity of EVOO through a computational multi-level data analysis procedure, focusing on the identification of shared biological functions between them. Specifically, publicly available data from genomics, transcriptomics and proteomics related to EVOO consumption and CLL were collected from several resources and analyzed through a computational pipeline, highlighting common molecular mechanisms and biological processes. Computational verification of a number of the highlighted functional terms associating CLL and EVOO has been performed as well. RESULTS Our investigation revealed four molecular pathways and three biological processes that overlap between mechanisms associated with CLL and those impacted by the consumption of EVOO. To further investigate the common biological functions, we focused on AKT1-related terms, aiming to investigate the potential importance of AKT1 in the anti- CLL effects associated with EVOO. CONCLUSIONS Overall, the results provide valuable insights into the potential beneficial effect of EVOO in CLL and highlight EVOO's bioactive compounds as promising candidates for future investigations.
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Affiliation(s)
- Marilena M Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Eleni Melliou
- Laboratory of Pharmacognosy and Natural Products Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Prokopios Magiatis
- Laboratory of Pharmacognosy and Natural Products Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece.
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
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5
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Zhu Y, Benos PV, Chikina M. A hybrid constrained continuous optimization approach for optimal causal discovery from biological data. Bioinformatics 2024; 40:ii87-ii97. [PMID: 39230691 PMCID: PMC11373380 DOI: 10.1093/bioinformatics/btae411] [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] [Indexed: 09/05/2024] Open
Abstract
MOTIVATION Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods. RESULTS We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm's strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics. AVAILABILITY AND IMPLEMENTATION https://github.com/zhu-yh1/PC-NOTEARS.
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Affiliation(s)
- Yuehua Zhu
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15217, United States
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, United States
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15217, United States
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Venafra V, Sacco F, Perfetto L. SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks. NPJ Syst Biol Appl 2024; 10:95. [PMID: 39179556 PMCID: PMC11343843 DOI: 10.1038/s41540-024-00417-6] [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: 01/30/2024] [Accepted: 07/31/2024] [Indexed: 08/26/2024] Open
Abstract
Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.
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Affiliation(s)
- Veronica Venafra
- Ph.D. Program in Cellular and Molecular Biology, Department of Biology, University of Rome 'Tor Vergata', Rome, Italy
- Department of Biology, University of Rome 'Tor Vergata', Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome 'Tor Vergata', Rome, Italy.
| | - Livia Perfetto
- Department of Biology and Biotechnologies 'C.Darwin', University of Rome 'La Sapienza', Rome, Italy.
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7
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López-Cortés A, Cabrera-Andrade A, Echeverría-Garcés G, Echeverría-Espinoza P, Pineda-Albán M, Elsitdie N, Bueno-Miño J, Cruz-Segundo CM, Dorado J, Pazos A, Gonzáles-Díaz H, Pérez-Castillo Y, Tejera E, Munteanu CR. Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses. Sci Rep 2024; 14:19359. [PMID: 39169044 PMCID: PMC11339426 DOI: 10.1038/s41598-024-68565-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 07/25/2024] [Indexed: 08/23/2024] Open
Abstract
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .
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Affiliation(s)
- Andrés López-Cortés
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador.
| | - Alejandro Cabrera-Andrade
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
- Escuela de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito, Ecuador
| | - Gabriela Echeverría-Garcés
- Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública "Leopoldo Izquieta Pérez", Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
| | | | - Micaela Pineda-Albán
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Nicole Elsitdie
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - José Bueno-Miño
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Carlos M Cruz-Segundo
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Tecnológico de Estudios Superiores de Jocotitlán, Jocotitlán, Mexico
| | - Julian Dorado
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
- Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain
| | - Humberto Gonzáles-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, Biscay, Spain
| | | | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), University of A Coruna, A Coruña, Spain
- Biomedical Research Institute of A Coruna (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruña, Spain
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8
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Martinez K, Agirre J, Akune Y, Aoki-Kinoshita KF, Arighi C, Axelsen KB, Bolton E, Bordeleau E, Edwards NJ, Fadda E, Feizi T, Hayes C, Ives CM, Joshi HJ, Krishna Prasad K, Kossida S, Lisacek F, Liu Y, Lütteke T, Ma J, Malik A, Martin M, Mehta AY, Neelamegham S, Panneerselvam K, Ranzinger R, Ricard-Blum S, Sanou G, Shanker V, Thomas PD, Tiemeyer M, Urban J, Vita R, Vora J, Yamamoto Y, Mazumder R. Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy. Database (Oxford) 2024; 2024:baae073. [PMID: 39137905 PMCID: PMC11321244 DOI: 10.1093/database/baae073] [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: 04/01/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 08/15/2024]
Abstract
Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled "Functional impact of glycans and their curation" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.
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Affiliation(s)
- Karina Martinez
- Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, 2300 I St. NW, Washington, DC 20052, United States
| | - Jon Agirre
- York Structural Biology Laboratory, Department of Chemistry, University of York, Wentworth Way, York YO10 5DD, United Kingdom
| | - Yukie Akune
- The Glycosciences Laboratory, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, United Kingdom
| | - Kiyoko F Aoki-Kinoshita
- Glycan and Life Systems Integration Center (GaLSIC), Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Cecilia Arighi
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, United States
| | - Kristian B Axelsen
- Swiss-Prot Group, Swiss Institute of Bioinformatics (SIB), CMU, 1 rue Michel Servet, Geneva 4 1211, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Emily Bordeleau
- Michael Smith Laboratories, The University of British Columbia, 2185 East Mall, Vancouver, British Columbia V6T 1Z4, Canada
| | - Nathan J Edwards
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, 2115 Wisconsin Ave NW, Washington, DC 20007, United States
| | - Elisa Fadda
- Department of Chemistry and Hamilton Institute, Maynooth University, Kilcock Road, Maynooth, Co. Kildare W23 AH3Y, Ireland
| | - Ten Feizi
- The Glycosciences Laboratory, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, United Kingdom
| | - Catherine Hayes
- Proteome Informatics Group, Swiss Institute of Bioinformatics (SIB), route de Drize 7, Geneva CH-1227, Switzerland
| | - Callum M Ives
- Department of Chemistry and Hamilton Institute, Maynooth University, Kilcock Road, Maynooth, Co. Kildare W23 AH3Y, Ireland
| | - Hiren J Joshi
- Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, Copenhagen DK-2200, Denmark
| | - Khakurel Krishna Prasad
- ELI Beamlines Facility, The Extreme Light Infrastructure ERIC, Za Radnicí 835, Dolní Břežany 25241, Czech Republic
| | - Sofia Kossida
- IMGT, The International ImMunoGeneTics Information System, National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), 141 rue de la Cardonille, Montpellier 34 090, France
| | - Frederique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics (SIB), route de Drize 7, Geneva CH-1227, Switzerland
| | - Yan Liu
- The Glycosciences Laboratory, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, United Kingdom
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Gießen, Frankfurter Str. 100, Gießen 35392, Germany
| | - Junfeng Ma
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3900 Reservior Road NW, Washington, DC 20007, United States
| | - Adnan Malik
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Maria Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Akul Y Mehta
- Department of Surgery, Beth Israel Deaconess Medical Center, National Center for Functional Glycomics, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States
| | - Sriram Neelamegham
- Departments of Chemical & Biological Engineering, Biomedical Engineering and Medicine, University at Buffalo, State University of New York, 906 Furnas Hall, Buffalo, NY 14260, United States
| | - Kalpana Panneerselvam
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - René Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, United States
| | - Sylvie Ricard-Blum
- Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, University Lyon 1, CNRS, 43 Boulevard du 11 novembre 1918, Villeurbanne cedex F-69622, France
| | - Gaoussou Sanou
- IMGT, The International ImMunoGeneTics Information System, National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), 141 rue de la Cardonille, Montpellier 34 090, France
| | - Vijay Shanker
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave, Newark, DE 19716, United States
| | - Paul D Thomas
- Department of Population and Public Health Sciences, University of Southern California, 2001 N Soto Street, Los Angeles, CA 90032, United States
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, United States
| | - James Urban
- Department of Chemistry and Molecular Biology, University of Gothenburg, Medicinaregatan 7 B, Gothenburg 41390, Sweden
| | - Randi Vita
- Immune Epitope Database and Analysis Project, La Jolla Institute for Allergy & Immunology, 9420 Athena Circle, La Jolla, CA 92037, United States
| | - Jeet Vora
- Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, 2300 I St. NW, Washington, DC 20052, United States
| | - Yasunori Yamamoto
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa, Chiba 277-0871, Japan
| | - Raja Mazumder
- Department of Biochemistry & Molecular Medicine, The George Washington University School of Medicine and Health Sciences, 2300 I St. NW, Washington, DC 20052, United States
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9
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Garg K, Kumar A, Kizhakkethil V, Kumar P, Singh S. Overlap in oncogenic and pro-inflammatory pathways associated with areca nut and nicotine exposure. CANCER PATHOGENESIS AND THERAPY 2024; 2:187-194. [PMID: 39027148 PMCID: PMC11252521 DOI: 10.1016/j.cpt.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 09/08/2023] [Accepted: 09/14/2023] [Indexed: 07/20/2024]
Abstract
Background Betel nut/areca nut/Areca catechu is one of the most commonly used psychoactive substance, and is also a major preventable cause of cancer. Unlike other psychoactive substances, such as nicotine, the mechanisms underlying addiction to areca nuts and related oncogenesis remain elusive. Recent reports suggest a possible overlap in the mechanisms of action of nicotine and areca nuts in the human body. Thus, this study aimed to investigate the interactome of human proteins associated with areca nut exposure and the intricate similarities and differences in the effects of the two psychoactive substances on humans. Methods A list of proteins associated with areca nut use was obtained from the available literature using terms from Medical Subject Headings (MeSH). Protein-protein interaction (PPI) networks and functional enrichment were analyzed. The results obtained for both psychoactive substances were compared. Results Given the limited number of common proteins (36/226, 16%) in the two sets, a substantial overlap (612/1176 nodes, 52%) was observed in the PPI networks, as well as in Gene Ontology. Areca nuts mainly affect signaling pathways through three hub proteins (alpha serine/threonine-protein kinase, tumor protein 53, and interleukin-6), which are common to both psychoactive substances, as well as two unique hub proteins (epidermal growth factor receptor and master regulator of cell cycle entry and proliferative metabolism). Areca nut-related proteins are associated with unique pathways, such as extracellular matrix organization, lipid storage, and metabolism, which are not found in nicotine-associated proteins. Conclusions Areca nuts affect regulatory mechanisms, leading to systemic toxicity and oncogenesis. Areca nuts also affect unique pathways that can be studied as potential markers of exposure, as well as targets for anticancer therapeutic agents.
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Affiliation(s)
- Krati Garg
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology (IIT) Delhi, Delhi 110016, India
| | - Anuj Kumar
- Division of Molecular Biology, ICMR-National Institute of Cancer Prevention and Research (ICMR-NICPR), Indian Council of Medical Research, Noida, Uttar Pradesh 201301, India
| | - Vidisha Kizhakkethil
- Department of Biotechnology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632024, India
| | - Pramod Kumar
- Division of Molecular Biology, ICMR-National Institute of Cancer Prevention and Research (ICMR-NICPR), Indian Council of Medical Research, Noida, Uttar Pradesh 201301, India
| | - Shalini Singh
- ICMR-National Institute of Cancer Prevention & Research (ICMR-NICPR), Indian Council of Medical Research, Noida, Uttar Pradesh 201301, India
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10
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Kwok T, Huerta-White T, Briegel K, Singh A, Yeguvapalli S, Chitrala KN. Bioinformatics analysis of the potential biomarkers of Multiple Sclerosis and Guillain-Barré syndrome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.595759. [PMID: 38853933 PMCID: PMC11160789 DOI: 10.1101/2024.05.29.595759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Recent research emphasizes the intricate interplay of genetics and epigenetics in neurological disorders, notably Multiple Sclerosis (MS) and Guillain-Barre Syndrome (GBS), both of which exhibit cardiovascular dysregulation, with GBS often featuring serious bradyarrhythmias requiring prompt recognition and treatment. While cardiovascular autonomic dysfunction in MS is typically less severe, orthostatic intolerance affects around half of MS patients. Their distinction lies in their autoimmune responses, MS is an autoimmune disease affecting the central nervous system, causes demyelination and axon damage, leading to cognitive, ocular, and musculoskeletal dysfunction. In contrast, GBS primarily affects the peripheral nervous system, resulting in paralysis and respiratory complications. Despite their differences, both diseases share environmental risk factors such as viral infections and Vitamin D deficiency. This study aims to explore shared gene expression pathways, functional annotations, and molecular pathways between MS and GBS to enhance diagnostics, pathogenesis understanding, and treatment strategies through molecular analysis techniques. Through the gene expression analysis, five significant genes were found UTS2, TNFSF10, GBP1, VCAN, FOS. Results shows that Common DEGs are linked to apoptosis, bacterial infections, and atherosclerosis. Molecular docking analysis suggests Aflatoxin B1 as a potential therapeutic compound due to its high binding affinity with common differentially expressed proteins.
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Affiliation(s)
| | | | - Karl Briegel
- Department of Engineering Technology, Division of Technology, University of Houston, 13850, University Blvd, Room SAB1 214, Sugar Land, TX 77479
| | - Aaisha Singh
- Department of Engineering Technology, Division of Technology, University of Houston, 13850, University Blvd, Room SAB1 214, Sugar Land, TX 77479
| | - Suneetha Yeguvapalli
- Department of Engineering Technology, Division of Technology, University of Houston, 13850, University Blvd, Room SAB1 214, Sugar Land, TX 77479
| | - Kumaraswamy Naidu Chitrala
- Department of Engineering Technology, Division of Technology, University of Houston, 13850, University Blvd, Room SAB1 214, Sugar Land, TX 77479
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11
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Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl 2024; 10:53. [PMID: 38760412 PMCID: PMC11101644 DOI: 10.1038/s41540-024-00378-w] [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: 10/20/2023] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.
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Affiliation(s)
| | - Trivadi Sundaram Ganesan
- Department of Medical Oncology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Anbumathi Palanisamy
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, India.
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12
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Arora C, Matic M, Bisceglia L, Di Chiaro P, De Oliveira Rosa N, Carli F, Clubb L, Nemati Fard LA, Kargas G, Diaferia GR, Vukotic R, Licata L, Wu G, Natoli G, Gutkind JS, Raimondi F. The landscape of cancer-rewired GPCR signaling axes. CELL GENOMICS 2024; 4:100557. [PMID: 38723607 PMCID: PMC11099383 DOI: 10.1016/j.xgen.2024.100557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 02/17/2024] [Accepted: 04/10/2024] [Indexed: 05/15/2024]
Abstract
We explored the dysregulation of G-protein-coupled receptor (GPCR) ligand systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes. Multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes and are associated to specific transcriptional programs and to patient survival patterns. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many involving receptor-biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including, e.g., muscarinic, adenosine, 5-hydroxytryptamine, and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens, which we further validated. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it).
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Affiliation(s)
- Chakit Arora
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Marin Matic
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Luisa Bisceglia
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Pierluigi Di Chiaro
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy
| | - Natalia De Oliveira Rosa
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Francesco Carli
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Lauren Clubb
- Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lorenzo Amir Nemati Fard
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Giorgos Kargas
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
| | - Giuseppe R Diaferia
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy
| | - Ranka Vukotic
- Azienda Ospedaliero-Universitaria Pisana, Via Roma, 67, 56126 Pisa, Italy
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Gioacchino Natoli
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy
| | - J Silvio Gutkind
- Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Francesco Raimondi
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy; Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
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13
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Meimetis N, Lauffenburger DA, Nilsson A. Inference of drug off-target effects on cellular signaling using interactome-based deep learning. iScience 2024; 27:109509. [PMID: 38591003 PMCID: PMC11000001 DOI: 10.1016/j.isci.2024.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
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14
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Latini S, Venafra V, Massacci G, Bica V, Graziosi S, Pugliese GM, Iannuccelli M, Frioni F, Minnella G, Marra JD, Chiusolo P, Pepe G, Helmer Citterich M, Mougiakakos D, Böttcher M, Fischer T, Perfetto L, Sacco F. Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction. eLife 2024; 12:RP90532. [PMID: 38564252 PMCID: PMC10987088 DOI: 10.7554/elife.90532] [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: 04/04/2024] Open
Abstract
Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
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Affiliation(s)
- Sara Latini
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | - Veronica Venafra
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | | | - Valeria Bica
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | - Simone Graziosi
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | | | | | - Filippo Frioni
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Gessica Minnella
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico A. Gemelli IRCCSRomeItaly
| | - John Donald Marra
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Patrizia Chiusolo
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Gerardo Pepe
- Department of Biology, University of Rome Tor VergataRomeItaly
| | | | - Dimitros Mougiakakos
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Department of Hematology and Oncology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Martin Böttcher
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Department of Hematology and Oncology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Thomas Fischer
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Institute of Molecular and Clinical Immunology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Livia Perfetto
- Department of Biology, University of Rome Tor VergataRomeItaly
- Department of Biology, Fondazione Human TechnopoleMilanItaly
| | - Francesca Sacco
- Department of Biology, University of Rome Tor VergataRomeItaly
- Telethon Institute of Genetics and Medicine (TIGEM)PozzuoliItaly
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15
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Kumar S, Sarmah DT, Paul A, Chatterjee S. Exploration of functional relations among differentially co-expressed genes identifies regulators in glioblastoma. Comput Biol Chem 2024; 109:108024. [PMID: 38335855 DOI: 10.1016/j.compbiolchem.2024.108024] [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: 09/15/2023] [Revised: 12/15/2023] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights.
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Affiliation(s)
- Shivam Kumar
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
| | - Dipanka Tanu Sarmah
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
| | - Abhijit Paul
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India.
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16
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Nishida K, Maruyama J, Kaizu K, Takahashi K, Yugi K. Transomics2cytoscape: an automated software for interpretable 2.5-dimensional visualization of trans-omic networks. NPJ Syst Biol Appl 2024; 10:16. [PMID: 38374087 PMCID: PMC10876688 DOI: 10.1038/s41540-024-00342-8] [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: 04/05/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape .
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Affiliation(s)
- Kozo Nishida
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei-shi, Tokyo, 184-8588, Japan
| | - Junichi Maruyama
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kazunari Kaizu
- Center for Biosystems Dynamics Research (BDR), RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Koichi Takahashi
- Center for Biosystems Dynamics Research (BDR), RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan
| | - Katsuyuki Yugi
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan.
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
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17
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Khodair AI, El-Hallouty SM, Cagle-White B, Abdel Aziz MH, Hanafy MK, Mowafy S, Hamdy NM, Kassab SE. Camptothecin structure simplification elaborated new imidazo[2,1-b]quinazoline derivative as a human topoisomerase I inhibitor with efficacy against bone cancer cells and colon adenocarcinoma. Eur J Med Chem 2024; 265:116049. [PMID: 38185054 DOI: 10.1016/j.ejmech.2023.116049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/17/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024]
Abstract
Camptothecin is a pentacyclic natural alkaloid that inhibits the hTop1 enzyme involved in DNA transcription and cancer cell growth. Camptothecin structure pitfalls prompted us to design new congeners using a structure simplification strategy to reduce the ring extension number from pentacyclic to tetracyclic while maintaining potential stacking of the new compounds with the DNA base pairs at the Top1-mediated cleavage complex and aqueous solubility, as well as minimizing compound-liver toxicity. The principal axis of this study was the verification of hTop1 inhibiting activity as a possible mechanism of action and the elaboration of new simplified inhibitors with improved pharmacodynamic and pharmacokinetic profiling using three structure panels (A-C) of (isoquinolinoimidazoquinazoline), (imidazoquinazoline), and (imidazoisoquinoline), respectively. DNA relaxation assay identified five compounds as hTop1 inhibitors belonging to the imidazoisoquinolines 3a,b, the imidazoquinazolines 12, and the isoquinolinoimidazoquinazolines 7a,b. In an MTT cytotoxicity assay against different cancer cell lines, compound 12 was the most potent against HOS bone cancer cells (IC50 = 1.47 μM). At the same time, the other inhibitors had no detectable activity against any cancer cell type. Compound (12) demonstrated great penetrating power in the HOS cancer cells' 3D-multicellular tumor spheroid model. Bioinformatics research of the hTop1 gene revealed that the TP53 cell proliferative gene is in the network of hTop1. The finding is confirmed empirically using the gene expression assay that proved the increase in p53 expression. The impact of structure simplification on compound 12 profile, characterized by the absence of acute oral liver toxicity when compared to Doxorubicin as a standard inhibitor, the lethal dose measured on Swiss Albino female mice and reported at LD50 = 250 mg/kg, and therapeutic significance in reducing colon adenocarcinoma tumor volume by 75.36 % after five weeks of treatment with compound 12. The molecular docking solutions of the active CPT-based derivative 12 and the inactive congener 14 into the active site of hTop1 and the activity cliffing of such MMP directed us to recommend the addition of HBD and HBA variables to compound 12 imidazoquinazoline core scaffold to enhance the potency via hydrogen bond formation with the major groove amino acids (Asp533, Lys532) as well as maintaining the hydrogen bond with the minor groove amino acid Arg364.
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Affiliation(s)
- Ahmed I Khodair
- Chemistry Department, Faculty of Science, Kafrelsheikh University, 33516, Kafrelsheikh, Egypt.
| | - Salwa M El-Hallouty
- Drug Bioassay-Cell Culture Laboratory, Department of Pharmacognosy, National Research Centre, Dokki, Giza 12622, Egypt
| | - Brittnee Cagle-White
- Department of Pharmaceutical Sciences and Health Outcomes, Fisch College of Pharmacy, The University of Texas at Tyler, Tyler, TX, TX 75799, USA
| | - May H Abdel Aziz
- Department of Pharmaceutical Sciences and Health Outcomes, Fisch College of Pharmacy, The University of Texas at Tyler, Tyler, TX, TX 75799, USA
| | - Mahmoud Kh Hanafy
- Drug Bioassay-Cell Culture Laboratory, Department of Pharmacognosy, National Research Centre, Dokki, Giza 12622, Egypt; Research Centre for Idling Brain Science, Department of Biochemistry, Graduate School of Medicine and Pharmaceutical Science, University of Toyama, 930-0194, Japan
| | - Samar Mowafy
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Cairo, 11431, Egypt
| | - Nadia M Hamdy
- Biochemistry Dept., Faculty of Pharmacy, Ain Shams University, Cairo, 11566, Egypt.
| | - Shaymaa E Kassab
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Damanhour University, Damanhour, El-Buhaira, 22516, Egypt.
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18
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Ramos-Medina MJ, Echeverría-Garcés G, Kyriakidis NC, León Cáceres Á, Ortiz-Prado E, Bautista J, Pérez-Meza ÁA, Abad-Sojos A, Nieto-Jaramillo K, Espinoza-Ferrao S, Ocaña-Paredes B, López-Cortés A. CardiOmics signatures reveal therapeutically actionable targets and drugs for cardiovascular diseases. Heliyon 2024; 10:e23682. [PMID: 38187312 PMCID: PMC10770621 DOI: 10.1016/j.heliyon.2023.e23682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/27/2023] [Accepted: 12/09/2023] [Indexed: 01/09/2024] Open
Abstract
Cardiovascular diseases are the leading cause of death worldwide, with heart failure being a complex condition that affects millions of individuals. Single-nucleus RNA sequencing has recently emerged as a powerful tool for unraveling the molecular mechanisms behind cardiovascular diseases. This cutting-edge technology enables the identification of molecular signatures, intracellular networks, and spatial relationships among cardiac cells, including cardiomyocytes, mast cells, lymphocytes, macrophages, lymphatic endothelial cells, endocardial cells, endothelial cells, epicardial cells, adipocytes, fibroblasts, neuronal cells, pericytes, and vascular smooth muscle cells. Despite these advancements, the discovery of essential therapeutic targets and drugs for precision cardiology remains a challenge. To bridge this gap, we conducted comprehensive in silico analyses of single-nucleus RNA sequencing data, functional enrichment, protein interactome network, and identification of the shortest pathways to physiological phenotypes. This integrated multi-omics analysis generated CardiOmics signatures, which allowed us to pinpoint three therapeutically actionable targets (ADRA1A1, PPARG, and ROCK2) and 15 effective drugs, including adrenergic receptor agonists, adrenergic receptor antagonists, norepinephrine precursors, PPAR receptor agonists, and Rho-associated kinase inhibitors, involved in late-stage cardiovascular disease clinical trials.
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Affiliation(s)
- María José Ramos-Medina
- German Cancer Research Center (DKFZ), Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Gabriela Echeverría-Garcés
- Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública “Leopoldo Izquieta Pérez”, Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
| | - Nikolaos C. Kyriakidis
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Ángela León Cáceres
- Heidelberg Institute of Global Health, Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
- Instituto de Salud Pública, Facultad de Medicina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
| | - Esteban Ortiz-Prado
- One Health Research Group, Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Jhommara Bautista
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Álvaro A. Pérez-Meza
- Escuela de Medicina, Colegio de Ciencias de La Salud COCSA, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | | | - Karol Nieto-Jaramillo
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuqui, Ecuador
| | | | - Belén Ocaña-Paredes
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | - Andrés López-Cortés
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
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Pastrello C, Kotlyar M, Abovsky M, Lu R, Jurisica I. PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful. Nucleic Acids Res 2024; 52:D663-D671. [PMID: 37994706 PMCID: PMC10767947 DOI: 10.1093/nar/gkad1027] [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: 09/14/2023] [Revised: 10/16/2023] [Accepted: 10/20/2023] [Indexed: 11/24/2023] Open
Abstract
Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.
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Affiliation(s)
- Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Max Kotlyar
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Mark Abovsky
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Richard Lu
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Toronto, Ontario M5T 0S8, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Krembil Discovery Tower, Toronto, ON M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto, Toronto, ON M5G 1L7, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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20
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Alvarado CX, Makarious MB, Weller CA, Vitale D, Koretsky MJ, Bandres-Ciga S, Iwaki H, Levine K, Singleton A, Faghri F, Nalls MA, Leonard HL. omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases. Am J Hum Genet 2024; 111:150-164. [PMID: 38181731 PMCID: PMC10806756 DOI: 10.1016/j.ajhg.2023.12.006] [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: 07/19/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.
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Affiliation(s)
- Chelsea X Alvarado
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Mary B Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA; Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; UCL Movement Disorders Centre, University College London, London WC1N 3BG, UK
| | - Cory A Weller
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Dan Vitale
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Mathew J Koretsky
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA
| | - Hirotaka Iwaki
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Kristin Levine
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Andrew Singleton
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Faraz Faghri
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Hampton L Leonard
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
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21
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Whitlock JH, Wilk EJ, Howton TC, Clark AD, Lasseigne BN. The landscape of SETBP1 gene expression and transcription factor activity across human tissues. PLoS One 2024; 19:e0296328. [PMID: 38165902 PMCID: PMC10760659 DOI: 10.1371/journal.pone.0296328] [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: 10/26/2023] [Accepted: 12/11/2023] [Indexed: 01/04/2024] Open
Abstract
The SET binding protein 1 (SETBP1) gene encodes a transcription factor (TF) involved in various cellular processes. Variants in SETBP1 can result in three different diseases determined by the introduction (germline vs. somatic) and location of the variant. Germline variants cause the ultra-rare pediatric Schinzel Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disorder (SETBP1-HD), characterized by severe multisystemic abnormalities with neurodegeneration or a less severe brain phenotype accompanied by hypotonia and strabismus, respectively. Somatic variants in SETBP1 are associated with hematological malignancies and cancer development in other tissues in adults. To better understand the tissue-specific mechanisms involving SETBP1, we analyzed publicly available RNA-sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) project. We found SETBP1 and its known target genes were widely expressed across 31 adult human tissues. K-means clustering identified three distinct expression patterns of SETBP1 targets across tissues. Functional enrichment analysis (FEA) of each cluster revealed gene sets related to transcriptional regulation, DNA binding, and mitochondrial function. TF activity analysis of SETBP1 and its target TFs revealed tissue-specific TF activity, underscoring the role of tissue context-driven regulation and suggesting its impact in SETBP1-associated disease. In addition to uncovering tissue-specific molecular signatures of SETBP1 expression and TF activity, we provide a Shiny web application to facilitate exploring TF activity across human tissues for 758 TFs. This study provides insight into the landscape of SETBP1 expression and TF activity across 31 non-diseased human tissues and reveals tissue-specific expression and activity of SETBP1 and its targets. In conjunction with the web application we constructed, our framework enables researchers to generate hypotheses related to the role tissue backgrounds play with respect to gene expression and TF activity in different disease contexts.
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Affiliation(s)
- Jordan H. Whitlock
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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22
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Iannuccelli M, Vitriolo A, Licata L, Lo Surdo P, Contino S, Cheroni C, Capocefalo D, Castagnoli L, Testa G, Cesareni G, Perfetto L. Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders. Mol Psychiatry 2024; 29:186-196. [PMID: 38102483 PMCID: PMC11078740 DOI: 10.1038/s41380-023-02317-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 10/14/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
Autism spectrum disorder (ASD) comprises a large group of neurodevelopmental conditions featuring, over a wide range of severity and combinations, a core set of manifestations (restricted sociality, stereotyped behavior and language impairment) alongside various comorbidities. Common and rare variants in several hundreds of genes and regulatory regions have been implicated in the molecular pathogenesis of ASD along a range of causation evidence strength. Despite significant progress in elucidating the impact of few paradigmatic individual loci, such sheer complexity in the genetic architecture underlying ASD as a whole has hampered the identification of convergent actionable hubs hypothesized to relay between the vastness of risk alleles and the core phenotypes. In turn this has limited the development of strategies that can revert or ameliorate this condition, calling for a systems-level approach to probe the cross-talk of cooperating genes in terms of causal interaction networks in order to make convergences experimentally tractable and reveal their clinical actionability. As a first step in this direction, we have captured from the scientific literature information on the causal links between the genes whose variants have been associated with ASD and the whole human proteome. This information has been annotated in a computer readable format in the SIGNOR database and is made freely available in the resource website. To link this information to cell functions and phenotypes, we have developed graph algorithms that estimate the functional distance of any protein in the SIGNOR causal interactome to phenotypes and pathways. The main novelty of our approach resides in the possibility to explore the mechanistic links connecting the suggested gene-phenotype relations.
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Affiliation(s)
- Marta Iannuccelli
- Department of Biology, University of Rome Tor Vergata, Via Della Ricerca Scientifica, 00133, Rome, Italy
| | - Alessandro Vitriolo
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Via Adamello 16, 20139, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Santa Sofia 9, 20122, Milan, Italy
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Via Della Ricerca Scientifica, 00133, Rome, Italy
- Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
| | - Prisca Lo Surdo
- Department of Biology, University of Rome Tor Vergata, Via Della Ricerca Scientifica, 00133, Rome, Italy
- Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
| | - Silvia Contino
- Department of Biology, University of Rome Tor Vergata, Via Della Ricerca Scientifica, 00133, Rome, Italy
| | - Cristina Cheroni
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Santa Sofia 9, 20122, Milan, Italy
| | - Daniele Capocefalo
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Santa Sofia 9, 20122, Milan, Italy
| | - Luisa Castagnoli
- Department of Biology, University of Rome Tor Vergata, Via Della Ricerca Scientifica, 00133, Rome, Italy
| | - Giuseppe Testa
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy.
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Via Adamello 16, 20139, Milan, Italy.
- Department of Oncology and Hemato-Oncology, University of Milan, Via Santa Sofia 9, 20122, Milan, Italy.
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Via Della Ricerca Scientifica, 00133, Rome, Italy.
| | - Livia Perfetto
- Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy.
- Department of Biology and Biotechnologies 'Charles Darwin', Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.
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23
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Abstract
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.
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Affiliation(s)
- Tim James
- Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
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24
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Minaeva M, Domingo J, Rentzsch P, Lappalainen T. Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.31.573765. [PMID: 38260658 PMCID: PMC10802353 DOI: 10.1101/2023.12.31.573765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Understanding the role of transcription and transcription factors in cellular identity and disease, such as cancer and autoimmunity, is essential. However, comprehensive data resources for cell line-specific transcription factor-to-target gene annotations are currently limited. To address this, we developed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and transcription factor binding data, we generated regulons for four common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving transcription factor knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable tool and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.
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Affiliation(s)
- Mariia Minaeva
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, 17165, Sweden
| | | | - Philipp Rentzsch
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, 17165, Sweden
| | - Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, 17165, Sweden
- New York Genome Center, New York, NY 10013, USA
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25
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Fernando MB, Fan Y, Zhang Y, Kammourh S, Murphy AN, Ghorbani S, Onatzevitch R, Pero A, Padilla C, Flaherty EK, Prytkova IK, Cao L, Williams S, Fang G, Slesinger PA, Brennand KJ. Precise Therapeutic Targeting of Distinct NRXN1+/- Mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.28.564543. [PMID: 37961635 PMCID: PMC10634884 DOI: 10.1101/2023.10.28.564543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
As genetic studies continue to identify risk loci that are significantly associated with risk for neuropsychiatric disease, a critical unanswered question is the extent to which diverse mutations--sometimes impacting the same gene-- will require tailored therapeutic strategies. Here we consider this in the context of rare neuropsychiatric disorder-associated copy number variants (2p16.3) resulting in heterozygous deletions in NRXN1, a pre-synaptic cell adhesion protein that serves as a critical synaptic organizer in the brain. Complex patterns of NRXN1 alternative splicing are fundamental to establishing diverse neurocircuitry, vary between the cell types of the brain, and are differentially impacted by unique (non-recurrent) deletions. We contrast the cell-type-specific impact of patient-specific mutations in NRXN1 using human induced pluripotent stem cells, finding that perturbations in NRXN1 splicing result in divergent cell-type-specific synaptic outcomes. Via distinct loss-of-function (LOF) and gain-of-function (GOF) mechanisms, NRXN1+/- deletions cause decreased synaptic activity in glutamatergic neurons, yet increased synaptic activity in GABAergic neurons. Stratification of patients by LOF and GOF mechanisms will facilitate individualized restoration of NRXN1 isoform repertoires; towards this, antisense oligonucleotides knockdown mutant isoform expression and alters synaptic transcriptional signatures, while treatment with β-estradiol rescues synaptic function in glutamatergic neurons. Given the increasing number of mutations predicted to engender both LOF and GOF mechanisms in brain disease, our findings add nuance to future considerations of precision medicine.
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Affiliation(s)
- Michael B. Fernando
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Friedman Brain Institute, Black Family Stem Cell Institute, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06520
| | - Yu Fan
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Yanchun Zhang
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sarah Kammourh
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Aleta N. Murphy
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Friedman Brain Institute, Black Family Stem Cell Institute, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sadaf Ghorbani
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06520
- Haukeland University Hospital, Bergen, Norway
| | - Ryan Onatzevitch
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Adriana Pero
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Christopher Padilla
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Erin K. Flaherty
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Iya K. Prytkova
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Lei Cao
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sarah Williams
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Friedman Brain Institute, Black Family Stem Cell Institute, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Gang Fang
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Paul A. Slesinger
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kristen J. Brennand
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Friedman Brain Institute, Black Family Stem Cell Institute, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06520
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Stefanucci L, Collins J, Sims MC, Barrio-Hernandez I, Sun L, Burren OS, Perfetto L, Bender I, Callahan TJ, Fleming K, Guerrero JA, Hermjakob H, Martin MJ, Stephenson J, Paneerselvam K, Petrovski S, Porras P, Robinson PN, Wang Q, Watkins X, Frontini M, Laskowski RA, Beltrao P, Di Angelantonio E, Gomez K, Laffan M, Ouwehand WH, Mumford AD, Freson K, Carss K, Downes K, Gleadall N, Megy K, Bruford E, Vuckovic D. The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants. Blood 2023; 142:2055-2068. [PMID: 37647632 PMCID: PMC10733830 DOI: 10.1182/blood.2023020118] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.
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Affiliation(s)
- Luca Stefanucci
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Janine Collins
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Barts Health NHS Trust, London, United Kingdom
| | - Matthew C. Sims
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
| | - Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Luanluan Sun
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Oliver S. Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
- Department of Biology and Biotechnology “C.Darwin,” Sapienza University of Rome, Rome, Italy
| | - Isobel Bender
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Tiffany J. Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
| | - Kathryn Fleming
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
| | - Jose A. Guerrero
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Barts Health NHS Trust, London, United Kingdom
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Maria J. Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - James Stephenson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - NIHR BioResource
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, Barts Health NHS Trust, London, United Kingdom
- Department of Haematology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
- Department of Biology and Biotechnology “C.Darwin,” Sapienza University of Rome, Rome, Italy
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
- Centre for Genomics Research, Discovery Sciences, AstraZeneca, Cambridge, United Kingdom
- Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
- Genomic Medicine, The Jackson Laboratory, Farmington, CT
- Institute for Systems Genomics, University of Connecticut, Farmington, CT
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences RILD Building, University of Exeter Medical School, Exeter, United Kingdom
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Health Data Science Centre, Human Technopole, Milan, Italy
- Haemophilia Centre and Thrombosis Unit, Royal Free London NHS Foundation Trust, London, United Kingdom
- Department of Haematology, Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Immunology and Inflammation, Centre for Haematology, Imperial College London, London, United Kingdom
- Department of Haematology, University College London Hospitals NHS Trust, London, United Kingdom
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Leuven, Belgium
- Cambridge Genomics Laboratory, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Kalpana Paneerselvam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, AstraZeneca, Cambridge, United Kingdom
- Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Peter N. Robinson
- Genomic Medicine, The Jackson Laboratory, Farmington, CT
- Institute for Systems Genomics, University of Connecticut, Farmington, CT
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Xavier Watkins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences RILD Building, University of Exeter Medical School, Exeter, United Kingdom
| | - Roman A. Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Emanuele Di Angelantonio
- British Heart Foundation, BHF Centre of Research Excellence, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Public Health and Primary Care, BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Health Data Science Centre, Human Technopole, Milan, Italy
| | - Keith Gomez
- Haemophilia Centre and Thrombosis Unit, Royal Free London NHS Foundation Trust, London, United Kingdom
| | - Mike Laffan
- Department of Haematology, Imperial College Healthcare NHS Trust, London, United Kingdom
- Department of Immunology and Inflammation, Centre for Haematology, Imperial College London, London, United Kingdom
| | - Willem H. Ouwehand
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Haematology, University College London Hospitals NHS Trust, London, United Kingdom
| | - Andrew D. Mumford
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
| | - Kathleen Freson
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, KULeuven, Leuven, Belgium
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge Genomics Laboratory, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Nick Gleadall
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Karyn Megy
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elspeth Bruford
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Dragana Vuckovic
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
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27
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da Cunha JI, Barauna AMD, Garcez RC. Prechordal structures act cooperatively in early trabeculae development of gnathostome skull. Cells Dev 2023; 176:203879. [PMID: 37844659 DOI: 10.1016/j.cdev.2023.203879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 10/04/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023]
Abstract
The vertebrate skull is formed by mesoderm and neural crest (NC) cells. The mesoderm contributes to the skull chordal domain, with the notochord playing an essential role in this process. The NC contributes to the skull prechordal domain, prompting investigation into the embryonic structures involved in prechordal neurocranium cartilage formation. The trabeculae cartilage, a structure of the prechordal neurocranium, arises at the convergence of prechordal plate (PCP), ventral midline (VM) cells of the diencephalon, and dorsal oral ectoderm. This study examines the molecular participation of these embryonic structures in gnathostome trabeculae development. PCP-secreted SHH induces its expression in VM cells of the diencephalon, initiating a positive feedback loop involving SIX3 and GLI1. SHH secreted by the VM cells of the diencephalon acts on the dorsal oral ectoderm, stimulating condensation of NC cells to form trabeculae. SHH from the prechordal region affects the expression of SOX9 in NC cells. BMP7 and SHH secreted by PCP induce NKX2.1 expression in VM cells of the diencephalon, but this does not impact trabeculae formation. Molecular cooperation between PCP, VM cells of the diencephalon, and dorsal oral ectoderm is crucial for craniofacial development by NC cells in the prechordal domain.
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Affiliation(s)
- Jaqueline Isoppo da Cunha
- Graduate Program of Cell and Developmental Biology, Federal University of Santa Catarina, Florianopolis, SC, Brazil; Stem Cell and Tissue Regeneration Laboratory (LACERT), Federal University of Santa Catarina, Florianopolis, SC, Brazil
| | - Alessandra Maria Duarte Barauna
- Graduate Program of Cell and Developmental Biology, Federal University of Santa Catarina, Florianopolis, SC, Brazil; Stem Cell and Tissue Regeneration Laboratory (LACERT), Federal University of Santa Catarina, Florianopolis, SC, Brazil
| | - Ricardo Castilho Garcez
- Graduate Program of Cell and Developmental Biology, Federal University of Santa Catarina, Florianopolis, SC, Brazil; Stem Cell and Tissue Regeneration Laboratory (LACERT), Federal University of Santa Catarina, Florianopolis, SC, Brazil; Department of Cell Biology, Embryology, and Genetics, Federal University of Santa Catarina, Florianopolis, SC, Brazil.
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28
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Hammad R, Eldosoky MA, Elmadbouly AA, Aglan RB, AbdelHamid SG, Zaky S, Ali E, Abd El Hakam FEZ, Mosaad AM, Abdelmageed NA, Kotb FM, Kotb HG, Hady AA, Abo-Elkheir OI, Kujumdshiev S, Sack U, Lambert C, Hamdy NM. Monocytes subsets altered distribution and dysregulated plasma hsa-miR-21-5p and hsa-miR-155-5p in HCV-linked liver cirrhosis progression to hepatocellular carcinoma. J Cancer Res Clin Oncol 2023; 149:15349-15364. [PMID: 37639012 PMCID: PMC10620275 DOI: 10.1007/s00432-023-05313-w] [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: 07/04/2023] [Accepted: 08/16/2023] [Indexed: 08/29/2023]
Abstract
PURPOSE The authors aim to investigate the altered monocytes subsets distribution in liver cirrhosis (LC) and subsequent hepatocellular carcinoma (HCC) in association with the expression level of plasma Homo sapiens (has)-miR-21-5p and hsa-miR-155-5p. A step toward non-protein coding (nc) RNA precision medicine based on the immune perturbation manifested as altered monocytes distribution, on top of LC and HCC. METHODS Seventy-nine patients diagnosed with chronic hepatitis C virus (CHCV) infection with LC were enrolled in the current study. Patients were sub-classified into LC group without HCC (n = 40), LC with HCC (n = 39), and 15 apparently healthy controls. Monocyte subsets frequencies were assessed by flow cytometry. Real-time quantitative PCR was used to measure plasma hsa-miR-21-5p and hsa-miR-155-5p expression. RESULTS Hsa-miR-21-5p correlated with intermediate monocytes (r = 0.30, p = 0.007), while hsa-miR-155-5p negatively correlated with non-classical monocytes (r = - 0.316, p = 0.005). ROC curve analysis revealed that combining intermediate monocytes frequency and hsa-miR-21 yielded sensitivity = 79.5%, specificity = 75%, and AUC = 0.84. In comparison, AFP yielded a lower sensitivity = 69% and 100% specificity with AUC = 0.85. Logistic regression analysis proved that up-regulation of intermediate monocytes frequency and hsa-miR-21-5p were independent risk factors for LC progression to HCC, after adjustment for co-founders. CONCLUSION Monocyte subsets differentiation in HCC was linked to hsa-miR-21-5p and hsa-miR-155-5p. Combined up-regulation of intermediate monocytes frequency and hsa-miR-21-5p expression could be considered a sensitive indicator of LC progression to HCC. Circulating intermediate monocytes and hsa-miR-21-5p were independent risk factors for HCC evolution, clinically and in silico proved.
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Affiliation(s)
- Reham Hammad
- Clinical Pathology Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Mona A Eldosoky
- Clinical Pathology Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Asmaa A Elmadbouly
- Clinical Pathology Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Reda Badr Aglan
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Shibîn el Kôm, 35211, Menoufia, Egypt
| | - Sherihan G AbdelHamid
- Biochemistry Department, Faculty of Pharmacy, Ain Shams University, Abbasia, Cairo, 11566, Egypt
| | - Samy Zaky
- Hepatology, Gastroenterology and Infectious Diseases Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Elham Ali
- Molecular Biology, Zoology and Entomology Department, Faculty of Science (Girls), Al-Azhar University, Nasr City, Cairo, 11754, Egypt
| | | | - Alshaimaa M Mosaad
- Hepatology, Gastroenterology and Infectious Diseases Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Neamat A Abdelmageed
- Hepatology, Gastroenterology and Infectious Diseases Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Fatma M Kotb
- Internal Medicine Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Hend G Kotb
- Internal Medicine Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Ahmed A Hady
- Clinical Oncology and Nuclear Medicine Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Omaima I Abo-Elkheir
- Community Medicine and Public Health Department, Faculty of Medicine (Girls), Al-Azhar University, Nasr City, Cairo, 11884, Egypt
| | - Sandy Kujumdshiev
- Institute of Clinical Immunology, University Medical Center Leipzig, Johannisallee 30, 04103, Leipzig, Germany
- DHGS German University of Health and Sport, Berlin, Germany
| | - Ulrich Sack
- Institute of Clinical Immunology, University Medical Center Leipzig, Johannisallee 30, 04103, Leipzig, Germany
| | - Claude Lambert
- Cytometry Unit, Immunology Laboratory, Saint-Etienne University Hospital, Saint-Étienne, Lyon, France
| | - Nadia M Hamdy
- Biochemistry Department, Faculty of Pharmacy, Ain Shams University, Abbasia, Cairo, 11566, Egypt.
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29
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Somers J, Fenner M, Kong G, Thirumalaisamy D, Yashar WM, Thapa K, Kinali M, Nikolova O, Babur Ö, Demir E. A framework for considering prior information in network-based approaches to omics data analysis. Proteomics 2023; 23:e2200402. [PMID: 37986684 DOI: 10.1002/pmic.202200402] [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: 07/19/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/22/2023]
Abstract
For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.
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Affiliation(s)
- Julia Somers
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Madeleine Fenner
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Garth Kong
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Dharani Thirumalaisamy
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - William M Yashar
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Kisan Thapa
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Meric Kinali
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Olga Nikolova
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
- Division of Oncological Sciences, Oregon Health and Science University, Portland, Oregon, USA
| | - Özgün Babur
- Computer Science Department, University of Massachusetts Boston, College of Science and Mathematics, Boston, Massachusetts, USA
| | - Emek Demir
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
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30
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Massacci G, Perfetto L, Sacco F. The Cyclin-dependent kinase 1: more than a cell cycle regulator. Br J Cancer 2023; 129:1707-1716. [PMID: 37898722 PMCID: PMC10667339 DOI: 10.1038/s41416-023-02468-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/26/2023] [Accepted: 10/13/2023] [Indexed: 10/30/2023] Open
Abstract
The Cyclin-dependent kinase 1, as a serine/threonine protein kinase, is more than a cell cycle regulator as it was originally identified. During the last decade, it has been shown to carry out versatile functions during the last decade. From cell cycle control to gene expression regulation and apoptosis, CDK1 is intimately involved in many cellular events that are vital for cell survival. Here, we provide a comprehensive catalogue of the CDK1 upstream regulators and substrates, describing how this kinase is implicated in the control of key 'cell cycle-unrelated' biological processes. Finally, we describe how deregulation of CDK1 expression and activation has been closely associated with cancer progression and drug resistance.
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Affiliation(s)
- Giorgia Massacci
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133, Rome, Italy
| | - Livia Perfetto
- Department of Biology and Biotechnologies "Charles Darwin", University of Rome La Sapienza, Piazzale Aldo Moro 5, 00185, Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133, Rome, Italy.
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31
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Whitlock JH, Wilk EJ, Howton TC, Clark AD, Lasseigne BN. The landscape of SETBP1 gene expression and transcription factor activity across human tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.551337. [PMID: 37873221 PMCID: PMC10592643 DOI: 10.1101/2023.08.08.551337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background The SET binding protein 1 (SETBP1) gene encodes a transcription factor (TF) involved in various cellular processes. Distinct SETBP1 variants have been linked to three different diseases. Germline variants cause the ultra-rare pediatric Schinzel Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disorder (SETBP1-HD), characterized by severe multisystemic abnormalities with neurodegeneration or a less severe brain phenotype accompanied by hypotonia and strabismus, respectively. Somatic variants in SETBP1 are associated with hematological malignancies and cancer development in other tissues in adults. Results To better understand the tissue-specific mechanisms involving SETBP1, we analyzed publicly available RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project. We found SETBP1, and its known target genes were widely expressed across 31 adult human tissues. K-means clustering identified three distinct expression patterns of SETBP1 targets across tissues. Functional enrichment analysis (FEA) of each cluster revealed gene sets related to transcription regulation, DNA binding, and mitochondrial function. TF activity analysis of SETBP1 and its target TFs revealed tissue-specific TF activity, underscoring the role of tissue context-driven regulation and suggesting its impact in SETBP1-associated disease. In addition to uncovering tissue-specific molecular signatures of SETBP1 expression and TF activity, we provide a Shiny web application to facilitate exploring TF activity across human tissues for 758 TFs. Conclusions This study provides insight into the landscape of SETBP1 expression and TF activity across 31 non-diseased human tissues and reveals tissue-specific expression and activity of SETBP1 and its targets. In conjunction with the web application we constructed, our framework enables researchers to generate hypotheses related to the role tissue backgrounds play with respect to gene expression and TF activity in different disease contexts.
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Affiliation(s)
- Jordan H. Whitlock
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
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32
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Arora C, Matic M, DiChiaro P, Rosa NDO, Carli F, Clubb L, Fard LAN, Kargas G, Diaferia G, Vukotic R, Licata L, Wu G, Natoli G, Gutkind JS, Raimondi F. The landscape of cancer rewired GPCR signaling axes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532291. [PMID: 37398064 PMCID: PMC10312480 DOI: 10.1101/2023.03.13.532291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
We explored the dysregulation of GPCR ligand signaling systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes, which revealed that multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes. We showed that biosynthetic pathway enrichment from enzyme expression recapitulated pathway activity signatures from metabolomics datasets, providing valuable surrogate information for GPCRs responding to organic ligands. We found that several GPCRs signaling components were significantly associated with patient survival in a cancer type-specific fashion. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many pairs involving receptor-biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including e.g., muscarinic, adenosine, 5-hydroxytryptamine and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it).
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Affiliation(s)
- Chakit Arora
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
| | - Marin Matic
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
| | - Pierluigi DiChiaro
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy
| | - Natalia De Oliveira Rosa
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
| | - Francesco Carli
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
| | - Lauren Clubb
- Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lorenzo Amir Nemati Fard
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
| | - Giorgos Kargas
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
| | - Giuseppe Diaferia
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy
| | - Ranka Vukotic
- Azienda Ospedaliero-Universitaria Pisana, Via Roma, 67, 56126 Pisa
| | - Luana Licata
- Department of Biology, University of Rome ‘Tor Vergata’, Rome 00133, Italy
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Gioacchino Natoli
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy
| | - J. Silvio Gutkind
- Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Francesco Raimondi
- Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy
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Rodrigues-Ferreira S, Morin M, Guichaoua G, Moindjie H, Haykal MM, Collier O, Stoven V, Nahmias C. A Network of 17 Microtubule-Related Genes Highlights Functional Deregulations in Breast Cancer. Cancers (Basel) 2023; 15:4870. [PMID: 37835564 PMCID: PMC10571893 DOI: 10.3390/cancers15194870] [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: 08/31/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023] Open
Abstract
A wide panel of microtubule-associated proteins and kinases is involved in coordinated regulation of the microtubule cytoskeleton and may thus represent valuable molecular markers contributing to major cellular pathways deregulated in cancer. We previously identified a panel of 17 microtubule-related (MT-Rel) genes that are differentially expressed in breast tumors showing resistance to taxane-based chemotherapy. In the present study, we evaluated the expression, prognostic value and functional impact of these genes in breast cancer. We show that 14 MT-Rel genes (KIF4A, ASPM, KIF20A, KIF14, TPX2, KIF18B, KIFC1, AURKB, KIF2C, GTSE1, KIF15, KIF11, RACGAP1, STMN1) are up-regulated in breast tumors compared with adjacent normal tissue. Six of them (KIF4A, ASPM, KIF20A, KIF14, TPX2, KIF18B) are overexpressed by more than 10-fold in tumor samples and four of them (KIF11, AURKB, TPX2 and KIFC1) are essential for cell survival. Overexpression of all 14 genes, and underexpression of 3 other MT-Rel genes (MAST4, MAPT and MTUS1) are associated with poor breast cancer patient survival. A Systems Biology approach highlighted three major functional networks connecting the 17 MT-Rel genes and their partners, which are centered on spindle assembly, chromosome segregation and cytokinesis. Our studies identified mitotic Aurora kinases and their substrates as major targets for therapeutic approaches against breast cancer.
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Affiliation(s)
- Sylvie Rodrigues-Ferreira
- Gustave Roussy Cancer Center, F-94800 Villejuif, France; (S.R.-F.); (M.M.); (H.M.); (M.M.H.)
- INSERM U981, Université Paris-Saclay, F-94800 Villejuif, France
- Inovarion, F-75005 Paris, France
| | - Morgane Morin
- Gustave Roussy Cancer Center, F-94800 Villejuif, France; (S.R.-F.); (M.M.); (H.M.); (M.M.H.)
- INSERM U981, Université Paris-Saclay, F-94800 Villejuif, France
| | - Gwenn Guichaoua
- CBIO (Centre de Bioinformatique), Mines Paris-PSL, PSL Research University, F-75005 Paris, France;
- INSERM U900, Institut Curie, F-75005 Paris, France
| | - Hadia Moindjie
- Gustave Roussy Cancer Center, F-94800 Villejuif, France; (S.R.-F.); (M.M.); (H.M.); (M.M.H.)
- INSERM U981, Université Paris-Saclay, F-94800 Villejuif, France
| | - Maria M. Haykal
- Gustave Roussy Cancer Center, F-94800 Villejuif, France; (S.R.-F.); (M.M.); (H.M.); (M.M.H.)
- INSERM U981, Université Paris-Saclay, F-94800 Villejuif, France
| | - Olivier Collier
- MODAL’X, UPL, Université Paris Nanterre, CNRS, F-92000 Nanterre, France;
| | - Véronique Stoven
- CBIO (Centre de Bioinformatique), Mines Paris-PSL, PSL Research University, F-75005 Paris, France;
- INSERM U900, Institut Curie, F-75005 Paris, France
| | - Clara Nahmias
- Gustave Roussy Cancer Center, F-94800 Villejuif, France; (S.R.-F.); (M.M.); (H.M.); (M.M.H.)
- INSERM U981, Université Paris-Saclay, F-94800 Villejuif, France
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34
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Zeng Y, He Y, Zheng R, Li M. Inferring single-cell gene regulatory network by non-redundant mutual information. Brief Bioinform 2023; 24:bbad326. [PMID: 37715282 DOI: 10.1093/bib/bbad326] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/12/2023] [Accepted: 08/08/2023] [Indexed: 09/17/2023] Open
Abstract
Gene regulatory network plays a crucial role in controlling the biological processes of living creatures. Deciphering the complex gene regulatory networks from experimental data remains a major challenge in system biology. Recent advances in single-cell RNA sequencing technology bring massive high-resolution data, enabling computational inference of cell-specific gene regulatory networks (GRNs). Many relevant algorithms have been developed to achieve this goal in the past years. However, GRN inference is still less ideal due to the extra noises involved in pseudo-time information and large amounts of dropouts in datasets. Here, we present a novel GRN inference method named Normi, which is based on non-redundant mutual information. Normi manipulates these problems by employing a sliding size-fixed window approach on the entire trajectory and conducts average smoothing strategy on the gene expression of the cells in each window to obtain representative cells. To further alleviate the impact of dropouts, we utilize the mixed KSG estimator to quantify the high-order time-delayed mutual information among genes, then filter out the redundant edges by adopting Max-Relevance and Min Redundancy algorithm. Moreover, we determined the optimal time delay for each gene pair by distance correlation. Normi outperforms other state-of-the-art GRN inference methods on both simulated data and single-cell RNA sequencing (scRNA-seq) datasets, demonstrating its superiority in robustness. The performance of Normi in real scRNA-seq data further reveals its ability to identify the key regulators and crucial biological processes.
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Affiliation(s)
- Yanping Zeng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yongxin He
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Alvarado CX, Makarious MB, Weller CA, Vitale D, Koretsky MJ, Bandres-Ciga S, Iwaki H, Levine K, Singleton A, Faghri F, Nalls MA, Leonard HL. omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.06.23288266. [PMID: 37090611 PMCID: PMC10120805 DOI: 10.1101/2023.04.06.23288266] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer's disease, 3 amyotrophic lateral sclerosis, 5 Lewy body dementia, 46 Parkinson's disease, and 9 Progressive supranuclear palsy target genes passing multiple test corrections (pSMR_multi < 2.95×10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics - classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these 69.8% are expressed in the disease relevant cell type from single nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as Riluzole in AD. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community [https://nih-card-ndd-smr-home-syboky.streamlit.app/].
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Affiliation(s)
- Chelsea X. Alvarado
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
| | - Mary B. Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, 20814
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK, WC1N 3BG
- UCL Movement Disorders Centre, University College London, London, UK, WC1N 3BG
| | - Cory A. Weller
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
| | - Dan Vitale
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
| | - Mathew J. Koretsky
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
| | - Sara Bandres-Ciga
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
| | - Hirotaka Iwaki
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, 20814
| | - Kristin Levine
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
| | - Andrew Singleton
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, 20814
| | - Faraz Faghri
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, 20814
| | - Mike A. Nalls
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, 20814
| | - Hampton L. Leonard
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 20814
- Data Tecnica International, Washington, DC, USA, 20037
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA, 20814
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
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Li X, Dai A, Tran R, Wang J. Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective. Breast Cancer Res Treat 2023:10.1007/s10549-023-06996-y. [PMID: 37329459 DOI: 10.1007/s10549-023-06996-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 05/25/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND microRNA (miRNAs) are small, non-coding RNAs that mediate post-transcriptional gene silencing. Numerous studies have demonstrated the critical role of miRNAs in the development of breast cancer and ovarian cancer. To reduce potential bias from individual studies, a more comprehensive approach of exploring miRNAs in cancer research is essential. This study aims to explore the role of miRNAs in the development of breast cancer and ovarian cancer. METHODS Abstracts of the publications were tokenized and the biomedical terms (miRNA, gene, disease, species) were identified and extracted for vectorization. Predictive analyses were conducted with four machine learning models: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes. Both holdout validation and cross-validation were utilized. Feature importance will be identified for miRNA-cancer networks construction. RESULTS We found that miR-182 is highly specific to female cancers. miR-182 targets different genes in regulating breast cancer and ovarian cancer. Naïve Bayes provided a promising prediction model for breast cancer and ovarian cancer with miRNAs and genes combination, with an accuracy score greater than 60%. Feature importance identified miR-155 and miR-199 are critical for breast cancer and ovarian cancer prediction, with miR-155 being highly related to breast cancer, whereas miR-199 being more associated with ovarian cancer. CONCLUSION Our approach effectively identified potential miRNA biomarkers associated with breast cancer and ovarian cancer, providing a solid foundation for generating novel research hypotheses and guiding future experimental studies.
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Affiliation(s)
- Xin Li
- Ophthalmology Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, Shandong, China
| | - Andrea Dai
- Oakland University William Beaumont School of Medicine, Rochester, MI, 48309, USA
| | - Richard Tran
- Masters Program in Computer Science, University of Chicago, Chicago, IL, 20833, USA
| | - Jie Wang
- Applied Data Science Program, Syracuse University, Syracuse, NY, 13244, USA.
- MDSight, LLC, Brookeville, MD, 20833, USA.
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37
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Licata L, Via A, Turina P, Babbi G, Benevenuta S, Carta C, Casadio R, Cicconardi A, Facchiano A, Fariselli P, Giordano D, Isidori F, Marabotti A, Martelli PL, Pascarella S, Pinelli M, Pippucci T, Russo R, Savojardo C, Scafuri B, Valeriani L, Capriotti E. Resources and tools for rare disease variant interpretation. Front Mol Biosci 2023; 10:1169109. [PMID: 37234922 PMCID: PMC10206239 DOI: 10.3389/fmolb.2023.1169109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.
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Affiliation(s)
- Luana Licata
- Department of Biology, University of Rome Tor Vergata, Roma, Italy
| | - Allegra Via
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Claudio Carta
- National Centre for Rare Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Andrea Cicconardi
- Department of Physics, University of Genova, Genova, Italy
- Italiano di Tecnologia—IIT, Genova, Italy
| | - Angelo Facchiano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Federica Isidori
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Anna Marabotti
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Michele Pinelli
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
| | - Tommaso Pippucci
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Russo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Napoli, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Bernardina Scafuri
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | | | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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38
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Atta H, Alzahaby N, Hamdy NM, Emam SH, Sonousi A, Ziko L. New trends in synthetic drugs and natural products targeting 20S proteasomes in cancers. Bioorg Chem 2023; 133:106427. [PMID: 36841046 DOI: 10.1016/j.bioorg.2023.106427] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/15/2023] [Accepted: 02/12/2023] [Indexed: 02/19/2023]
Abstract
Cancer is a global health challenge that remains to be a field of extensive research aiming to find new anticancer therapeutics. The 20S proteasome complex is one of the targets of anticancerdrugs, as it is correlated with several cancer types. Herein, we aim to discuss the 20S proteasome subunits and investigatethe currently studied proteasome inhibitors targeting the catalytically active proteasome subunits. In this review, we summarize the proteindegradation mechanism of the 20S proteasome complex and compareit with the 26S proteasome complex. Afterwards, the localization of the 20S proteasome is summarized as well as its use as a diagnosticandprognostic marker. The FDA-approved proteasome inhibitors (PIs) under clinical trials are summarized and their current limited use in solid tumors is also reviewed in addition to the expression of theβ5 subunit in differentcell lines. The review discusses in-silico analysis of the active subunit of the 20S proteasome complex. For development of new proteasome inhibitor drugs, the natural products inhibiting the 20S proteasome are summarized, as well as novel methodologies and challenges for the natural product discovery and current information about the biosynthetic gene clusters encoding them. We herein briefly summarize some resistancemechanismsto the proteasomeinhibitors. Additionally, we focus on the three main classes of proteasome inhibitors: 1] boronic acid, 2] beta-lactone and 3] epoxide inhibitor classes, as well as other PI classes, and their IC50 values and their structure-activity relationship (SAR). Lastly,we summarize several future prospects of developing new proteasome inhibitors towards the treatment of tumors, especially solid tumors.
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Affiliation(s)
- Hind Atta
- School of Life and Medical Sciences, University of Hertfordshire Hosted By Global Academic Foundation, Egypt
| | - Nouran Alzahaby
- Biochemistry Department, Faculty of Pharmacy, Ain Shams University, Abassia 11566, Cairo, Egypt
| | - Nadia M Hamdy
- Biochemistry Department, Faculty of Pharmacy, Ain Shams University, Abassia 11566, Cairo, Egypt
| | - Soha H Emam
- Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt
| | - Amr Sonousi
- School of Life and Medical Sciences, University of Hertfordshire Hosted By Global Academic Foundation, Egypt; Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt
| | - Laila Ziko
- School of Life and Medical Sciences, University of Hertfordshire Hosted By Global Academic Foundation, Egypt; Biology Department, School of Sciences and Engineering, American University in Cairo, Egypt.
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Franciosa G, Locard-Paulet M, Jensen LJ, Olsen JV. Recent advances in kinase signaling network profiling by mass spectrometry. Curr Opin Chem Biol 2023; 73:102260. [PMID: 36657259 DOI: 10.1016/j.cbpa.2022.102260] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023]
Abstract
Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.
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Affiliation(s)
- Giulia Franciosa
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marie Locard-Paulet
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars J Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jesper V Olsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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