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Li XL, Zhang JQ, Shen XJ, Zhang Y, Guo DA. Overview and limitations of database in global traditional medicines: A narrative review. Acta Pharmacol Sin 2024:10.1038/s41401-024-01353-1. [PMID: 39095509 DOI: 10.1038/s41401-024-01353-1] [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: 07/27/2023] [Accepted: 07/02/2024] [Indexed: 08/04/2024] Open
Abstract
The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.
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Affiliation(s)
- Xiao-Lan Li
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jian-Qing Zhang
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Xuan-Jing Shen
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yu Zhang
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - De-An Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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2
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RPJ, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLoS Comput Biol 2024; 20:e1011814. [PMID: 38527092 PMCID: PMC10994553 DOI: 10.1371/journal.pcbi.1011814] [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: 01/10/2024] [Revised: 04/04/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, Denver, Colorado, United States of America
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Rachel PJ Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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Ma YM, Zhao L. Mechanism and Therapeutic Prospect of miRNAs in Neurodegenerative Diseases. Behav Neurol 2023; 2023:8537296. [PMID: 38058356 PMCID: PMC10697780 DOI: 10.1155/2023/8537296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 08/30/2023] [Accepted: 10/21/2023] [Indexed: 12/08/2023] Open
Abstract
MicroRNAs (miRNAs) are the smallest class of noncoding RNAs, which widely exist in animals and plants. They can inhibit translation or overexpression by combining with mRNA and participate in posttranscriptional regulation of genes, resulting in reduced expression of target proteins, affecting the development, growth, aging, metabolism, and other physiological and pathological processes of animals and plants. It is a powerful negative regulator of gene expression. It mediates the information exchange between different cellular pathways in cellular homeostasis and stress response and regulates the differentiation, plasticity, and neurotransmission of neurons. In neurodegenerative diseases, in addition to the complex interactions between genetic susceptibility and environmental factors, miRNAs can serve as a promising diagnostic tool for diseases. They can also increase or reduce neuronal damage by regulating the body's signaling pathways, immune system, stem cells, gut microbiota, etc. They can not only affect the occurrence of diseases and exacerbate disease progression but also promote neuronal repair and reduce apoptosis, to prevent and slow down the development of diseases. This article reviews the research progress of miRNAs on the mechanism and treatment of neurodegenerative diseases in the nervous system. This trial is registered with NCT01819545, NCT02129452, NCT04120493, NCT04840823, NCT02253732, NCT02045056, NCT03388242, NCT01992029, NCT04961450, NCT03088839, NCT04137926, NCT02283073, NCT04509271, NCT02859428, and NCT05243017.
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Affiliation(s)
- Ya-Min Ma
- Acupuncture and Massage Department of Nanyang Traditional Chinese Medicine Hospital, Wo Long District, Nanyang City 473000, China
| | - Lan Zhao
- Tianjin Key Laboratory of Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Xiqing District, Tianjin 300381, China
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Blaser MC, Buffolo F, Halu A, Turner ME, Schlotter F, Higashi H, Pantano L, Clift CL, Saddic LA, Atkins SK, Rogers MA, Pham T, Vromman A, Shvartz E, Sukhova GK, Monticone S, Camussi G, Robson SC, Body SC, Muehlschlegel JD, Singh SA, Aikawa M, Aikawa E. Multiomics of Tissue Extracellular Vesicles Identifies Unique Modulators of Atherosclerosis and Calcific Aortic Valve Stenosis. Circulation 2023; 148:661-678. [PMID: 37427430 PMCID: PMC10527599 DOI: 10.1161/circulationaha.122.063402] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 06/02/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND Fewer than 50% of patients who develop aortic valve calcification have concomitant atherosclerosis, implying differential pathogenesis. Although circulating extracellular vesicles (EVs) act as biomarkers of cardiovascular diseases, tissue-entrapped EVs are associated with early mineralization, but their cargoes, functions, and contributions to disease remain unknown. METHODS Disease stage-specific proteomics was performed on human carotid endarterectomy specimens (n=16) and stenotic aortic valves (n=18). Tissue EVs were isolated from human carotid arteries (normal, n=6; diseased, n=4) and aortic valves (normal, n=6; diseased, n=4) by enzymatic digestion, (ultra)centrifugation, and a 15-fraction density gradient validated by proteomics, CD63-immunogold electron microscopy, and nanoparticle tracking analysis. Vesiculomics, comprising vesicular proteomics and small RNA-sequencing, was conducted on tissue EVs. TargetScan identified microRNA targets. Pathway network analyses prioritized genes for validation in primary human carotid artery smooth muscle cells and aortic valvular interstitial cells. RESULTS Disease progression drove significant convergence (P<0.0001) of carotid artery plaque and calcified aortic valve proteomes (2318 proteins). Each tissue also retained a unique subset of differentially enriched proteins (381 in plaques; 226 in valves; q<0.05). Vesicular gene ontology terms increased 2.9-fold (P<0.0001) among proteins modulated by disease in both tissues. Proteomics identified 22 EV markers in tissue digest fractions. Networks of proteins and microRNA targets changed by disease progression in both artery and valve EVs revealed shared involvement in intracellular signaling and cell cycle regulation. Vesiculomics identified 773 proteins and 80 microRNAs differentially enriched by disease exclusively in artery or valve EVs (q<0.05); multiomics integration found tissue-specific EV cargoes associated with procalcific Notch and Wnt signaling in carotid arteries and aortic valves, respectively. Knockdown of tissue-specific EV-derived molecules FGFR2, PPP2CA, and ADAM17 in human carotid artery smooth muscle cells and WNT5A, APP, and APC in human aortic valvular interstitial cells significantly modulated calcification. CONCLUSIONS The first comparative proteomics study of human carotid artery plaques and calcified aortic valves identifies unique drivers of atherosclerosis versus aortic valve stenosis and implicates EVs in advanced cardiovascular calcification. We delineate a vesiculomics strategy to isolate, purify, and study protein and RNA cargoes from EVs entrapped in fibrocalcific tissues. Integration of vesicular proteomics and transcriptomics by network approaches revealed novel roles for tissue EVs in modulating cardiovascular disease.
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Affiliation(s)
- Mark C. Blaser
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Fabrizio Buffolo
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Arda Halu
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mandy E. Turner
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Florian Schlotter
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Hideyuki Higashi
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lorena Pantano
- T H Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Cassandra L. Clift
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Louis A. Saddic
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Samantha K. Atkins
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Maximillian A. Rogers
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Tan Pham
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Amélie Vromman
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Eugenia Shvartz
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Galina K Sukhova
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Silvia Monticone
- Division of Internal Medicine and Hypertension, Department of Medical Sciences, University of Torino, Torino, Italy
| | - Giovanni Camussi
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Simon C. Robson
- Center for Inflammation Research, Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
| | - Simon C. Body
- Boston University School of Medicine, Boston, MA, USA
| | - Jochen D. Muehlschlegel
- Center for Perioperative Genomics, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sasha A. Singh
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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5
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Rathore D, Marino MJ, Nita-Lazar A. Omics and systems view of innate immune pathways. Proteomics 2023; 23:e2200407. [PMID: 37269203 DOI: 10.1002/pmic.202200407] [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: 02/14/2023] [Revised: 04/16/2023] [Accepted: 05/23/2023] [Indexed: 06/04/2023]
Abstract
Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.
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Affiliation(s)
- Deepali Rathore
- Functional Cellular Networks Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Matthew J Marino
- Functional Cellular Networks Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
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Hosseini-Gerami L, Higgins IA, Collier DA, Laing E, Evans D, Broughton H, Bender A. Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis. BMC Bioinformatics 2023; 24:154. [PMID: 37072707 PMCID: PMC10111792 DOI: 10.1186/s12859-023-05277-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 04/06/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks. RESULTS According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 'landmark' genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets. CONCLUSIONS Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.
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Affiliation(s)
- Layla Hosseini-Gerami
- Department of Chemistry, Centre for Molecular Informatics, Cambridge, UK
- Ignota Labs, London, UK
| | | | - David A Collier
- Eli Lilly and Company, Bracknell, UK
- Social, Genetic and Developmental Psychiatry Centre, IoPPN, Kings's College London, London, UK
- Genetic and Genomic Consulting Ltd, Farnham, UK
| | - Emma Laing
- Eli Lilly and Company, Bracknell, UK
- GSK, Stevenage, UK
| | - David Evans
- Eli Lilly and Company, Bracknell, UK
- DeepMind, London, UK
| | - Howard Broughton
- Centre de Investigación, Eli Lilly and Company, Alcobendas, Spain
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, Cambridge, UK.
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Shin SY, Nguyen LK. SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks. Methods Mol Biol 2023; 2634:357-381. [PMID: 37074588 DOI: 10.1007/978-1-0716-3008-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.
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Affiliation(s)
- Sung-Young Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia.
- Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia.
- Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
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8
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Dovrolis N, Filidou E, Tarapatzi G, Kokkotis G, Spathakis M, Kandilogiannakis L, Drygiannakis I, Valatas V, Arvanitidis K, Karakasiliotis I, Vradelis S, Manolopoulos VG, Paspaliaris V, Bamias G, Kolios G. Co-expression of fibrotic genes in inflammatory bowel disease; A localized event? Front Immunol 2022; 13:1058237. [PMID: 36632136 PMCID: PMC9826764 DOI: 10.3389/fimmu.2022.1058237] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/08/2022] [Indexed: 12/27/2022] Open
Abstract
Introduction Extracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis. Methods and results In this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn's disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively. Discussion These findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies.
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Affiliation(s)
- Nikolas Dovrolis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece,*Correspondence: George Kolios, ; Nikolas Dovrolis,
| | - Eirini Filidou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Gesthimani Tarapatzi
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Georgios Kokkotis
- Gastrointestinal (GI) Unit, 3 Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Michail Spathakis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Leonidas Kandilogiannakis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Ioannis Drygiannakis
- Gastroenterology and Hepatology Research Laboratory, Medical School, University of Crete, Heraklion, Greece
| | - Vassilis Valatas
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Gastroenterology and Hepatology Research Laboratory, Medical School, University of Crete, Heraklion, Greece
| | - Konstantinos Arvanitidis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Ioannis Karakasiliotis
- Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Stergios Vradelis
- Second Department of Internal Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, Alexandroupolis, Greece
| | - Vangelis G. Manolopoulos
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | | | - Giorgos Bamias
- Gastrointestinal (GI) Unit, 3 Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - George Kolios
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece,Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece,*Correspondence: George Kolios, ; Nikolas Dovrolis,
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9
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Gable AL, Szklarczyk D, Lyon D, Matias Rodrigues JF, von Mering C. Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments. Brief Bioinform 2022; 23:bbac355. [PMID: 36088548 PMCID: PMC9487593 DOI: 10.1093/bib/bbac355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/13/2022] [Accepted: 07/30/2022] [Indexed: 11/14/2022] Open
Abstract
A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.
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Affiliation(s)
- Annika L Gable
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Damian Szklarczyk
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - David Lyon
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Christian von Mering
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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10
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Giuseppe A, Chiara P, Yun N, Igor J. Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture. Brief Bioinform 2022; 23:6691914. [PMID: 36063560 DOI: 10.1093/bib/bbac368] [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: 05/16/2022] [Revised: 07/25/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
Biological pathways are a broadly used formalism for representing and interpreting the cascade of biochemical reactions underlying cellular and biological mechanisms. Pathway representation provides an ontological link among biomolecules such as RNA, DNA, small molecules, proteins, protein complexes, hormones and genes. Frequently, pathway annotations are used to identify mechanisms linked to genes within affected biological contexts. This important role and the simplicity and elegance in representing complex interactions led to an explosion of pathway representations and databases. Unfortunately, the lack of overlap across databases results in inconsistent enrichment analysis results, unless databases are integrated. However, due to absence of consensus, guidelines or gold standards in pathway definition and representation, integration of data across pathway databases is not straightforward. Despite multiple attempts to provide consolidated pathways, highly related, redundant, poorly overlapping or ambiguous pathways continue to render pathways analysis inconsistent and hard to interpret. Ontology-based integration will promote unbiased, comprehensive yet streamlined analysis of experiments, and will reduce the number of enriched pathways when performing pathway enrichment analysis. Moreover, appropriate and consolidated pathways provide better training data for pathway prediction algorithms. In this manuscript, we describe the current methods for pathway consolidation, their strengths and pitfalls, and highlight directions for future improvements to this research area.
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Affiliation(s)
- Agapito Giuseppe
- Department of Law, Economics and Social Sciences, University Magna Græcia of Catanzaro, Italy.,Data Analytic Research Center, University Magna Græcia of Catanzaro, Italy
| | - Pastrello Chiara
- Osteoarthritis Research Program, Division of Orthopaedics, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
| | - Niu Yun
- Osteoarthritis Research Program, Division of Orthopaedics, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
| | - Jurisica Igor
- Osteoarthritis Research Program, Division of Orthopaedics, Schroeder Arthritis Institute, University Health Network, Toronto, Canada.,Departments of Medical Biophysics and Computer Science Canada, University of Toronto, Toronto, Canada.,Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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11
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Jin LQ, Zhou Y, Li YS, Zhang G, Hu J, Selzer ME. Transcriptomes of Injured Lamprey Axon Tips: Single-Cell RNA-Seq Suggests Differential Involvement of MAPK Signaling Pathways in Axon Retraction and Regeneration after Spinal Cord Injury. Cells 2022; 11:cells11152320. [PMID: 35954164 PMCID: PMC9367414 DOI: 10.3390/cells11152320] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 02/01/2023] Open
Abstract
Axotomy in the CNS activates retrograde signals that can trigger regeneration or cell death. Whether these outcomes use different injury signals is not known. Local protein synthesis in axon tips plays an important role in axon retraction and regeneration. Microarray and RNA-seq studies on cultured mammalian embryonic or early postnatal peripheral neurons showed that axon growth cones contain hundreds to thousands of mRNAs. In the lamprey, identified reticulospinal neurons vary in the probability that their axons will regenerate after axotomy. The bad regenerators undergo early severe axon retraction and very delayed apoptosis. We micro-aspirated axoplasms from 10 growing, 9 static and 5 retracting axon tips of spinal cord transected lampreys and performed single-cell RNA-seq, analyzing the results bioinformatically. Genes were identified that were upregulated selectively in growing (n = 38), static (20) or retracting tips (18). Among them, map3k2, csnk1e and gtf2h were expressed in growing tips, mapk8(1) was expressed in static tips and prkcq was expressed in retracting tips. Venn diagrams revealed more than 40 components of MAPK signaling pathways, including jnk and p38 isoforms, which were differentially distributed in growing, static and/or retracting tips. Real-time q-PCR and immunohistochemistry verified the colocalization of map3k2 and csnk1e in growing axon tips. Thus, differentially regulated MAPK and circadian rhythm signaling pathways may be involved in activating either programs for axon regeneration or axon retraction and apoptosis.
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Affiliation(s)
- Li-Qing Jin
- Shriners Hospitals Pediatric Research Center, The Lewis Katz School of Medicine (LKSOM) at Temple University, Philadelphia, PA 19140, USA; (G.Z.); (J.H.)
- Department of Neural Sciences, Lewis Katz School of Medicine (LKSOM), 3500 North Broad Street, Philadelphia, PA 19140, USA
- Correspondence: (L.-Q.J.); (M.E.S.)
| | - Yan Zhou
- Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA;
| | - Yue-Sheng Li
- DNA Sequence & Genomics Core Facility at the NHLBI, Bethesda, MD 20892, USA;
| | - Guixin Zhang
- Shriners Hospitals Pediatric Research Center, The Lewis Katz School of Medicine (LKSOM) at Temple University, Philadelphia, PA 19140, USA; (G.Z.); (J.H.)
- Department of Neural Sciences, Lewis Katz School of Medicine (LKSOM), 3500 North Broad Street, Philadelphia, PA 19140, USA
| | - Jianli Hu
- Shriners Hospitals Pediatric Research Center, The Lewis Katz School of Medicine (LKSOM) at Temple University, Philadelphia, PA 19140, USA; (G.Z.); (J.H.)
- Department of Neural Sciences, Lewis Katz School of Medicine (LKSOM), 3500 North Broad Street, Philadelphia, PA 19140, USA
| | - Michael E. Selzer
- Shriners Hospitals Pediatric Research Center, The Lewis Katz School of Medicine (LKSOM) at Temple University, Philadelphia, PA 19140, USA; (G.Z.); (J.H.)
- Department of Neural Sciences, Lewis Katz School of Medicine (LKSOM), 3500 North Broad Street, Philadelphia, PA 19140, USA
- Department of Neurology, Lewis Katz School of Medicine (LKSOM), 3500 North Broad Street, Philadelphia, PA 19140, USA
- Correspondence: (L.-Q.J.); (M.E.S.)
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12
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Vignet P, Coquet J, Auber S, Boudet M, Siegel A, Théret N. Discrete modeling for integration and analysis of large-scale signaling networks. PLoS Comput Biol 2022; 18:e1010175. [PMID: 35696426 PMCID: PMC9232147 DOI: 10.1371/journal.pcbi.1010175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/24/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states. The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene). Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states.
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Affiliation(s)
- Pierre Vignet
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jean Coquet
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Sébastien Auber
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Matéo Boudet
- IGEPP, Agrocampus Ouest, INRAE, Université de Rennes 1, Le Rheu, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- * E-mail: (AS); (NT)
| | - Nathalie Théret
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- * E-mail: (AS); (NT)
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13
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OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization. Comput Struct Biotechnol J 2022; 20:2280-2291. [PMID: 35615022 PMCID: PMC9120235 DOI: 10.1016/j.csbj.2022.05.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/29/2022] Open
Abstract
OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein–protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.
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14
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Jiang W, Jones JC, Shankavaram U, Sproull M, Camphausen K, Krauze AV. Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications. Cancers (Basel) 2022; 14:2227. [PMID: 35565358 PMCID: PMC9105298 DOI: 10.3390/cancers14092227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
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Affiliation(s)
- Will Jiang
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Jennifer C. Jones
- Translational Nanobiology Section, Laboratory of Pathology, NIH/NCI/CCR, Bethesda, MD 20892, USA;
| | - Uma Shankavaram
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
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15
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Nicolas A, Deplanche M, Commere PH, Diot A, Genthon C, Marques da Silva W, Azevedo V, Germon P, Jamme H, Guédon E, Le Loir Y, Laurent F, Bierne H, Berkova N. Transcriptome Architecture of Osteoblastic Cells Infected With Staphylococcus aureus Reveals Strong Inflammatory Responses and Signatures of Metabolic and Epigenetic Dysregulation. Front Cell Infect Microbiol 2022; 12:854242. [PMID: 35531332 PMCID: PMC9067450 DOI: 10.3389/fcimb.2022.854242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/03/2022] [Indexed: 11/21/2022] Open
Abstract
Staphylococcus aureus is an opportunistic pathogen that causes a range of devastating diseases including chronic osteomyelitis, which partially relies on the internalization and persistence of S. aureus in osteoblasts. The identification of the mechanisms of the osteoblast response to intracellular S. aureus is thus crucial to improve the knowledge of this infectious pathology. Since the signal from specifically infected bacteria-bearing cells is diluted and the results are confounded by bystander effects of uninfected cells, we developed a novel model of long-term infection. Using a flow cytometric approach we isolated only S. aureus-bearing cells from mixed populations that allows to identify signals specific to intracellular infection. Here we present an in-depth analysis of the effect of long-term S. aureus infection on the transcriptional program of human osteoblast-like cells. After RNA-seq and KEGG and Reactome pathway enrichment analysis, the remodeled transcriptomic profile of infected cells revealed exacerbated immune and inflammatory responses, as well as metabolic dysregulations that likely influence the intracellular life of bacteria. Numerous genes encoding epigenetic regulators were downregulated. The later included genes coding for components of chromatin-repressive complexes (e.g., NuRD, BAHD1 and PRC1) and epifactors involved in DNA methylation. Sets of genes encoding proteins of cell adhesion or neurotransmission were also deregulated. Our results suggest that intracellular S. aureus infection has a long-term impact on the genome and epigenome of host cells, which may exert patho-physiological dysfunctions additionally to the defense response during the infection process. Overall, these results not only improve our conceptual understanding of biological processes involved in the long-term S. aureus infections of osteoblast-like cells, but also provide an atlas of deregulated host genes and biological pathways and identify novel markers and potential candidates for prophylactic and therapeutic approaches.
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Affiliation(s)
- Aurélie Nicolas
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Institut Agro, Science et Technologie du Lait et de l’OEuf (STLO), Rennes, France
| | - Martine Deplanche
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Institut Agro, Science et Technologie du Lait et de l’OEuf (STLO), Rennes, France
| | - Pierre-Henri Commere
- Cytometry and Biomarkers Centre de Ressources et Recherches Technologiques (C2RT), Institut Pasteur, Paris, France
| | - Alan Diot
- Centre International de Recherche en Infectiologie, CIRI, Inserm U1111, Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche 5308 (UMR5308), Ecole Normale Supérieure (ENS) de Lyon, Universit´ Claude Bernard Lyon 1 (UCBL1), Lyon, France
- Hospices Civils de Lyon, French National Reference Centre for Staphylococci, Lyon, France
| | - Clemence Genthon
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Unité Service 1426 (US1426), Transcriptome Plateforme Technologique (GeT-PlaGe), Genotoul, Castanet-Tolosan, France
| | - Wanderson Marques da Silva
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Institut Agro, Science et Technologie du Lait et de l’OEuf (STLO), Rennes, France
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Vasco Azevedo
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Pierre Germon
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Université François Rabelais, Infectiologie et Santé Publique (ISP), Tours, France
| | - Hélène Jamme
- Université Paris-Saclay, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Biologie de la Reproduction, Environnement, Epigénétique et Développement (BREED), Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, Biologie de la Reproduction, Environnement, Epigénétique et Développement (BREED), Maisons-Alfort, France
| | - Eric Guédon
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Institut Agro, Science et Technologie du Lait et de l’OEuf (STLO), Rennes, France
| | - Yves Le Loir
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Institut Agro, Science et Technologie du Lait et de l’OEuf (STLO), Rennes, France
| | - Fréderic Laurent
- Centre International de Recherche en Infectiologie, CIRI, Inserm U1111, Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche 5308 (UMR5308), Ecole Normale Supérieure (ENS) de Lyon, Universit´ Claude Bernard Lyon 1 (UCBL1), Lyon, France
- Hospices Civils de Lyon, French National Reference Centre for Staphylococci, Lyon, France
| | - Hélène Bierne
- Université Paris-Saclay, Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Nadia Berkova
- Institut National de Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Institut Agro, Science et Technologie du Lait et de l’OEuf (STLO), Rennes, France
- *Correspondence: Nadia Berkova,
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Catozzi S, Ternet C, Gourrege A, Wynne K, Oliviero G, Kiel C. Reconstruction and analysis of a large-scale binary Ras-effector signaling network. Cell Commun Signal 2022; 20:24. [PMID: 35246154 PMCID: PMC8896392 DOI: 10.1186/s12964-022-00823-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/18/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic. METHODS A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein-protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions. RESULTS The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways. CONCLUSIONS Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a 'reference interactome' and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input-output relationships, crosstalk, and pathway redundancy. Video Abstract.
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Affiliation(s)
- Simona Catozzi
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Camille Ternet
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Alize Gourrege
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Giorgio Oliviero
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Christina Kiel
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland. .,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland. .,Department of Molecular Medicine, University of Pavia, 27100, Pavia, Italy.
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17
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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18
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Rise K, Tessem MB, Drabløs F, Rye MB. FunHoP: Enhanced Visualization and Analysis of Functionally Homologous Proteins in Complex Metabolic Networks. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:848-859. [PMID: 33741524 PMCID: PMC9170767 DOI: 10.1016/j.gpb.2021.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 05/08/2019] [Accepted: 08/18/2019] [Indexed: 11/28/2022]
Abstract
Cytoscape is often used for visualization and analysis of metabolic pathways. For example, based on KEGG data, a reader for KEGG Markup Language (KGML) is used to load files into Cytoscape. However, although multiple genes can be responsible for the same reaction, the KGML-reader KEGGScape only presents the first listed gene in a network node for a given reaction. This can lead to incorrect interpretations of the pathways. Our new method, FunHoP, shows all possible genes in each node, making the pathways more complete. FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq. Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads, and weighting the reads on gene length and ratio, a new expression value is calculated for the node as a whole. Differential expression at node level is then applied to the networks. Using prostate cancer as model, we integrate RNA-seq data from two patient cohorts with metabolism data from literature. Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically. Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.
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Affiliation(s)
- Kjersti Rise
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim NO-7491, Norway.
| | - May-Britt Tessem
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim NO-7491, Norway
| | - Finn Drabløs
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim NO-7491, Norway
| | - Morten B Rye
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim NO-7491, Norway; Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim NO-7491, Norway.
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19
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Riddell N, Murphy MJ, Crewther SG. Electroretinography and Gene Expression Measures Implicate Phototransduction and Metabolic Shifts in Chick Myopia and Hyperopia Models. Life (Basel) 2021; 11:life11060501. [PMID: 34072440 PMCID: PMC8228081 DOI: 10.3390/life11060501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 12/26/2022] Open
Abstract
The Retinal Ion-Driven Fluid Efflux (RIDE) model theorizes that phototransduction-driven changes in trans-retinal ion and fluid transport underlie the development of myopia (short-sightedness). In support of this model, previous functional studies have identified the attenuation of outer retinal contributions to the global flash electroretinogram (gfERG) following weeks of myopia induction in chicks, while discovery-driven transcriptome studies have identified changes to the expression of ATP-driven ion transport and mitochondrial metabolism genes in the retina/RPE/choroid at the mid- to late-induction time-points. Less is known about the early time-points despite biometric analyses demonstrating changes in eye growth by 3 h in the chick lens defocus model. Thus, the present study compared gfERG and transcriptome profiles between 3 h and 3 days of negative lens-induced myopia and positive lens-induced hyperopia in chicks. Photoreceptor (a-wave and d-wave) and bipolar (b-wave and late-stage d-wave) cell responses were suppressed following negative lens-wear, particularly at the 3–4 h and 3-day time-points when active shifts in the rate of ocular growth were expected. Transcriptome measures revealed the up-regulation of oxidative phosphorylation genes following 6 h of negative lens-wear, concordant with previous reports at 2 days in this model. Signal transduction pathways, with core genes involved in glutamate and G-protein coupled receptor signalling, were down-regulated at 6 h. These findings contribute to a growing body of evidence for the dysregulation of phototransduction and mitochondrial metabolism in animal models of myopia.
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20
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Abstract
Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.
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Affiliation(s)
- Mark C. Blaser
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon Kraler
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
| | - Thomas F. Lüscher
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
- Heart Division, Royal Brompton & Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College, London, UK
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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21
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Kim BK, Lee HS, Lee SY, Park HW. Different Biological Pathways Between Good and Poor Inhaled Corticosteroid Responses in Asthma. Front Med (Lausanne) 2021; 8:652824. [PMID: 33816533 PMCID: PMC8012484 DOI: 10.3389/fmed.2021.652824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 02/25/2021] [Indexed: 12/18/2022] Open
Abstract
Gene regulatory networks address how transcription factors (TFs) and their regulatory roles in gene expression determine the responsiveness to anti-asthma therapy. The purpose of this study was to assess gene regulatory networks of adult patients with asthma who showed good or poor lung function improvements in response to inhaled corticosteroids (ICSs). A total of 47 patients with asthma were recruited and classified as good responders (GRs) and poor responders (PRs) based on their responses to ICSs. Genome-wide gene expression was measured using peripheral blood mononuclear cells obtained in a stable state. We used Passing Attributes between Networks for Data Assimilations to construct the gene regulatory networks associated with GRs and PRs to ICSs. We identified the top-10 TFs that showed large differences in high-confidence edges between the GR and PR aggregate networks. These top-10 TFs and their differentially-connected genes in the PR and GR aggregate networks were significantly enriched in distinct biological pathways, such as TGF-β signaling, cell cycle, and IL-4 and IL-13 signaling pathways. We identified multiple TFs and related biological pathways influencing ICS responses in asthma. Our results provide potential targets to overcome insensitivity to corticosteroids in patients with asthma.
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Affiliation(s)
- Byung-Keun Kim
- Department of Internal Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Hyun-Seung Lee
- Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, South Korea
| | - Suh-Young Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Heung-Woo Park
- Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, South Korea.,Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.,Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
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22
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Schlotter F, de Freitas RCC, Rogers MA, Blaser MC, Wu PJ, Higashi H, Halu A, Iqbal F, Andraski AB, Rodia CN, Kuraoka S, Wen JR, Creager M, Pham T, Hutcheson JD, Body SC, Kohan AB, Sacks FM, Aikawa M, Singh SA, Aikawa E. ApoC-III is a novel inducer of calcification in human aortic valves. J Biol Chem 2021; 296:100193. [PMID: 33334888 PMCID: PMC7948477 DOI: 10.1074/jbc.ra120.015700] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/07/2020] [Accepted: 12/15/2020] [Indexed: 01/02/2023] Open
Abstract
Calcific aortic valve disease (CAVD) occurs when subpopulations of valve cells undergo specific differentiation pathways, promoting tissue fibrosis and calcification. Lipoprotein particles carry oxidized lipids that promote valvular disease, but low-density lipoprotein-lowering therapies have failed in clinical trials, and there are currently no pharmacological interventions available for this disease. Apolipoproteins are known promoters of atherosclerosis, but whether they possess pathogenic properties in CAVD is less clear. To search for a possible link, we assessed 12 apolipoproteins in nonfibrotic/noncalcific and fibrotic/calcific aortic valve tissues by proteomics and immunohistochemistry to understand if they were enriched in calcified areas. Eight apolipoproteins (apoA-I, apoA-II, apoA-IV, apoB, apoC-III, apoD, apoL-I, and apoM) were enriched in the calcific versus nonfibrotic/noncalcific tissues. Apo(a), apoB, apoC-III, apoE, and apoJ localized within the disease-prone fibrosa and colocalized with calcific regions as detected by immunohistochemistry. Circulating apoC-III on lipoprotein(a) is a potential biomarker of aortic stenosis incidence and progression, but whether apoC-III also induces aortic valve calcification is unknown. We found that apoC-III was increased in fibrotic and calcific tissues and observed within the calcification-prone fibrosa layer as well as around calcification. In addition, we showed that apoC-III induced calcification in primary human valvular cell cultures via a mitochondrial dysfunction/inflammation-mediated pathway. This study provides a first assessment of a broad array of apolipoproteins in CAVD tissues, demonstrates that specific apolipoproteins associate with valvular calcification, and implicates apoC-III as an active and modifiable driver of CAVD beyond its potential role as a biomarker.
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Affiliation(s)
- Florian Schlotter
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Renata C C de Freitas
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Maximillian A Rogers
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark C Blaser
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Pin-Jou Wu
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hideyuki Higashi
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Arda Halu
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Farwah Iqbal
- Division of Cardiovascular Medicine, Department of Medicine, Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Allison B Andraski
- Department of Nutrition and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Cayla N Rodia
- Department of Nutritional Sciences, University of Connecticut, Storrs, Connecticut, USA
| | - Shiori Kuraoka
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer R Wen
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Creager
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tan Pham
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua D Hutcheson
- Department of Biomedical Engineering, Florida International University, Miami, Florida, USA
| | - Simon C Body
- Department of Anesthesiology, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Alison B Kohan
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Frank M Sacks
- Department of Nutrition and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Masanori Aikawa
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Division of Cardiovascular Medicine, Department of Medicine, Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sasha A Singh
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Elena Aikawa
- Division of Cardiovascular Medicine, Department of Medicine, Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Division of Cardiovascular Medicine, Department of Medicine, Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Human Pathology, Sechenov First Moscow State Medical University, Moscow, Russia.
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23
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Monraz Gomez LC, Kondratova M, Sompairac N, Lonjou C, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Atlas of Cancer Signaling Network: A Resource of Multi-Scale Biological Maps to Study Disease Mechanisms. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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24
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Hong W, Li S, Cai Y, Zhang T, Yang Q, He B, Yu J, Chen Z. The Target MicroRNAs and Potential Underlying Mechanisms of Yiqi-Bushen-Tiaozhi Recipe against-Non-Alcoholic Steatohepatitis. Front Pharmacol 2020; 11:529553. [PMID: 33281601 PMCID: PMC7688626 DOI: 10.3389/fphar.2020.529553] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 10/08/2020] [Indexed: 12/12/2022] Open
Abstract
MicroRNAs (miRNAs) have emerged as potential therapeutic targets for non-alcoholic fatty liver disease/non-alcoholic steatohepatitis (NAFLD/NASH). Traditional Chineses Medicine (TCM) plays an important role in the prevention or treatment of NAFLD/NASH. However, miRNA targets of TCM against NASH still remain largely unknown. Here, we showed that Yiqi-Bushen-Tiaozhi (YBT) recipe effectively attenuated diet-induced NASH in C57BL/6 mice. To identify the miRNA targets of YBT and understand the potential underlying mechanisms, we performed network pharmacology using miRNA and mRNA deep sequencing data combined with Ingenuity Pathway Analysis (IPA). Mmu-let-7a-5p, mmu-let-7b-5p, mmu-let-7g-3p and mmu-miR-106b-3p were screened as the main targets of YBT. Our results suggested that YBT might alleviate NASH by regulating the expression of these miRNAs that potentially modulate inflammation/immunity and oxidative stress. This study provides useful information for guiding future studies on the mechanism of YBT against NASH by regulating miRNAs.
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Affiliation(s)
- Wei Hong
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
| | - Songsong Li
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
| | - Yueqin Cai
- Laboratory Animal Research Center of Zhejiang Chinese Medical University, Hangzhou, China
| | - Tingting Zhang
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
| | - Qingrou Yang
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
| | - Beihui He
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
| | - Jianshun Yu
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
| | - Zhiyun Chen
- The Second Central Laboratory, The First Affliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,Key Laboratory of Integrative Chinese and Western Medicine for the Diagnosis and Treatment of Circulatory Diseases of Zhejiang Province, Hangzhou, China
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25
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Kong Y, Yu T. forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction. Bioinformatics 2020; 36:3507-3515. [PMID: 32163118 DOI: 10.1093/bioinformatics/btaa164] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 02/07/2020] [Accepted: 03/08/2020] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This 'n≪p' property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relationships between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection. RESULTS To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data. AVAILABILITY AND IMPLEMENTATION The method is available at https://github.com/yunchuankong/forgeNet. CONTACT tianwei.yu@emory.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yunchuan Kong
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
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26
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Ghulam A, Lei X, Guo M, Bian C. A Review of Pathway Databases and Related Methods Analysis. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191018162505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Pathway analysis integrates most of the computational tools for the investigation of
high-level and complex human diseases. In the field of bioinformatics research, biological pathways
analysis is an important part of systems biology. The molecular complexities of biological
pathways are difficult to understand in human diseases, which can be explored through pathway
analysis. In this review, we describe essential information related to pathway databases and their
mechanisms, algorithms and methods. In the pathway database analysis, we present a brief introduction
on how to gain knowledge from fundamental pathway data in regard to specific human
pathways and how to use pathway databases and pathway analysis to predict diseases during an
experiment. We also provide detailed information related to computational tools that are used in
complex pathway data analysis, the roles of these tools in the bioinformatics field and how to store
the pathway data. We illustrate various methodological difficulties that are faced during pathway
analysis. The main ideas and techniques for the pathway-based examination approaches are presented.
We provide the list of pathway databases and analytical tools. This review will serve as a
helpful manual for pathway analysis databases.
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Affiliation(s)
- Ali Ghulam
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Min Guo
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xian, China
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27
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Choice of Differentiation Media Significantly Impacts Cell Lineage and Response to CFTR Modulators in Fully Differentiated Primary Cultures of Cystic Fibrosis Human Airway Epithelial Cells. Cells 2020; 9:cells9092137. [PMID: 32967385 PMCID: PMC7565948 DOI: 10.3390/cells9092137] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/13/2020] [Accepted: 09/16/2020] [Indexed: 12/13/2022] Open
Abstract
In vitro cultures of primary human airway epithelial cells (hAECs) grown at air–liquid interface have become a valuable tool to study airway biology under normal and pathologic conditions, and for drug discovery in lung diseases such as cystic fibrosis (CF). An increasing number of different differentiation media, are now available, making comparison of data between studies difficult. Here, we investigated the impact of two common differentiation media on phenotypic, transcriptomic, and physiological features of CF and non-CF epithelia. Cellular architecture and density were strongly impacted by the choice of medium. RNA-sequencing revealed a shift in airway cell lineage; one medium promoting differentiation into club and goblet cells whilst the other enriched the growth of ionocytes and multiciliated cells. Pathway analysis identified differential expression of genes involved in ion and fluid transport. Physiological assays (intracellular/extracellular pH, Ussing chamber) specifically showed that ATP12A and CFTR function were altered, impacting pH and transepithelial ion transport in CF hAECs. Importantly, the two media differentially affected functional responses to CFTR modulators. We argue that the effect of growth conditions should be appropriately determined depending on the scientific question and that our study can act as a guide for choosing the optimal growth medium for specific applications.
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28
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Perscheid C. Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches. Brief Bioinform 2020; 22:5881664. [PMID: 32761115 DOI: 10.1093/bib/bbaa151] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
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Affiliation(s)
- Cindy Perscheid
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
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29
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Ferrandi PJ, Khan MM, Paez HG, Pitzer CR, Alway SE, Mohamed JS. Transcriptome Analysis of Skeletal Muscle Reveals Altered Proteolytic and Neuromuscular Junction Associated Gene Expressions in a Mouse Model of Cerebral Ischemic Stroke. Genes (Basel) 2020; 11:genes11070726. [PMID: 32629989 PMCID: PMC7397267 DOI: 10.3390/genes11070726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 12/24/2022] Open
Abstract
Stroke is a leading cause of mortality and long-term disability in patients worldwide. Skeletal muscle is the primary systemic target organ of stroke that induces muscle wasting and weakness, which predominantly contribute to functional disability in stroke patients. Currently, no pharmacological drug is available to treat post-stroke muscle morbidities as the mechanisms underlying post-stroke muscle wasting remain poorly understood. To understand the stroke-mediated molecular changes occurring at the transcriptional level in skeletal muscle, the gene expression profiles and enrichment pathways were explored in a mouse model of cerebral ischemic stroke via high-throughput RNA sequencing and extensive bioinformatic analyses. RNA-seq revealed that the elevated muscle atrophy observed in response to stroke was associated with the altered expression of genes involved in proteolysis, cell cycle, extracellular matrix remodeling, and the neuromuscular junction (NMJ). These data suggest that stroke primarily targets muscle protein degradation and NMJ pathway proteins to induce muscle atrophy. Collectively, we for the first time have found a novel genome-wide transcriptome signature of post-stroke skeletal muscle in mice. Our study will provide critical information to further elucidate specific gene(s) and pathway(s) that can be targeted to mitigate accountable for post-stroke muscle atrophy and related weakness.
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Affiliation(s)
- Peter J. Ferrandi
- Laboratory of Muscle and Nerve, Department of Diagnostic and Health Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA;
- Center for Muscle, Metabolism and Neuropathology, Division of Rehabilitation Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (M.M.K.); (H.G.P.); (C.R.P.); (S.E.A.)
| | - Mohammad Moshahid Khan
- Center for Muscle, Metabolism and Neuropathology, Division of Rehabilitation Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (M.M.K.); (H.G.P.); (C.R.P.); (S.E.A.)
- Department of Neurology, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Hector G. Paez
- Center for Muscle, Metabolism and Neuropathology, Division of Rehabilitation Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (M.M.K.); (H.G.P.); (C.R.P.); (S.E.A.)
- Laboratory of Muscle Biology and Sarcopenia, Department of Physical Therapy, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Christopher R. Pitzer
- Center for Muscle, Metabolism and Neuropathology, Division of Rehabilitation Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (M.M.K.); (H.G.P.); (C.R.P.); (S.E.A.)
- Laboratory of Muscle Biology and Sarcopenia, Department of Physical Therapy, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Stephen E. Alway
- Center for Muscle, Metabolism and Neuropathology, Division of Rehabilitation Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (M.M.K.); (H.G.P.); (C.R.P.); (S.E.A.)
- Laboratory of Muscle Biology and Sarcopenia, Department of Physical Therapy, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Junaith S. Mohamed
- Laboratory of Muscle and Nerve, Department of Diagnostic and Health Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA;
- Center for Muscle, Metabolism and Neuropathology, Division of Rehabilitation Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (M.M.K.); (H.G.P.); (C.R.P.); (S.E.A.)
- Correspondence: ; Tel.: +1-901-448-8560
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30
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Chang HC, Chu CP, Lin SJ, Hsiao CK. Network hub-node prioritization of gene regulation with intra-network association. BMC Bioinformatics 2020; 21:101. [PMID: 32164570 PMCID: PMC7069025 DOI: 10.1186/s12859-020-3444-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 03/06/2020] [Indexed: 11/10/2022] Open
Abstract
Background To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. Results Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. Conclusions The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.
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Affiliation(s)
- Hung-Ching Chang
- Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Road, Taipei, 10055, Taiwan
| | - Chiao-Pei Chu
- Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Road, Taipei, 10055, Taiwan
| | - Shu-Ju Lin
- Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Chuhsing Kate Hsiao
- Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, No. 17, Xu-Zhou Road, Taipei, 10055, Taiwan. .,Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.
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Profiling the Protein Targets of Unmodified Bio‐Active Molecules with Drug Affinity Responsive Target Stability and Liquid Chromatography/Tandem Mass Spectrometry. Proteomics 2020; 20:e1900325. [DOI: 10.1002/pmic.201900325] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/28/2019] [Indexed: 12/17/2022]
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Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer. Cancers (Basel) 2020; 12:cancers12020271. [PMID: 31979117 PMCID: PMC7073226 DOI: 10.3390/cancers12020271] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 12/13/2022] Open
Abstract
Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.
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Pirooznia M, Niranjan T, Chen YC, Tunc I, Goes FS, Avramopoulos D, Potash JB, Huganir RL, Zandi PP, Wang T. Affected Sib-Pair Analyses Identify Signaling Networks Associated With Social Behavioral Deficits in Autism. Front Genet 2019; 10:1186. [PMID: 31827489 PMCID: PMC6892440 DOI: 10.3389/fgene.2019.01186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 10/25/2019] [Indexed: 11/29/2022] Open
Abstract
Autism spectrum disorders (ASDs) are characterized by deficits in three core behavioral domains: reciprocal social interactions, communication, and restricted interests and/or repetitive behaviors. Several hundreds of risk genes for autism have been identified, however, it remains a challenge to associate these genes with specific core behavioral deficits. In multiplex autism families, affected sibs often show significant differences in severity of individual core phenotypes. We hypothesize that a higher mutation burden contributes to a larger difference in the severity of specific core phenotypes between affected sibs. We tested this hypothesis on social behavioral deficits in autism. We sequenced synaptome genes (n = 1,886) in affected male sib-pairs (n = 274) in families from the Autism Genetics Research Exchange (AGRE) and identified rare (MAF ≤ 1%) and predicted functional variants. We selected affected sib-pairs with a large (≥10; n = 92 pairs) or a small (≤4; n = 108 pairs) difference in total cumulative Autism Diagnostic Interview-Revised (ADI-R) social scores (SOCT_CS). We compared burdens of unshared variants present only in sibs with severe social deficits and found a higher burden in SOCT_CS≥10 compared to SOCT_CS ≤ 4 (SOCT_CS≥10: 705.1 ± 16.2; SOCT_CS ≤ 4, 668.3 ± 9.0; p = 0.025). Unshared SOCT_CS≥10 genes only in sibs with severe social deficits are significantly enriched in the SFARI gene set. Network analyses of these genes using InWeb_IM, molecular signatures database (MSigDB), and GeNetMeta identified enrichment for phosphoinositide 3-kinase (PI3K)-AKT-mammalian target of rapamycin (mTOR) (Enrichment Score [eScore] p value = 3.36E−07; n = 8 genes) and Nerve growth factor (NGF) (eScore p value = 8.94E−07; n = 9 genes) networks. These studies support a key role for these signaling networks in social behavioral deficits and present a novel approach to associate risk genes and signaling networks with core behavioral domains in autism.
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Affiliation(s)
- Mehdi Pirooznia
- Bioinformatics and Computational Biology Core Facility, National Heart Lung and Blood Institute, NIH, Bethesda, MD, United States.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Tejasvi Niranjan
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yun-Ching Chen
- Bioinformatics and Computational Biology Core Facility, National Heart Lung and Blood Institute, NIH, Bethesda, MD, United States
| | - Ilker Tunc
- Bioinformatics and Computational Biology Core Facility, National Heart Lung and Blood Institute, NIH, Bethesda, MD, United States
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Dimitrios Avramopoulos
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - James B Potash
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard L Huganir
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Mental Health and Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD, United States
| | - Tao Wang
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Paananen J, Fortino V. An omics perspective on drug target discovery platforms. Brief Bioinform 2019; 21:1937-1953. [PMID: 31774113 PMCID: PMC7711264 DOI: 10.1093/bib/bbz122] [Citation(s) in RCA: 92] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/23/2019] [Accepted: 07/27/2019] [Indexed: 01/28/2023] Open
Abstract
The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.
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Affiliation(s)
- Jussi Paananen
- Institute of Biomedicine, University of Eastern Finland, Finland.,Blueprint Genetics Ltd, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Finland
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35
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Label propagation defines signaling networks associated with recurrently mutated cancer genes. Sci Rep 2019; 9:9401. [PMID: 31253832 PMCID: PMC6599034 DOI: 10.1038/s41598-019-45603-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 06/11/2019] [Indexed: 11/09/2022] Open
Abstract
Human tumors have distinct profiles of genomic alterations, and each of these alterations has the potential to cause unique changes to cellular homeostasis. Detailed analyses of these changes could reveal downstream effects of genomic alterations, contributing to our understanding of their roles in tumor development and progression. Across a range of tumor types, including bladder, lung, and endometrial carcinoma, we determined genes that are frequently altered in The Cancer Genome Atlas patient populations, then examined the effects of these alterations on signaling and regulatory pathways. To achieve this, we used a label propagation-based methodology to generate networks from gene expression signatures associated with defined mutations. Individual networks offered a large-scale view of signaling changes represented by gene signatures, which in turn reflected the scope of molecular events that are perturbed in the presence of a given genomic alteration. Comparing different networks to one another revealed common biological pathways impacted by distinct genomic alterations, highlighting the concept that tumors can dysregulate key pathways through multiple, seemingly unrelated mechanisms. Finally, altered genes inducing common changes to the signaling network were used to search for genomic markers of drug response, connecting shared perturbations to differential drug sensitivity.
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36
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Wang LL, Thomas Hayman G, Smith JR, Tutaj M, Shimoyama ME, Gennari JH. Predicting instances of pathway ontology classes for pathway integration. J Biomed Semantics 2019; 10:11. [PMID: 31196182 PMCID: PMC6567466 DOI: 10.1186/s13326-019-0202-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 05/22/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology as a unifying ontology for organizing pathway data. We aim to associate pathway instances from different databases to the appropriate class in the Pathway Ontology. RESULTS Using a supervised machine learning approach, we trained neural networks to predict mappings between Reactome pathways and Pathway Ontology (PW) classes. For 2222 Reactome classes, the neural network (NN) model generated 10,952 class recommendations. We compared against a baseline bag-of-words (BOW) model for predicting correct PW classes. A 5% subset of Reactome pathways (111 pathways) was randomly selected, and the corresponding class recommendations from both models were evaluated by two curators. The precision of the BOW model was higher (0.49 for BOW and 0.39 for NN), but the recall was lower (0.42 for BOW and 0.78 for NN). Around 78% of Reactome pathways received pertinent recommendations from the NN model. CONCLUSIONS The neural predictive model produced meaningful class recommendations that assisted PW curators in selecting appropriate class mappings for Reactome pathways. Our methods can be used to reduce the manual effort associated with ontology curation, and more broadly, for augmenting the curators' ability to organize and integrate data from pathway databases using the Pathway Ontology.
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Affiliation(s)
- Lucy Lu Wang
- Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St, Seattle, 98109, WA, USA.
| | - G Thomas Hayman
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - Jennifer R Smith
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - Monika Tutaj
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - Mary E Shimoyama
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St, Seattle, 98109, WA, USA
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Domingo-Fernández D, Mubeen S, Marín-Llaó J, Hoyt CT, Hofmann-Apitius M. PathMe: merging and exploring mechanistic pathway knowledge. BMC Bioinformatics 2019; 20:243. [PMID: 31092193 PMCID: PMC6521546 DOI: 10.1186/s12859-019-2863-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 04/29/2019] [Indexed: 12/12/2022] Open
Abstract
Background The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions. Results Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies. Conclusions This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level. Electronic supplementary material The online version of this article (10.1186/s12859-019-2863-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. .,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Josep Marín-Llaó
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
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38
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Jiménez J, Sabbadin D, Cuzzolin A, Martínez-Rosell G, Gora J, Manchester J, Duca J, De Fabritiis G. PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks. J Chem Inf Model 2019; 59:1172-1181. [PMID: 30586501 DOI: 10.1021/acs.jcim.8b00711] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .
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Affiliation(s)
- José Jiménez
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Davide Sabbadin
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Alberto Cuzzolin
- Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Gerard Martínez-Rosell
- Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Jacob Gora
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States.,Department of Mathematics and Computer Science , Freie Universität Berlin , Takustr. 9 , 14195 Berlin , Germany
| | - John Manchester
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - José Duca
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - Gianni De Fabritiis
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.,Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23 , 08010 Barcelona , Spain
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Domingo-Fernández D, Hoyt CT, Bobis-Álvarez C, Marín-Llaó J, Hofmann-Apitius M. ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases. NPJ Syst Biol Appl 2018; 5:3. [PMID: 30564458 PMCID: PMC6292919 DOI: 10.1038/s41540-018-0078-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 10/31/2018] [Accepted: 11/02/2018] [Indexed: 11/09/2022] Open
Abstract
Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinson’s disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at https://compath.scai.fraunhofer.de/.
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Affiliation(s)
- Daniel Domingo-Fernández
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Charles Tapley Hoyt
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Carlos Bobis-Álvarez
- 3Faculty of Medicine and Health Sciences, University of Oviedo, 33006 Oviedo, Spain
| | - Josep Marín-Llaó
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,4Rovira i Virgili University, 43003 Tarragona, Spain
| | - Martin Hofmann-Apitius
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
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Kong Y, Yu T. A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics 2018; 34:3727-3737. [PMID: 29850911 PMCID: PMC6198851 DOI: 10.1093/bioinformatics/bty429] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 04/30/2018] [Accepted: 05/23/2018] [Indexed: 12/16/2022] Open
Abstract
Motivation Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (i) there are tens of thousands of features and only hundreds of training samples, (ii) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. Results To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to integrate external relational information of features into the deep neural network architecture. The method is able to achieve sparse connection between network layers to prevent overfitting. To validate the method's capability, we conducted both simulation experiments and real data analysis using a breast invasive carcinoma RNA-seq dataset and a kidney renal clear cell carcinoma RNA-seq dataset from The Cancer Genome Atlas. The resulting high classification accuracy and easily interpretable feature selection results suggest the method is a useful addition to the current graph-guided classification models and feature selection procedures. Availability and implementation The method is available at https://github.com/yunchuankong/GEDFN. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yunchuan Kong
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA
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Halu A, Wang JG, Iwata H, Mojcher A, Abib AL, Singh SA, Aikawa M, Sharma A. Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation. eLife 2018; 7:37059. [PMID: 30303482 PMCID: PMC6179386 DOI: 10.7554/elife.37059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/30/2018] [Indexed: 02/06/2023] Open
Abstract
The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling. When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels. Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease. Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop. These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.
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Affiliation(s)
- Arda Halu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.,Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Jian-Guo Wang
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Hiroshi Iwata
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Alexander Mojcher
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Ana Luisa Abib
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
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Kondratova M, Sompairac N, Barillot E, Zinovyev A, Kuperstein I. Signalling maps in cancer research: construction and data analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4964960. [PMID: 29688383 PMCID: PMC5890450 DOI: 10.1093/database/bay036] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 03/19/2018] [Indexed: 12/22/2022]
Abstract
Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.
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Affiliation(s)
- Maria Kondratova
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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43
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Solov'eva NV, Kuvshinova YV, Kichuk IV, Chausova SV, Vil'yanov VB, Kremenitskaya SA. [Dichotomous classification of autism spectrum disorders: syndromal and non-syndromal forms]. Zh Nevrol Psikhiatr Im S S Korsakova 2018; 118:107-112. [PMID: 29863703 DOI: 10.17116/jnevro201811841107-112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In foreign literature on research into the etiopathogenesis of autism spectrum disorders (ASDs), the division of this group of diseases into two forms is getting more and more frequent. These two forms are 'syndromal' and 'non-syndromal' forms of autistic disorders. The literature review aims to cover the issues of the dichotomous classification of ASDs based on the genetic and molecular psychiatric views on the etiopathogenesis of this group of diseases. It also covers the purpose of this classification, the opportunities of its usage in routine clinical practice and the network resources, which allow classifying a form of ASD correctly. Special attention is paid to the multidisciplinary approach to dichotomous classification and its difference from the clinical view on the systematization of autism and the importance of this method for selection of target therapy.
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Affiliation(s)
- N V Solov'eva
- Scientific Centre of Personalized Psychiatry, Moscow, Russia
| | - Ya V Kuvshinova
- Scientific Centre of Personalized Psychiatry, Moscow, Russia
| | - I V Kichuk
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - S V Chausova
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - V B Vil'yanov
- Scientific Centre of Personalized Psychiatry, Moscow, Russia
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44
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Monraz Gomez LC, Kondratova M, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Application of Atlas of Cancer Signalling Network in preclinical studies. Brief Bioinform 2018; 20:701-716. [DOI: 10.1093/bib/bby031] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 03/28/2018] [Indexed: 12/27/2022] Open
Affiliation(s)
- L Cristobal Monraz Gomez
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Maria Kondratova
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Jean-Marie Ravel
- Genetic Laboratory, Nancy's Regional University Hospital, Vandœuvre-lès-Nancy and INSERM UMR 954, Lorraine University, Vandœuvre-lès-Nancy
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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45
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Chowdhury S, Sinha N, Ganguli P, Bhowmick R, Singh V, Nandi S, Sarkar RR. BIOPYDB: A Dynamic Human Cell Specific Biochemical Pathway Database with Advanced Computational Analyses Platform. J Integr Bioinform 2018; 15:/j/jib.ahead-of-print/jib-2017-0072/jib-2017-0072.xml. [PMID: 29547394 PMCID: PMC6340122 DOI: 10.1515/jib-2017-0072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 01/29/2018] [Indexed: 12/03/2022] Open
Abstract
BIOPYDB: BIOchemical PathwaY DataBase is developed as a manually curated, readily updatable, dynamic resource of human cell specific pathway information along with integrated computational platform to perform various pathway analyses. Presently, it comprises of 46 pathways, 3189 molecules, 5742 reactions and 6897 different types of diseases linked with pathway proteins, which are referred by 520 literatures and 17 other pathway databases. With its repertoire of biochemical pathway data, and computational tools for performing Topological, Logical and Dynamic analyses, BIOPYDB offers both the experimental and computational biologists to acquire a comprehensive understanding of signaling cascades in the cells. Automated pathway image reconstruction, cross referencing of pathway molecules and interactions with other databases and literature sources, complex search operations to extract information from other similar resources, integrated platform for pathway data sharing and computation, etc. are the novel and useful features included in this database to make it more acceptable and attractive to the users of pathway research communities. The RESTful API service is also made available to the advanced users and developers for accessing this database more conveniently through their own computer programmes.
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Affiliation(s)
- Saikat Chowdhury
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India.,Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, Maharashtra 411008, India
| | - Noopur Sinha
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India.,Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, Maharashtra 411008, India
| | - Piyali Ganguli
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India.,Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, Maharashtra 411008, India
| | - Rupa Bhowmick
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India.,Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, Maharashtra 411008, India
| | - Vidhi Singh
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India
| | - Sutanu Nandi
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India.,Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, Maharashtra 411008, India
| | - Ram Rup Sarkar
- CSIR- National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, Maharashtra 411008, India.,Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, Maharashtra 411008, India
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46
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Lee HS, Park T. Nuclear receptor and VEGF pathways for gene-blood lead interactions, on bone mineral density, in Korean smokers. PLoS One 2018; 13:e0193323. [PMID: 29518117 PMCID: PMC5843219 DOI: 10.1371/journal.pone.0193323] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 02/08/2018] [Indexed: 11/19/2022] Open
Abstract
Osteoporosis has a complex etiology and is considered a multifactorial polygenic disease, in which genetic determinants are modulated by hormonal, lifestyle, environmental, and nutritional factors. Therefore, investigating these multiple factors, and the interactions between them, might lead to a better understanding of osteoporosis pathogenesis, and possible therapeutic interventions. The objective of this study was to identify the relationship between three blood metals (Pb, Cd, and Al), in smoking and nonsmoking patients' sera, and prevalence of osteoporosis. In particular, we focused on gene-environment interactions of metal exposure, including a dataset obtained through genome-wide association study (GWAS). Subsequently, we conducted a pathway-based analysis, using a GWAS dataset, to elucidate how metal exposure influences susceptibility to osteoporosis. In this study, we evaluated blood metal exposures for estimating the prevalence of osteoporosis in 443 participants (aged 53.24 ± 8.29), from the Republic of Korea. Those analyses revealed a negative association between lead blood levels and bone mineral density in current smokers (p trend <0.01). By further using GWAS-based pathway analysis, we found nuclear receptor (FDR<0.05) and VEGF pathways (FDR<0.05) to be significantly upregulated by blood lead burden, with regard to the prevalence of osteoporosis, in current smokers. These findings suggest that the intracellular pathways of angiogenesis and nuclear hormonal signaling can modulate interactions between lead exposure and genetic variation, with regard to susceptibility to diminished bone mineral density. Our findings may provide new leads for understanding the mechanisms underlying the development of osteoporosis, including possible interventions.
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Affiliation(s)
- Ho-Sun Lee
- Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Gwanak 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea
- Daegu Institution, National Forensic Service, Hogukro, Waegwon-eup, Chilgok-gun, Gyeomgsamgbuk-do, Republic of Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Gwanak 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea
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47
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Chaudhuri R, Krycer JR, Fazakerley DJ, Fisher-Wellman KH, Su Z, Hoehn KL, Yang JYH, Kuncic Z, Vafaee F, James DE. The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes. Sci Rep 2018; 8:1774. [PMID: 29379070 PMCID: PMC5789081 DOI: 10.1038/s41598-018-20104-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 01/12/2018] [Indexed: 02/06/2023] Open
Abstract
Insulin resistance is a major risk factor for metabolic diseases such as Type 2 diabetes. Although the underlying mechanisms of insulin resistance remain elusive, oxidative stress is a unifying driver by which numerous extrinsic signals and cellular stresses trigger insulin resistance. Consequently, we sought to understand the cellular response to oxidative stress and its role in insulin resistance. Using cultured 3T3-L1 adipocytes, we established a model of physiologically-derived oxidative stress by inhibiting the cycling of glutathione and thioredoxin, which induced insulin resistance as measured by impaired insulin-stimulated 2-deoxyglucose uptake. Using time-resolved transcriptomics, we found > 2000 genes differentially-expressed over 24 hours, with specific metabolic and signalling pathways enriched at different times. We explored this coordination using a knowledge-based hierarchical-clustering approach to generate a temporal transcriptional cascade and identify key transcription factors responding to oxidative stress. This response shared many similarities with changes observed in distinct insulin resistance models. However, an anti-oxidant reversed insulin resistance phenotypically but not transcriptionally, implying that the transcriptional response to oxidative stress is insufficient for insulin resistance. This suggests that the primary site by which oxidative stress impairs insulin action occurs post-transcriptionally, warranting a multi-level ‘trans-omic’ approach when studying time-resolved responses to cellular perturbations.
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Affiliation(s)
- Rima Chaudhuri
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - James R Krycer
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Zhiduan Su
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Kyle L Hoehn
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Jean Yee Hwa Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Zdenka Kuncic
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Physics and Australian Institute for Nanoscale Science and Technology, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW, 2052, Australia.
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia. .,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia. .,Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
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48
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A Two-Stage Whole-Genome Gene Expression Association Study of Young-Onset Hypertension in Han Chinese Population of Taiwan. Sci Rep 2018; 8:1800. [PMID: 29379041 PMCID: PMC5789005 DOI: 10.1038/s41598-018-19520-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 01/03/2018] [Indexed: 12/31/2022] Open
Abstract
Hypertension is an important public health problem in the world. Since the intermediate position of the gene expression between genotype and phenotype makes it suitable to link genotype to phenotype, we carried out a two-stage whole-genome gene expression association study to find differentially expressed genes and pathways for hypertension. In the first stage, 126 cases and 149 controls were used to find out the differentially expressed genes. In the second stage, an independent set of samples (127 cases and 150 controls) was used to validate the results. Additionally, we conducted a gene set enrichment analysis (GSEA) to search for differentially affected pathways. A total of nine genes were implicated in the first stage (Bonferroni-corrected p-value < 0.05). Among these genes, ZRANB1, FAM110A, PREP, ANKRD9 and LAMB2 were also differentially expressed in an existing database of hypertensive mouse model (GSE19817). A total of 16 pathways were identified by the GSEA. ZRANB1 and six pathways identified are related to TNF-α. Three pathways are related to interleukin, one to metabolic syndrome, and one to Hedgehog signaling. Identification of these genes and pathways suggest the importance of 1. inflammation, 2. visceral fat metabolism, and 3. adipocytes and osteocytes homeostasis in hypertension mechanisms and complications.
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Csabai L, Ölbei M, Budd A, Korcsmáros T, Fazekas D. SignaLink: Multilayered Regulatory Networks. Methods Mol Biol 2018; 1819:53-73. [PMID: 30421399 DOI: 10.1007/978-1-4939-8618-7_3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein-protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.
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Affiliation(s)
| | - Márton Ölbei
- Earlham Institute, Norwich Research Park, Norwich, UK.,Quadram Institute, Norwich Research Park, Norwich, UK
| | - Aidan Budd
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - Tamás Korcsmáros
- Eötvös Loránd University, Budapest, Hungary. .,Earlham Institute, Norwich Research Park, Norwich, UK. .,Quadram Institute, Norwich Research Park, Norwich, UK.
| | - Dávid Fazekas
- Eötvös Loránd University, Budapest, Hungary.,Earlham Institute, Norwich Research Park, Norwich, UK
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50
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Felgueiras J, Silva JV, Fardilha M. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools. J Proteomics 2018; 171:127-140. [DOI: 10.1016/j.jprot.2017.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/26/2017] [Accepted: 05/13/2017] [Indexed: 02/02/2023]
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