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Kaur A, Raji, Verma V, Goel RK. Strategic pathway analysis for dual management of epilepsy and comorbid depression: a systems biology perspective. In Silico Pharmacol 2024; 12:36. [PMID: 38699778 PMCID: PMC11061056 DOI: 10.1007/s40203-024-00208-1] [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: 01/10/2024] [Accepted: 04/01/2024] [Indexed: 05/05/2024] Open
Abstract
Depression is a common psychiatric comorbidity among patients with epilepsy (PWE), affecting more than a third of PWE. Management of depression may improve quality of life of epileptic patients. Unfortunately, available antidepressants worsen epilepsy by reducing the seizure threshold. This situation demands search of new safer target for combined directorate of epilepsy and comorbid depression. A system biology approach may be useful to find novel pathways/markers for the cure of both epilepsy and associated depression via analyzing available genomic and proteomic information. Hence, the system biology approach using curated 64 seed genes involved in temporal lobe epilepsy and mental depression was applied. The interplay of 600 potential proteins was revealed by the Disease Module Detection (DIAMOnD) Algorithm for the treatment of both epilepsy and comorbid depression using these seed genes. The gene enrichment analysis of seed and diamond genes through DAVID suggested 95 pathways. Selected pathways were refined based on their syn or anti role in epilepsy and depression. In conclusion, total 8 pathways and 27 DIAMOnD genes/proteins were finally deduced as potential new targets for modulation of selected pathways to manage epilepsy and comorbid depression. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-024-00208-1.
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Affiliation(s)
- Arvinder Kaur
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab India 147002
| | - Raji
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab India 147002
| | - Varinder Verma
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab India 147002
| | - Rajesh Kumar Goel
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab India 147002
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2
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Munquad S, Das AB. Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma. Heliyon 2024; 10:e27190. [PMID: 38468932 PMCID: PMC10926146 DOI: 10.1016/j.heliyon.2024.e27190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024] Open
Abstract
The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.
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Affiliation(s)
- Sana Munquad
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India
| | - Asim Bikas Das
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, 506004, Telangana, India
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3
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Zhang P, Zhang D, Zhou W, Wang L, Wang B, Zhang T, Li S. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Brief Bioinform 2023; 25:bbad518. [PMID: 38197310 PMCID: PMC10777171 DOI: 10.1093/bib/bbad518] [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: 09/02/2023] [Revised: 11/03/2023] [Accepted: 11/30/2023] [Indexed: 01/11/2024] Open
Abstract
Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Dingfan Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wuai Zhou
- China Mobile Information System Integration Co., Ltd, Beijing 100032, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Boyang Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingyu Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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4
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Morselli Gysi D, Barabási AL. Noncoding RNAs improve the predictive power of network medicine. Proc Natl Acad Sci U S A 2023; 120:e2301342120. [PMID: 37906646 PMCID: PMC10636370 DOI: 10.1073/pnas.2301342120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/09/2023] [Indexed: 11/02/2023] Open
Abstract
Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA02115
- Department of Physics, Northeastern University, Boston, MA02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA02115
- US Department of Veteran Affairs, Boston, MA02130
- Department of Network and Data Science, Central European University, Budapest1051, Hungary
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5
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Caprioli B, Eichler RAS, Silva RNO, Martucci LF, Reckziegel P, Ferro ES. Neurolysin Knockout Mice in a Diet-Induced Obesity Model. Int J Mol Sci 2023; 24:15190. [PMID: 37894869 PMCID: PMC10607720 DOI: 10.3390/ijms242015190] [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: 08/29/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Neurolysin oligopeptidase (E.C.3.4.24.16; Nln), a member of the zinc metallopeptidase M3 family, was first identified in rat brain synaptic membranes hydrolyzing neurotensin at the Pro-Tyr peptide bond. The previous development of C57BL6/N mice with suppression of Nln gene expression (Nln-/-), demonstrated the biological relevance of this oligopeptidase for insulin signaling and glucose uptake. Here, several metabolic parameters were investigated in Nln-/- and wild-type C57BL6/N animals (WT; n = 5-8), male and female, fed either a standard (SD) or a hypercaloric diet (HD), for seven weeks. Higher food intake and body mass gain was observed for Nln-/- animals fed HD, compared to both male and female WT control animals fed HD. Leptin gene expression was higher in Nln-/- male and female animals fed HD, compared to WT controls. Both WT and Nln-/- females fed HD showed similar gene expression increase of dipeptidyl peptidase 4 (DPP4), a peptidase related to glucagon-like peptide-1 (GLP-1) metabolism. The present data suggest that Nln participates in the physiological mechanisms related to diet-induced obesity. Further studies will be necessary to better understand the molecular mechanism responsible for the higher body mass gain observed in Nln-/- animals fed HD.
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Affiliation(s)
- Bruna Caprioli
- Pharmacology Department, Biomedical Sciences Institute (ICB), São Paulo 05508-000, SP, Brazil; (B.C.); (R.A.S.E.); (R.N.O.S.); (L.F.M.)
| | - Rosangela A. S. Eichler
- Pharmacology Department, Biomedical Sciences Institute (ICB), São Paulo 05508-000, SP, Brazil; (B.C.); (R.A.S.E.); (R.N.O.S.); (L.F.M.)
| | - Renée N. O. Silva
- Pharmacology Department, Biomedical Sciences Institute (ICB), São Paulo 05508-000, SP, Brazil; (B.C.); (R.A.S.E.); (R.N.O.S.); (L.F.M.)
| | - Luiz Felipe Martucci
- Pharmacology Department, Biomedical Sciences Institute (ICB), São Paulo 05508-000, SP, Brazil; (B.C.); (R.A.S.E.); (R.N.O.S.); (L.F.M.)
| | - Patricia Reckziegel
- Department of Clinical and Toxicological Analysis, Faculty of Pharmaceutical Sciences (FCF), University of São Paulo (USP), São Paulo 05508-000, SP, Brazil;
| | - Emer S. Ferro
- Pharmacology Department, Biomedical Sciences Institute (ICB), São Paulo 05508-000, SP, Brazil; (B.C.); (R.A.S.E.); (R.N.O.S.); (L.F.M.)
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Guthrie J, Ko¨stel Bal S, Lombardo SD, Mu¨ller F, Sin C, Hu¨tter CV, Menche J, Boztug K. AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation. SCIENCE ADVANCES 2023; 9:eadg6375. [PMID: 37656781 PMCID: PMC10848965 DOI: 10.1126/sciadv.adg6375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/01/2023] [Indexed: 09/03/2023]
Abstract
Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.
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Affiliation(s)
- Julia Guthrie
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Sevgi Ko¨stel Bal
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
| | - Salvo Danilo Lombardo
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Felix Mu¨ller
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Celine Sin
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
| | - Christiane V. R. Hu¨tter
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna BioCenter, A-1030 Vienna, Austria
| | - Jo¨rg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- Max Perutz Labs, Vienna BioCenter Campus, Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Structural and Computational Biology, University of Vienna, Dr.-Bohr-Gasse 9, 1030, Vienna Austria
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Kaan Boztug
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Zimmermannplatz 10, A-1090 Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria
- St. Anna Children’s Cancer Research Institute (CCRI), Zimmermannplatz 10, A-1090 Vienna, Austria
- St. Anna Children’s Hospital, Kinderspitalgasse 6, A-1090, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics and Adolescent Medicine, Währinger Gürtel 18-20, A-1090 Vienna, Austria
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7
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Zaied RE, Fadason T, O'Sullivan JM. De novo identification of complex traits associated with asthma. Front Immunol 2023; 14:1231492. [PMID: 37680636 PMCID: PMC10480836 DOI: 10.3389/fimmu.2023.1231492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023] Open
Abstract
Introduction Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Methods Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. Results We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Discussion Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.
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Affiliation(s)
- Roan E Zaied
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Medical Research Council (MRC) Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
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Sadegh S, Skelton J, Anastasi E, Maier A, Adamowicz K, Möller A, Kriege NM, Kronberg J, Haller T, Kacprowski T, Wipat A, Baumbach J, Blumenthal DB. Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond. Nat Commun 2023; 14:1662. [PMID: 36966134 PMCID: PMC10039912 DOI: 10.1038/s41467-023-37349-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/13/2023] [Indexed: 03/27/2023] Open
Abstract
A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.
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Affiliation(s)
- Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - James Skelton
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Elisa Anastasi
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Anna Möller
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nils M Kriege
- Faculty of Computer Science, University of Vienna, Vienna, Austria
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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9
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Shang J, Zhu X, Sun Y, Li F, Kong X, Liu JX. DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer. BMC Bioinformatics 2023; 24:13. [PMID: 36624376 PMCID: PMC9830734 DOI: 10.1186/s12859-023-05136-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection. RESULTS In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods. CONCLUSIONS The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.
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Affiliation(s)
- Junliang Shang
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Xuhui Zhu
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Yan Sun
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Feng Li
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Xiangzhen Kong
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Jin-Xing Liu
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
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Pacheco Pachado M, Casas AI, Elbatreek MH, Nogales C, Guney E, Espay AJ, Schmidt HH. Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes. J Alzheimers Dis 2023; 96:47-56. [PMID: 37742653 PMCID: PMC10657714 DOI: 10.3233/jad-230694] [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] [Accepted: 08/22/2023] [Indexed: 09/26/2023]
Abstract
Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.
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Affiliation(s)
- Mayra Pacheco Pachado
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ana I. Casas
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Universitätsklinikum Essen, Klinik für Neurologie, Essen, Germany
| | - Mahmoud H. Elbatreek
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Cristian Nogales
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Emre Guney
- Discovery and Data Science (DDS) Unit, STALICLA R&D SL, Barcelona, Spain
| | - Alberto J. Espay
- James J. and Joan A. Gardner Family Center for Parkinson’s Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, USA
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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11
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Loscalzo J. Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning. FASEB J 2023; 37:e22660. [PMID: 36468661 PMCID: PMC10107166 DOI: 10.1096/fj.202201683r] [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: 10/14/2022] [Revised: 10/27/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.
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Affiliation(s)
- Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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12
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Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
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13
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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14
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Voitalov I, Zhang L, Kilpatrick C, Withers JB, Saleh A, Akmaev VR, Ghiassian SD. The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis. Sci Rep 2022; 12:21685. [PMID: 36522454 PMCID: PMC9755270 DOI: 10.1038/s41598-022-26276-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Tumor necrosis factor-[Formula: see text] inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome-a network of protein-protein interactions in human cells-consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.
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Affiliation(s)
- Ivan Voitalov
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Lixia Zhang
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Casey Kilpatrick
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Johanna B. Withers
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
| | - Alif Saleh
- Scipher Medicine Corporation, 221 Crescent St Suite 103A, Waltham, MA 02453 USA
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15
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Kondoh K, Akahori H, Muto Y, Terada T. Identification of Key Genes and Pathways Associated with Preeclampsia by a WGCNA and an Evolutionary Approach. Genes (Basel) 2022; 13:genes13112134. [PMID: 36421809 PMCID: PMC9690438 DOI: 10.3390/genes13112134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 11/18/2022] Open
Abstract
Preeclampsia (PE) is the serious obstetric-related disease characterized by newly onset hypertension and causes damage to the kidneys, brain, liver, and more. To investigate genes with key roles in PE’s pathogenesis and their contributions, we used a microarray dataset of normotensive and PE patients and conducted a weighted gene co-expression network analysis (WGCNA). Cyan and magenta modules that are highly enriched with differentially expressed genes (DEGs) were revealed. By using the molecular complex detection (MCODE) algorithm, we identified five significant clusters in the cyan module protein–protein interaction (PPI) network and nine significant clusters in the magenta module PPI network. Our analyses indicated that (i) human accelerated region (HAR) genes are enriched in the magenta-associated C6 cluster, and (ii) positive selection (PS) genes are enriched in the cyan-associated C3 and C5 clusters. We propose these enriched HAR and PS genes, i.e., EIF4E, EIF5, EIF3M, DDX17, SRSF11, PSPC1, SUMO1, CAPZA1, PSMD14, and MNAT1, including highly connected hub genes, HNRNPA1, RBMX, PRKDC, and RANBP2, as candidate key genes for PE’s pathogenesis. A further clarification of the functions of these PPI clusters and key enriched genes will contribute to the discovery of diagnostic biomarkers for PE and therapeutic intervention targets.
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Affiliation(s)
- Kuniyo Kondoh
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu-City 501-1193, Gifu, Japan
- School of Nursing, Gifu University of Health Sciences, 2-92, Higashiuzura, Gifu-City 500-8281, Gifu, Japan
| | - Hiromichi Akahori
- Department of Functional Bioscience, Gifu University School of Medicine, 1-1, Yanagido, Gifu-City 501-1193, Gifu, Japan
| | - Yoshinori Muto
- Institute for Glyco-Core Research (iGCORE), Gifu University, 1-1 Yanagido, Gifu-City 501-1193, Gifu, Japan
| | - Tomoyoshi Terada
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu-City 501-1193, Gifu, Japan
- Department of Functional Bioscience, Gifu University School of Medicine, 1-1, Yanagido, Gifu-City 501-1193, Gifu, Japan
- Correspondence: ; Tel.: +81-58-293-3241
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Integrative network analysis interweaves the missing links in cardiomyopathy diseasome. Sci Rep 2022; 12:19670. [PMID: 36385157 PMCID: PMC9668833 DOI: 10.1038/s41598-022-24246-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy's genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1. Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.
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17
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Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis. Transl Res 2022; 246:78-86. [PMID: 35306220 DOI: 10.1016/j.trsl.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/08/2022] [Indexed: 11/22/2022]
Abstract
This cross-cohort study aimed to (1) determine a network-based molecular signature that predicts the likelihood of inadequate response to the tumor necrosis factor-ɑ inhibitor (TNFi) therapy, infliximab, in ulcerative colitis (UC) patients, and (2) address biomarker irreproducibility across different cohort studies. Whole-transcriptome microarray data were derived from biopsies of affected colon tissue from 2 cohorts of infliximab-treated UC patients (training N = 24 and validation N = 22). Response was defined as endoscopic and histologic healing at 4-6 weeks and 8 weeks, respectively. From the training cohort, genes with RNA expression that significantly correlated with clinical response outcomes were mapped onto the Human Interactome network map of protein-protein interactions to identify a largest connected component (LCC) of proteins indicative of infliximab response status in UC. Expression levels of transcripts within the LCC were fed into a probabilistic neural network model to generate a classifier that predicts inadequate response to infliximab. A classifier predictive of inadequate response to infliximab was generated and tested in a cross-cohort, blinded fashion; the AUC was 0.83 and inadequate response was predicted with a 100% positive predictive value and 64% sensitivity. Genes separately identified from the 2 cohorts that correlated with response to infliximab appeared distinct but mapped onto the same network region of the Human Interactome, reflecting a common underlying biology of response among UC patients. Cross-cohort validation of a classifier predictive of infliximab response status in UC patients indicates that a molecular signature of non-response to TNFi therapies is present in patients' baseline gene expression data. The goal is to develop a diagnostic test that predicts which patients will have an inadequate response to targeted therapies and define new targets and pathways for therapeutic development.
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18
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Shi Q, Luo J, Chen W, He Q, Long J, Zhang B. Circ_0060531 knockdown ameliorates IL-22-induced keratinocyte damage by binding to miR-330-5p to decrease GAB1 expression. Autoimmunity 2022; 55:243-253. [PMID: 35293807 DOI: 10.1080/08916934.2022.2037127] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 01/14/2022] [Accepted: 01/30/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND Psoriasis is a chronic immune-mediated skin disease. Recent studies showed its pathogenesis involved circular RNA (circRNA). However, the role of circ_0060531 in psoriasis development and the behind mechanism remain to be explored. METHODS Psoriasis cell model was constructed by treating keratinocytes (HaCaT cells) using interleukin 22 (IL-22). Expression of circ_0060531, microRNA-330-5p (miR-330-5p) and GRB2 associated binder 1 (GAB1) was determined by quantitative real-time polymerase chain reaction. The functional effects of circ_0060531 on IL-22-caused cell injury were investigated by 3-(4,5-Dimethylthazol-2-yl)-2,5-diphenyltetrazolium bromide, 5-Ethynyl-29-deoxyuridine, wound-healing and enzyme-linked immunosorbent assays. Protein expression was analysed by Western blot. The interactions among circ_0060531, miR-330-5p and GAB1 were identified by dual-luciferase reporter or RNA immunoprecipitation assay. RESULTS Circ_0060531 and GAB1 expression were significantly increased, while miR-330-5p was decreased in psoriatic skin biopsies and IL-22-stimulated HaCaT cells in comparison with controls. In function, circ_0060531 knockdown assuaged IL-22-induced cell proliferation, cell migration and inflammation. Besides, circ_0060531 acted as a miR-330-5p sponge, and regulated the processes of IL-22-treated HaCaT cells by binding to the miRNA. Under the treatment of IL-22, miR-330-5p mediated HaCaT cell damage by targeting GAB1. Importantly, circ_0060531 modulated GAB1 production by interacting with miR-330-5p. CONCLUSION Circ_0060531 knockdown assuaged IL-22-induced keratinocyte dysfunction through miR-330-5p/GAB1 pathway, proving a novel target for the therapy of psoriasis.
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Affiliation(s)
- Quan Shi
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan City, China
| | - Jing Luo
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan City, China
| | - Weiming Chen
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan City, China
| | - Qi He
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan City, China
| | - Jianwen Long
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan City, China
| | - Bo Zhang
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan City, China
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19
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Velásquez-Zapata V, Elmore JM, Fuerst G, Wise RP. An interolog-based barley interactome as an integration framework for immune signaling. Genetics 2022; 221:iyac056. [PMID: 35435213 PMCID: PMC9157089 DOI: 10.1093/genetics/iyac056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
The barley MLA nucleotide-binding leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many fungal diseases, including powdery mildew, stem-, and stripe rust. We used interolog inference to construct a barley protein interactome (Hordeum vulgare predicted interactome, HvInt) comprising 66,133 edges and 7,181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared with the experimentally validated Arabidopsis interactome of 11,253 proteins and 73,960 interactions, verifying that the 2 networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific "omics" datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and infection-time-course RNA sequencing of immune signaling mutants, we assembled resistant and susceptible subnetworks. The resulting differentially coexpressed (resistant - susceptible) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to mildew resistance locus a (Mla) through trans eQTL associations. Lastly, we anchored HvInt with new and previously identified interactors of the MLA coiled coli + nucleotide-binding domains and extended these to additional MLA alleles, orthologs, and NLR outgroups to predict receptor localization and conservation of signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA 50011, USA
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
| | - James Mitch Elmore
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA 50011, USA
| | - Gregory Fuerst
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA 50011, USA
| | - Roger P Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA 50011, USA
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA 50011, USA
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20
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Li A, Chen JY, Hsu CL, Oyang YJ, Huang HC, Juan HF. A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis. Pharmaceutics 2022; 14:pharmaceutics14050971. [PMID: 35631558 PMCID: PMC9147547 DOI: 10.3390/pharmaceutics14050971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/22/2022] [Accepted: 04/29/2022] [Indexed: 12/04/2022] Open
Abstract
Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.
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Affiliation(s)
- Albert Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Jhih-Yu Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Chia-Lang Hsu
- Department of Medical Research, National Taiwan University Hospital, Taipei 106, Taiwan;
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 106, Taiwan
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Hsuan-Cheng Huang
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (H.-C.H.); (H.-F.J.)
| | - Hsueh-Fen Juan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
- Department of Life Science, National Taiwan University, Taipei 106, Taiwan
- Center for Computational and Systems Biology, National Taiwan University, Taipei 106, Taiwan
- Correspondence: (H.-C.H.); (H.-F.J.)
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Lamb CA, Saifuddin A, Powell N, Rieder F. The Future of Precision Medicine to Predict Outcomes and Control Tissue Remodeling in Inflammatory Bowel Disease. Gastroenterology 2022; 162:1525-1542. [PMID: 34995532 PMCID: PMC8983496 DOI: 10.1053/j.gastro.2021.09.077] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/20/2021] [Accepted: 09/23/2021] [Indexed: 02/06/2023]
Abstract
Inflammatory bowel disease is characterized by significant interindividual heterogeneity. With a wider selection of pharmacologic and nonpharmacologic interventions available and in advanced developmental stages, a priority for the coming decade is to determine accurate methods of predicting treatment response and disease course. Precision medicine strategies will allow tailoring of preventative and therapeutic decisions to individual patient needs. In this review, we consider the future of precision medicine in inflammatory bowel disease. We discuss the critical need to extend from research focused on short-term symptomatic response to integrative multi-omic systems biology strategies to identify and validate biomarkers that underpin precision approaches. Crucially, the international community has collective responsibility to provide well-phenotyped and -curated longitudinal datasets for scientific discovery and validation. Research must also study broader aspects of the immune response, including components of the extracellular matrix, to better understand biological pathways initiating and perpetuating tissue fibrosis and longer-term disease complications.
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Affiliation(s)
- Christopher A Lamb
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Gastroenterology, Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, United Kingdom.
| | - Aamir Saifuddin
- St Mark's Academic Institute, London North West University Hospitals National Health Service Trust, London, United Kingdom; Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Nick Powell
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Florian Rieder
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio; Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, Ohio
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22
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Röhl A, Baek SH, Kachroo P, Morrow JD, Tantisira K, Silverman EK, Weiss ST, Sharma A, Glass K, DeMeo DL. Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease. Respir Res 2022; 23:69. [PMID: 35331221 PMCID: PMC8944072 DOI: 10.1186/s12931-022-01963-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 02/08/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation. METHODS In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks. RESULTS We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways. CONCLUSIONS The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.
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Affiliation(s)
- Annika Röhl
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Seung Han Baek
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jarrett D Morrow
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kelan Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pediatric Respiratory Medicine, University of California San Diego, San Diego, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Center for Complex Network Research, Northeastern University, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
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Xiang J, Meng X, Zhao Y, Wu FX, Li M. HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure. Brief Bioinform 2022; 23:6547263. [PMID: 35275996 DOI: 10.1093/bib/bbac072] [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: 10/20/2021] [Revised: 01/18/2022] [Accepted: 02/13/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.
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Affiliation(s)
- Ju Xiang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China; Department of Basic Medical Sciences & Academician Workstation, Changsha Medical University, Changsha, Hunan 410219, China
| | - Xiangmao Meng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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24
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Bernett J, Krupke D, Sadegh S, Baumbach J, Fekete SP, Kacprowski T, List M, Blumenthal DB. Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics 2022; 38:1600-1606. [PMID: 34984440 DOI: 10.1093/bioinformatics/btab876] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/29/2021] [Accepted: 12/31/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically include non-robust steps in their workflows, i.e. the computed subnetworks vary when running the DMMMs multiple times on equivalent input. This lack of robustness has a negative effect on the trustworthiness of the obtained subnetworks and is hence detrimental for the widespread adoption of DMMMs in the biomedical sciences. RESULTS To overcome this problem, we present a new DMMM called ROBUST (robust disease module mining via enumeration of diverse prize-collecting Steiner trees). In a large-scale empirical evaluation, we show that ROBUST outperforms competing methods in terms of robustness, scalability and, in most settings, functional relevance of the produced modules, measured via KEGG (Kyoto Encyclopedia of Genes and Genomes) gene set enrichment scores and overlap with DisGeNET disease genes. AVAILABILITY AND IMPLEMENTATION A Python 3 implementation and scripts to reproduce the results reported in this article are available on GitHub: https://github.com/bionetslab/robust, https://github.com/bionetslab/robust-eval. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Judith Bernett
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Dominik Krupke
- Department of Computer Science, TU Braunschweig, 38106 Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Sándor P Fekete
- Department of Computer Science, TU Braunschweig, 38106 Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany
| | - Tim Kacprowski
- Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany.,Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technical University of Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
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25
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Exploiting protein family and protein network data to identify novel drug targets for bladder cancer. Oncotarget 2022; 13:105-117. [PMID: 35035776 PMCID: PMC8758182 DOI: 10.18632/oncotarget.28175] [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: 06/01/2021] [Accepted: 12/08/2021] [Indexed: 12/11/2022] Open
Abstract
Bladder cancer remains one of the most common forms of cancer and yet there are limited small molecule targeted therapies. Here, we present a computational platform to identify new potential targets for bladder cancer therapy. Our method initially exploited a set of known driver genes for bladder cancer combined with predicted bladder cancer genes from mutationally enriched protein domain families. We enriched this initial set of genes using protein network data to identify a comprehensive set of 323 putative bladder cancer targets. Pathway and cancer hallmarks analyses highlighted putative mechanisms in agreement with those previously reported for this cancer and revealed protein network modules highly enriched in potential drivers likely to be good targets for targeted therapies. 21 of our potential drug targets are targeted by FDA approved drugs for other diseases — some of them are known drivers or are already being targeted for bladder cancer (FGFR3, ERBB3, HDAC3, EGFR). A further 4 potential drug targets were identified by inheriting drug mappings across our in-house CATH domain functional families (FunFams). Our FunFam data also allowed us to identify drug targets in families that are less prone to side effects i.e., where structurally similar protein domain relatives are less dispersed across the human protein network. We provide information on our novel potential cancer driver genes, together with information on pathways, network modules and hallmarks associated with the predicted and known bladder cancer drivers and we highlight those drivers we predict to be likely drug targets.
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26
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Santos SDS, Torres M, Galeano D, Sánchez MDM, Cernuzzi L, Paccanaro A. Machine learning and network medicine approaches for drug repositioning for COVID-19. PATTERNS (NEW YORK, N.Y.) 2022; 3:100396. [PMID: 34778851 PMCID: PMC8576113 DOI: 10.1016/j.patter.2021.100396] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/21/2021] [Accepted: 11/01/2021] [Indexed: 12/13/2022]
Abstract
We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.
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Affiliation(s)
- Suzana de Siqueira Santos
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- COVID-19 International Research Team
| | - Mateo Torres
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- COVID-19 International Research Team
| | - Diego Galeano
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- Facultad de Ingenieria, Universidad Nacional de Asunción, Luque 110948, Paraguay
- COVID-19 International Research Team
| | | | - Luca Cernuzzi
- Universidad Católica “Nuestra Señora de la Asunción”, Asunción C.C. 1683, Paraguay
| | - Alberto Paccanaro
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham TW20 0EX, UK
- COVID-19 International Research Team
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27
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Ghiassian SD, Withers JB, Santolini M, Saleh A, Akmaev VR. RETRACTED: Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis. Transl Res 2022; 239:35-43. [PMID: 33965585 DOI: 10.1016/j.trsl.2021.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 03/12/2021] [Accepted: 04/28/2021] [Indexed: 11/25/2022]
Abstract
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors after consulting with the Editors. During a follow-up study, the authors regretfully discovered that the microarray probe-to-gene mapping was incorrect. Although the methodology and primary findings remain the same, the identity of the biomarker genes are incorrect as a result of this honest mistake. The extent of the changes to correct this information necessitated the publication of a corrected version of this article: https://doi.org/10.1016/j.trsl.2022.03.006.
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Affiliation(s)
| | | | - Marc Santolini
- Center for Research and Interdisciplinarity (CRI), University Paris Descartes, Paris, France
| | - Alif Saleh
- Scipher Medicine Corporation, Waltham, Massachusetts
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28
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Buphamalai P, Kokotovic T, Nagy V, Menche J. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nat Commun 2021; 12:6306. [PMID: 34753928 PMCID: PMC8578255 DOI: 10.1038/s41467-021-26674-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/19/2021] [Indexed: 01/26/2023] Open
Abstract
Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.
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Affiliation(s)
- Pisanu Buphamalai
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Campus Vienna BioCenter 5, 1030, Vienna, Austria
| | - Tomislav Kokotovic
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Vanja Nagy
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, 1090, Vienna, Austria.
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Campus Vienna BioCenter 5, 1030, Vienna, Austria.
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090, Vienna, Austria.
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29
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Liu J, Zhu H, Qiu J. Locally Adjust Networks Based on Connectivity and Semantic Similarities for Disease Module Detection. Front Genet 2021; 12:726596. [PMID: 34759955 PMCID: PMC8575408 DOI: 10.3389/fgene.2021.726596] [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: 06/17/2021] [Accepted: 09/22/2021] [Indexed: 11/13/2022] Open
Abstract
For studying the pathogenesis of complex diseases, it is important to identify the disease modules in the system level. Since the protein-protein interaction (PPI) networks contain a number of incomplete and incorrect interactome, most existing methods often lead to many disease proteins isolating from disease modules. In this paper, we propose an effective disease module identification method IDMCSS, where the used human PPI networks are obtained by adding some potential missing interactions from existing PPI networks, as well as removing some potential incorrect interactions. In IDMCSS, a network adjustment strategy is developed to add or remove links around disease proteins based on both topological and semantic information. Next, neighboring proteins of disease proteins are prioritized according to a suggested similarity between each of them and disease proteins, and the protein with the largest similarity with disease proteins is added into a candidate disease protein set one by one. The stopping criterion is set to the boundary of the disease proteins. Finally, the connected subnetwork having the largest number of disease proteins is selected as a disease module. Experimental results on asthma demonstrate the effectiveness of the method in comparison to existing algorithms for disease module identification. It is also shown that the proposed IDMCSS can obtain the disease modules having crucial biological processes of asthma and 12 targets for drug intervention can be predicted.
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Affiliation(s)
- Jia Liu
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
| | - Huole Zhu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, China
| | - Jianfeng Qiu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, China
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30
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Rivero-García I, Castresana-Aguirre M, Guglielmo L, Guala D, Sonnhammer ELL. Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19. Sci Rep 2021; 11:20687. [PMID: 34667255 PMCID: PMC8526804 DOI: 10.1038/s41598-021-99721-y] [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: 03/09/2021] [Accepted: 09/30/2021] [Indexed: 12/14/2022] Open
Abstract
This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.
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Affiliation(s)
- Inés Rivero-García
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Miguel Castresana-Aguirre
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Luca Guglielmo
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Dimitri Guala
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Erik L. L. Sonnhammer
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
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31
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Tanaka M, Matsumoto K, Satake R, Yoshida Y, Inoue M, Hasegawa S, Suzuki T, Iwata M, Iguchi K, Nakamura M. Gentamicin-induced hearing loss: A retrospective study using the Food and Drug Administration Adverse Event Reporting System and a toxicological study using drug-gene network analysis. Heliyon 2021; 7:e07429. [PMID: 34401547 PMCID: PMC8353315 DOI: 10.1016/j.heliyon.2021.e07429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/15/2021] [Accepted: 06/24/2021] [Indexed: 11/25/2022] Open
Abstract
The objectives of the study were to evaluate the relationship between gentamicin (GEN) and hearing loss using the Food and Drug Administration Adverse Event Reporting system (FAERS) database and elucidate the potential toxicological mechanism of GEN-induced hearing loss through a drug–gene network analysis. Using the preferred terms and standardized queries from the Medical Dictionary for Regulatory Activities, we calculated the reporting odds ratios (RORs). We extracted GEN-associated genes (seed genes) and analyzed drug−gene interactions using the ClueGO plug-in in the Cytoscape software and the DIseAse MOdule Detection (DIAMOnD) algorithm. The lower limit of the 95% confidence interval (CI) of the ROR for aminoglycosides (AG) antibacterials was over 1, and the ROR was 5.5 (5.1–6.0). We retrieved 17 seed genes related to GEN from the PharmGKB and Drug Gene Interaction databases. In total, 1018 human genes interacting with GEN were investigated using ClueGO. Through Molecular Complex Detection (MCODE) analysis, we identified 17 local gene clusters. The nodes and edges of the highest-ranked local gene cluster named “Cluster 1” were 30 and 433, respectively. According to the ClueGO analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Cluster 1 genes were highly enriched in “oxidative phosphorylation.” According to the ClueGO analysis using ClinVar, Cluster 1 genes were highly enriched in “mitochondrial diseases,” “mitochondrial complex I deficiency,” “hereditary hearing loss and deafness,” and “Leigh syndrome.” We identified 60 GEN-associated genes using the DIAMOnD algorithm. Several GEN-associated genes in the DIAMOnD algorithm were highly enriched in “PI3K-Akt signaling pathway,” “Ras signaling pathway,” “focal adhesion,” “MAPK signaling pathway,” “regulation of actin cytoskeleton,” “oxidative phosphorylation,” and “ECM-receptor interaction.” Our analysis demonstrated an association between several AGs and hearing loss using the FAERS database. Drug−gene network analysis demonstrated that GEN may be associated with oxidative phosphorylation-associated genes and integrin genes, which may be associated with hearing loss.
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Affiliation(s)
- Mizuki Tanaka
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Kiyoka Matsumoto
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Riko Satake
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Yu Yoshida
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Misaki Inoue
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Shiori Hasegawa
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan.,Department of Pharmacy, Kobe City Medical Center General Hospital, 2-1-1 Minatojima Minamimachi, Chuo-ku, Kobe, 650-0047, Japan
| | - Takaaki Suzuki
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan.,Gifu Prefectural Government, 2-1-1 Yabutaminami, Gifu, 500-8570, Japan
| | - Mari Iwata
- Kifune Pharmacy, 2-23-2 Hasuike, Yanaizu-cho, Gifu, 501-6103, Japan
| | - Kazuhiro Iguchi
- Laboratory of Community Pharmacy, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
| | - Mitsuhiro Nakamura
- Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4 Daigaku-Nishi, Gifu, 501-1196, Japan
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32
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Gokuladhas S, Schierding W, Golovina E, Fadason T, O’Sullivan J. Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach. Front Immunol 2021; 12:693142. [PMID: 34484189 PMCID: PMC8415031 DOI: 10.3389/fimmu.2021.693142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/28/2021] [Indexed: 01/08/2023] Open
Abstract
Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.
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Affiliation(s)
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Evgeniia Golovina
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Tayaza Fadason
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Justin O’Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Brain Research New Zealand, The University of Auckland, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
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33
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Jung SM, Park KS, Kim KJ. Integrative analysis of lung molecular signatures reveals key drivers of systemic sclerosis-associated interstitial lung disease. Ann Rheum Dis 2021; 81:108-116. [PMID: 34380701 DOI: 10.1136/annrheumdis-2021-220493] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 07/25/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Interstitial lung disease is a significant comorbidity and the leading cause of mortality in patients with systemic sclerosis. Transcriptomic data of systemic sclerosis-associated interstitial lung disease (SSc-ILD) were analysed to evaluate the salient molecular and cellular signatures in comparison with those in related pulmonary diseases and to identify the key driver genes and target molecules in the disease module. METHODS A transcriptomic dataset of lung tissues from patients with SSc-ILD (n=52), idiopathic pulmonary fibrosis (IPF) (n=549), non-specific interstitial pneumonia (n=49) and pulmonary arterial hypertension (n=81) and from normal healthy controls (n=331) was subjected to filtration of differentially expressed genes, functional enrichment analysis, network-based key driver analysis and kernel-based diffusion scoring. The association of enriched pathways with clinical parameters was evaluated in patients with SSc-ILD. RESULTS SSc-ILD shared key pathogenic pathways with other fibrosing pulmonary diseases but was distinguishable in some pathological processes. SSc-ILD showed general similarity with IPF in molecular and cellular signatures but stronger signals for myofibroblasts, which in SSc-ILD were in a senescent and apoptosis-resistant state. The p53 signalling pathway was the most enriched signature in lung tissues and lung fibroblasts of SSc-ILD, and was significantly correlated with carbon monoxide diffusing capacity of lung, cellular senescence and apoptosis. EEF2, EFF2K, PHKG2, VCAM1, PRKACB, ITGA4, CDK1, CDK2, FN1 and HDAC1 were key regulators with high diffusion scores in the disease module. CONCLUSIONS Integrative transcriptomic analysis of lung tissues revealed key signatures of fibrosis in SSc-ILD. A network-based Bayesian approach provides deep insights into key regulatory genes and molecular targets applicable to treating SSc-ILD.
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Affiliation(s)
- Seung Min Jung
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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34
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Tian Y, Su X, Su Y, Zhang X. EMODMI: A Multi-Objective Optimization Based Method to Identify Disease Modules. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1109/tetci.2020.3014923] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Gong L, Bates S, Li J, Qiao D, Glass K, Wei W, Hsu VW, Zhou X, Silverman EK. Connecting COPD GWAS genes: FAM13A controls TGFβ2 secretion by modulating AP-3 transport. Am J Respir Cell Mol Biol 2021; 65:532-543. [PMID: 34166600 DOI: 10.1165/rcmb.2021-0016oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a common, complex disease and a major cause of morbidity and mortality. Although multiple genetic determinants of COPD have been implicated by genome-wide association studies (GWAS), the pathophysiologic significance of these associations remains largely unknown. From a COPD protein-protein interaction network module, we selected a network path between two COPD GWAS genes for validation studies: FAM13A-AP3D1-CTGF-TGFB2. We find that TGFβ2, FAM13A, and AP3D1 (but not CTGF) form a cellular protein complex. Functional characterization suggests that this complex mediates the secretion of TGFβ2 through an AP-3-dependent pathway, with FAM13A acting as a negative regulator by targeting a late stage of this transport that involves the dissociation of coat-cargo interaction. Moreover, we find that TGFβ2 is a transmembrane protein that engages the AP-3 complex for delivery to the late endosomal compartments for subsequent secretion through exosomes. These results identify a pathophysiologic context that unifies the biological network role of two COPD GWAS proteins and reveal novel mechanisms of cargo transport through an intracellular pathway.
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Affiliation(s)
- Lu Gong
- Brigham and Women's Hospital, 1861, Channing Division, Boston, Massachusetts, United States
| | - Samuel Bates
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Jian Li
- Brigham And Women's Hospital, Boston, United States
| | - Dandi Qiao
- Brigham and Women's Hospital and Harvard Medical School, Medicine, Boston, Massachusetts, United States.,Harvard School of Public Health, Biostatistics, Boston, Massachusetts, United States
| | - Kimberly Glass
- Brigham and Women\'s Hospital Channing Division of Network Medicine, 1869, Boston, Massachusetts, United States
| | - Wenyi Wei
- Beth Israel Deaconess Medical Center, 1859, Department of Pathology, Boston, Massachusetts, United States.,Harvard Medical School , Boston , Massachusetts, United States
| | - Victor W Hsu
- Brigham and Women's Hospital, 1861, Division of Rheumatology, Inflammation, and Immunity, Boston, Massachusetts, United States
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Edwin K Silverman
- Brigham and Women's Hospital, 1861, Channing Division of Network Medicine, Boston, Massachusetts, United States;
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A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study. Rheumatol Ther 2021; 8:1159-1176. [PMID: 34148193 PMCID: PMC8214458 DOI: 10.1007/s40744-021-00330-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 06/03/2021] [Indexed: 12/12/2022] Open
Abstract
Introduction Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility. Methods The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI. Results The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics. Conclusion The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients. Supplementary Information The online version contains supplementary material available at 10.1007/s40744-021-00330-y. A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.
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Benincasa G, DeMeo DL, Glass K, Silverman EK, Napoli C. Epigenetics and pulmonary diseases in the horizon of precision medicine: a review. Eur Respir J 2021; 57:13993003.03406-2020. [PMID: 33214212 DOI: 10.1183/13993003.03406-2020] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023]
Abstract
Epigenetic mechanisms represent potential molecular routes which could bridge the gap between genetic background and environmental risk factors contributing to the pathogenesis of pulmonary diseases. In patients with COPD, asthma and pulmonary arterial hypertension (PAH), there is emerging evidence of aberrant epigenetic marks, mainly including DNA methylation and histone modifications which directly mediate reversible modifications to the DNA without affecting the genomic sequence. Post-translational events and microRNAs can be also regulated epigenetically and potentially participate in disease pathogenesis. Thus, novel pathogenic mechanisms and putative biomarkers may be detectable in peripheral blood, sputum, nasal and buccal swabs or lung tissue. Besides, DNA methylation plays an important role during the early phases of fetal development and may be impacted by environmental exposures, ultimately influencing an individual's susceptibility to COPD, asthma and PAH later in life. With the advances in omics platforms and the application of computational biology tools, modelling the epigenetic variability in a network framework, rather than as single molecular defects, provides insights into the possible molecular pathways underlying the pathogenesis of COPD, asthma and PAH. Epigenetic modifications may have clinical applications as noninvasive biomarkers of pulmonary diseases. Moreover, combining molecular assays with network analysis of epigenomic data may aid in clarifying the multistage transition from a "pre-disease" to "disease" state, with the goal of improving primary prevention of lung diseases and its subsequent clinical management.We describe epigenetic mechanisms known to be associated with pulmonary diseases and discuss how network analysis could improve our understanding of lung diseases.
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Affiliation(s)
- Giuditta Benincasa
- Dept of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Dawn L DeMeo
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Claudio Napoli
- Dept of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy .,Clinical Dept of Internal and Specialty Medicine (DAI), University Hospital (AOU), University of Campania "Luigi Vanvitelli", Naples, Italy
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Abstract
BACKGROUND Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results. METHODS Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed. RESULTS The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies. CONCLUSIONS The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.
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Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Gastroenterology, Hepatology and Nutrition, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Morselli Gysi D, do Valle Í, Zitnik M, Ameli A, Gan X, Varol O, Ghiassian SD, Patten JJ, Davey RA, Loscalzo J, Barabási AL. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc Natl Acad Sci U S A 2021; 118:e2025581118. [PMID: 33906951 PMCID: PMC8126852 DOI: 10.1073/pnas.2025581118] [Citation(s) in RCA: 176] [Impact Index Per Article: 58.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Ítalo do Valle
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, MA 02115
- Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138
| | - Asher Ameli
- Department of Physics, Northeastern University, Boston, MA 02115
- Data Science Department, Scipher Medicine, Waltham, MA 02453
| | - Xiao Gan
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Onur Varol
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | | | - J J Patten
- Department of Microbiology, National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118
| | - Robert A Davey
- Department of Microbiology, National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA 02115;
- Department of Physics, Northeastern University, Boston, MA 02115
- Department of Network and Data Science, Central European University, Budapest 1051, Hungary
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40
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Wang B, Dong X, Hu J, Ma X, Han C, Wang Y, Gao L. The peripheral and core regions of virus-host network of COVID-19. Brief Bioinform 2021; 22:6265188. [PMID: 33956950 PMCID: PMC8136014 DOI: 10.1093/bib/bbab169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/30/2021] [Accepted: 04/11/2021] [Indexed: 12/16/2022] Open
Abstract
Two thousand nineteen novel coronavirus SARS-CoV-2, the pathogen of COVID-19, has caused a catastrophic pandemic, which has a profound and widespread impact on human lives and social economy globally. However, the molecular perturbations induced by the SARS-CoV-2 infection remain unknown. In this paper, from the perspective of omnigenic, we analyze the properties of the neighborhood perturbed by SARS-CoV-2 in the human interactome and disclose the peripheral and core regions of virus-host network (VHN). We find that the virus-host proteins (VHPs) form a significantly connected VHN, among which highly perturbed proteins aggregate into an observable core region. The non-core region of VHN forms a large scale but relatively low perturbed periphery. We further validate that the periphery is non-negligible and conducive to identifying comorbidities and detecting drug repurposing candidates for COVID-19. We particularly put forward a flower model for COVID-19, SARS and H1N1 based on their peripheral regions, and the flower model shows more correlations between COVID-19 and other two similar diseases in common functional pathways and candidate drugs. Overall, our periphery-core pattern can not only offer insights into interconnectivity of SARS-CoV-2 VHPs but also facilitate the research on therapeutic drugs.
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Affiliation(s)
- Bingbo Wang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Xianan Dong
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Jie Hu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Xiujuan Ma
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Chao Han
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Yajun Wang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
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Cremades-Jimeno L, de Pedro MÁ, López-Ramos M, Sastre J, Mínguez P, Fernández IM, Baos S, Cárdaba B. Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology. Front Immunol 2021; 12:640791. [PMID: 33936056 PMCID: PMC8081895 DOI: 10.3389/fimmu.2021.640791] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/15/2021] [Indexed: 01/29/2023] Open
Abstract
Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study of 94 asthma/respiratory allergy biomarker candidates, we defined a group of potential biomarkers to distinguish clinical phenotypes (i.e. nonallergic asthma, allergic asthma, respiratory allergy without asthma) and disease severity. Here, we analyze our experimental results using complex algorithmic approaches that establish holistic disease models (systems biology), combining these insights with information available in specialized databases developed worldwide. With this approach, we aim to prioritize the most relevant biomarkers according to their specificity and mechanistic implication with molecular motifs of the diseases. The Therapeutic Performance Mapping System (Anaxomics’ TPMS technology) was used to generate one mathematical model per disease: allergic asthma (AA), non-allergic asthma (NA), and respiratory allergy (RA), defining specific molecular motifs for each. The relationship of our molecular biomarker candidates and each disease was analyzed by artificial neural networks (ANNs) scores. These analyses prioritized molecular biomarkers specific to the diseases and to particular molecular motifs. As a first step, molecular characterization of the pathophysiological processes of AA defined 16 molecular motifs: 2 specific for AA, 2 shared with RA, and 12 shared with NA. Mechanistic analysis showed 17 proteins that were strongly related to AA. Eleven proteins were associated with RA and 16 proteins with NA. Specificity analysis showed that 12 proteins were specific to AA, 7 were specific to RA, and 2 to NA. Finally, a triggering analysis revealed a relevant role for AKT1, STAT1, and MAPK13 in all three conditions and for TLR4 in asthmatic diseases (AA and NA). In conclusion, this study has enabled us to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.
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Affiliation(s)
- Lucía Cremades-Jimeno
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - María Ángeles de Pedro
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - María López-Ramos
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Joaquín Sastre
- Allergy Department, Fundación Jiménez Díaz, Madrid, Spain.,Center for Biomedical Network of Respiratory Diseases (CIBERES), ISCIII, Madrid, Spain
| | - Pablo Mínguez
- Department of Genetics, IIS-Fundación Jiménez Díaz, UAM, Madrid, Spain.,Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain
| | | | - Selene Baos
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Blanca Cárdaba
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain.,Center for Biomedical Network of Respiratory Diseases (CIBERES), ISCIII, Madrid, Spain
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42
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Lombardo SD, Basile MS, Ciurleo R, Bramanti A, Arcidiacono A, Mangano K, Bramanti P, Nicoletti F, Fagone P. A Network Medicine Approach for Drug Repurposing in Duchenne Muscular Dystrophy. Genes (Basel) 2021; 12:genes12040543. [PMID: 33918694 PMCID: PMC8069953 DOI: 10.3390/genes12040543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/23/2021] [Accepted: 04/07/2021] [Indexed: 01/01/2023] Open
Abstract
Duchenne muscular dystrophy (DMD) is a progressive hereditary muscular disease caused by a lack of dystrophin, leading to membrane instability, cell damage, and inflammatory response. However, gene-editing alone is not enough to restore the healthy phenotype and additional treatments are required. In the present study, we have first conducted a meta-analysis of three microarray datasets, GSE38417, GSE3307, and GSE6011, to identify the differentially expressed genes (DEGs) between healthy donors and DMD patients. We have then integrated this analysis with the knowledge obtained from DisGeNET and DIAMOnD, a well-known algorithm for drug–gene association discoveries in the human interactome. The data obtained allowed us to identify novel possible target genes and were used to predict potential therapeutical options that could reverse the pathological condition.
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Affiliation(s)
- Salvo Danilo Lombardo
- Department of Structural & Computational Biology at the Max Perutz Labs, University of Vienna, 1010 Vienna, Austria;
| | - Maria Sofia Basile
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.S.B.); (R.C.); (A.B.); (P.B.)
| | - Rosella Ciurleo
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.S.B.); (R.C.); (A.B.); (P.B.)
| | - Alessia Bramanti
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.S.B.); (R.C.); (A.B.); (P.B.)
| | - Antonio Arcidiacono
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 89, 95123 Catania, Italy; (A.A.); (K.M.); (P.F.)
| | - Katia Mangano
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 89, 95123 Catania, Italy; (A.A.); (K.M.); (P.F.)
| | - Placido Bramanti
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo, Contrada Casazza, 98124 Messina, Italy; (M.S.B.); (R.C.); (A.B.); (P.B.)
| | - Ferdinando Nicoletti
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 89, 95123 Catania, Italy; (A.A.); (K.M.); (P.F.)
- Correspondence:
| | - Paolo Fagone
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 89, 95123 Catania, Italy; (A.A.); (K.M.); (P.F.)
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Lazareva O, Baumbach J, List M, Blumenthal DB. On the limits of active module identification. Brief Bioinform 2021; 22:6189770. [PMID: 33782690 DOI: 10.1093/bib/bbab066] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 01/29/2021] [Indexed: 12/12/2022] Open
Abstract
In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein-protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.
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Affiliation(s)
- Olga Lazareva
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany.,Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Markus List
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - David B Blumenthal
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
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do Valle IF, Roweth HG, Malloy MW, Moco S, Barron D, Battinelli E, Loscalzo J, Barabási AL. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. NATURE FOOD 2021; 2:143-155. [PMID: 37117448 DOI: 10.1038/s43016-021-00243-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 02/16/2021] [Indexed: 04/30/2023]
Abstract
Polyphenols, natural products present in plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we develop a network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighbourhoods of the human interactome, whose network proximity to disease proteins is predictive of the molecule's known therapeutic effects. The methodology recovers known associations, such as the effect of epigallocatechin-3-O-gallate on type 2 diabetes, and predicts that rosmarinic acid has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health. We experimentally confirm that rosmarinic acid inhibits platelet aggregation and α-granule secretion through inhibition of protein tyrosine phosphorylation, offering direct support for the predicted molecular mechanism. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability and drug interaction.
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Affiliation(s)
- Italo F do Valle
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA
| | - Harvey G Roweth
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael W Malloy
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sofia Moco
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Denis Barron
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Elisabeth Battinelli
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Network and Data Science, Central European University, Budapest, Hungary.
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He Q, Liu N, Hu F, Shi Q, Pi X, Chen H, Li J, Zhang B. Circ_0061012 contributes to IL-22-induced proliferation, migration and invasion in keratinocytes through miR-194-5p/GAB1 axis in psoriasis. Biosci Rep 2021; 41:BSR20203130. [PMID: 33393621 PMCID: PMC7809556 DOI: 10.1042/bsr20203130] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/28/2022] Open
Abstract
Psoriasis is a chronic inflammation-associated skin disorder featured by excessive proliferation and abnormal differentiation of keratinocytes. Here, we intended to investigate the role of circular RNA 0061012 (circ_0061012) in psoriasis progression. The expression of circ_0061012, SLMO2-ATP5E readthrough (SLMO2-ATP5E) messenger RNA (mRNA), microRNA-194-5p (miR-194-5p) and GRB2 associated binding protein 1 (GAB1) mRNA was determined by quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation and metastasis were analyzed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and transwell assays. Western blot assay was used to measure the protein levels of Ki67, matrix metallopeptidase 9 (MMP9) and GAB1. Dual-luciferase reporter assay and RNA immune co-precipitation (RIP) assay were used to verify the interaction between miR-194-5p and circ_0061012 or GAB1. Circ_0061012 abundance was significantly enhanced in lesional skin samples from psoriasis patients than that in normal skin specimens from healthy volunteers. Interleukin-22 (IL-22) treatment increased the expression of circ_0061012 in a dose-dependent manner. Circ_0061012 silencing alleviated IL-22-induced promoting effects in the proliferation, migration and invasion of HaCaT cells. Circ_0061012 interacted with miR-194-5p, and miR-194-5p knockdown counteracted circ_0061012 silencing-mediated influences in IL-22-induced HaCaT cells. GAB1 was a target of miR-194-5p in HaCaT cells, and miR-194-5p hampered proliferation and metastasis which were induced by IL-22 partly through targeting GAB1. Circ_0061012 elevated the expression of GAB1 through sponging miR-194-5p in HaCaT cells. Circ_0061012 accelerated IL-22-induced proliferation and metastasis in HaCaT cells through enhancing GAB1 expression via sponging miR-194-5p in psoriasis.
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Affiliation(s)
- Qi He
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Nian Liu
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Feng Hu
- Department of Dermatology, Wuhan No. 1 Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Quan Shi
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Xianming Pi
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Hongxiang Chen
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiawen Li
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bo Zhang
- Department of Dermatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
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46
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Leroux M, Boutchueng-Djidjou M, Faure R. Insulin's Discovery: New Insights on Its Hundredth Birthday: From Insulin Action and Clearance to Sweet Networks. Int J Mol Sci 2021; 22:ijms22031030. [PMID: 33494161 PMCID: PMC7864324 DOI: 10.3390/ijms22031030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 11/28/2022] Open
Abstract
In 2021, the 100th anniversary of the isolation of insulin and the rescue of a child with type 1 diabetes from death will be marked. In this review, we highlight advances since the ingenious work of the four discoverers, Frederick Grant Banting, John James Rickard Macleod, James Bertram Collip and Charles Herbert Best. Macleoad closed his Nobel Lecture speech by raising the question of the mechanism of insulin action in the body. This challenge attracted many investigators, and the question remained unanswered until the third part of the 20th century. We summarize what has been learned, from the discovery of cell surface receptors, insulin action, and clearance, to network and precision medicine.
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Wang X, Wang ZY, Zheng JH, Li S. TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches. Chin J Nat Med 2021; 19:1-11. [PMID: 33516447 DOI: 10.1016/s1875-5364(21)60001-8] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Indexed: 12/22/2022]
Abstract
Traditional Chinese medicine (TCM) is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of diseases. The holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and system. TCM gave birth to a new method featuring holistic and systematic "network target", a core theory and method of network pharmacology. TCM is also an important research object of network pharmacology. TCM network pharmacology, which aims to understand the network-based biological basis of complex diseases, TCM syndromes and herb treatments, plays a critical role in the origin and development process of network pharmacology. This review introduces new progresses of TCM network pharmacology in recent years, including predicting herb targets, understanding biological foundation of diseases and syndromes, network regulation mechanisms of herbal formulae, and identifying disease and syndrome biomarkers based on biological network. These studies show a trend of combining computational, experimental and clinical approaches, which is a promising direction of TCM network pharmacology research in the future. Considering that TCM network pharmacology is still a young research field, it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.
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Affiliation(s)
- Xin Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Zi-Yi Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Jia-Hui Zheng
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.
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Banerjee S, Velásquez-Zapata V, Fuerst G, Elmore JM, Wise RP. NGPINT: a next-generation protein-protein interaction software. Brief Bioinform 2020; 22:6046042. [PMID: 33367498 DOI: 10.1093/bib/bbaa351] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/23/2020] [Accepted: 11/02/2020] [Indexed: 12/27/2022] Open
Abstract
Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein-protein interactions in model and non-model organisms.
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Affiliation(s)
- Sagnik Banerjee
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, 50011, USA.,Department of Statistics, Iowa State University, Ames, IA, 50011, USA
| | - Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, 50011, USA.,Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA, 50011, USA
| | - Gregory Fuerst
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA, 50011, USA.,Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA, 50011, USA
| | - J Mitch Elmore
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA, 50011, USA.,Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA, 50011, USA
| | - Roger P Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, 50011, USA.,Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA, 50011, USA.,Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA, 50011, USA
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49
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Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data. Sci Rep 2020; 10:21466. [PMID: 33293650 PMCID: PMC7722755 DOI: 10.1038/s41598-020-78511-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 11/17/2020] [Indexed: 01/09/2023] Open
Abstract
The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.
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50
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Ohsawa S, Umemura T, Terada T, Muto Y. Network and Evolutionary Analysis of Human Epigenetic Regulators to Unravel Disease Associations. Genes (Basel) 2020; 11:genes11121457. [PMID: 33291839 PMCID: PMC7761991 DOI: 10.3390/genes11121457] [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: 11/04/2020] [Revised: 11/29/2020] [Accepted: 11/30/2020] [Indexed: 12/15/2022] Open
Abstract
We carried out a system-level analysis of epigenetic regulators (ERs) and detailed the protein–protein interaction (PPI) network characteristics of disease-associated ERs. We found that most diseases associated with ERs can be clustered into two large groups, cancer diseases and developmental diseases. ER genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate with one another. We used the disease module detection (DIAMOnD) algorithm to expand the PPI subnetworks into a comprehensive cancer disease ER network (CDEN) and developmental disease ER network (DDEN). Using the transcriptome from early mouse developmental stages, we identified the gene co-expression modules significantly enriched for the CDEN and DDEN gene sets, which indicated the stage-dependent roles of ER-related disease genes during early embryonic development. The evolutionary rate and phylogenetic age distribution analysis indicated that the evolution of CDEN and DDEN genes was mostly constrained, and these genes exhibited older evolutionary age. Our analysis of human polymorphism data revealed that genes belonging to DDEN and Seed-DDEN were more likely to show signs of recent positive selection in human history. This finding suggests a potential association between positive selection of ERs and risk of developmental diseases through the mechanism of antagonistic pleiotropy.
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Affiliation(s)
- Shinji Ohsawa
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu 501-1193, Japan; (S.O.); (T.T.)
- Department of Nursing, Ogaki Women’s College, 1-109, Nishinokawa-cho, Ogaki 503-8554, Japan
| | - Toshiaki Umemura
- Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, 2630, Sugitani, Toyama 930-0194, Japan;
| | - Tomoyoshi Terada
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu 501-1193, Japan; (S.O.); (T.T.)
- Department of Functional Bioscience, Gifu University School of Medicine, 1-1, Yanagido, Gifu 501-1193, Japan
| | - Yoshinori Muto
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu 501-1193, Japan; (S.O.); (T.T.)
- Department of Functional Bioscience, Gifu University School of Medicine, 1-1, Yanagido, Gifu 501-1193, Japan
- Correspondence: ; Tel.: +81-58-293-3241
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