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Hakobyan S, Stepanyan A, Nersisyan L, Binder H, Arakelyan A. PSF toolkit: an R package for pathway curation and topology-aware analysis. Front Genet 2023; 14:1264656. [PMID: 37680201 PMCID: PMC10482229 DOI: 10.3389/fgene.2023.1264656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 08/09/2023] [Indexed: 09/09/2023] Open
Abstract
Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.
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Affiliation(s)
- Siras Hakobyan
- Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
| | | | | | - Hans Binder
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Arsen Arakelyan
- Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
- Russian-Armenian University, Yerevan, Armenia
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Lin J, Xue Y, Su W, Zhang Z, Wei Q, Huang T. Identification of Dysregulated Mechanisms and Candidate Gene Markers in Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2022; 17:475-487. [PMID: 35281477 PMCID: PMC8904782 DOI: 10.2147/copd.s349694] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 02/27/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment. Methods The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the "least absolute shrinkage and selection operator" in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset. Results A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4. Conclusion The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.
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Affiliation(s)
- Jie Lin
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Yanlong Xue
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Wenyan Su
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Zan Zhang
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
| | - Qiu Wei
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China,Correspondence: Qiu Wei; Tianxia Huang, Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, 89 Qixing Road, Nanning, Guangxi, 530022, People’s Republic of China, Tel +86 7712636163, Fax +86 7712617892, Email ;
| | - Tianxia Huang
- Department of Respiratory and Critical Care, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China,Department of Respiratory and Critical Care, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China
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Arakelyan A, Melkonyan A, Hakobyan S, Boyarskih U, Simonyan A, Nersisyan L, Nikoghosyan M, Filipenko M, Binder H. Transcriptome Patterns of BRCA1- and BRCA2- Mutated Breast and Ovarian Cancers. Int J Mol Sci 2021; 22:1266. [PMID: 33525353 PMCID: PMC7865215 DOI: 10.3390/ijms22031266] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/06/2021] [Accepted: 01/07/2021] [Indexed: 02/06/2023] Open
Abstract
Mutations in the BRCA1 and BRCA2 genes are known risk factors and drivers of breast and ovarian cancers. So far, few studies have been focused on understanding the differences in transcriptome and functional landscapes associated with the disease (breast vs. ovarian cancers), gene (BRCA1 vs. BRCA2), and mutation type (germline vs. somatic). In this study, we were aimed at systemic evaluation of the association of BRCA1 and BRCA2 germline and somatic mutations with gene expression, disease clinical features, outcome, and treatment. We performed BRCA1/2 mutation centered RNA-seq data analysis of breast and ovarian cancers from the TCGA repository using transcriptome and phenotype "portrayal" with multi-layer self-organizing maps and functional annotation. The results revealed considerable differences in BRCA1- and BRCA2-dependent transcriptome landscapes in the studied cancers. Furthermore, our data indicated that somatic and germline mutations for both genes are characterized by deregulation of different biological functions and differential associations with phenotype characteristics and poly(ADP-ribose) polymerase (PARP)-inhibitor gene signatures. Overall, this study demonstrates considerable variation in transcriptomic landscapes of breast and ovarian cancers associated with the affected gene (BRCA1 vs. BRCA2), as well as the mutation type (somatic vs. germline). These results warrant further investigations with larger groups of mutation carriers aimed at refining the understanding of molecular mechanisms of breast and ovarian cancers.
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Affiliation(s)
- Arsen Arakelyan
- Group of Bioinformatics, Institute of Molecular Biology National Academy of Sciences of Armenia, 0014 Yerevan, Armenia; (S.H.); (A.S.); (L.N.); (M.N.)
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia
| | - Ani Melkonyan
- Laboratory of Human Genomics and Immunomics, Institute of Molecular Biology National Academy of Sciences of Armenia, 0014 Yerevan, Armenia;
| | - Siras Hakobyan
- Group of Bioinformatics, Institute of Molecular Biology National Academy of Sciences of Armenia, 0014 Yerevan, Armenia; (S.H.); (A.S.); (L.N.); (M.N.)
| | - Uljana Boyarskih
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences (SB RAS), 630090 Novosibirsk, Russia; (U.B.); (M.F.)
| | - Arman Simonyan
- Group of Bioinformatics, Institute of Molecular Biology National Academy of Sciences of Armenia, 0014 Yerevan, Armenia; (S.H.); (A.S.); (L.N.); (M.N.)
| | - Lilit Nersisyan
- Group of Bioinformatics, Institute of Molecular Biology National Academy of Sciences of Armenia, 0014 Yerevan, Armenia; (S.H.); (A.S.); (L.N.); (M.N.)
| | - Maria Nikoghosyan
- Group of Bioinformatics, Institute of Molecular Biology National Academy of Sciences of Armenia, 0014 Yerevan, Armenia; (S.H.); (A.S.); (L.N.); (M.N.)
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia
| | - Maxim Filipenko
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences (SB RAS), 630090 Novosibirsk, Russia; (U.B.); (M.F.)
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany;
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Arakelyan A, Nersisyan L, Nikoghosyan M, Hakobyan S, Simonyan A, Hopp L, Loeffler-Wirth H, Binder H. Transcriptome-Guided Drug Repositioning. Pharmaceutics 2019; 11:E677. [PMID: 31842375 PMCID: PMC6969900 DOI: 10.3390/pharmaceutics11120677] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/17/2019] [Accepted: 12/11/2019] [Indexed: 02/06/2023] Open
Abstract
Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies "drug target" spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs' action for approved diseases (ulcerative colitis and Crohn's disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn's disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.
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Affiliation(s)
- Arsen Arakelyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
| | - Lilit Nersisyan
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Maria Nikoghosyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Siras Hakobyan
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Arman Simonyan
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051 Yerevan, Armenia; (M.N.); (A.S.)
- Group of Bioinformatics, Institute of Molecular Biology NAS RA, 0014 Yerevan, Armenia; (L.N.); (S.H.)
| | - Lydia Hopp
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany; (L.H.); (H.L.-W.)
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Lee D, Cho KH. Topological estimation of signal flow in complex signaling networks. Sci Rep 2018; 8:5262. [PMID: 29588498 PMCID: PMC5869720 DOI: 10.1038/s41598-018-23643-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 03/16/2018] [Indexed: 12/15/2022] Open
Abstract
In a cell, any information about extra- or intra-cellular changes is transferred and processed through a signaling network and dysregulation of signal flow often leads to disease such as cancer. So, understanding of signal flow in the signaling network is critical to identify drug targets. Owing to the development of high-throughput measurement technologies, the structure of a signaling network is becoming more available, but detailed kinetic parameter information about molecular interactions is still very limited. A question then arises as to whether we can estimate the signal flow based only on the structure information of a signaling network. To answer this question, we develop a novel algorithm that can estimate the signal flow using only the topological information and apply it to predict the direction of activity change in various signaling networks. Interestingly, we find that the average accuracy of the estimation algorithm is about 60–80% even though we only use the topological information. We also find that this predictive power gets collapsed if we randomly alter the network topology, showing the importance of network topology. Our study provides a basis for utilizing the topological information of signaling networks in precision medicine or drug target discovery.
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Affiliation(s)
- Daewon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
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Arakelyan A, Nersisyan L, Poghosyan D, Khondkaryan L, Hakobyan A, Löffler-Wirth H, Melanitou E, Binder H. Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles. PLoS One 2017; 12:e0187572. [PMID: 29099860 PMCID: PMC5669448 DOI: 10.1371/journal.pone.0187572] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 10/23/2017] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Autoinflammatory and autoimmune disorders are characterized by aberrant changes in innate and adaptive immunity that may lead from an initial inflammatory state to an organ specific damage. These disorders possess heterogeneity in terms of affected organs and clinical phenotypes. However, despite the differences in etiology and phenotypic variations, they share genetic associations, treatment responses and clinical manifestations. The mechanisms involved in their initiation and development remain poorly understood, however the existence of some clear similarities between autoimmune and autoinflammatory disorders indicates variable degrees of interaction between immune-related mechanisms. METHODS Our study aims at contributing to a holistic, pathway-centered view on the inflammatory condition of autoimmune and autoinflammatory diseases. We have evaluated similarities and specificities of pathway activity changes in twelve autoimmune and autoinflammatory disorders by performing meta-analysis of publicly available gene expression datasets generated from peripheral blood mononuclear cells, using a bioinformatics pipeline that integrates Self Organizing Maps and Pathway Signal Flow algorithms along with KEGG pathway topologies. RESULTS AND CONCLUSIONS The results reveal that clinically divergent disease groups share common pathway perturbation profiles. We identified pathways, similarly perturbed in all the studied diseases, such as PI3K-Akt, Toll-like receptor, and NF-kappa B signaling, that serve as integrators of signals guiding immune cell polarization, migration, growth, survival and differentiation. Further, two clusters of diseases were identified based on specifically dysregulated pathways: one gathering mostly autoimmune and the other mainly autoinflammatory diseases. Cluster separation was driven not only by apparent involvement of pathways implicated in adaptive immunity in one case, and inflammation in the other, but also by processes not explicitly related to immune response, but rather representing various events related to the formation of specific pathophysiological environment. Thus, our data suggest that while all of the studied diseases are affected by activation of common inflammatory processes, disease-specific variations in their relative balance are also identified.
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Affiliation(s)
- Arsen Arakelyan
- Bioinformatics Group, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia
- Department of Bioinformatics and Bioengineering, Russian-Armenian University, Yerevan, Armenia
| | - Lilit Nersisyan
- Bioinformatics Group, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia
- Zaven and Sonia Akian College of Science and Engineering, American University of Armenia, Yerevan, Armenia
| | - David Poghosyan
- Group of Immune Response Regulation, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia
| | - Lusine Khondkaryan
- Group of Immune Response Regulation, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia
| | - Anna Hakobyan
- Bioinformatics Group, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia
| | - Henry Löffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Evie Melanitou
- Department of Parasitology and Insect Vectors, Institut Pasteur, Paris, France
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
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