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Saba KH, Difilippo V, Kovac M, Cornmark L, Magnusson L, Nilsson J, van den Bos H, Spierings DC, Bidgoli M, Jonson T, Sumathi VP, Brosjö O, Staaf J, Foijer F, Styring E, Nathrath M, Baumhoer D, Nord KH. Disruption of the TP53 locus in osteosarcoma leads to TP53 promoter gene fusions and restoration of parts of the TP53 signalling pathway. J Pathol 2024; 262:147-160. [PMID: 38010733 DOI: 10.1002/path.6219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/24/2023] [Accepted: 09/19/2023] [Indexed: 11/29/2023]
Abstract
TP53 is the most frequently mutated gene in human cancer. This gene shows not only loss-of-function mutations but also recurrent missense mutations with gain-of-function activity. We have studied the primary bone malignancy osteosarcoma, which harbours one of the most rearranged genomes of all cancers. This is odd since it primarily affects children and adolescents who have not lived the long life thought necessary to accumulate massive numbers of mutations. In osteosarcoma, TP53 is often disrupted by structural variants. Here, we show through combined whole-genome and transcriptome analyses of 148 osteosarcomas that TP53 structural variants commonly result in loss of coding parts of the gene while simultaneously preserving and relocating the promoter region. The transferred TP53 promoter region is fused to genes previously implicated in cancer development. Paradoxically, these erroneously upregulated genes are significantly associated with the TP53 signalling pathway itself. This suggests that while the classical tumour suppressor activities of TP53 are lost, certain parts of the TP53 signalling pathway that are necessary for cancer cell survival and proliferation are retained. In line with this, our data suggest that transposition of the TP53 promoter is an early event that allows for a new normal state of genome-wide rearrangements in osteosarcoma. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Karim H Saba
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Valeria Difilippo
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Michal Kovac
- Bone Tumour Reference Centre at the Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland
- Faculty of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia
| | - Louise Cornmark
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Linda Magnusson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Jenny Nilsson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Hilda van den Bos
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Diana Cj Spierings
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Mahtab Bidgoli
- Department of Clinical Genetics and Pathology, Laboratory Medicine, Medical Services, Skåne University Hospital, Lund, Sweden
| | - Tord Jonson
- Department of Clinical Genetics and Pathology, Laboratory Medicine, Medical Services, Skåne University Hospital, Lund, Sweden
| | - Vaiyapuri P Sumathi
- Department of Musculoskeletal Pathology, Royal Orthopaedic Hospital, Birmingham, UK
| | - Otte Brosjö
- Department of Orthopedics, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Staaf
- Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Floris Foijer
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Emelie Styring
- Department of Orthopedics, Lund University, Skåne University Hospital, Lund, Sweden
| | - Michaela Nathrath
- Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany
| | - Daniel Baumhoer
- Bone Tumour Reference Centre at the Institute of Pathology, University Hospital and University of Basel, Basel, Switzerland
| | - Karolin H Nord
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
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Buzzao D, Castresana-Aguirre M, Guala D, Sonnhammer ELL. Benchmarking enrichment analysis methods with the disease pathway network. Brief Bioinform 2024; 25:bbae069. [PMID: 38436561 PMCID: PMC10939300 DOI: 10.1093/bib/bbae069] [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: 09/29/2023] [Revised: 01/10/2024] [Accepted: 02/03/2024] [Indexed: 03/05/2024] Open
Abstract
Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.
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Affiliation(s)
- Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | | | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
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3
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Genna A, Alter J, Poletti M, Meirson T, Sneh T, Gendler M, Saleev N, Karagiannis GS, Wang Y, Cox D, Entenberg D, Oktay MH, Korcsmaros T, Condeelis JS, Gil-Henn H. FAK family proteins regulate in vivo breast cancer metastasis via distinct mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564212. [PMID: 37961438 PMCID: PMC10634866 DOI: 10.1101/2023.10.27.564212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Breast cancer is the most commonly diagnosed malignancy and the major leading cause of tumor-related deaths in women. It is estimated that the majority of breast tumor-related deaths are a consequence of metastasis, to which no cure exists at present. The FAK family proteins Proline-rich tyrosine kinase (PYK2) and focal adhesion kinase (FAK) are highly expressed in breast cancer, but the exact cellular and signaling mechanisms by which they regulate in vivo tumor cell invasiveness and consequent metastatic dissemination are mostly unknown. Using a PYK2 and FAK knockdown xenograft model we show here, for the first time, that ablation of either PYK2 or FAK decreases primary tumor size and significantly reduces Tumor MicroEnvironment of Metastasis (TMEM) doorway activation, leading to decreased intravasation and reduced spontaneous lung metastasis. Intravital imaging analysis further demonstrates that PYK2, but not FAK, regulates a motility phenotype switch between focal adhesion-mediated fast motility and invadopodia-dependent, ECM-degradation associated slow motility within the primary tumor. Furthermore, we validate our in vivo and intravital imaging results with integrated transcriptomic and proteomic data analysis from xenograft knockdown tumors and reveal new and distinct pathways by which these two homologous kinases regulate breast tumor cell invasiveness and consequent metastatic dissemination. Our findings identify PYK2 and FAK as novel mediators of mammary tumor progression and metastasis and as candidate therapeutic targets for breast cancer metastasis.
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Windels SFL, Malod-Dognin N, Pržulj N. Identifying cellular cancer mechanisms through pathway-driven data integration. Bioinformatics 2022; 38:4344-4351. [PMID: 35916710 PMCID: PMC9477533 DOI: 10.1093/bioinformatics/btac493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/14/2022] [Accepted: 07/30/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Cancer is a genetic disease in which accumulated mutations of driver genes induce a functional reorganization of the cell by reprogramming cellular pathways. Current approaches identify cancer pathways as those most internally perturbed by gene expression changes. However, driver genes characteristically perform hub roles between pathways. Therefore, we hypothesize that cancer pathways should be identified by changes in their pathway-pathway relationships. RESULTS To learn an embedding space that captures the relationships between pathways in a healthy cell, we propose pathway-driven non-negative matrix tri-factorization. In this space, we determine condition-specific (i.e. diseased and healthy) embeddings of pathways and genes. Based on these embeddings, we define our 'NMTF centrality' to measure a pathway's or gene's functional importance, and our 'moving distance', to measure the change in its functional relationships. We combine both measures to predict 15 genes and pathways involved in four major cancers, predicting 60 gene-cancer associations in total, covering 28 unique genes. To further exploit driver genes' tendency to perform hub roles, we model our network data using graphlet adjacency, which considers nodes adjacent if their interaction patterns form specific shapes (e.g. paths or triangles). We find that the predicted genes rewire pathway-pathway interactions in the immune system and provide literary evidence that many are druggable (15/28) and implicated in the associated cancers (47/60). We predict six druggable cancer-specific drug targets. AVAILABILITY AND IMPLEMENTATION The code and data are available at: https://gitlab.bsc.es/swindels/pathway_driven_nmtf. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sam F L Windels
- Department of Computer Science, University College London, London WC1E 6BT, UK,Barcelona Supercomputing Center, 08034 Barcelona, Spain
| | - Noël Malod-Dognin
- Department of Computer Science, University College London, London WC1E 6BT, UK,Barcelona Supercomputing Center, 08034 Barcelona, Spain
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Wang S, Zheng H, Choi JS, Lee JK, Li X, Hu H. A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference. Brief Funct Genomics 2022; 21:339-356. [PMID: 35822343 PMCID: PMC9479691 DOI: 10.1093/bfgp/elac019] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.
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Affiliation(s)
| | | | | | | | - Xiaoman Li
- Corresponding authors: Haiyan Hu, Department of Computer Science, University of Central Florida, Orlando, FL, USA. Tel.: +1-4078820134; Fax: +1-4078235835; E-mail: ; Xiaoman Li, Burnett School of Biomedical Science, University of Central Florida, Orlando, FL, USA. Tel.: +1-4078234811; Fax: +1-4078235835; E-mail:
| | - Haiyan Hu
- Corresponding authors: Haiyan Hu, Department of Computer Science, University of Central Florida, Orlando, FL, USA. Tel.: +1-4078820134; Fax: +1-4078235835; E-mail: ; Xiaoman Li, Burnett School of Biomedical Science, University of Central Florida, Orlando, FL, USA. Tel.: +1-4078234811; Fax: +1-4078235835; E-mail:
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Pirintsos S, Panagiotopoulos A, Bariotakis M, Daskalakis V, Lionis C, Sourvinos G, Karakasiliotis I, Kampa M, Castanas E. From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples. Molecules 2022; 27:molecules27134060. [PMID: 35807306 PMCID: PMC9268545 DOI: 10.3390/molecules27134060] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products’ drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.
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Affiliation(s)
- Stergios Pirintsos
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
- Botanical Garden, University of Crete, 74100 Rethymnon, Greece
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Correspondence: (S.P.); (E.C.)
| | - Athanasios Panagiotopoulos
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Michalis Bariotakis
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
| | - Vangelis Daskalakis
- Department of Chemical Engineering, Cyprus University of Technology, Limassol 3603, Cyprus;
| | - Christos Lionis
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Clinic of Social and Family Medicine, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - George Sourvinos
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - Ioannis Karakasiliotis
- Laboratory of Biology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Marilena Kampa
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Elias Castanas
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
- Correspondence: (S.P.); (E.C.)
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Castresana-Aguirre M, Guala D, Sonnhammer ELL. Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis. Front Genet 2022; 13:855766. [PMID: 35620466 PMCID: PMC9127507 DOI: 10.3389/fgene.2022.855766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 04/25/2022] [Indexed: 12/13/2022] Open
Abstract
Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.
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Affiliation(s)
- Miguel Castresana-Aguirre
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Dimitri Guala
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
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Guala D, Sonnhammer ELL. Network Crosstalk as a Basis for Drug Repurposing. Front Genet 2022; 13:792090. [PMID: 35350247 PMCID: PMC8958038 DOI: 10.3389/fgene.2022.792090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 01/27/2022] [Indexed: 11/23/2022] Open
Abstract
The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.
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Affiliation(s)
- Dimitri Guala
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
- Merck AB, Solna, Sweden
| | - Erik L. L. Sonnhammer
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
- *Correspondence: Erik L. L. Sonnhammer,
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10
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Castresana-Aguirre M, Persson E, Sonnhammer ELL. PathBIX-a web server for network-based pathway annotation with adaptive null models. BIOINFORMATICS ADVANCES 2021; 1:vbab010. [PMID: 36700096 PMCID: PMC9710673 DOI: 10.1093/bioadv/vbab010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/30/2021] [Indexed: 01/28/2023]
Abstract
Motivation Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation. Results We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways. Availability https://pathbix.sbc.su.se/. Contact erik.sonnhammer@scilifelab.se. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Miguel Castresana-Aguirre
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm 17121, Sweden
| | - Emma Persson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm 17121, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm 17121, Sweden,To whom correspondence should be addressed.
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11
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Persson E, Castresana-Aguirre M, Buzzao D, Guala D, Sonnhammer ELL. FunCoup 5: Functional Association Networks in All Domains of Life, Supporting Directed Links and Tissue-Specificity. J Mol Biol 2021; 433:166835. [PMID: 33539890 DOI: 10.1016/j.jmb.2021.166835] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/18/2020] [Accepted: 01/15/2021] [Indexed: 02/07/2023]
Abstract
FunCoup (https://funcoup.sbc.su.se) is one of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence using a redundancy-weighted naïve Bayesian approach, combined with orthology transfer. FunCoup's high coverage comes from using eleven different types of evidence, and extensive transfer of information between species. Since the latest update of the database, the availability of source data has improved drastically, and user expectations on a tool for functional associations have grown. To meet these requirements, we have made a new release of FunCoup with updated source data and improved functionality. FunCoup 5 now includes 22 species from all domains of life, and the source data for evidences, gold standards, and genomes have been updated to the latest available versions. In this new release, directed regulatory links inferred from transcription factor binding can be visualized in the network viewer for the human interactome. Another new feature is the possibility to filter by genes expressed in a certain tissue in the network viewer. FunCoup 5 further includes the SARS-CoV-2 proteome, allowing users to visualize and analyze interactions between SARS-CoV-2 and human proteins in order to better understand COVID-19. This new release of FunCoup constitutes a major advance for the users, with updated sources, new species and improved functionality for analysis of the networks.
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Affiliation(s)
- Emma Persson
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Miguel Castresana-Aguirre
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.
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Treveil A, Bohar B, Sudhakar P, Gul L, Csabai L, Olbei M, Poletti M, Madgwick M, Andrighetti T, Hautefort I, Modos D, Korcsmaros T. ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways. PLoS Comput Biol 2021; 17:e1008685. [PMID: 33534793 PMCID: PMC7886129 DOI: 10.1371/journal.pcbi.1008685] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 02/16/2021] [Accepted: 01/10/2021] [Indexed: 12/21/2022] Open
Abstract
The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.
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Affiliation(s)
- Agatha Treveil
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Balazs Bohar
- Earlham Institute, Norwich, United Kingdom
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Padhmanand Sudhakar
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Lejla Gul
- Earlham Institute, Norwich, United Kingdom
| | - Luca Csabai
- Earlham Institute, Norwich, United Kingdom
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Marton Olbei
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Martina Poletti
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Matthew Madgwick
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tahila Andrighetti
- Earlham Institute, Norwich, United Kingdom
- Institute of Biosciences, São Paulo University, Botucatu, Brazil
| | | | - Dezso Modos
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
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