1
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Ceylan B, Düz E, Çakir T. Personalized Protein-Protein Interaction Networks Towards Unraveling the Molecular Mechanisms of Alzheimer's Disease. Mol Neurobiol 2024; 61:2120-2135. [PMID: 37855983 DOI: 10.1007/s12035-023-03690-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023]
Abstract
Alzheimer's disease (AD) is a highly heterogenous neurodegenerative disease, and several omic-based datasets were generated in the last decade from the patients with the disease. However, the vast majority of studies evaluate these datasets in bulk by considering all the patients as a single group, which obscures the molecular differences resulting from the heterogeneous nature of the disease. In this study, we adopted a personalized approach and analyzed the transcriptome data from 403 patients individually by mapping the data on a human protein-protein interaction network. Patient-specific subnetworks were discovered and analyzed in terms of the genes in the subnetworks, enriched functional terms, and known AD genes. We identified several affected pathways that could not be captured by the bulk comparison. We also showed that our personalized findings point to patterns of alterations consistent with the recently suggested AD subtypes.
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Affiliation(s)
- Betül Ceylan
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Elif Düz
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Tunahan Çakir
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
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2
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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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3
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Hartung M, Anastasi E, Mamdouh ZM, Nogales C, Schmidt HHHW, Baumbach J, Zolotareva O, List M. Cancer driver drug interaction explorer. Nucleic Acids Res 2022; 50:W138-W144. [PMID: 35580047 PMCID: PMC9252786 DOI: 10.1093/nar/gkac384] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/06/2022] [Accepted: 04/29/2022] [Indexed: 12/16/2022] Open
Abstract
Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.
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Affiliation(s)
- Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Elisa Anastasi
- School of Computing, Newcastle University, 2308 Newcastle upon Tyne, UK
| | - Zeinab M Mamdouh
- Department of Pharmacology and Personalised Medicine, Maastricht University, 6229 Maastricht, Netherlands.,Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zagazig University, 44519 Zagazig, Egypt
| | - Cristian Nogales
- Department of Pharmacology and Personalised Medicine, Maastricht University, 6229 Maastricht, Netherlands
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalised Medicine, Maastricht University, 6229 Maastricht, Netherlands
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Olga Zolotareva
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
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4
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Winkler S, Winkler I, Figaschewski M, Tiede T, Nordheim A, Kohlbacher O. De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet. BMC Bioinformatics 2022; 23:139. [PMID: 35439941 PMCID: PMC9020058 DOI: 10.1186/s12859-022-04670-6] [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: 06/05/2021] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Background With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. Results We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. Conclusion The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
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Affiliation(s)
- Sebastian Winkler
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany. .,International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.
| | - Ivana Winkler
- International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.,Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mirjam Figaschewski
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
| | - Thorsten Tiede
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
| | - Alfred Nordheim
- Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany.,Leibniz Institute on Aging (FLI), Jena, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany.,Translational Bioinformatics, University Hospital Tuebingen, Tübingen, Germany
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5
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Mechteridis K, Lauber M, Baumbach J, List M. KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem. Front Genet 2022; 12:812853. [PMID: 35173764 PMCID: PMC8842393 DOI: 10.3389/fgene.2021.812853] [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: 11/10/2021] [Accepted: 12/24/2021] [Indexed: 11/13/2022] Open
Abstract
De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package's potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.
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Affiliation(s)
- Konstantinos Mechteridis
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Michael Lauber
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, Faculty of Science, University of Southern Denmark, Odense, Denmark
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
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6
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CTCFL regulates the PI3K-Akt pathway and it is a target for personalized ovarian cancer therapy. NPJ Syst Biol Appl 2022; 8:5. [PMID: 35132075 PMCID: PMC8821627 DOI: 10.1038/s41540-022-00214-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 01/05/2022] [Indexed: 12/04/2022] Open
Abstract
High-grade serous ovarian carcinoma (HGSC) is the most lethal gynecologic malignancy due to the lack of reliable biomarkers, effective treatment, and chemoresistance. Improving the diagnosis and the development of targeted therapies is still needed. The molecular pathomechanisms driving HGSC progression are not fully understood though crucial for effective diagnosis and identification of novel targeted therapy options. The oncogene CTCFL (BORIS), the paralog of CTCF, is a transcriptional factor highly expressed in ovarian cancer (but in rarely any other tissue in females) with cancer-specific characteristics and therapeutic potential. In this work, we seek to understand the regulatory functions of CTCFL to unravel new target genes with clinical relevance. We used in vitro models to evaluate the transcriptional changes due to the presence of CTCFL, followed by a selection of gene candidates using de novo network enrichment analysis. The resulting mechanistic candidates were further assessed regarding their prognostic potential and druggability. We show that CTCFL-driven genes are involved in cytoplasmic membrane functions; in particular, the PI3K-Akt initiators EGFR1 and VEGFA, as well as ITGB3 and ITGB6 are potential drug targets. Finally, we identified the CTCFL targets ACTBL2, MALT1 and PCDH7 as mechanistic biomarkers to predict survival in HGSC. Finally, we elucidated the value of CTCFL in combination with its targets as a prognostic marker profile for HGSC progression and as putative drug targets.
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7
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Alveolar Regeneration in COVID-19 Patients: A Network Perspective. Int J Mol Sci 2021; 22:ijms222011279. [PMID: 34681944 PMCID: PMC8538208 DOI: 10.3390/ijms222011279] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 12/12/2022] Open
Abstract
A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.
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8
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Maden SF, Acuner SE. Mapping Transcriptome Data to Protein-Protein Interaction Networks of Inflammatory Bowel Diseases Reveals Disease-Specific Subnetworks. Front Genet 2021; 12:688447. [PMID: 34484291 PMCID: PMC8416454 DOI: 10.3389/fgene.2021.688447] [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: 03/30/2021] [Accepted: 07/19/2021] [Indexed: 12/31/2022] Open
Abstract
Inflammatory bowel disease (IBD) is the common name for chronic disorders associated with the inflammation of the gastrointestinal tract. IBD is triggered by environmental factors in genetically susceptible individuals and has a significant number of incidences worldwide. Crohn’s disease (CD) and ulcerative colitis (UC) are the two distinct types of IBD. While involvement in ulcerative colitis is limited to the colon, Crohn’s disease may involve the whole gastrointestinal tract. Although these two disorders differ in macroscopic inflammation patterns, they share various molecular pathogenesis, yet the diagnosis can remain unclear, and it is important to reveal their molecular signatures in the network level. Improved molecular understanding may reveal disease type-specific and even individual-specific targets. To this aim, we determine the subnetworks specific to UC and CD by mapping transcriptome data to protein–protein interaction (PPI) networks using two different approaches [KeyPathwayMiner (KPM) and stringApp] and perform the functional enrichment analysis of the resulting disease type-specific subnetworks. TP63 was identified as the hub gene in the UC-specific subnet and p63 tumor protein, being in the same family as p53 and p73, has been studied in literature for the risk associated with colorectal cancer and IBD. APP was identified as the hub gene in the CD-specific subnet, and it has an important role in the pathogenesis of Alzheimer’s disease (AD). This relation suggests that some similar genetic factors may be effective in both AD and CD. Last, in order to understand the biological meaning of these disease-specific subnets, they were functionally enriched. It is important to note that chemokines—special types of cytokines—and antibacterial response are important in UC-specific subnets, whereas cytokines and antimicrobial responses as well as cancer-related pathways are important in CD-specific subnets. Overall, these findings reveal the differences between IBD subtypes at the molecular level and can facilitate diagnosis for UC and CD as well as provide potential molecular targets that are specific to disease subtypes.
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Affiliation(s)
- Sefika Feyza Maden
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Saliha Ece Acuner
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
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9
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Salgado-Albarrán M, Navarro-Delgado EI, Del Moral-Morales A, Alcaraz N, Baumbach J, González-Barrios R, Soto-Reyes E. Comparative transcriptome analysis reveals key epigenetic targets in SARS-CoV-2 infection. NPJ Syst Biol Appl 2021; 7:21. [PMID: 34031419 PMCID: PMC8144203 DOI: 10.1038/s41540-021-00181-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/08/2021] [Indexed: 02/04/2023] Open
Abstract
COVID-19 is an infection caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2), which has caused a global outbreak. Current research efforts are focused on the understanding of the molecular mechanisms involved in SARS-CoV-2 infection in order to propose drug-based therapeutic options. Transcriptional changes due to epigenetic regulation are key host cell responses to viral infection and have been studied in SARS-CoV and MERS-CoV; however, such changes are not fully described for SARS-CoV-2. In this study, we analyzed multiple transcriptomes obtained from cell lines infected with MERS-CoV, SARS-CoV, and SARS-CoV-2, and from COVID-19 patient-derived samples. Using integrative analyses of gene co-expression networks and de-novo pathway enrichment, we characterize different gene modules and protein pathways enriched with Transcription Factors or Epifactors relevant for SARS-CoV-2 infection. We identified EP300, MOV10, RELA, and TRIM25 as top candidates, and more than 60 additional proteins involved in the epigenetic response during viral infection that has therapeutic potential. Our results show that targeting the epigenetic machinery could be a feasible alternative to treat COVID-19.
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Affiliation(s)
- Marisol Salgado-Albarrán
- grid.7220.70000 0001 2157 0393Departamento de Ciencias Naturales, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, Mexico ,grid.6936.a0000000123222966Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Erick I. Navarro-Delgado
- grid.419167.c0000 0004 1777 1207Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Mexico City, Mexico
| | - Aylin Del Moral-Morales
- grid.7220.70000 0001 2157 0393Departamento de Ciencias Naturales, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, Mexico
| | - Nicolas Alcaraz
- grid.5254.60000 0001 0674 042XThe Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jan Baumbach
- grid.9026.d0000 0001 2287 2617Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany ,grid.10825.3e0000 0001 0728 0170Computational BioMedicine Lab, Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Rodrigo González-Barrios
- grid.419167.c0000 0004 1777 1207Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Mexico City, Mexico
| | - Ernesto Soto-Reyes
- grid.7220.70000 0001 2157 0393Departamento de Ciencias Naturales, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, Mexico
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10
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Louadi Z, Yuan K, Gress A, Tsoy O, Kalinina OV, Baumbach J, Kacprowski T, List M. DIGGER: exploring the functional role of alternative splicing in protein interactions. Nucleic Acids Res 2021; 49:D309-D318. [PMID: 32976589 PMCID: PMC7778957 DOI: 10.1093/nar/gkaa768] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 12/20/2022] Open
Abstract
Alternative splicing plays a major role in regulating the functional repertoire of the proteome. However, isoform-specific effects to protein-protein interactions (PPIs) are usually overlooked, making it impossible to judge the functional role of individual exons on a systems biology level. We overcome this barrier by integrating protein-protein interactions, domain-domain interactions and residue-level interactions information to lift exon expression analysis to a network level. Our user-friendly database DIGGER is available at https://exbio.wzw.tum.de/digger and allows users to seamlessly switch between isoform and exon-centric views of the interactome and to extract sub-networks of relevant isoforms, making it an essential resource for studying mechanistic consequences of alternative splicing.
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Affiliation(s)
- Zakaria Louadi
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Kevin Yuan
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Alexander Gress
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), 66123 Saarbrücken, Germany
| | - Olga Tsoy
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), 66123 Saarbrücken, Germany.,Faculty of Medicine, Saarland University, 66421 Homburg, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense M, Denmark
| | - Tim Kacprowski
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
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11
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Matschinske J, Salgado-Albarrán M, Sadegh S, Bongiovanni D, Baumbach J, Blumenthal DB. Individuating Possibly Repurposable Drugs and Drug Targets for COVID-19 Treatment Through Hypothesis-Driven Systems Medicine Using CoVex. Assay Drug Dev Technol 2020; 18:348-355. [PMID: 33164550 PMCID: PMC7703307 DOI: 10.1089/adt.2020.1010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has developed into a pandemic causing major disruptions and hundreds of thousands of deaths in wide parts of the world. As of July 3, 2020, neither vaccines nor approved drugs for effective treatment are available. In this article, we showcase how to individuate drug targets and potentially repurposable drugs in silico using CoVex a recently presented systems medicine platform for COVID-19 drug repurposing. Starting from initial hypotheses, CoVex leverages network algorithms to individuate host proteins involved in COVID-19 disease mechanisms, as well as existing drugs targeting these potential drug targets. Our analysis reveals GLA, PLAT, and GGCX as potential drug targets, and urokinase, argatroban, dabigatran etexilate, betrixaban, ximelagatran and anisindione as potentially repurposable drugs.
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Affiliation(s)
- Julian Matschinske
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City, Mexico
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Dario Bongiovanni
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS, Humanitas University, Rozzano, Milan, Italy
| | - Jan Baumbach
- 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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12
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Perscheid C. Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches. Brief Bioinform 2020; 22:5881664. [PMID: 32761115 DOI: 10.1093/bib/bbaa151] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
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Affiliation(s)
- Cindy Perscheid
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
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13
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Sadegh S, Matschinske J, Blumenthal DB, Galindez G, Kacprowski T, List M, Nasirigerdeh R, Oubounyt M, Pichlmair A, Rose TD, Salgado-Albarrán M, Späth J, Stukalov A, Wenke NK, Yuan K, Pauling JK, Baumbach J. Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing. Nat Commun 2020; 11:3518. [PMID: 32665542 PMCID: PMC7360763 DOI: 10.1038/s41467-020-17189-2] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 06/12/2020] [Indexed: 11/09/2022] Open
Abstract
Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at https://exbio.wzw.tum.de/covex/.
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Affiliation(s)
- Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Julian Matschinske
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - David B Blumenthal
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Gihanna Galindez
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Tim Kacprowski
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Reza Nasirigerdeh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Mhaned Oubounyt
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Andreas Pichlmair
- Institute of Virology, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Tim Daniel Rose
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), 05300, Mexico City, Mexico
| | - Julian Späth
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Alexey Stukalov
- Institute of Virology, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Nina K Wenke
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Kevin Yuan
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Josch K Pauling
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, München, Germany.
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
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14
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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15
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Soerensen M, Hozakowska-Roszkowska DM, Nygaard M, Larsen MJ, Schwämmle V, Christensen K, Christiansen L, Tan Q. A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins. Front Neurosci 2020; 14:233. [PMID: 32327964 PMCID: PMC7160301 DOI: 10.3389/fnins.2020.00233] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/02/2020] [Indexed: 12/02/2022] Open
Abstract
Monozygotic twins are genetically identical but rarely phenotypically identical. Epigenetic and transcriptional variation could influence this phenotypic discordance. Investigation of intra-pair differences in molecular markers and a given phenotype in monozygotic twins controls most of the genetic contribution, enabling studies of the molecular features of the phenotype. This study aimed to identify genes associated with cognition in later life using integrated enrichment analyses of the results of blood-derived intra-pair epigenome-wide and transcriptome-wide association analyses of cognition in 452 middle-aged and old-aged monozygotic twins (56–80 years). Integrated analyses were performed with an unsupervised approach using KeyPathwayMiner, and a supervised approach using the KEGG and Reactome databases. The supervised approach identified several enriched gene sets, including “neuroactive ligand receptor interaction” (p-value = 1.62∗10-2), “Neurotrophin signaling” (p-value = 2.52∗10-3), “Alzheimer’s disease” (p-value = 1.20∗10-2), and “long-term depression” (p-value = 1.62∗10-2). The unsupervised approach resulted in a 238 gene network, including the Alzheimer’s disease gene APP (Amyloid Beta Precursor Protein) as an exception node, and several novel candidate genes. The strength of the unsupervised method is that it can reveal previously uncharacterized sub-pathways and detect interplay between biological processes, which remain undetected by the current supervised methods. In conclusion, this study identified several previously reported cognition genes and pathways and, additionally, puts forward novel candidates for further verification and validation.
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Affiliation(s)
- Mette Soerensen
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Center for Individualized Medicine in Arterial Diseases, Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark.,Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Dominika Marzena Hozakowska-Roszkowska
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Marianne Nygaard
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Martin J Larsen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.,Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Kaare Christensen
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.,Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Lene Christiansen
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Qihua Tan
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.,Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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16
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Abstract
Biomolecular networks such as protein-protein interaction networks provide a static picture of the interplay of genes and their products, and, consequently, they fail to capture dynamic changes taking place during the development of complex diseases. KeyPathwayMiner is a software platform designed to fill this gap by integrating previous knowledge captured in molecular interaction networks with OMICS datasets (DNA microarrays, RNA sequencing, genome-wide methylation studies, etc.) to extract connected subnetworks with a high number of deregulated genes. This protocol describes how to use KeyPathwayMiner for integrated analysis of multi-omics datasets in the network analysis tool Cytoscape and in a stand-alone web application available at https://keypathwayminer.compbio.sdu.dk .
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Affiliation(s)
- Nicolas Alcaraz
- Department of Biology, The Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark
| | - Anne Hartebrodt
- TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Markus List
- TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
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17
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Abstract
Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.
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Affiliation(s)
- Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, QC, Canada
| | - Shuzhao Li
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC, Canada. .,Department of Animal Science, McGill University, Montreal, QC, Canada. .,Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada.
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18
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Salgado-Albarrán M, González-Barrios R, Guerra-Calderas L, Alcaraz N, Estefanía Sánchez-Correa T, Castro-Hernández C, Sánchez-Pérez Y, Aréchaga-Ocampo E, García-Carrancá A, Cantú de León D, Herrera LA, Baumbach J, Soto-Reyes E. The epigenetic factor BORIS (CTCFL) controls the androgen receptor regulatory network in ovarian cancer. Oncogenesis 2019; 8:41. [PMID: 31406110 PMCID: PMC6690894 DOI: 10.1038/s41389-019-0150-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 05/08/2019] [Accepted: 06/01/2019] [Indexed: 01/24/2023] Open
Abstract
The identification of prognostic biomarkers is a priority for patients suffering from high-grade serous ovarian cancer (SOC), which accounts for >70% of ovarian cancer (OC) deaths. Meanwhile, borderline ovarian cancer (BOC) is a low malignancy tumor and usually patients undergo surgery with low probabilities of recurrence. However, SOC remains the most lethal neoplasm due to the lack of biomarkers for early diagnosis and prognosis. In this regard, BORIS (CTCFL), a CTCF paralog, is a promising cancer biomarker that is overexpressed and controls transcription in several cancer types, mainly in OC. Studies suggest that BORIS has an important function in OC by altering gene expression, but the effect and extent to which BORIS influences transcription in OC from a genome-wide perspective is unclear. Here, we sought to identify BORIS target genes in an OC cell line (OVCAR3) with potential biomarker use in OC tumor samples. To achieve this, we performed in vitro knockout and knockdown experiments of BORIS in OVCAR3 cell line followed by expression microarrays and bioinformatics network enrichment analysis to identify relevant BORIS target genes. In addition, ex vivo expression data analysis of 373 ovarian cancer patients were evaluated to identify the expression patterns of BORIS target genes. In vitro, we uncovered 130 differentially expressed genes and obtained the BORIS-associated regulatory network, in which the androgen receptor (AR) acts as a major transcription factor. Also, FN1, FAM129A, and CD97 genes, which are related to chemoresistance and metastases in OC, were identified. In SOC patients, we observed that malignancy is associated with high levels of BORIS expression while BOC patients show lower levels. Our study suggests that BORIS acts as a main regulator, and has the potential to be used as a prognostic biomarker and to yield novel drug targets among the genes BORIS controls in SOC patients.
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Affiliation(s)
- Marisol Salgado-Albarrán
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, 05300, Mexico.,Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Rodrigo González-Barrios
- Cancer Biomedical Research Unit, Instituto Nacional de Cancerología (INCan), Mexico City, Mexico
| | - Lissania Guerra-Calderas
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, 05300, Mexico
| | - Nicolás Alcaraz
- The Bioinformatics Centre Section for RNA and Computational Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Yesennia Sánchez-Pérez
- Cancer Biomedical Research Unit, Instituto Nacional de Cancerología (INCan), Mexico City, Mexico
| | - Elena Aréchaga-Ocampo
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, 05300, Mexico
| | | | - David Cantú de León
- Cancer Biomedical Research Unit, Instituto Nacional de Cancerología (INCan), Mexico City, Mexico
| | - Luis A Herrera
- Cancer Biomedical Research Unit, Instituto Nacional de Cancerología (INCan), Mexico City, Mexico
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Ernesto Soto-Reyes
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), Mexico City, 05300, Mexico.
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19
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Alcaraz N, List M, Batra R, Vandin F, Ditzel HJ, Baumbach J. De novo pathway-based biomarker identification. Nucleic Acids Res 2017; 45:e151. [PMID: 28934488 PMCID: PMC5766193 DOI: 10.1093/nar/gkx642] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 07/13/2017] [Indexed: 02/07/2023] Open
Abstract
Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.
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Affiliation(s)
- Nicolas Alcaraz
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.,The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Markus List
- Computational Biology and Applied Algorithms, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Richa Batra
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Munich, Germany.,Department of Dermatology and Allergy, Technical University of Munich, 80802 Munich, Germany
| | - Fabio Vandin
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Department of Information and Engineering, University of Padowa, 35122 Padowa, Italy
| | - Henrik J Ditzel
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.,Department of Oncology, Odense University Hospital, 5000 Odense, Denmark
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Computational Systems Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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20
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Papaleo E, Gromova I, Gromov P. Gaining insights into cancer biology through exploration of the cancer secretome using proteomic and bioinformatic tools. Expert Rev Proteomics 2017; 14:1021-1035. [PMID: 28967788 DOI: 10.1080/14789450.2017.1387053] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Tumor-associated proteins released by cancer cells and by tumor stroma cells, referred as 'cancer secretome', represent a valuable resource for discovery of potential cancer biomarkers. The last decade was marked by a great increase in number of studies focused on various aspects of cancer secretome including, composition and identification of components externalized by malignant cells and by the components of tumor microenvironment. Areas covered: Here, we provide an overview of achievements in the proteomic analysis of the cancer secretome, elicited through the tumor-associated interstitial fluid recovered from malignant tissues ex vivo or the protein component of conditioned media obtained from cultured cancer cells in vitro. We summarize various bioinformatic tools and approaches and critically appraise their outcomes, focusing on problems and challenges that arise when applied for the analysis of cancer secretomic databases. Expert commentary: Recent achievements in the omics- analysis of structural and metabolic aspects of altered cancer secretome contribute greatly to the various hallmarks of cancer including the identification of clinically significant biomarkers and potential targets for therapeutic intervention.
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Affiliation(s)
- Elena Papaleo
- a Danish Cancer Society Research Center, Computational Biology Laboratory , Copenhagen , Denmark
| | - Irina Gromova
- b Danish Cancer Society Research Center, Genome Integrity Unit, Breast Cancer Biology Group , Copenhagen , Denmark
| | - Pavel Gromov
- b Danish Cancer Society Research Center, Genome Integrity Unit, Breast Cancer Biology Group , Copenhagen , Denmark
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21
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Differential Effects of Vitamins A and D on the Transcriptional Landscape of Human Monocytes during Infection. Sci Rep 2017; 7:40599. [PMID: 28094291 PMCID: PMC5240108 DOI: 10.1038/srep40599] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 12/06/2016] [Indexed: 12/16/2022] Open
Abstract
Vitamin A and vitamin D are essential nutrients with a wide range of pleiotropic effects in humans. Beyond their well-documented roles in cellular differentiation, embryogenesis, tissue maintenance and bone/calcium homeostasis, both vitamins have attracted considerable attention due to their association with-immunological traits. Nevertheless, our knowledge of their immunomodulatory potential during infection is restricted to single gene-centric studies, which do not reflect the complexity of immune processes. In the present study, we performed a comprehensive RNA-seq-based approach to define the whole immunomodulatory role of vitamins A and D during infection. Using human monocytes as host cells, we characterized the differential role of both vitamins upon infection with three different pathogens: Aspergillus fumigatus, Candida albicans and Escherichia coli. Both vitamins showed an unexpected ability to counteract the pathogen-induced transcriptional responses. Upon infection, we identified 346 and 176 immune-relevant genes that were regulated by atRA and vitD, respectively. This immunomodulatory activity was dependent on the inflammatory stimulus, allowing us to distinguish regulatory patterns which were specific for each stimulatory setting. Moreover, we explored possible direct and indirect mechanisms of vitamin-mediated regulation of the immune response. Our findings highlight the importance of vitamin-monitoring in critically ill patients. Moreover, our results underpin the potential of atRA and vitD as therapeutic options for anti-inflammatory treatment.
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22
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Alcaraz N, List M, Dissing-Hansen M, Rehmsmeier M, Tan Q, Mollenhauer J, Ditzel HJ, Baumbach J. Robust de novo pathway enrichment with KeyPathwayMiner 5. F1000Res 2016; 5:1531. [PMID: 27540470 PMCID: PMC4965696 DOI: 10.12688/f1000research.9054.1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/22/2016] [Indexed: 01/26/2023] Open
Abstract
Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.
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Affiliation(s)
- Nicolas Alcaraz
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark
| | - Markus List
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany
| | - Martin Dissing-Hansen
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Marc Rehmsmeier
- Integrated Research Institute (IRI) for the Life Sciences and Department of Biology, Humboldt-Universitat zu Berlin, 10099 Berlin, Germany
| | - Qihua Tan
- Institute of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, 5000 Odense, Denmark
| | - Jan Mollenhauer
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark
| | - Henrik J Ditzel
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark; Lundbeckfonden Center of Excellence in Nanomedicine NanoCAN, University of Southern Denmark, 5000 Odense, Denmark; Department of Oncology, Odense University Hospital, 5000 Odense, Denmark
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Max Planck Institute for Informatics, 66123 Saarbrucken, Germany
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