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Bavais J, Chevallier J, Spinelli L, van de Pavert S, Puthier D. SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics data. NAR Genom Bioinform 2025; 7:lqaf043. [PMID: 40248490 PMCID: PMC12004220 DOI: 10.1093/nargab/lqaf043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 03/19/2025] [Accepted: 04/09/2025] [Indexed: 04/19/2025] Open
Abstract
The standard pipeline to analyze single-cell RNA-seq or spatial transcriptomics data focuses on a gene-centric approach that overlooks the collective behavior of genes. However, understanding cell populations necessitates recognizing intricate combinations of activated and repressed pathways. Therefore, a broader view of gene behavior offers more accurate insights into cellular heterogeneity in single-cell or spatial transcriptomics data. Here, we describe SciGeneX (Single-cell informative Gene eXplorer), a R package implementing a neighborhood analysis and a graph partitioning method to generate co-expression gene modules. These modules, whether shared or restricted to cell populations, collectively reflect cellular heterogeneity. Their combinations are able to highlight specific cell populations, even rare ones. SciGeneX uncovers rare and novel cell populations that were not observed before in human thymus spatial transcriptomics data. We show that SciGeneX outperforms existing methods on both artificial and experimental datasets. Overall, SciGeneX will aid in unravelling cellular and molecular diversity in single-cell and spatial transcriptomics studies.
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Affiliation(s)
- Julie Bavais
- Aix-Marseille Univ, INSERM, TAGC, Turing Centre for Living systems, 13288 Marseille, France
- Aix-Marseille Univ, CNRS, INSERM, CIML, Turing Centre for Living systems, 13009 Marseille, France
| | - Jessica Chevallier
- Aix-Marseille Univ, INSERM, TAGC, Turing Centre for Living systems, 13288 Marseille, France
- Aix-Marseille Univ, CNRS, INSERM, CIML, Turing Centre for Living systems, 13009 Marseille, France
| | - Lionel Spinelli
- Aix-Marseille Univ, INSERM, TAGC, Turing Centre for Living systems, 13288 Marseille, France
- Aix-Marseille Univ, CNRS, INSERM, CIML, Turing Centre for Living systems, 13009 Marseille, France
| | - Serge A van de Pavert
- Aix-Marseille Univ, CNRS, INSERM, CIML, Turing Centre for Living systems, 13009 Marseille, France
| | - Denis Puthier
- Aix-Marseille Univ, INSERM, TAGC, MarMaRa Institute, Turing Centre for Living systems, Transcriptomics and Genomics Marseille Luminy (TGML), 13288 Marseille, France
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Dawood M, Hamdoun S, Efferth T. Multifactorial Modes of Action of Arsenic Trioxide in Cancer Cells as Analyzed by Classical and Network Pharmacology. Front Pharmacol 2018; 9:143. [PMID: 29535630 PMCID: PMC5835320 DOI: 10.3389/fphar.2018.00143] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 02/09/2018] [Indexed: 12/13/2022] Open
Abstract
Arsenic trioxide is a traditional remedy in Chinese Medicine since ages. Nowadays, it is clinically used to treat acute promyelocytic leukemia (APL) by targeting PML/RARA. However, the drug's activity is broader and the mechanisms of action in other tumor types remain unclear. In this study, we investigated molecular modes of action by classical and network pharmacological approaches. CEM/ADR5000 resistance leukemic cells were similar sensitive to As2O3 as their wild-type counterpart CCRF-CEM (resistance ratio: 1.88). Drug-resistant U87.MG ΔEGFR glioblastoma cells harboring mutated epidermal growth factor receptor were even more sensitive (collateral sensitive) than wild-type U87.MG cells (resistance ratio: 0.33). HCT-116 colon carcinoma p53-/- knockout cells were 7.16-fold resistant toward As2O3 compared to wild-type cells. Forty genes determining cellular responsiveness to As2O3 were identified by microarray and COMPARE analyses in 58 cell lines of the NCI panel. Hierarchical cluster analysis-based heat mapping revealed significant differences between As2O3 sensitive cell lines and resistant cell lines with p-value: 1.86 × 10-5. The genes were subjected to Galaxy Cistrome gene promoter transcription factor analysis to predict the binding of transcription factors. We have exemplarily chosen NF-kB and AP-1, and indeed As2O3 dose-dependently inhibited the promoter activity of these two transcription factors in reporter cell lines. Furthermore, the genes identified here and those published in the literature were assembled and subjected to Ingenuity Pathway Analysis for comprehensive network pharmacological approaches that included all known factors of resistance of tumor cells to As2O3. In addition to pathways related to the anticancer effects of As2O3, several neurological pathways were identified. As arsenic is well-known to exert neurotoxicity, these pathways might account for neurological side effects. In conclusion, the activity of As2O3 is not restricted to acute promyelocytic leukemia. In addition to PML/RARA, numerous other genes belonging to diverse functional classes may also contribute to its cytotoxicity. Network pharmacology is suited to unravel the multifactorial modes of action of As2O3.
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Affiliation(s)
| | | | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany
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Donaires FS, Godoy PRDV, Leandro GS, Puthier D, Sakamoto-Hojo ET. E2F transcription factors associated with up-regulated genes in glioblastoma. Cancer Biomark 2017; 18:199-208. [PMID: 27983535 DOI: 10.3233/cbm-161628] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Glioblastoma is considered to the most common and malignant brain tumor in adults. Patients have a median survival of approximately one year from diagnosis due to poor response to therapy. OBJECTIVE We applied bioinformatics approaches to predict transcription factors (TF) that are deregulated in glioblastoma in an attempt to point out molecular targets for therapy. METHODS Up-regulated genes in glioblastoma selected from public microarray data were submitted to two TF association analyses. Thereafter, the expression levels of TF obtained in the overlap of analyses were assessed by RT-qPCR carried out in seven glioblastoma cell lines (T98, U251, U138, U87, U343, M059J, and M059K). RESULTS E2F1 and E2F4 were highlighted in both TF analyses. However, only E2F1 was confirmed as significantly up-regulated in all glioblastoma cell lines in vitro. CONCLUSION E2F1 is a potential common regulator of differentially expressed genes in glioblastoma, despite the genetic heterogeneity of tumor cells.
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Affiliation(s)
- Flávia S Donaires
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Paulo R D V Godoy
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Giovana S Leandro
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Denis Puthier
- Technological Advances for Genomics and Clinics (TAGC), UMR, S 1090 INSERM Aix-Marseille Université, U928 Parc Scientifique de Luminy Case 928 163, Avenue de Luminy, 13288 Marseille Cedex 9, France
| | - Elza T Sakamoto-Hojo
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil.,Department of Biology, Faculty of Philosophy, Sciences, and Letters at Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
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Bergon A, Belzeaux R, Comte M, Pelletier F, Hervé M, Gardiner EJ, Beveridge NJ, Liu B, Carr V, Scott RJ, Kelly B, Cairns MJ, Kumarasinghe N, Schall U, Blin O, Boucraut J, Tooney PA, Fakra E, Ibrahim EC. CX3CR1 is dysregulated in blood and brain from schizophrenia patients. Schizophr Res 2015; 168:434-43. [PMID: 26285829 DOI: 10.1016/j.schres.2015.08.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Revised: 08/05/2015] [Accepted: 08/06/2015] [Indexed: 12/31/2022]
Abstract
The molecular mechanisms underlying schizophrenia remain largely unknown. Although schizophrenia is a mental disorder, there is increasing evidence to indicate that inflammatory processes driven by diverse environmental factors play a significant role in its development. With gene expression studies having been conducted across a variety of sample types, e.g., blood and postmortem brain, it is possible to investigate convergent signatures that may reveal interactions between the immune and nervous systems in schizophrenia pathophysiology. We conducted two meta-analyses of schizophrenia microarray gene expression data (N=474) and non-psychiatric control (N=485) data from postmortem brain and blood. Then, we assessed whether significantly dysregulated genes in schizophrenia could be shared between blood and brain. To validate our findings, we selected a top gene candidate and analyzed its expression by RT-qPCR in a cohort of schizophrenia subjects stabilized by atypical antipsychotic monotherapy (N=29) and matched controls (N=31). Meta-analyses highlighted inflammation as the major biological process associated with schizophrenia and that the chemokine receptor CX3CR1 was significantly down-regulated in schizophrenia. This differential expression was also confirmed in our validation cohort. Given both the recent data demonstrating selective CX3CR1 expression in subsets of neuroimmune cells, as well as behavioral and neuropathological observations of CX3CR1 deficiency in mouse models, our results of reduced CX3CR1 expression adds further support for a role played by monocyte/microglia in the neurodevelopment of schizophrenia.
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Affiliation(s)
- Aurélie Bergon
- INSERM, TAGC UMR_S 1090, 13288 Marseille Cedex 09, France; Aix Marseille Université, TAGC UMR_S 1090, 13288 Marseille Cedex 09, France
| | - Raoul Belzeaux
- Aix Marseille Université, CNRS, CRN2M UMR 7286, 13344 Marseille Cedex 15, France; FondaMental, Fondation de Recherche et de Soins en Santé Mentale, 94000 Créteil, France; AP-HM, Hôpital Sainte Marguerite, Pôle de Psychiatrie Universitaire Solaris, 13009 Marseille, France
| | - Magali Comte
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone UMR 7289, 13005 Marseille, France
| | - Florence Pelletier
- Aix Marseille Université, CNRS, CRN2M UMR 7286, 13344 Marseille Cedex 15, France; FondaMental, Fondation de Recherche et de Soins en Santé Mentale, 94000 Créteil, France
| | - Mylène Hervé
- Aix Marseille Université, CNRS, CRN2M UMR 7286, 13344 Marseille Cedex 15, France; FondaMental, Fondation de Recherche et de Soins en Santé Mentale, 94000 Créteil, France
| | - Erin J Gardiner
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia
| | - Natalie J Beveridge
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia
| | - Bing Liu
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Kids Cancer Alliance, Cancer Institute NSW, Sydney, Australia
| | - Vaughan Carr
- Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia; School of Psychiatry, University of New South Wales, Randwick, NSW 2301, Australia; Department of Psychiatry, Monash University, Clayton, VIC 3168, Australia
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia
| | - Brian Kelly
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia
| | - Nishantha Kumarasinghe
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia; University of Sri Jayewardenepura, Nugegoda, Sri Lanka; National Institute of Mental Health, Angoda, Sri Lanka
| | - Ulrich Schall
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia
| | - Olivier Blin
- CIC-UPCET et Pharmacologie Clinique, Hôpital de la Timone, 13005 Marseille, France
| | - José Boucraut
- Aix Marseille Université, CNRS, CRN2M UMR 7286, 13344 Marseille Cedex 15, France; FondaMental, Fondation de Recherche et de Soins en Santé Mentale, 94000 Créteil, France
| | - Paul A Tooney
- School of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia; Centre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia; Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia; Schizophrenia Research Institute, Darlinghurst, NSW 2010 Australia
| | - Eric Fakra
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone UMR 7289, 13005 Marseille, France; CHU de Saint-Etienne, Pôle de Psychiatrie, 42100 Saint-Etienne, France
| | - El Chérif Ibrahim
- Aix Marseille Université, CNRS, CRN2M UMR 7286, 13344 Marseille Cedex 15, France; FondaMental, Fondation de Recherche et de Soins en Santé Mentale, 94000 Créteil, France.
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Malaney P, Pathak RR, Xue B, Uversky VN, Davé V. Intrinsic disorder in PTEN and its interactome confers structural plasticity and functional versatility. Sci Rep 2014; 3:2035. [PMID: 23783762 PMCID: PMC3687229 DOI: 10.1038/srep02035] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Accepted: 06/03/2013] [Indexed: 12/19/2022] Open
Abstract
IDPs, while structurally poor, are functionally rich by virtue of their flexibility and modularity. However, how mutations in IDPs elicit diseases, remain elusive. Herein, we have identified tumor suppressor PTEN as an intrinsically disordered protein (IDP) and elucidated the molecular principles by which its intrinsically disordered region (IDR) at the carboxyl-terminus (C-tail) executes its functions. Post-translational modifications, conserved eukaryotic linear motifs and molecular recognition features present in the C-tail IDR enhance PTEN's protein-protein interactions that are required for its myriad cellular functions. PTEN primary and secondary interactomes are also enriched in IDPs, most being cancer related, revealing that PTEN functions emanate from and are nucleated by the C-tail IDR, which form pliable network-hubs. Together, PTEN higher order functional networks operate via multiple IDP-IDP interactions facilitated by its C-tail IDR. Targeting PTEN IDR and its interaction hubs emerges as a new paradigm for treatment of PTEN related pathologies.
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Affiliation(s)
- Prerna Malaney
- Morsani College of Medicine, Department of Pathology and Cell Biology, University of South Florida, Tampa, FL 33612, USA
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6
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Evangelista AF, Collares CVA, Xavier DJ, Macedo C, Manoel-Caetano FS, Rassi DM, Foss-Freitas MC, Foss MC, Sakamoto-Hojo ET, Nguyen C, Puthier D, Passos GA, Donadi EA. Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation. BMC Med Genomics 2014; 7:28. [PMID: 24885568 PMCID: PMC4066312 DOI: 10.1186/1755-8794-7-28] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 03/27/2014] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures. METHODS Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool. RESULTS The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin. CONCLUSION This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.
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Affiliation(s)
- Adriane F Evangelista
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Cristhianna VA Collares
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
- Division Clinical Immunology, Faculty of Medicine of Ribeirão Preto, (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Danilo J Xavier
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Claudia Macedo
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Fernanda S Manoel-Caetano
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Diane M Rassi
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Maria C Foss-Freitas
- Division Clinical Immunology, Faculty of Medicine of Ribeirão Preto, (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Milton C Foss
- Division Clinical Immunology, Faculty of Medicine of Ribeirão Preto, (USP), 14049-900 Ribeirão Preto, SP, Brazil
| | - Elza T Sakamoto-Hojo
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
- Department of Biology, Faculty of Philosophy, Sciences and Letters, (USP), 14040-900 Ribeirão Preto, SP, Brazil
| | - Catherine Nguyen
- INSERM U1090, TAGC, Aix-Marseille Université IFR137, 13100 Marseille, France
| | - Denis Puthier
- INSERM U1090, TAGC, Aix-Marseille Université IFR137, 13100 Marseille, France
| | - Geraldo A Passos
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
- Disciplines of Genetics and Molecular Biology, Department of Morphology, Physiology and Basic Pathology, School of Dentistry of Ribeirão Preto, USP, 14040-904 Ribeirão Preto, SP, Brazil
| | - Eduardo A Donadi
- Molecular Immunogenetics Group, Department of Genetics, Faculty of Medicine of Ribeirão Preto, University of São Paulo (USP), 14049-900 Ribeirão Preto, SP, Brazil
- Division Clinical Immunology, Faculty of Medicine of Ribeirão Preto, (USP), 14049-900 Ribeirão Preto, SP, Brazil
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Fahrenbach JP, Andrade J, McNally EM. The CO-Regulation Database (CORD): a tool to identify coordinately expressed genes. PLoS One 2014; 9:e90408. [PMID: 24599084 PMCID: PMC3944024 DOI: 10.1371/journal.pone.0090408] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2013] [Accepted: 02/01/2014] [Indexed: 02/03/2023] Open
Abstract
Background Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene. Results Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (CDKN2A), vimentin (VIM), MyoD (MYOD1). CDKN2A, VIM, and MYOD1 all displayed gene correlations consistent with known interacting genes. Conclusions We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at http://cord-db.org.
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Affiliation(s)
- John P. Fahrenbach
- Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
| | - Jorge Andrade
- Center for Research Informatics, The University of Chicago, Chicago, Illinois, United States of America
| | - Elizabeth M. McNally
- Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
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Santra T, Kolch W, Kholodenko BN. Navigating the multilayered organization of eukaryotic signaling: a new trend in data integration. PLoS Comput Biol 2014; 10:e1003385. [PMID: 24550716 PMCID: PMC3923657 DOI: 10.1371/journal.pcbi.1003385] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells
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Affiliation(s)
- Tapesh Santra
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Ireland
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Ireland
- * E-mail:
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Wrangle J, Wang W, Koch A, Easwaran H, Mohammad HP, Vendetti F, VanCriekinge W, DeMeyer T, Du Z, Parsana P, Rodgers K, Yen RW, Zahnow CA, Taube JM, Brahmer JR, Tykodi SS, Easton K, Carvajal RD, Jones PA, Laird PW, Weisenberger DJ, Tsai S, Juergens RA, Topalian SL, Rudin CM, Brock MV, Pardoll D, Baylin SB. Alterations of immune response of Non-Small Cell Lung Cancer with Azacytidine. Oncotarget 2013; 4:2067-79. [PMID: 24162015 PMCID: PMC3875770 DOI: 10.18632/oncotarget.1542] [Citation(s) in RCA: 304] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Accepted: 10/25/2013] [Indexed: 12/14/2022] Open
Abstract
Innovative therapies are needed for advanced Non-Small Cell Lung Cancer (NSCLC). We have undertaken a genomics based, hypothesis driving, approach to query an emerging potential that epigenetic therapy may sensitize to immune checkpoint therapy targeting PD-L1/PD-1 interaction. NSCLC cell lines were treated with the DNA hypomethylating agent azacytidine (AZA - Vidaza) and genes and pathways altered were mapped by genome-wide expression and DNA methylation analyses. AZA-induced pathways were analyzed in The Cancer Genome Atlas (TCGA) project by mapping the derived gene signatures in hundreds of lung adeno (LUAD) and squamous cell carcinoma (LUSC) samples. AZA up-regulates genes and pathways related to both innate and adaptive immunity and genes related to immune evasion in a several NSCLC lines. DNA hypermethylation and low expression of IRF7, an interferon transcription factor, tracks with this signature particularly in LUSC. In concert with these events, AZA up-regulates PD-L1 transcripts and protein, a key ligand-mediator of immune tolerance. Analysis of TCGA samples demonstrates that a significant proportion of primary NSCLC have low expression of AZA-induced immune genes, including PD-L1. We hypothesize that epigenetic therapy combined with blockade of immune checkpoints - in particular the PD-1/PD-L1 pathway - may augment response of NSCLC by shifting the balance between immune activation and immune inhibition, particularly in a subset of NSCLC with low expression of these pathways. Our studies define a biomarker strategy for response in a recently initiated trial to examine the potential of epigenetic therapy to sensitize patients with NSCLC to PD-1 immune checkpoint blockade.
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Affiliation(s)
- John Wrangle
- The Johns Hopkins University, School of Medicine, Oncology Center-Hematology/Medical Oncology, Baltimore, Maryland
| | - Wei Wang
- The Johns Hopkins University, School of Medicine, Human Genetics Graduate Program, Baltimore, Maryland
| | - Alexander Koch
- Departments of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Hariharan Easwaran
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Helai P. Mohammad
- GlaxoSmithKline Pharmaceuticals, Cancer Epigenetics and Oncology, Collegeville, Pennsylvania
| | - Frank Vendetti
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Wim VanCriekinge
- Departments of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Tim DeMeyer
- Departments of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Zhengzong Du
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Princy Parsana
- The Johns Hopkins University, Advanced Academic Bioinformatics, Baltimore, Maryland
| | - Kristen Rodgers
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Ray-Whay Yen
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Cynthia A. Zahnow
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Janis M. Taube
- The Johns Hopkins University, School of Medicine, Dermatology and Oral Pathology, Baltimore, Maryland
| | - Julie R. Brahmer
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Scott S. Tykodi
- University of Washington and Fred Hutchison Cancer Research Center, Seattle Cancer Care Alliance, Seattle, Washington
| | - Keith Easton
- University of Washington and Fred Hutchison Cancer Research Center, Seattle Cancer Care Alliance, Seattle, Washington
| | | | - Peter A. Jones
- USC Epigenome Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Peter W. Laird
- USC Epigenome Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Daniel J. Weisenberger
- USC Epigenome Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Salina Tsai
- The Johns Hopkins University, School of Medicine, Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland
| | - Rosalyn A. Juergens
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Suzanne L. Topalian
- The Johns Hopkins University, School of Medicine, Surgery, Baltimore, Maryland
| | - Charles M. Rudin
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Malcolm V. Brock
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Drew Pardoll
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
| | - Stephen B. Baylin
- The Johns Hopkins University, School of Medicine, Oncology, Baltimore, Maryland
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10
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Saadoun D, Terrier B, Bannock J, Vazquez T, Massad C, Kang I, Joly F, Rosenzwajg M, Sene D, Benech P, Musset L, Klatzmann D, Meffre E, Cacoub P. Expansion of autoreactive unresponsive CD21-/low B cells in Sjögren's syndrome-associated lymphoproliferation. ACTA ACUST UNITED AC 2013; 65:1085-96. [PMID: 23279883 DOI: 10.1002/art.37828] [Citation(s) in RCA: 182] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2012] [Accepted: 12/07/2012] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Primary Sjögren's syndrome (SS) is an autoimmune disease associated with a high risk of developing non-Hodgkin's lymphoma. This study was undertaken to determine the nature of B cells driving lymphoproliferation in primary SS. METHODS B cell subsets and function were analyzed in peripheral blood from 66 adult patients with primary SS (including 14 patients with B cell lymphoproliferative disease [LPD]) and 30 healthy donors, using flow cytometry, calcium mobilization, and gene array analysis. The reactivity of recombinant antibodies isolated from single B cells from patients with primary SS and LPD was tested using an enzyme-linked immunosorbent assay. RESULTS We observed an expansion of an unusual CD21-/low B cell population that correlated with lymphoproliferation in patients with primary SS. A majority of CD21-/low B cells from patients with primary SS expressed autoreactive antibodies, which recognized nuclear and cytoplasmic structures. These B cells belonged to the memory compartment, since their Ig genes were mutated. They were unable to induce calcium flux, become activated, or proliferate in response to B cell receptor and/or CD40 triggering, suggesting that these autoreactive B cells may be anergic. However, CD21-/low B cells from patients with primary SS remained responsive to Toll-like receptor (TLR) stimulation. Molecules specifically expressed in CD21-/low B cells that are likely to induce their unresponsive stage were detected in gene array analyses. CONCLUSION Patients with primary SS who display high frequencies of autoreactive and unresponsive CD21-/low B cells are susceptible to developing lymphoproliferation. These cells remain in peripheral blood controlled by functional anergy instead of being eliminated, and chronic antigenic stimulation through TLR stimulation may create a favorable environment for breaking tolerance and activating these cells.
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Affiliation(s)
- D Saadoun
- CNRS UMR 7211, INSERM U959, Groupe Hospitalier Pitié-Salpêtrière, and Université Pierre et Marie Curie, Paris 6, Paris, France.
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11
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Lepoivre C, Bergon A, Lopez F, Perumal NB, Nguyen C, Imbert J, Puthier D. TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks. BMC Bioinformatics 2012; 13:19. [PMID: 22292669 PMCID: PMC3395838 DOI: 10.1186/1471-2105-13-19] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2011] [Accepted: 01/31/2012] [Indexed: 01/04/2023] Open
Abstract
Background Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. Results We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation. Conclusions The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.
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Affiliation(s)
- Cyrille Lepoivre
- TAGC UMR_S 928, Inserm, Parc Scientifique de Luminy, Marseille, France
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12
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Tseng GC, Ghosh D, Feingold E. Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res 2012; 40:3785-99. [PMID: 22262733 PMCID: PMC3351145 DOI: 10.1093/nar/gkr1265] [Citation(s) in RCA: 285] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.
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Affiliation(s)
- George C Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
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13
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Abstract
MCL is a general purpose cluster algorithm for both weighted and unweighted networks. The algorithm utilises network topology as well as edge weights, is highly scalable and has been applied in a wide variety of bioinformatic methods. In this chapter, we give protocols and case studies for clustering of networks derived from, respectively, protein sequence similarities and gene expression profile correlations.
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Affiliation(s)
- Stijn van Dongen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
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14
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Terrier B, Joly F, Vazquez T, Benech P, Rosenzwajg M, Carpentier W, Garrido M, Ghillani-Dalbin P, Klatzmann D, Cacoub P, Saadoun D. Expansion of Functionally Anergic CD21−/lowMarginal Zone-like B Cell Clones in Hepatitis C Virus Infection-Related Autoimmunity. THE JOURNAL OF IMMUNOLOGY 2011; 187:6550-63. [DOI: 10.4049/jimmunol.1102022] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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15
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Baron D, Magot A, Ramstein G, Steenman M, Fayet G, Chevalier C, Jourdon P, Houlgatte R, Savagner F, Pereon Y. Immune response and mitochondrial metabolism are commonly deregulated in DMD and aging skeletal muscle. PLoS One 2011; 6:e26952. [PMID: 22096509 PMCID: PMC3212519 DOI: 10.1371/journal.pone.0026952] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2011] [Accepted: 10/06/2011] [Indexed: 01/12/2023] Open
Abstract
Duchenne Muscular Dystrophy (DMD) is a complex process involving multiple pathways downstream of the primary genetic insult leading to fatal muscle degeneration. Aging muscle is a multifactorial neuromuscular process characterized by impaired muscle regeneration leading to progressive atrophy. We hypothesized that these chronic atrophying situations may share specific myogenic adaptative responses at transcriptional level according to tissue remodeling. Muscle biopsies from four young DMD and four AGED subjects were referred to a group of seven muscle biopsies from young subjects without any neuromuscular disorder and explored through a dedicated expression microarray. We identified 528 differentially expressed genes (out of 2,745 analyzed), of which 328 could be validated by an exhaustive meta-analysis of public microarray datasets referring to DMD and Aging in skeletal muscle. Among the 328 validated co-expressed genes, 50% had the same expression profile in both groups and corresponded to immune/fibrosis responses and mitochondrial metabolism. Generalizing these observed meta-signatures with large compendia of public datasets reinforced our results as they could be also identified in other pathological processes and in diverse physiological conditions. Focusing on the common gene signatures in these two atrophying conditions, we observed enrichment in motifs for candidate transcription factors that may coordinate either the immune/fibrosis responses (ETS1, IRF1, NF1) or the mitochondrial metabolism (ESRRA). Deregulation in their expression could be responsible, at least in part, for the same transcriptome changes initiating the chronic muscle atrophy. This study suggests that distinct pathophysiological processes may share common gene responses and pathways related to specific transcription factors.
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16
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Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A. NCBI GEO: archive for functional genomics data sets--10 years on. Nucleic Acids Res 2010; 39:D1005-10. [PMID: 21097893 PMCID: PMC3013736 DOI: 10.1093/nar/gkq1184] [Citation(s) in RCA: 831] [Impact Index Per Article: 55.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20,000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.
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Affiliation(s)
- Tanya Barrett
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 45 Center Drive, Bethesda, MD 20892, USA.
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17
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Sex-related differences in gene expression following Coxiella burnetii infection in mice: potential role of circadian rhythm. PLoS One 2010; 5:e12190. [PMID: 20730052 PMCID: PMC2921390 DOI: 10.1371/journal.pone.0012190] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2010] [Accepted: 07/22/2010] [Indexed: 12/20/2022] Open
Abstract
Background Q fever, a zoonosis due to Coxiella burnetii infection, exhibits sexual dimorphism; men are affected more frequently and severely than women for a given exposure. Here we explore whether the severity of C. burnetii infection in mice is related to differences in male and female gene expression profiles. Methodology/Principal Findings Mice were infected with C. burnetii for 24 hours, and gene expression was measured in liver cells using microarrays. Multiclass analysis identified 2,777 probes for which expression was specifically modulated by C. burnetti infection. Only 14% of the modulated genes were sex-independent, and the remaining 86% were differentially expressed in males and females. Castration of males and females showed that sex hormones were responsible for more than 60% of the observed gene modulation, and this reduction was most pronounced in males. Using functional annotation of modulated genes, we identified four clusters enriched in males that were related to cell-cell adhesion, signal transduction, defensins and cytokine/Jak-Stat pathways. Up-regulation of the IL-10 and Stat-3 genes may account for the high susceptibility of men with Q fever to C. burnetii infection and autoantibody production. Two clusters were identified in females, including the circadian rhythm pathway, which consists of positive (Clock, Arntl) and negative (Per) limbs of a feedback loop. We found that Clock and Arntl were down-modulated whereas Per was up-regulated; these changes may be associated with efficient bacterial elimination in females but not in males, in which an exacerbated host response would be prominent. Conclusion This large-scale study revealed for the first time that circadian rhythm plays a major role in the anti-infectious response of mice, and it provides a new basis for elucidating the role of sexual dimorphism in human infections.
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18
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Acharya KK, Chandrashekar DS, Chitturi N, Shah H, Malhotra V, Sreelakshmi KS, Deepti H, Bajpai A, Davuluri S, Bora P, Rao L. A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database. BMC Genomics 2010; 11:467. [PMID: 20699007 PMCID: PMC3091663 DOI: 10.1186/1471-2164-11-467] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2010] [Accepted: 08/11/2010] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND In the recent years, there has been a rise in gene expression profiling reports. Unfortunately, it has not been possible to make maximum use of available gene expression data. Many databases and programs can be used to derive the possible expression patterns of mammalian genes, based on existing data. However, these available resources have limitations. For example, it is not possible to obtain a list of genes that are expressed in certain conditions. To overcome such limitations, we have taken up a new strategy to predict gene expression patterns using available information, for one tissue at a time. RESULTS The first step of this approach involved manual collection of maximum data derived from large-scale (genome-wide) gene expression studies, pertaining to mammalian testis. These data have been compiled into a Mammalian Gene Expression Testis-database (MGEx-Tdb). This process resulted in a richer collection of gene expression data compared to other databases/resources, for multiple testicular conditions. The gene-lists collected this way in turn were exploited to derive a 'consensus' expression status for each gene, across studies. The expression information obtained from the newly developed database mostly agreed with results from multiple small-scale studies on selected genes. A comparative analysis showed that MGEx-Tdb can retrieve the gene expression information more efficiently than other commonly used databases. It has the ability to provide a clear expression status (transcribed or dormant) for most genes, in the testis tissue, under several specific physiological/experimental conditions and/or cell-types. CONCLUSIONS Manual compilation of gene expression data, which can be a painstaking process, followed by a consensus expression status determination for specific locations and conditions, can be a reliable way of making use of the existing data to predict gene expression patterns. MGEx-Tdb provides expression information for 14 different combinations of specific locations and conditions in humans (25,158 genes), 79 in mice (22,919 genes) and 23 in rats (14,108 genes). It is also the first system that can predict expression of genes with a 'reliability-score', which is calculated based on the extent of agreements and contradictions across gene-sets/studies. This new platform is publicly available at the following web address: http://resource.ibab.ac.in/MGEx-Tdb/.
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Affiliation(s)
- Kshitish K Acharya
- Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Electronic City, Bangalore - 560100, Karnataka state, India.
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19
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Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data. Adv Bioinformatics 2010:423589. [PMID: 20369061 PMCID: PMC2847380 DOI: 10.1155/2010/423589] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2009] [Accepted: 01/05/2010] [Indexed: 12/26/2022] Open
Abstract
High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge.
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20
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Genomics Portals: integrative web-platform for mining genomics data. BMC Genomics 2010; 11:27. [PMID: 20070909 PMCID: PMC2824719 DOI: 10.1186/1471-2164-11-27] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2009] [Accepted: 01/13/2010] [Indexed: 12/21/2022] Open
Abstract
Background A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Results Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. Conclusion The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org.
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Herrmann C, Bérard S, Tichit L. SimCT: a generic tool to visualize ontology-based relationships for biological objects. ACTA ACUST UNITED AC 2009; 25:3197-8. [PMID: 19776214 PMCID: PMC2778334 DOI: 10.1093/bioinformatics/btp553] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
UNLABELLED We present a web-based service, SimCT, which allows to graphically display the relationships between biological objects (e.g. genes or proteins) based on their annotations to a biomedical ontology. The result is presented as a tree of these objects, which can be viewed and explored through a specific java applet designed to highlight relevant features. Unlike the numerous tools that search for overrepresented terms, SimCT draws a simplified representation of biological terms present in the set of objects, and can be applied to any ontology for which annotation data is available. Being web-based, it does not require prior installation, and provides an intuitive, easy-to-use service. AVAILABILITY http://tagc.univ-mrs.fr/SimCT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Carl Herrmann
- TAGC-U928 Inserm, Faculté des Sciences, Université de la Méditerranée, Campus de Luminy Case 928, Marseille, France.
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