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Brunson T, Sanati N, Matthews L, Haw R, Beavers D, Shorser S, Sevilla C, Viteri G, Conley P, Rothfels K, Hermjakob H, Stein L, D’Eustachio P, Wu G. Illuminating Dark Proteins using Reactome Pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543335. [PMID: 37333417 PMCID: PMC10274615 DOI: 10.1101/2023.06.05.543335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.
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Affiliation(s)
| | - Nasim Sanati
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Deidre Beavers
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Solomon Shorser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Patrick Conley
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A1, Canada
| | | | - Guanming Wu
- Oregon Health & Science University, Portland, OR 97239, USA
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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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Satyam R, Yousef M, Qazi S, Bhat AM, Raza K. COVIDium: a COVID-19 resource compendium. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6377761. [PMID: 34585731 PMCID: PMC8500058 DOI: 10.1093/database/baab057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/14/2021] [Accepted: 09/11/2021] [Indexed: 12/24/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 that causes coronavirus disease 2019 (COVID-19) disrupted the normal functioning throughout the world since early 2020 and it continues to do so. Nonetheless, the global pandemic was taken up as a challenge by researchers across the globe to discover an effective cure, either in the form of a drug or vaccine. This resulted in an unprecedented surge of experimental and computational data and publications, which often translated their findings in the form of databases (DBs) and tools. Over 160 such DBs and more than 80 software tools were developed, which are uncharacterized, unannotated, deployed at different universal resource locators and are challenging to reach out through a normal web search. Besides, most of the DBs/tools are present on preprints and are either underutilized or unrecognized because of their inability to make it to top Google search hits. Henceforth, there was a need to crawl and characterize these DBs and create a compendium for easy referencing. The current article is one such concerted effort in this direction to create a COVID-19 resource compendium (COVIDium) that would facilitate the researchers to find suitable DBs and tools for their research studies. COVIDium tries to classify the DBs and tools into 11 broad categories for quick navigation. It also provides end-users some generic hit terms to filter the DB entries for quick access to the resources. Additionally, the DB provides Tracker Dashboard, Neuro Resources, references to COVID-19 datasets and protein-protein interactions. This compendium will be periodically updated to accommodate new resources. Database URL: The COVIDium is accessible through http://kraza.in/covidium/.
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Affiliation(s)
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Jerusalem St 11, Safed, Zefat 1320611, Israel
| | - Sahar Qazi
- Department of Computer Science, Jamia Millia Islamia, Maulana Mohammad Ali Jauhar Marg, Jamia Nagar, Okhla, New Delhi 110025, India
| | - Adil Manzoor Bhat
- Department of Computer Science, Jamia Millia Islamia, Maulana Mohammad Ali Jauhar Marg, Jamia Nagar, Okhla, New Delhi 110025, India
| | - Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, Maulana Mohammad Ali Jauhar Marg, Jamia Nagar, Okhla, New Delhi 110025, India
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4
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Richens JL, Bramble JP, Spencer HL, Cantlay F, Butler M, O'Shea P. Towards defining the Mechanisms of Alzheimer's disease based on a contextual analysis of molecular pathways. AIMS GENETICS 2021. [DOI: 10.3934/genet.2016.1.25] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
AbstractAlzheimer's disease (AD) is posing an increasingly profound problem to society. Our genuine understanding of the pathogenesis of AD is inadequate and as a consequence, diagnostic and therapeutic strategies are currently insufficient. The understandable focus of many studies is the identification of molecules with high diagnostic utility however the opportunity to obtain a further understanding of the mechanistic origins of the disease from such putative biomarkers is often overlooked. This study examines the involvement of biomarkers in AD to shed light on potential mechanisms and pathways through which they are implicated in the pathology of this devastating neurodegenerative disorder. The computational tools required to analyse ever-growing datasets in the context of AD are also discussed.
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Affiliation(s)
- Joanna L. Richens
- Cell Biophysics Group, School of Life Sciences, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Jonathan P. Bramble
- Cell Biophysics Group, School of Life Sciences, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Hannah L. Spencer
- Cell Biophysics Group, School of Life Sciences, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Fiona Cantlay
- Cell Biophysics Group, School of Life Sciences, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Molly Butler
- Cell Biophysics Group, School of Life Sciences, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Paul O'Shea
- Cell Biophysics Group, School of Life Sciences, University of Nottingham, University Park, Nottingham, United Kingdom
- Address as of 1st July 2016: Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada
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The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2017; 2017:9084507. [PMID: 29359159 PMCID: PMC5735586 DOI: 10.1155/2017/9084507] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/15/2017] [Accepted: 08/29/2017] [Indexed: 02/08/2023]
Abstract
Aim The incidence of Alzheimer's disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. Methods We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. Results We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. Conclusion These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD.
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Hill DP, D'Eustachio P, Berardini TZ, Mungall CJ, Renedo N, Blake JA. Modeling biochemical pathways in the gene ontology. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw126. [PMID: 27589964 PMCID: PMC5009323 DOI: 10.1093/database/baw126] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 08/10/2016] [Indexed: 12/05/2022]
Abstract
The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.
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Affiliation(s)
- David P Hill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Peter D'Eustachio
- Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Tanya Z Berardini
- Arabidopsis Information Resource, Phoenix Bioinformatics, Redwood City, CA 94063, USA
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Jaber Z, Aouad P, Al Medawar M, Bahmad H, Abou-Abbass H, Kobeissy F. Application of Systems Biology to Neuroproteomics: The Path to Enhanced Theranostics in Traumatic Brain Injury. Methods Mol Biol 2016; 1462:139-155. [PMID: 27604717 DOI: 10.1007/978-1-4939-3816-2_9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The application of systems biology tools in analyzing heterogeneous data from multiple sources has become a necessity, especially in biomarker discovery. Such tools were developed with several approaches to address different types of research questions and hypotheses. In the field of neurotrauma and traumatic brain injury (TBI), three distinct approaches have been used so far as systems biology tools, namely functional group categorization, pathway analysis, and protein-protein interaction (PPI) networks. The databases allow for query of the system to identify candidate targets which can be further studied to elucidate potential downstream biomarkers indicative of disease progression, severity, and improvement. The various systems biology tools, databases, and strategies that can be implemented on available TBI data in neuroproteomic studies are discussed in this chapter.
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Affiliation(s)
- Zaynab Jaber
- Department of Biochemistry, Graduate School and University Center of CUNY, 365 Fifth Avenue, New York, NY, 10016, USA.
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
| | - Patrick Aouad
- Department of Biology, Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon
| | - Mohamad Al Medawar
- Division of Vascular Endothelium and Microcirculation, Department of Medicine III, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Hisham Bahmad
- Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
| | - Hussein Abou-Abbass
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Firas Kobeissy
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
- Department of Psychiatry, Center for Neuroproteomics and Biomarkers Research, University of Florida, 4000 SW 23rd St., Apt. 5-204, Gainesville, FL, 32608, USA.
- Banyan Biomarkers, Inc, Alachua, FL, USA.
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Scott CC, Vossio S, Vacca F, Snijder B, Larios J, Schaad O, Guex N, Kuznetsov D, Martin O, Chambon M, Turcatti G, Pelkmans L, Gruenberg J. Wnt directs the endosomal flux of LDL-derived cholesterol and lipid droplet homeostasis. EMBO Rep 2015; 16:741-52. [PMID: 25851648 DOI: 10.15252/embr.201540081] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 03/06/2015] [Indexed: 01/24/2023] Open
Abstract
The Wnt pathway, which controls crucial steps of the development and differentiation programs, has been proposed to influence lipid storage and homeostasis. In this paper, using an unbiased strategy based on high-content genome-wide RNAi screens that monitored lipid distribution and amounts, we find that Wnt3a regulates cellular cholesterol. We show that Wnt3a stimulates the production of lipid droplets and that this stimulation strictly depends on endocytosed, LDL-derived cholesterol and on functional early and late endosomes. We also show that Wnt signaling itself controls cholesterol endocytosis and flux along the endosomal pathway, which in turn modulates cellular lipid homeostasis. These results underscore the importance of endosome functions for LD formation and reveal a previously unknown regulatory mechanism of the cellular programs controlling lipid storage and endosome transport under the control of Wnt signaling.
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Affiliation(s)
- Cameron C Scott
- Department of Biochemistry, University of Geneva, Geneva, Switzerland
| | - Stefania Vossio
- Department of Biochemistry, University of Geneva, Geneva, Switzerland
| | - Fabrizio Vacca
- Department of Biochemistry, University of Geneva, Geneva, Switzerland
| | - Berend Snijder
- Faculty of Sciences, Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Jorge Larios
- Department of Biochemistry, University of Geneva, Geneva, Switzerland
| | - Olivier Schaad
- Department of Biochemistry, University of Geneva, Geneva, Switzerland
| | - Nicolas Guex
- Vital-IT Group, Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Dmitry Kuznetsov
- Vital-IT Group, Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Olivier Martin
- Vital-IT Group, Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Marc Chambon
- Biomolecular Screening Facility, SV-PTECH-PTCB, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Gerardo Turcatti
- Biomolecular Screening Facility, SV-PTECH-PTCB, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Lucas Pelkmans
- Faculty of Sciences, Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Jean Gruenberg
- Department of Biochemistry, University of Geneva, Geneva, Switzerland
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Kerkentzes K, Lagani V, Tsamardinos I, Vyberg M, Røe OD. Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue. Front Oncol 2014; 4:251. [PMID: 25325012 PMCID: PMC4178426 DOI: 10.3389/fonc.2014.00251] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 09/02/2014] [Indexed: 11/22/2022] Open
Abstract
Objective: Novel statistical methods and increasingly more accurate gene annotations can transform “old” biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches. Methods: The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways. Results: Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93–100% (AUC = 0.93–1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue. Conclusion: Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.
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Affiliation(s)
- Konstantinos Kerkentzes
- Department of Computer Science, University of Crete , Heraklion , Greece ; Institute of Computer Science, Foundation of Research and Technology - Hellas , Heraklion , Greece
| | - Vincenzo Lagani
- Institute of Computer Science, Foundation of Research and Technology - Hellas , Heraklion , Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete , Heraklion , Greece ; Institute of Computer Science, Foundation of Research and Technology - Hellas , Heraklion , Greece
| | - Mogens Vyberg
- Institute of Pathology, Aalborg University Hospital , Aalborg , Denmark
| | - Oluf Dimitri Røe
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology , Trondheim , Norway ; Department of Oncology, Clinical Cancer Research Center, Aalborg University Hospital , Aalborg , Denmark ; Cancer Clinic, Levanger Hospital, Nord-Trøndelag Health Trust , Levanger , Norway
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10
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Altered gene transcription in human cells treated with Ludox® silica nanoparticles. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014; 11:8867-90. [PMID: 25170680 PMCID: PMC4198995 DOI: 10.3390/ijerph110908867] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Revised: 07/08/2014] [Accepted: 08/05/2014] [Indexed: 12/13/2022]
Abstract
Silica (SiO2) nanoparticles (NPs) have found extensive applications in industrial manufacturing, biomedical and biotechnological fields. Therefore, the increasing exposure to such ultrafine particles requires studies to characterize their potential cytotoxic effects in order to provide exhaustive information to assess the impact of nanomaterials on human health. The understanding of the biological processes involved in the development and maintenance of a variety of pathologies is improved by genome-wide approaches, and in this context, gene set analysis has emerged as a fundamental tool for the interpretation of the results. In this work we show how the use of a combination of gene-by-gene and gene set analyses can enhance the interpretation of results of in vitro treatment of A549 cells with Ludox® colloidal amorphous silica nanoparticles. By gene-by-gene and gene set analyses, we evidenced a specific cell response in relation to NPs size and elapsed time after treatment, with the smaller NPs (SM30) having higher impact on inflammatory and apoptosis processes than the bigger ones. Apoptotic process appeared to be activated by the up-regulation of the initiator genes TNFa and IL1b and by ATM. Moreover, our analyses evidenced that cell treatment with Ludox® silica nanoparticles activated the matrix metalloproteinase genes MMP1, MMP10 and MMP9. The information derived from this study can be informative about the cytotoxicity of Ludox® and other similar colloidal amorphous silica NPs prepared by solution processes.
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11
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Genetics of the human metabolome, what is next? Biochim Biophys Acta Mol Basis Dis 2014; 1842:1923-1931. [PMID: 24905732 DOI: 10.1016/j.bbadis.2014.05.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Revised: 05/04/2014] [Accepted: 05/28/2014] [Indexed: 11/23/2022]
Abstract
Increases in throughput and decreases in costs have facilitated large scale metabolomics studies, the simultaneous measurement of large numbers of biochemical components in biological samples. Initial large scale studies focused on biomarker discovery for disease or disease progression and helped to understand biochemical pathways underlying disease. The first population-based studies that combined metabolomics and genome wide association studies (mGWAS) have increased our understanding of the (genetic) regulation of biochemical conversions. Measurements of metabolites as intermediate phenotypes are a potentially very powerful approach to uncover how genetic variation affects disease susceptibility and progression. However, we still face many hurdles in the interpretation of mGWAS data. Due to the composite nature of many metabolites, single enzymes may affect the levels of multiple metabolites and, conversely, levels of single metabolites may be affected by multiple enzymes. Here, we will provide a global review of the current status of mGWAS. We will specifically discuss the application of prior biological knowledge present in databases to the interpretation of mGWAS results and discuss the potential of mathematical models. As the technology continuously improves to detect metabolites and to measure genetic variation, it is clear that comprehensive systems biology based approaches are required to further our insight in the association between genes, metabolites and disease. This article is part of a Special Issue entitled: From Genome to Function.
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12
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Haverland NA, Fox HS, Ciborowski P. Quantitative proteomics by SWATH-MS reveals altered expression of nucleic acid binding and regulatory proteins in HIV-1-infected macrophages. J Proteome Res 2014; 13:2109-19. [PMID: 24564501 PMCID: PMC3993959 DOI: 10.1021/pr4012602] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human immunodeficiency virus type 1 (HIV-1) infection remains a worldwide epidemic, and innovative therapies to combat the virus are needed. Developing a host-oriented antiviral strategy capable of targeting the biomolecules that are directly or indirectly required for viral replication may provide advantages over traditional virus-centric approaches. We used quantitative proteomics by SWATH-MS in conjunction with bioinformatic analyses to identify host proteins, with an emphasis on nucleic acid binding and regulatory proteins, which could serve as candidates in the development of host-oriented antiretroviral strategies. Using SWATH-MS, we identified and quantified the expression of 3608 proteins in uninfected and HIV-1-infected monocyte-derived macrophages. Of these 3608 proteins, 420 were significantly altered upon HIV-1 infection. Bioinformatic analyses revealed functional enrichment for RNA binding and processing as well as transcription regulation. Our findings highlight a novel subset of proteins and processes that are involved in the host response to HIV-1 infection. In addition, we provide an original and transparent methodology for the analysis of label-free quantitative proteomics data generated by SWATH-MS that can be readily adapted to other biological systems.
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Affiliation(s)
- Nicole A Haverland
- Department of Pharmacology and Experimental Neuroscience University of Nebraska Medical Center , Durham Research Center I, 985800 Nebraska Medical Center Omaha, Nebraska 68198-5800, United States
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13
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Richens JL, Morgan K, O'Shea P. Reverse engineering of Alzheimer's disease based on biomarker pathways analysis. Neurobiol Aging 2014; 35:2029-38. [PMID: 24684789 DOI: 10.1016/j.neurobiolaging.2014.02.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Revised: 02/18/2014] [Accepted: 02/26/2014] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) poses an increasingly profound problem to society, yet progress toward a genuine understanding of the disease remains worryingly slow. Perhaps, the most outstanding problem with the biology of AD is the question of its mechanistic origins, that is, it remains unclear wherein the molecular failures occur that underlie the disease. We demonstrate how molecular biomarkers could help define the nature of AD in terms of the early biochemical events that correlate with disease progression. We use a novel panel of biomolecules that appears in cerebrospinal fluid of AD patients. As changes in the relative abundance of these molecular markers are associated with progression to AD from mild cognitive impairment, we make the assumption that by tracking their origins we can identify the biochemical conditions that predispose their presence and consequently cause the onset of AD. We couple these protein markers with an analysis of a series of genetic factors and together this hypothesis essentially allows us to redefine AD in terms of the molecular pathways that underlie the disease.
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Affiliation(s)
- Joanna L Richens
- Cell Biophysics Group, School of Life Sciences, Faculty of Medicine & Health Sciences, University Park, University of Nottingham, Nottingham, UK
| | - Kevin Morgan
- Humans Genetics Research Group, School of Life Sciences, Faculty of Medicine & Health Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, UK
| | - Paul O'Shea
- Cell Biophysics Group, School of Life Sciences, Faculty of Medicine & Health Sciences, University Park, University of Nottingham, Nottingham, UK.
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14
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Brooksbank C, Bergman MT, Apweiler R, Birney E, Thornton J. The European Bioinformatics Institute's data resources 2014. Nucleic Acids Res 2014; 42:D18-25. [PMID: 24271396 PMCID: PMC3964968 DOI: 10.1093/nar/gkt1206] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 11/01/2013] [Accepted: 11/04/2013] [Indexed: 12/18/2022] Open
Abstract
Molecular Biology has been at the heart of the 'big data' revolution from its very beginning, and the need for access to biological data is a common thread running from the 1965 publication of Dayhoff's 'Atlas of Protein Sequence and Structure' through the Human Genome Project in the late 1990s and early 2000s to today's population-scale sequencing initiatives. The European Bioinformatics Institute (EMBL-EBI; http://www.ebi.ac.uk) is one of three organizations worldwide that provides free access to comprehensive, integrated molecular data sets. Here, we summarize the principles underpinning the development of these public resources and provide an overview of EMBL-EBI's database collection to complement the reviews of individual databases provided elsewhere in this issue.
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Affiliation(s)
- Catherine Brooksbank
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mary Todd Bergman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rolf Apweiler
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Janet Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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15
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Amaral A, Castillo J, Ramalho-Santos J, Oliva R. The combined human sperm proteome: cellular pathways and implications for basic and clinical science. Hum Reprod Update 2013; 20:40-62. [DOI: 10.1093/humupd/dmt046] [Citation(s) in RCA: 184] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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