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Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
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Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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2
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Graph Neural Network for Protein-Protein Interaction Prediction: A Comparative Study. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27186135. [PMID: 36144868 PMCID: PMC9501426 DOI: 10.3390/molecules27186135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/17/2022]
Abstract
Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein-protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein-protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.
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Khazen G, Gyulkhandanian A, Issa T, Maroun RC. Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes. Comput Struct Biotechnol J 2021; 19:5184-5197. [PMID: 34630938 PMCID: PMC8476896 DOI: 10.1016/j.csbj.2021.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/23/2021] [Accepted: 09/12/2021] [Indexed: 02/03/2023] Open
Abstract
Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.
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Affiliation(s)
- Georges Khazen
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Aram Gyulkhandanian
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
| | - Tina Issa
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Rachid C Maroun
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
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4
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Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:1-53. [PMID: 34340765 DOI: 10.1016/bs.apcsb.2021.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. Identifying intracellular signaling modules and exploring pathways associated with breast cancer recurrence. Sci Rep 2021; 11:385. [PMID: 33432018 PMCID: PMC7801429 DOI: 10.1038/s41598-020-79603-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/18/2020] [Indexed: 11/09/2022] Open
Abstract
Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .
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Affiliation(s)
- Xi Chen
- grid.438526.e0000 0001 0694 4940Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203 USA ,grid.430264.7Center for Computational Biology, Flatiron Institute, Simons Foundation, 162 Fifth Avenue, New York, NY 10010 USA
| | - Jinghua Gu
- grid.438526.e0000 0001 0694 4940Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203 USA
| | - Andrew F. Neuwald
- grid.411024.20000 0001 2175 4264Institute for Genome Sciences and Department Biochemistry and Molecular Biology, University of Maryland School of Medicine, 670 W. Baltimore Street, Baltimore, MD 21201 USA
| | - Leena Hilakivi-Clarke
- grid.17635.360000000419368657Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912 USA
| | - Robert Clarke
- grid.17635.360000000419368657Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912 USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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Bajpai AK, Davuluri S, Tiwary K, Narayanan S, Oguru S, Basavaraju K, Dayalan D, Thirumurugan K, Acharya KK. Systematic comparison of the protein-protein interaction databases from a user's perspective. J Biomed Inform 2020; 103:103380. [DOI: 10.1016/j.jbi.2020.103380] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 11/08/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023]
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Borisov N, Sorokin M, Garazha A, Buzdin A. Quantitation of Molecular Pathway Activation Using RNA Sequencing Data. Methods Mol Biol 2020; 2063:189-206. [PMID: 31667772 DOI: 10.1007/978-1-0716-0138-9_15] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.
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Affiliation(s)
- Nicolas Borisov
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
| | - Maxim Sorokin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Anton Buzdin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
- Omicsway Corp., Walnut, CA, USA.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
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8
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Han H, Jain AD, Truica MI, Izquierdo-Ferrer J, Anker JF, Lysy B, Sagar V, Luan Y, Chalmers ZR, Unno K, Mok H, Vatapalli R, Yoo YA, Rodriguez Y, Kandela I, Parker JB, Chakravarti D, Mishra RK, Schiltz GE, Abdulkadir SA. Small-Molecule MYC Inhibitors Suppress Tumor Growth and Enhance Immunotherapy. Cancer Cell 2019; 36:483-497.e15. [PMID: 31679823 PMCID: PMC6939458 DOI: 10.1016/j.ccell.2019.10.001] [Citation(s) in RCA: 242] [Impact Index Per Article: 48.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 08/19/2019] [Accepted: 09/30/2019] [Indexed: 01/16/2023]
Abstract
Small molecules that directly target MYC and are also well tolerated in vivo will provide invaluable chemical probes and potential anti-cancer therapeutic agents. We developed a series of small-molecule MYC inhibitors that engage MYC inside cells, disrupt MYC/MAX dimers, and impair MYC-driven gene expression. The compounds enhance MYC phosphorylation on threonine-58, consequently increasing proteasome-mediated MYC degradation. The initial lead, MYC inhibitor 361 (MYCi361), suppressed in vivo tumor growth in mice, increased tumor immune cell infiltration, upregulated PD-L1 on tumors, and sensitized tumors to anti-PD1 immunotherapy. However, 361 demonstrated a narrow therapeutic index. An improved analog, MYCi975 showed better tolerability. These findings suggest the potential of small-molecule MYC inhibitors as chemical probes and possible anti-cancer therapeutic agents.
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Affiliation(s)
- Huiying Han
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Atul D Jain
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL 60208, USA
| | - Mihai I Truica
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Javier Izquierdo-Ferrer
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL 60208, USA
| | - Jonathan F Anker
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Barbara Lysy
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Vinay Sagar
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yi Luan
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Zachary R Chalmers
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Kenji Unno
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Hanlin Mok
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Rajita Vatapalli
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Young A Yoo
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yara Rodriguez
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Irawati Kandela
- Center for Developmental Therapeutics, Northwestern University, Evanston, IL 60208, USA
| | - J Brandon Parker
- Division of Reproductive Science in Medicine, Department of OB/GYN, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Debabrata Chakravarti
- Division of Reproductive Science in Medicine, Department of OB/GYN, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago IL 60611, USA
| | - Rama K Mishra
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL 60208, USA; Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago IL 60611, USA
| | - Gary E Schiltz
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL 60208, USA; The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago IL 60611, USA
| | - Sarki A Abdulkadir
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
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9
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Hatz S, Spangler S, Bender A, Studham M, Haselmayer P, Lacoste AMB, Willis VC, Martin RL, Gurulingappa H, Betz U. Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson. PLoS One 2019; 14:e0214619. [PMID: 30958864 PMCID: PMC6453528 DOI: 10.1371/journal.pone.0214619] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/16/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship. METHODS Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers. RESULTS Watson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%. CONCLUSION Taken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.
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Affiliation(s)
- Sonja Hatz
- Merck KGaA, Frankfurter Straße, Darmstadt, Germany
| | - Scott Spangler
- IBM Watson Health, Almaden, California, United States of America
| | - Andrew Bender
- EMD Serono, Middlesex Turnpike, Billerica, United States of America
| | - Matthew Studham
- EMD Serono, Middlesex Turnpike, Billerica, United States of America
| | | | | | - Van C. Willis
- IBM Watson Health, Cambridge, Massachusetts, United States of America
| | - Richard L. Martin
- IBM Watson Health, Cambridge, Massachusetts, United States of America
| | | | - Ulrich Betz
- Merck KGaA, Frankfurter Straße, Darmstadt, Germany
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Goncalves TM, Southey BR, Rodriguez-Zas SL. Interplay Between Amphetamine and Activity Level in Gene Networks of the Mouse Striatum. Bioinform Biol Insights 2018; 12:1177932218815152. [PMID: 30559594 PMCID: PMC6291885 DOI: 10.1177/1177932218815152] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 10/18/2018] [Indexed: 01/09/2023] Open
Abstract
The psychostimulant amphetamine can be prescribed to ameliorate the symptoms of narcolepsy, attention-deficit hyperactivity disorder and to facilitate weight loss. This stimulant can also have negative effects including toxicity and addiction risk. The impact of amphetamine on gene networks is partially understood and this study addresses this gap in consideration of the physical activity. The striata of mice exposed to either amphetamine or saline treatment were compared in a mouse line selected for home cage physical overactivity, a phenotype that can be mitigated with amphetamine, and in a contemporary control line using RNA-seq. Genes presenting opposite expression patterns between treatments across lines included a pseudogene of coiled-coil-helix-coiled-coil-helix domain containing 2 gene (Chchd2), ribonuclease P RNA component H1 (Rpph1), short stature homeobox 2 (Shox2), transient receptor potential melastatin 6 (Trpm6), and tumor necrosis factor receptor superfamily, member 9 (Tnfrsf9). Genes presenting consistent treatment patterns across lines, albeit at different levels of significance included cholecystokinin (Cck), vasoactive intestinal polypeptide (Vip), arginine vasopressin (Avp), oxytocin/neurophysin (Oxt), thyrotropin releasing hormone (Trh), neurotensin (Nts), angiotensinogen (Agt), galanin (Gal), prolactin receptor (Prlr), and calcitonin receptor (Calcr). Potassium inwardly rectifying channel, subfamily J, member 6 (Kcnj6), and retinoic acid-related (RAR)-related orphan receptor alpha (Rora) were similarly differentially expressed between treatments across lines. Functional categories enriched among the genes presenting line-dependent amphetamine effect included genes coding for neuropeptides and associated with memory and neuroplasticity and synaptic signaling, energy, and redox processes. A line-dependent association between amphetamine exposure and the synaptic signaling genes neurogranin (Nrgn) and synaptic membrane exocytosis 1(Rims1) was highlighted in the gene networks. Our findings advance the understanding of molecular players and networks affected by amphetamine in support of the development of activity-targeted therapies that may capitalize on the benefits of this psychostimulant.
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Affiliation(s)
- Tassia M Goncalves
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Bruce R Southey
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Sandra L Rodriguez-Zas
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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11
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Molecular pathway activation – New type of biomarkers for tumor morphology and personalized selection of target drugs. Semin Cancer Biol 2018; 53:110-124. [DOI: 10.1016/j.semcancer.2018.06.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 06/19/2018] [Accepted: 06/19/2018] [Indexed: 02/06/2023]
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12
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Yu K, Lung PY, Zhao T, Zhao P, Tseng YY, Zhang J. Automatic extraction of protein-protein interactions using grammatical relationship graph. BMC Med Inform Decis Mak 2018; 18:42. [PMID: 30066644 PMCID: PMC6069288 DOI: 10.1186/s12911-018-0628-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm. Methods Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database. Results We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature. Conclusions We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.
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Affiliation(s)
- Kaixian Yu
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA. .,Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA.
| | - Pei-Yau Lung
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL, 32306, USA
| | - Peixiang Zhao
- Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
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13
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Nikitin D, Penzar D, Garazha A, Sorokin M, Tkachev V, Borisov N, Poltorak A, Prassolov V, Buzdin AA. Profiling of Human Molecular Pathways Affected by Retrotransposons at the Level of Regulation by Transcription Factor Proteins. Front Immunol 2018; 9:30. [PMID: 29441061 PMCID: PMC5797644 DOI: 10.3389/fimmu.2018.00030] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 01/04/2018] [Indexed: 12/22/2022] Open
Abstract
Endogenous retroviruses and retrotransposons also termed retroelements (REs) are mobile genetic elements that were active until recently in human genome evolution. REs regulate gene expression by actively reshaping chromatin structure or by directly providing transcription factor binding sites (TFBSs). We aimed to identify molecular processes most deeply impacted by the REs in human cells at the level of TFBS regulation. By using ENCODE data, we identified ~2 million TFBS overlapping with putatively regulation-competent human REs located in 5-kb gene promoter neighborhood (~17% of all TFBS in promoter neighborhoods; ~9% of all RE-linked TFBS). Most of REs hosting TFBS were highly diverged repeats, and for the evolutionary young (0–8% diverged) elements we identified only ~7% of all RE-linked TFBS. The gene-specific distributions of RE-linked TFBS generally correlated with the distributions for all TFBS. However, several groups of molecular processes were highly enriched in the RE-linked TFBS regulation. They were strongly connected with the immunity and response to pathogens, with the negative regulation of gene transcription, ubiquitination, and protein degradation, extracellular matrix organization, regulation of STAT signaling, fatty acids metabolism, regulation of GTPase activity, protein targeting to Golgi, regulation of cell division and differentiation, development and functioning of perception organs and reproductive system. By contrast, the processes most weakly affected by the REs were linked with the conservative aspects of embryo development. We also identified differences in the regulation features by the younger and older fractions of the REs. The regulation by the older fraction of the REs was linked mainly with the immunity, cell adhesion, cAMP, IGF1R, Notch, Wnt, and integrin signaling, neuronal development, chondroitin sulfate and heparin metabolism, and endocytosis. The younger REs regulate other aspects of immunity, cell cycle progression and apoptosis, PDGF, TGF beta, EGFR, and p38 signaling, transcriptional repression, structure of nuclear lumen, catabolism of phospholipids, and heterocyclic molecules, insulin and AMPK signaling, retrograde Golgi-ER transport, and estrogen signaling. The immunity-linked pathways were highly represented in both categories, but their functional roles were different and did not overlap. Our results point to the most quickly evolving molecular pathways in the recent and ancient evolution of human genome.
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Affiliation(s)
- Daniil Nikitin
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.,D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Dmitry Penzar
- The Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Andrew Garazha
- D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Maxim Sorokin
- OmicsWay Corp., Walnut, CA, United States.,National Research Centre Kurchatov Institute, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Nicolas Borisov
- OmicsWay Corp., Walnut, CA, United States.,National Research Centre Kurchatov Institute, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
| | - Alexander Poltorak
- Program in Immunology, Sackler Graduate School, Tufts University, Boston, MA, United States
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Anton A Buzdin
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.,D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,OmicsWay Corp., Walnut, CA, United States.,National Research Centre Kurchatov Institute, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
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14
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Chen M, Wang J, Luo Y, Huang K, Shi X, Liu Y, Li J, Lai Z, Xue S, Gao H, Chen A, Chen D. Identify Down syndrome transcriptome associations using integrative analysis of microarray database and correlation-interaction network. Hum Genomics 2018; 12:2. [PMID: 29351810 PMCID: PMC5775600 DOI: 10.1186/s40246-018-0133-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Accepted: 01/05/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) have previously been emerged as key players in a series of biological processes. Dysregulation of lncRNA is correlated to human diseases including neurological disorders. Here, we developed a multi-step bioinformatics analysis to study the functions of a particular Down syndrome-associated gene DSCR9 including the lncRNAs. The method is named correlation-interaction-network (COIN), based on which a pipeline is implemented. Co-expression gene network analysis and biological network analysis results are presented. METHODS We identified the regulation function of DSCR9, a lncRNA transcribed from the Down syndrome critical region (DSCR) of chromosome 21, by analyzing its co-expression genes from over 1700 sets and nearly 60,000 public Affymetrix human U133-Plus 2 transcriptional profiling microarrays. After proper evaluations, a threshold is chosen to filter the data and get satisfactory results. Microarray data resource is from EBI database and protein-protein interaction (PPI) network information is incorporated from the most complete network databases. PPI integration strategy guarantees complete information regarding DSCR9. Enrichment analysis is performed to identify significantly correlated pathways. RESULTS We found that the most significant pathways associated with the top DSCR9 co-expressed genes were shown to be involved in neuro-active ligand-receptor interaction (GLP1R, HTR4, P2RX2, UCN3, and UTS2R), calcium signaling pathway (CACNA1F, CACNG4, HTR4, P2RX2, and SLC8A3), neuronal system (KCNJ5 and SYN1) by the KEGG, and GO analysis. The A549 and U251 cell lines with stable DSCR9 overexpression were constructed. We validated 10 DSCR9 co-expression genes by qPCR in both cell lines with over 70% accuracy. CONCLUSIONS DSCR9 was highly correlated with genes that were known as important factors in the developments and functions of nervous system, indicating that DSCR9 may regulate neurological proteins regarding Down syndrome and other neurological-related diseases. The pipeline can be properly adjusted to other applications.
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Affiliation(s)
- Min Chen
- Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.,Obstetrics and Gynecology Institute of Guangzhou, Guangzhou, 510150, China.,The Medical Centre for Critical Pregnant Women in Guangzhou, Guangzhou, 510150, China.,Key Laboratory for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China.,Key Laboratory for Reproduction and Genetics of Guangdong Higher Education Institutes, Guangzhou, China
| | - Jiayan Wang
- Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.,Obstetrics and Gynecology Institute of Guangzhou, Guangzhou, 510150, China.,The Medical Centre for Critical Pregnant Women in Guangzhou, Guangzhou, 510150, China.,Key Laboratory for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China
| | - Yingjun Luo
- Mendel Genes Inc, Manhattan Beach, CA, Manhattan Beach, CA, 90266, USA
| | - Kailing Huang
- Mendel Genes Inc, Manhattan Beach, CA, Manhattan Beach, CA, 90266, USA
| | - Xiaoshun Shi
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanhui Liu
- Mendel Genes Inc, Manhattan Beach, CA, Manhattan Beach, CA, 90266, USA
| | - Jin Li
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Zhengfei Lai
- Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
| | - Shuya Xue
- Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.,Obstetrics and Gynecology Institute of Guangzhou, Guangzhou, 510150, China.,The Medical Centre for Critical Pregnant Women in Guangzhou, Guangzhou, 510150, China.,Key Laboratory for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China.,Key Laboratory for Reproduction and Genetics of Guangdong Higher Education Institutes, Guangzhou, China
| | - Haimei Gao
- Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.,Obstetrics and Gynecology Institute of Guangzhou, Guangzhou, 510150, China.,The Medical Centre for Critical Pregnant Women in Guangzhou, Guangzhou, 510150, China.,Key Laboratory for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China.,Key Laboratory for Reproduction and Genetics of Guangdong Higher Education Institutes, Guangzhou, China
| | - Allen Chen
- Department of Mathematics, University of California, Berkeley, CA, 94720, USA.,Mendel Genes Inc, Manhattan Beach, CA, Manhattan Beach, CA, 90266, USA
| | - Dunjin Chen
- Department of Fetal Medicine and Prenatal Diagnosis, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China. .,Obstetrics and Gynecology Institute of Guangzhou, Guangzhou, 510150, China. .,The Medical Centre for Critical Pregnant Women in Guangzhou, Guangzhou, 510150, China. .,Key Laboratory for Major Obstetric Diseases of Guangdong Province, Guangzhou, 510150, China. .,Key Laboratory for Reproduction and Genetics of Guangdong Higher Education Institutes, Guangzhou, China.
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15
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Schönbach C, Li J, Ma L, Horton P, Sjaugi MF, Ranganathan S. A bioinformatics potpourri. BMC Genomics 2018; 19:920. [PMID: 29363432 PMCID: PMC5780851 DOI: 10.1186/s12864-017-4326-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018.
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Affiliation(s)
- Christian Schönbach
- International Research Center for Medical Sciences, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, 860-0811 Japan
| | - Jinyan Li
- The Advanced Analytics Institute, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Lan Ma
- Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055 People’s Republic of China
| | - Paul Horton
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, 135-0064 Japan
| | | | - Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109 Australia
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16
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Felgueiras J, Silva JV, Fardilha M. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools. J Proteomics 2018; 171:127-140. [DOI: 10.1016/j.jprot.2017.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/26/2017] [Accepted: 05/13/2017] [Indexed: 02/02/2023]
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17
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Abstract
Protein complex-based feature selection (PCBFS) provides unparalleled reproducibility with high phenotypic relevance on proteomics data. Currently, there are five PCBFS paradigms, but not all representative methods have been implemented or made readily available. To allow general users to take advantage of these methods, we developed the R-package NetProt, which provides implementations of representative feature-selection methods. NetProt also provides methods for generating simulated differential data and generating pseudocomplexes for complex-based performance benchmarking. The NetProt open source R package is available for download from https://github.com/gohwils/NetProt/releases/ , and online documentation is available at http://rpubs.com/gohwils/204259 .
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Affiliation(s)
- Wilson Wen Bin Goh
- School of Pharmaceutical Science and Technology, Tianjin University , 92 Weijin Road, Tianjin 300072, China.,School of Biological Sciences, Nanyang Technological University , 60 Nanyang Drive, Singapore 637551.,Department of Computer Science, National University of Singapore , 13 Computing Drive, Singapore 117417
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore , 13 Computing Drive, Singapore 117417.,Department of Pathology, National University of Singapore , 5 Lower Kent Ridge Road, Singapore 119074
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18
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Ramsahai E, Walkins K, Tripathi V, John M. The use of gene interaction networks to improve the identification of cancer driver genes. PeerJ 2017; 5:e2568. [PMID: 28149674 PMCID: PMC5274523 DOI: 10.7717/peerj.2568] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 09/14/2016] [Indexed: 01/17/2023] Open
Abstract
Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.
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Affiliation(s)
- Emilie Ramsahai
- Department of Mathematics & Statistics, The Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus , Trinidad and Tobago
| | - Kheston Walkins
- Department of Preclinical Sciences, The University of the West Indies, St. Augustine , Trinidad and Tobago
| | - Vrijesh Tripathi
- Department of Mathematics & Statistics, The Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus , Trinidad and Tobago
| | - Melford John
- Department of Preclinical Sciences, The University of the West Indies, St. Augustine , Trinidad and Tobago
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19
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Buzdin AA, Prassolov V, Zhavoronkov AA, Borisov NM. Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data. Methods Mol Biol 2017; 1613:53-83. [PMID: 28849558 DOI: 10.1007/978-1-4939-7027-8_4] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.
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Affiliation(s)
- Anton A Buzdin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR.
- Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia.
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- Laboratory of Bioinformatics, D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova street 32, Mosow, 119991, Russia
| | - Alex A Zhavoronkov
- Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Nikolay M Borisov
- Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia
- Department of Personalized Medicine, First Oncology Research and Advisory Center, Moscow, Russia
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20
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Chisanga D, Keerthikumar S, Mathivanan S, Chilamkurti N. Network Tools for the Analysis of Proteomic Data. Methods Mol Biol 2017; 1549:177-197. [PMID: 27975292 DOI: 10.1007/978-1-4939-6740-7_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data.Computational tools, including pathway databases and network building tools, have therefore been developed to store, analyze, interpret, and learn from proteomics data. These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.
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Affiliation(s)
- David Chisanga
- Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciencesy, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Shivakumar Keerthikumar
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Suresh Mathivanan
- Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Naveen Chilamkurti
- Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciencesy, La Trobe University, Melbourne, VIC, 3086, Australia.
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21
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Lee SA, Huang KC. Epigenetic profiling of human brain differential DNA methylation networks in schizophrenia. BMC Med Genomics 2016; 9:68. [PMID: 28117656 PMCID: PMC5260790 DOI: 10.1186/s12920-016-0229-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Background Epigenetics of schizophrenia provides important information on how the environmental factors affect the genetic architecture of the disease. DNA methylation plays a pivotal role in etiology for schizophrenia. Previous studies have focused mostly on the discovery of schizophrenia-associated SNPs or genetic variants. As postmortem brain samples became available, more and more recent studies surveyed transcriptomics of the diseases. In this study, we constructed protein-protein interaction (PPI) network using the disease associated SNP (or genetic variants), differentially expressed disease genes and differentially methylated disease genes (or promoters). By combining the different datasets and topological analyses of the PPI network, we established a more comprehensive understanding of the development and genetics of this devastating mental illness. Results We analyzed the previously published DNA methylation profiles of prefrontal cortex from 335 healthy controls and 191 schizophrenic patients. These datasets revealed 2014 CpGs identified as GWAS risk loci with the differential methylation profile in schizophrenia, and 1689 schizophrenic differential methylated genes (SDMGs) identified with predominant hypomethylation. These SDMGs, combined with the PPIs of these genes, were constructed into the schizophrenic differential methylation network (SDMN). On the SDMN, there are 10 hypermethylated SDMGs, including GNA13, CAPNS1, GABPB2, GIT2, LEFTY1, NDUFA10, MIOS, MPHOSPH6, PRDM14 and RFWD2. The hypermethylation to differential expression network (HyDEN) were constructed to determine how the hypermethylated promoters regulate gene expression. The enrichment analyses of biochemical pathways in HyDEN, including TNF alpha, PDGFR-beta signaling, TGF beta Receptor, VEGFR1 and VEGFR2 signaling, regulation of telomerase, hepatocyte growth factor receptor signaling, ErbB1 downstream signaling and mTOR signaling pathway, suggested that the malfunctioning of these pathways contribute to the symptoms of schizophrenia. Conclusions The epigenetic profiles of DNA differential methylation from schizophrenic brain samples were investigated to understand the regulatory roles of SDMGs. The SDMGs interplays with SCZCGs in a coordinated fashion in the disease mechanism of schizophrenia. The protein complexes and pathways involved in SDMN may be responsible for the etiology and potential treatment targets. The SDMG promoters are predominantly hypomethylated. Increasing methylation on these promoters is proposed as a novel therapeutic approach for schizophrenia. Electronic supplementary material The online version of this article (doi:10.1186/s12920-016-0229-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sheng-An Lee
- Department of Information Management, Kainan University, Taoyuan, Taiwan
| | - Kuo-Chuan Huang
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan. .,Department of Nursing, Ching Kuo Institute of Management and Health, Keelung, Taiwan.
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22
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Yang C, Fu R, Zhuang Z, Wang S. Studies on the biological functions of CPS1 in AFB1 induced hepatocarcinogenesis. Gene 2016; 591:255-261. [PMID: 27425868 DOI: 10.1016/j.gene.2016.07.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 06/23/2016] [Accepted: 07/12/2016] [Indexed: 01/10/2023]
Abstract
Carbamyl phosphate synthetase 1 (CPS1) was down-regulated in hepatocellular carcinoma (HCC), as treated by aflatoxin B1 (AFB1), a potent hepatocarcinogenesis mycotoxin. In this study, we firstly confirmed that AFB1 down-regulated the expression of CPS1 in a dose-dependent manner. At the meantime, both siRNA knock down of CPS1 and AFB1 treatment inhibited cell proliferation, and induced cell apoptosis. To further analysis the function of CPS1, the interacting proteins of CPS1 were searched by Co-IP, and three interacting proteins including type II cytoskeletal 1 (KRT1), albumin (ALB), and ubiquitin C (UBC) were found. Both KRT1 and ALB were new interacting proteins for CPS1. Our further study showed that CPS1 was regulating interacted and colocalized with KRT1 and ALB, and the intensity correlation was changed by AFB1. KRT1, ALB and CPS1 were all reported to play an important role in differentiation and tissue specialization. These results may offer an increasing understand that CPS1 might have a function in differentiation.
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Affiliation(s)
- Chi Yang
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of the Education Ministry, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Institute of Edible Fungi, National and Local Joint Engineering Research Center for Breeding & Cultivation of Featured Edible Fungi, Fujian Academy of Agricultural Sciences, Fuzhou 350014, China
| | - Rao Fu
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of the Education Ministry, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zhenhong Zhuang
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of the Education Ministry, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Shihua Wang
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of the Education Ministry, School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
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23
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Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes. Sci Rep 2016; 6:21223. [PMID: 26868667 PMCID: PMC4751475 DOI: 10.1038/srep21223] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 01/19/2016] [Indexed: 02/02/2023] Open
Abstract
Most protein complex detection methods utilize unsupervised techniques to cluster densely connected nodes in a protein-protein interaction (PPI) network, in spite of the fact that many true complexes are not dense subgraphs. Supervised methods have been proposed recently, but they do not answer why a group of proteins are predicted as a complex, and they have not investigated how to detect new complexes of one species by training the model on the PPI data of another species. We propose a novel supervised method to address these issues. The key idea is to discover emerging patterns (EPs), a type of contrast pattern, which can clearly distinguish true complexes from random subgraphs in a PPI network. An integrative score of EPs is defined to measure how likely a subgraph of proteins can form a complex. New complexes thus can grow from our seed proteins by iteratively updating this score. The performance of our method is tested on eight benchmark PPI datasets and compared with seven unsupervised methods, two supervised and one semi-supervised methods under five standards to assess the quality of the predicted complexes. The results show that in most cases our method achieved a better performance, sometimes significantly.
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Dang TN, Murray P, Forbes AG. PathwayMatrix: visualizing binary relationships between proteins in biological pathways. BMC Proc 2015; 9:S3. [PMID: 26361499 PMCID: PMC4547148 DOI: 10.1186/1753-6561-9-s6-s3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Background Molecular activation pathways are inherently complex, and understanding relations across many biochemical reactions and reaction types is difficult. Visualizing and analyzing a pathway is a challenge due to the network size and the diversity of relations between proteins and molecules. Results In this paper, we introduce PathwayMatrix, a visualization tool that presents the binary relations between proteins in the pathway via the use of an interactive adjacency matrix. We provide filtering, lensing, clustering, and brushing and linking capabilities in order to present relevant details about proteins within a pathway. Conclusions We evaluated PathwayMatrix by conducting a series of in-depth interviews with domain experts who provided positive feedback, leading us to believe that our visualization technique could be helpful for the larger community of researchers utilizing pathway visualizations. PathwayMatrix is freely available at https://github.com/CreativeCodingLab/PathwayMatrix.
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Affiliation(s)
- Tuan Nhon Dang
- Department of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA
| | - Paul Murray
- Department of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA
| | - Angus Graeme Forbes
- Department of Computer Science M/C 152, University of Illinois at Chicago, 851 S. Morgan, Room 1120, Chicago 60607-7053, IL, USA
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25
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Exploring novel mechanistic insights in Alzheimer's disease by assessing reliability of protein interactions. Sci Rep 2015; 5:13634. [PMID: 26346705 PMCID: PMC4562155 DOI: 10.1038/srep13634] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 08/03/2015] [Indexed: 01/08/2023] Open
Abstract
Protein interaction networks are widely used in computational biology as a graphical means of representing higher-level systemic functions in a computable form. Although, many algorithms exist that seamlessly collect and measure protein interaction information in network models, they often do not provide novel mechanistic insights using quantitative criteria. Measuring information content and knowledge representation in network models about disease mechanisms becomes crucial particularly when exploring new target candidates in a well-defined functional context of a potential disease mechanism. To this end, we have developed a knowledge-based scoring approach that uses literature-derived protein interaction features to quantify protein interaction confidence. Thereby, we introduce the novel concept of knowledge cliffs, regions of the interaction network where a significant gap between high scoring and low scoring interactions is observed, representing a divide between established and emerging knowledge on disease mechanism. To show the application of this approach, we constructed and assessed reliability of a protein-protein interaction model specific to Alzheimer’s disease, which led to screening, and prioritization of four novel protein candidates. Evaluation of the identified candidates showed that two of them are already followed in clinical trials for testing potential AD drugs.
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Borisov NM, Terekhanova NV, Aliper AM, Venkova LS, Smirnov PY, Roumiantsev S, Korzinkin MB, Zhavoronkov AA, Buzdin AA. Signaling pathways activation profiles make better markers of cancer than expression of individual genes. Oncotarget 2015; 5:10198-205. [PMID: 25415353 PMCID: PMC4259415 DOI: 10.18632/oncotarget.2548] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Identification of reliable and accurate molecular markers remains one of the major challenges of contemporary biomedicine. We developed a new bioinformatic technique termed OncoFinder that for the first time enables to quantatively measure activation of intracellular signaling pathways basing on transcriptomic data. Signaling pathways regulate all major cellular events in health and disease. Here, we showed that the Pathway Activation Strength (PAS) value itself may serve as the biomarker for cancer, and compared it with the "traditional" molecular markers based on the expression of individual genes. We applied OncoFinder to profile gene expression datasets for the nine human cancer types including bladder cancer, basal cell carcinoma, glioblastoma, hepatocellular carcinoma, lung adenocarcinoma, oral tongue squamous cell carcinoma, primary melanoma, prostate cancer and renal cancer, totally 292 cancer and 128 normal tissue samples taken from the Gene expression omnibus (GEO) repository. We profiled activation of 82 signaling pathways that involve ~2700 gene products. For 9/9 of the cancer types tested, the PAS values showed better area-under-the-curve (AUC) scores compared to the individual genes enclosing each of the pathways. These results evidence that the PAS values can be used as a new type of cancer biomarkers, superior to the traditional gene expression biomarkers.
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Jeanquartier F, Jean-Quartier C, Holzinger A. Integrated web visualizations for protein-protein interaction databases. BMC Bioinformatics 2015; 16:195. [PMID: 26077899 PMCID: PMC4466863 DOI: 10.1186/s12859-015-0615-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Accepted: 05/15/2015] [Indexed: 12/27/2022] Open
Abstract
Background Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. Results We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Conclusions Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.
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Affiliation(s)
- Fleur Jeanquartier
- Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria.
| | - Claire Jean-Quartier
- Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria.
| | - Andreas Holzinger
- Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. .,Institute for Information Systems & Computer Media Graz University of Technology, Inffeldgasse 16c, Graz, 8010, Austria.
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Li W, Freudenberg J, Oswald M. Principles for the organization of gene-sets. Comput Biol Chem 2015; 59 Pt B:139-49. [PMID: 26188561 DOI: 10.1016/j.compbiolchem.2015.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Accepted: 04/08/2015] [Indexed: 12/23/2022]
Abstract
A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.
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Affiliation(s)
- Wentian Li
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY, USA.
| | - Jan Freudenberg
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY, USA
| | - Michaela Oswald
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY, USA
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Hou J, Ma J, Yu KN, Li W, Cheng C, Bao L, Han W. Non-thermal plasma treatment altered gene expression profiling in non-small-cell lung cancer A549 cells. BMC Genomics 2015; 16:435. [PMID: 26116417 PMCID: PMC4483225 DOI: 10.1186/s12864-015-1644-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Accepted: 05/20/2015] [Indexed: 11/20/2022] Open
Abstract
Background Recent technological advances in atmospheric plasmas have made the creation of non-thermal atmospheric pressure plasma (NTP) possible for utilization in the medical field. Although accumulated evidence suggests that NTP induces cell death in various cancer cell types thus offering a promising alternative treatment strategy, the mechanism underlying its therapeutic effect is not fully understood. Results We analyzed relevant signaling cascades associated with the tumor protein p53, in particular the cell cycle arrest, DNA damage as well as the underlying apoptosis pathways. Based on our results, the major effect from plasma exposure was found to be the activation of MAPK and p53 signaling pathways, resulting in changes in gene expression of MEKK, GADD, FOS and JUN. Finally, a significant modulation in expression of genes related to cellular proliferation and differentiation was observed. Conclusion Overall, the presented data of the tumor transcriptome helped identify the key players in modulated gene expression following exposure to plasma at the molecular level, and also helped interpret the downstream processes. The present work laid the foundation for further studies to clarify the roles of multiple pathways in plasma-induced biological processes. Further investigation of these genes in other cell lines may reveal comprehensive mechanisms of plasma induced effects. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1644-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jue Hou
- Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
| | - Jie Ma
- Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. .,School of Life Sciences, University of Science and Technology of China, Hefei, China.
| | - K N Yu
- Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. .,Department of Physics and Materials Science, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong.
| | - Wei Li
- Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
| | - Cheng Cheng
- Institute of Plasma Physics, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei, China.
| | - Lingzhi Bao
- Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. .,, Mailbox 1110, 350 Shushanhu Road, Hefei, Anhui, 230031, P. R. China.
| | - Wei Han
- Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. .,, Mailbox 1110, 350 Shushanhu Road, Hefei, Anhui, 230031, P. R. China.
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Subramani S, Kalpana R, Monickaraj PM, Natarajan J. HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways. J Biomed Inform 2015; 54:121-31. [PMID: 25659452 DOI: 10.1016/j.jbi.2015.01.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Revised: 01/13/2015] [Accepted: 01/15/2015] [Indexed: 12/26/2022]
Abstract
The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks.
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Affiliation(s)
- Suresh Subramani
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, School of Life Sciences, Bharathiar University, Tamil Nadu, India.
| | - Raja Kalpana
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, School of Life Sciences, Bharathiar University, Tamil Nadu, India.
| | | | - Jeyakumar Natarajan
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, School of Life Sciences, Bharathiar University, Tamil Nadu, India.
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Tang H, Zhong F, Liu W, He F, Xie H. PathPPI: an integrated dataset of human pathways and protein-protein interactions. SCIENCE CHINA-LIFE SCIENCES 2015; 58:579-89. [PMID: 25591449 DOI: 10.1007/s11427-014-4766-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2014] [Accepted: 07/20/2014] [Indexed: 12/23/2022]
Abstract
Integration of pathway and protein-protein interaction (PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset (named PathPPI). More detailed interaction type and modification information on protein interactions can be preserved in PathPPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values (O AB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The PathPPI data was provided at http://proteomeview.hupo.org.cn/PathPPI/PathPPI.html.
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Affiliation(s)
- HaiLin Tang
- College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Changsha, 410073, China
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Abstract
The prediction of protein-protein interactions and kinase-specific phosphorylation sites on individual proteins is critical for correctly placing proteins within signaling pathways and networks. The importance of this type of annotation continues to increase with the continued explosion of genomic and proteomic data, particularly with emerging data categorizing posttranslational modifications on a large scale. A variety of computational tools are available for this purpose. In this chapter, we review the general methodologies for these types of computational predictions and present a detailed user-focused tutorial of one such method and computational tool, Scansite, which is freely available to the entire scientific community over the Internet.
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Affiliation(s)
- Tobias Ehrenberger
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Barter RL, Schramm SJ, Mann GJ, Yang YH. Network-based biomarkers enhance classical approaches to prognostic gene expression signatures. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 4:S5. [PMID: 25521200 PMCID: PMC4290694 DOI: 10.1186/1752-0509-8-s4-s5] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers. RESULTS We found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable. CONCLUSIONS Next-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add to traditional approaches? Our findings indicate there is value in the way in which different subspaces of the patient sample are captured differently among the various methods, highlighting the possibility of 'combination' classifiers capable of identifying which patients will be more accurately classified by one particular method over another. We have seen this clearly for the first time because of our in-depth analysis at the level of individual patients.
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Affiliation(s)
- Rebecca L Barter
- School of Mathematics and Statistics at The University of Sydney, F07, The University of Sydney, NSW, 2006, Australia
| | - Sarah-Jane Schramm
- Westmead Millennium Institute at The University of Sydney, 176 Hawkesbury Road, Westmead, NSW, 2145, Australia
- Melanoma Institute Australia, 40 Rocklands Rd, North Sydney, NSW, 2060, Australia
| | - Graham J Mann
- Westmead Millennium Institute at The University of Sydney, 176 Hawkesbury Road, Westmead, NSW, 2145, Australia
- Melanoma Institute Australia, 40 Rocklands Rd, North Sydney, NSW, 2060, Australia
| | - Yee Hwa Yang
- School of Mathematics and Statistics at The University of Sydney, F07, The University of Sydney, NSW, 2006, Australia
- Melanoma Institute Australia, 40 Rocklands Rd, North Sydney, NSW, 2060, Australia
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Moni MA, Liò P. Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies. BMC Bioinformatics 2014; 15:333. [PMID: 25344230 PMCID: PMC4363349 DOI: 10.1186/1471-2105-15-333] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Accepted: 09/19/2014] [Indexed: 01/02/2023] Open
Abstract
Background Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I’d rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes. Results We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the ϕ-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases. Conclusions Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-333) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohammad Ali Moni
- Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.
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Callegari S, Gastaldello S, Faridani OR, Masucci MG. Epstein-Barr virus encoded microRNAs target SUMO-regulated cellular functions. FEBS J 2014; 281:4935-50. [DOI: 10.1111/febs.13040] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 08/11/2014] [Accepted: 09/02/2014] [Indexed: 12/30/2022]
Affiliation(s)
- Simone Callegari
- Department of Cell and Molecular Biology; Karolinska Institutet; Stockholm Sweden
| | - Stefano Gastaldello
- Department of Cell and Molecular Biology; Karolinska Institutet; Stockholm Sweden
| | - Omid R. Faridani
- Department of Cell and Molecular Biology; Karolinska Institutet; Stockholm Sweden
| | - Maria G. Masucci
- Department of Cell and Molecular Biology; Karolinska Institutet; Stockholm Sweden
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Buzdin AA, Zhavoronkov AA, Korzinkin MB, Roumiantsev SA, Aliper AM, Venkova LS, Smirnov PY, Borisov NM. The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis. Front Mol Biosci 2014; 1:8. [PMID: 25988149 PMCID: PMC4428387 DOI: 10.3389/fmolb.2014.00008] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Accepted: 08/04/2014] [Indexed: 11/24/2022] Open
Abstract
The diversity of the installed sequencing and microarray equipment make it increasingly difficult to compare and analyze the gene expression datasets obtained using the different methods. Many applications requiring high-quality and low error rates cannot make use of available data using traditional analytical approaches. Recently, we proposed a new concept of signalome-wide analysis of functional changes in the intracellular pathways termed OncoFinder, a bioinformatic tool for quantitative estimation of the signaling pathway activation (SPA). We also developed methods to compare the gene expression data obtained using multiple platforms and minimizing the error rates by mapping the gene expression data onto the known and custom signaling pathways. This technique for the first time makes it possible to analyze the functional features of intracellular regulation on a mathematical basis. In this study we show that the OncoFinder method significantly reduces the errors introduced by transcriptome-wide experimental techniques. We compared the gene expression data for the same biological samples obtained by both the next generation sequencing (NGS) and microarray methods. For these different techniques we demonstrate that there is virtually no correlation between the gene expression values for all datasets analyzed (R2 < 0.1). In contrast, when the OncoFinder algorithm is applied to the data we observed clear-cut correlations between the NGS and microarray gene expression datasets. The SPA profiles obtained using NGS and microarray techniques were almost identical for the same biological samples allowing for the platform-agnostic analytical applications. We conclude that this feature of the OncoFinder enables to characterize the functional states of the transcriptomes and interactomes more accurately as before, which makes OncoFinder a method of choice for many applications including genetics, physiology, biomedicine, and molecular diagnostics.
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Affiliation(s)
- Anton A Buzdin
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences Moscow, Russia ; Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology Moscow, Russia ; Pathway Pharmaceuticals Wan Chai, Hong Kong
| | - Alex A Zhavoronkov
- Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology Moscow, Russia ; Pathway Pharmaceuticals Wan Chai, Hong Kong
| | - Mikhail B Korzinkin
- Pathway Pharmaceuticals Wan Chai, Hong Kong ; Laboratory of Systems Biology, A.I. Burnasyan Federal Medical Biophysical Center Moscow, Russia
| | - Sergey A Roumiantsev
- Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology Moscow, Russia
| | - Alexander M Aliper
- Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology Moscow, Russia ; Pathway Pharmaceuticals Wan Chai, Hong Kong
| | - Larisa S Venkova
- Pathway Pharmaceuticals Wan Chai, Hong Kong ; Laboratory of Systems Biology, A.I. Burnasyan Federal Medical Biophysical Center Moscow, Russia
| | - Philip Y Smirnov
- Pathway Pharmaceuticals Wan Chai, Hong Kong ; Laboratory of Systems Biology, A.I. Burnasyan Federal Medical Biophysical Center Moscow, Russia
| | - Nikolay M Borisov
- Pathway Pharmaceuticals Wan Chai, Hong Kong ; Laboratory of Systems Biology, A.I. Burnasyan Federal Medical Biophysical Center Moscow, Russia
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Schramm SJ, Jayaswal V, Goel A, Li SS, Yang YH, Mann GJ, Wilkins MR. Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations. Proteomics 2014; 13:3393-405. [PMID: 24166987 DOI: 10.1002/pmic.201200570] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Revised: 09/11/2013] [Accepted: 10/07/2013] [Indexed: 01/01/2023]
Abstract
High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.
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Affiliation(s)
- Sarah-Jane Schramm
- Sydney Medical School, Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, Sydney, NSW, Australia
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Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, Zhang B, Zhang C, Lamb JR, Xie T, Suver C, Molony C, Melquist S, Johnson AD, Fan G, Stone DJ, Schadt EE, Casaccia P, Emilsson V, Zhu J. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol 2014; 10:743. [PMID: 25080494 PMCID: PMC4299500 DOI: 10.15252/msb.20145304] [Citation(s) in RCA: 123] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10−12), while Dnmt3a KO signature does not (P = 0.017).
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Affiliation(s)
| | - Jimmy L Huynh
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Wang
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Seungyeul Yoo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua McElwee
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chunsheng Zhang
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - John R Lamb
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Tao Xie
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | | | - Cliona Molony
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Stacey Melquist
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | | | - Guoping Fan
- Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - David J Stone
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patrizia Casaccia
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valur Emilsson
- Icelandic Heart Association, Kopavogur, Iceland Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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A study of substrate specificity for a CTD phosphatase, SCP1, by proteomic screening of binding partners. Biochem Biophys Res Commun 2014; 448:189-94. [PMID: 24769477 DOI: 10.1016/j.bbrc.2014.04.089] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 04/17/2014] [Indexed: 11/21/2022]
Abstract
RNA polymerase II carboxyl-terminal domain (RNAPII CTD) phosphatases are a newly emerging family of phosphatases. Recently a CTD-specific phosphatase, small CTD phosphatase 1 (SCP1), has shown to act as an evolutionarily conserved transcriptional corepressor for inhibiting neuronal gene transcription in non-neuronal cells. In this study, using the established NIH/3T3 and HEK293T cells, which are expressing human SCP1 proteins under the tight control of expression by doxycycline, a proteomic screening was conducted to identify the binding partners for SCP1. Although the present findings provide the possibility for new avenues to provide to a better understanding of cellular physiology of SCP1, now these proteomic and some immunological approaches for SCP1 interactome might not represent the accurate physiological relevance in vivo. In this presentation, we focus the substrate specificity to delineate an appearance of the dephosphorylation reaction catalyzed by SCP1 phosphatase. We compared the phosphorylated sequences of the immunologically confirmed binding partners with SCP1 searched in HPRD. We found the similar sequences from CdcA3 and validated the efficiency of enzymatic catalysis for synthetic phosphopeptides the recombinant SCP1. This approach led to the identification of several interacting partners with SCP1. We suggest that CdcA3 could be an enzymatic substrate for SCP1 and that SCP1 might have the relationship with cell cycle regulation through enzymatic activity against CdcA3.
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Jadoon A, Cunningham P, McDermott LC. Arachidonic acid metabolism in the human placenta: identification of a putative lipoxygenase. Placenta 2014; 35:422-4. [PMID: 24767823 DOI: 10.1016/j.placenta.2014.03.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 03/26/2014] [Accepted: 03/30/2014] [Indexed: 12/17/2022]
Abstract
Arachidonic acid (ARA) metabolites maintain pregnancy and control parturition. We generated a network of 77 proteins involved in placental ARA metabolism to identify novel proteins in this pathway. We identified a long pathway within this network which showed that secretory and cytosolic phospholipase A2 proteins act in concert. The functions of all network proteins expressed in the placental decidua were determined by database searches. Thus ARA metabolism was linked to carbohydrate metabolism. One protein, transmembrane protein 62 (TMEM62), expressed in decidua was previously uncharacterized, and was identified as a putative lipoxygenase. TMEM62 may play a role in pregnancy and/or parturition.
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Affiliation(s)
- A Jadoon
- Diabetes and Nutritional Sciences Division, School of Medicine, Franklin Wilkins Building, Stamford Street, London SE1 9NH, UK
| | - P Cunningham
- Department of Biochemistry, School of Biomedical and Health Sciences, King's College London, Franklin Wilkins Building, Stamford Street, London SE1 9NH, UK
| | - L C McDermott
- Diabetes and Nutritional Sciences Division, School of Medicine, Franklin Wilkins Building, Stamford Street, London SE1 9NH, UK.
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Buzdin AA, Zhavoronkov AA, Korzinkin MB, Venkova LS, Zenin AA, Smirnov PY, Borisov NM. Oncofinder, a new method for the analysis of intracellular signaling pathway activation using transcriptomic data. Front Genet 2014; 5:55. [PMID: 24723936 PMCID: PMC3971199 DOI: 10.3389/fgene.2014.00055] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Accepted: 03/03/2014] [Indexed: 11/26/2022] Open
Abstract
We propose a new biomathematical method, OncoFinder, for both quantitative and qualitative analysis of the intracellular signaling pathway activation (SPA). This method is universal and may be used for the analysis of any physiological, stress, malignancy and other perturbed conditions at the molecular level. In contrast to the other existing techniques for aggregation and generalization of the gene expression data for individual samples, we suggest to distinguish the positive/activator and negative/repressor role of every gene product in each pathway. We show that the relative importance of each gene product in a pathway can be assessed using kinetic models for “low-level” protein interactions. Although the importance factors for the pathway members cannot be so far established for most of the signaling pathways due to the lack of the required experimental data, we showed that ignoring these factors can be sometimes acceptable and that the simplified formula for SPA evaluation may be applied for many cases. We hope that due to its universal applicability, the method OncoFinder will be widely used by the researcher community.
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Affiliation(s)
- Anton A Buzdin
- Pathway Pharmaceuticals, Limited Wan Chai, Hong Kong, Hong Kong ; D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology Moscow, Russia ; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Moscow, Russia ; Biological and Medical Physics, Moscow Institute of Physics and Technology Dolgoprudny, Russia
| | - Alex A Zhavoronkov
- Pathway Pharmaceuticals, Limited Wan Chai, Hong Kong, Hong Kong ; D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology Moscow, Russia ; Biological and Medical Physics, Moscow Institute of Physics and Technology Dolgoprudny, Russia
| | - Mikhail B Korzinkin
- Pathway Pharmaceuticals, Limited Wan Chai, Hong Kong, Hong Kong ; Burnasyan Federal Medical Biophysical Center Moscow, Russia
| | | | | | | | - Nikolay M Borisov
- Pathway Pharmaceuticals, Limited Wan Chai, Hong Kong, Hong Kong ; Biological and Medical Physics, Moscow Institute of Physics and Technology Dolgoprudny, Russia ; Burnasyan Federal Medical Biophysical Center Moscow, Russia
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Salazar GA, Meintjes A, Mulder N. PPI layouts: BioJS components for the display of Protein-Protein Interactions. F1000Res 2014; 3:50. [PMID: 25075288 PMCID: PMC4103490 DOI: 10.12688/f1000research.3-50.v1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/03/2014] [Indexed: 01/17/2023] Open
Abstract
SUMMARY We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. AVAILABILITY http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753.
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Affiliation(s)
- Gustavo A Salazar
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
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Rid R, Strasser W, Siegl D, Frech C, Kommenda M, Kern T, Hintner H, Bauer JW, Önder K. PRIMOS: an integrated database of reassessed protein-protein interactions providing web-based access to in silico validation of experimentally derived data. Assay Drug Dev Technol 2014; 11:333-46. [PMID: 23772554 DOI: 10.1089/adt.2013.506] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Steady improvements in proteomics present a bioinformatic challenge to retrieve, store, and process the accumulating and often redundant amount of information. In particular, a large-scale comparison and analysis of protein-protein interaction (PPI) data requires tools for data interpretation as well as validation. At this juncture, the Protein Interaction and Molecule Search (PRIMOS) platform represents a novel web portal that unifies six primary PPI databases (BIND, Biomolecular Interaction Network Database; DIP, Database of Interacting Proteins; HPRD, Human Protein Reference Database; IntAct; MINT, Molecular Interaction Database; and MIPS, Munich Information Center for Protein Sequences) into a single consistent repository, which currently includes more than 196,700 redundancy-removed PPIs. PRIMOS supports three advanced search strategies centering on disease-relevant PPIs, on inter- and intra-organismal crosstalk relations (e.g., pathogen-host interactions), and on highly connected protein nodes analysis ("hub" identification). The main novelties distinguishing PRIMOS from other secondary PPI databases are the reassessment of known PPIs, and the capacity to validate personal experimental data by our peer-reviewed, homology-based validation. This article focuses on definite PRIMOS use cases (presentation of embedded biological concepts, example applications) to demonstrate its broad functionality and practical value. PRIMOS is publicly available at http://primos.fh-hagenberg.at.
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Affiliation(s)
- Raphaela Rid
- Division of Molecular Dermatology, Department of Dermatology, Paracelsus Medical University Salzburg, Salzburg, Austria
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Ecological genomics of host behavior manipulation by parasites. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 781:169-90. [PMID: 24277300 DOI: 10.1007/978-94-007-7347-9_9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Among the vast array of niche exploitation strategies exhibited by millions of different species on Earth, parasitic lifestyles are characterized by extremely successful evolutionary outcomes. Some parasites even seem to have the ability to 'control' their host's behavior to fulfill their own vital needs. Research efforts in the past decades have focused on surveying the phylogenetic diversity and ecological nature of these host-parasite interactions, and trying to understand their evolutionary significance. However, to understand the proximal and ultimate causes of these behavioral alterations triggered by parasitic infections, the underlying molecular mechanisms governing them must be uncovered. Studies using ecological genomics approaches have identified key candidate molecules involved in host-parasite molecular cross-talk, but also molecules not expected to alter behavior. These studies have shown the importance of following up with functional analyses, using a comparative approach and including a time-series analysis. High-throughput methods surveying different levels of biological information, such as the transcriptome and the epigenome, suggest that specific biologically-relevant processes are affected by infection, that sex-specific effects at the level of behavior are recapitulated at the level of transcription, and that epigenetic control represents a key factor in managing life cycle stages of the parasite through temporal regulation of gene expression. Post-translational processes, such as protein-protein interactions (interactome) and post translational modifications (e.g. protein phosphorylation, phosphorylome), and processes modifying gene expression and translation, such as interactions with microRNAs (microRNAome), are examples of promising avenues to explore to obtain crucial insights into the proximal and ultimate causes of these fascinating and complex inter-specific interactions.
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Randhawa V, Sharma P, Bhushan S, Bagler G. Identification of key nodes of type 2 diabetes mellitus protein interactome and study of their interactions with phloridzin. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2013; 17:302-17. [PMID: 23692363 DOI: 10.1089/omi.2012.0115] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Network biology-inspired approaches could be used effectively in probing regulatory processes by which small molecules intervene with disease mechanisms. The present study aims at identification of key targets of type 2 diabetes mellitus (T2DM) by network analysis of the underlying protein interactome, and probing for mechanisms by which phloridzin could be critical at altering the disease phenotype. Towards this goal, we constructed a protein-protein interaction network associated with T2DM, starting from candidate genes and systems-level interactions data available. The relevance of the network constructed was verified with the help of gene ontology, node deletion, and biological essentiality studies. Using a network analysis method, MAPK1, EP300, and SMAD2 were identified as the most central proteins of potential therapeutic value. Phloridzin, a known antidiabetic agent, potentially interacts with proteins central to T2DM mechanisms. The structural understanding of interaction of phloridzin with these proteins of relevance to T2DM could provide better insight into its regulatory mechanisms and help in developing better therapeutic agents. The molecular docking results suggest that phloridzin is potentially involved in making critical interactions with MAPK1. These results could further be validated by experimental studies and could be used to design therapeutic agents for T2DM intervention.
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Affiliation(s)
- Vinay Randhawa
- Biotechnology Division, Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research (CSIR-IHBT), Palampur, India
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Jayaswal V, Schramm SJ, Mann GJ, Wilkins MR, Yang YH. VAN: an R package for identifying biologically perturbed networks via differential variability analysis. BMC Res Notes 2013; 6:430. [PMID: 24156242 PMCID: PMC4015612 DOI: 10.1186/1756-0500-6-430] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 10/18/2013] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for network-level analyses. Consequently, bioinformatics approaches that enable a joint analysis of high-throughput transcriptomics datasets and large-scale molecular interaction networks for identifying perturbed networks are gaining popularity. Typically, these approaches require the sequential application of multiple bioinformatics techniques - ID mapping, network analysis, and network visualization. Here, we present the Variability Analysis in Networks (VAN) software package: a collection of R functions to streamline this bioinformatics analysis. FINDINGS VAN determines whether there are network-level perturbations across biological states of interest. It first identifies hubs (densely connected proteins/microRNAs) in a network and then uses them to extract network modules (comprising of a hub and all its interaction partners). The function identifySignificantHubs identifies dysregulated modules (i.e. modules with changes in expression correlation between a hub and its interaction partners) using a single expression and network dataset. The function summarizeHubData identifies dysregulated modules based on a meta-analysis of multiple expression and/or network datasets. VAN also converts protein identifiers present in a MITAB-formatted interaction network to gene identifiers (UniProt identifier to Entrez identifier or gene symbol using the function generatePpiMap) and generates microRNA-gene interaction networks using TargetScan and Microcosm databases (generateMicroRnaMap). The function obtainCancerInfo is used to identify hubs (corresponding to significantly perturbed modules) that are already causally associated with cancer(s) in the Cancer Gene Census database. Additionally, VAN supports the visualization of changes to network modules in R and Cytoscape (visualizeNetwork and obtainPairSubset, respectively). We demonstrate the utility of VAN using a gene expression data from metastatic melanoma and a protein-protein interaction network from the Human Protein Reference Database. CONCLUSIONS Our package provides a comprehensive and user-friendly platform for the integrative analysis of -omics data to identify disease-associated network modules. This bioinformatics approach, which is essentially focused on the question of explaining phenotype with a 'network type' and in particular, how regulation is changing among different states of interest, is relevant to many questions including those related to network perturbations across developmental timelines.
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Affiliation(s)
- Vivek Jayaswal
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
| | - Sarah-Jane Schramm
- Westmead Millennium Institute for Medical Research, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Melanoma Institute Australia, Sydney, NSW, Australia
| | - Graham J Mann
- Westmead Millennium Institute for Medical Research, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Melanoma Institute Australia, Sydney, NSW, Australia
| | - Marc R Wilkins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- Systems Biology Initiative, University of New South Wales, Sydney, NSW, Australia
| | - Yee Hwa Yang
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Melanoma Institute Australia, Sydney, NSW, Australia
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Shi H, Xu J, Zhang G, Xu L, Li C, Wang L, Zhao Z, Jiang W, Guo Z, Li X. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC SYSTEMS BIOLOGY 2013; 7:101. [PMID: 24103777 PMCID: PMC4124764 DOI: 10.1186/1752-0509-7-101] [Citation(s) in RCA: 185] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Accepted: 10/03/2013] [Indexed: 12/19/2022]
Abstract
Background MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed. Results We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms. Conclusions In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.
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Affiliation(s)
- Hongbo Shi
- College of Bioinformatics Science and Technology and State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Harbin Medical University, Harbin, Heilongjiang 150081, PR China.
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Klapa MI, Tsafou K, Theodoridis E, Tsakalidis A, Moschonas NK. Reconstruction of the experimentally supported human protein interactome: what can we learn? BMC SYSTEMS BIOLOGY 2013; 7:96. [PMID: 24088582 PMCID: PMC4015887 DOI: 10.1186/1752-0509-7-96] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Accepted: 09/25/2013] [Indexed: 02/02/2023]
Abstract
BACKGROUND Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance. RESULTS First, we defined the UniProtKB manually reviewed human "complete" proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors. CONCLUSIONS Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human "complete" proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms.
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Affiliation(s)
- Maria I Klapa
- Department of General Biology, School of Medicine, University of Patras, Rio, Patras, Greece.
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Identification of biomarkers for hepatocellular carcinoma using network-based bioinformatics methods. Eur J Med Res 2013; 18:35. [PMID: 24083576 PMCID: PMC4016278 DOI: 10.1186/2047-783x-18-35] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Accepted: 08/30/2013] [Indexed: 01/06/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide. Despite several efforts to elucidate molecular mechanisms involved in this cancer, they are still not fully understood. Methods To acquire further insights into the molecular mechanisms of HCC, and to identify biomarkers for early diagnosis of HCC, we downloaded the gene expression profile on HCC with non-cancerous liver controls from the Gene Expression Omnibus (GEO) and analyzed these data using a combined bioinformatics approach. Results The dysregulated pathways and protein-protein interaction (PPI) network, including hub nodes that distinguished HCCs from non-cancerous liver controls, were identified. In total, 29 phenotype-related differentially expressed genes were included in the PPI network. Hierarchical clustering showed that the gene expression profile of these 29 genes was able to differentiate HCC samples from non-cancerous liver samples. Among these genes, CDC2 (Cell division control protein 2 homolog), MMP2 (matrix metalloproteinase-2) and DCN (Decorin were the hub nodes in the PPI network. Conclusions This study provides a portfolio of targets useful for future investigation. However, experimental studies should be conducted to verify our findings.
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Blumert C, Kalkhof S, Brocke-Heidrich K, Kohajda T, von Bergen M, Horn F. Analysis of the STAT3 interactome using in-situ biotinylation and SILAC. J Proteomics 2013; 94:370-86. [PMID: 24013128 DOI: 10.1016/j.jprot.2013.08.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Revised: 08/01/2013] [Accepted: 08/26/2013] [Indexed: 12/28/2022]
Abstract
UNLABELLED Signal transducer and activator of transcription 3 (STAT3) is activated by a variety of cytokines and growth factors. To generate a comprehensive data set of proteins interacting specifically with STAT3, we applied stable isotope labeling with amino acids in cell culture (SILAC). For high-affinity pull-down using streptavidin, we fused STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag), which did not affect STAT3 functions. By this approach, 3642 coprecipitated proteins were detected in human embryonic kidney-293 cells. Filtering using statistical and functional criteria finally extracted 136 proteins as putative interaction partners of STAT3. Both, a physical interaction network analysis and the enrichment of known and predicted interaction partners suggested that our filtering criteria successfully enriched true STAT3 interactors. Our approach identified numerous novel interactors, including ones previously predicted to associate with STAT3. By reciprocal coprecipitation, we were able to verify the physical association between STAT3 and selected interactors, including the novel interaction with TOX4, a member of the TOX high mobility group box family. Applying the same method, we next investigated the activation-dependency of the STAT3 interactome. Again, we identified both known and novel interactions. Thus, our approach allows to study protein-protein interaction effectively and comprehensively. BIOLOGICAL SIGNIFICANCE The location, activity, function, degradation, and synthesis of proteins are significantly regulated by interactions of proteins with other proteins, biopolymers and small molecules. Thus, the comprehensive characterization of interactions of proteins in a given proteome is the next milestone on the path to understanding the biochemistry of the cell. In order to generate a comprehensive interactome dataset of proteins specifically interacting with a selected bait protein, we fused our bait protein STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag). This bio-tag allows an affinity pull-down using streptavidin but affected neither the activation of STAT3 by tyrosine phosphorylation nor its transactivating potential. We combined SILAC for accurate relative protein quantification, subcellular fractionation to increase the coverage of interacting proteins, high-affinity pull-down and a stringent filtering method to successfully analyze the interactome of STAT3. With our approach we confirmed several already known and identified numerous novel STAT3 interactors. The approach applied provides a rapid and effective method, which is broadly applicable for studying protein-protein interactions and their dependency on post-translational modifications.
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Affiliation(s)
- Conny Blumert
- Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, 04103 Leipzig, Germany; Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, 04103 Leipzig, Germany
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