1
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Cardner M, Meyer-Schaller N, Christofori G, Beerenwinkel N. Inferring signalling dynamics by integrating interventional with observational data. Bioinformatics 2020; 35:i577-i585. [PMID: 31510686 PMCID: PMC6612850 DOI: 10.1093/bioinformatics/btz325] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Motivation In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial–mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT. Results In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance. Availability and implementation R package at https://github.com/cbg-ethz/timeseriesNEM. The RNA sequencing data and microscopy images can be explored through a Shiny app at https://emt.bsse.ethz.ch. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mathias Cardner
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | | | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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2
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Sverchkov Y, Ho YH, Gasch A, Craven M. Context-Specific Nested Effects Models. J Comput Biol 2020; 27:403-417. [PMID: 32053004 DOI: 10.1089/cmb.2019.0459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Advances in systems biology have made clear the importance of network models for capturing knowledge about complex relationships in gene regulation, metabolism, and cellular signaling. A common approach to uncovering biological networks involves performing perturbations on elements of the network, such as gene knockdown experiments, and measuring how the perturbation affects some reporter of the process under study. In this article, we develop context-specific nested effects models (CSNEMs), an approach to inferring such networks that generalizes nested effects models (NEMs). The main contribution of this work is that CSNEMs explicitly model the participation of a gene in multiple contexts, meaning that a gene can appear in multiple places in the network. Biologically, the representation of regulators in multiple contexts may indicate that these regulators have distinct roles in different cellular compartments or cell cycle phases. We present an evaluation of the method on simulated data as well as on data from a study of the sodium chloride stress response in Saccharomyces cerevisiae.
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Affiliation(s)
- Yuriy Sverchkov
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Yi-Hsuan Ho
- Department of Genetics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Audrey Gasch
- Department of Genetics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Mark Craven
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
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3
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Kiblawi S, Chasman D, Henning A, Park E, Poon H, Gould M, Ahlquist P, Craven M. Augmenting subnetwork inference with information extracted from the scientific literature. PLoS Comput Biol 2019; 15:e1006758. [PMID: 31246951 PMCID: PMC6619809 DOI: 10.1371/journal.pcbi.1006758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 07/10/2019] [Accepted: 01/04/2019] [Indexed: 11/20/2022] Open
Abstract
Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference There is a multitude of publicly available databases that contain information about biological entities (i.e., genes, proteins, and other small molecules) as well as information about how these entities interact together. However, these databases are often incomplete. There is a wealth of information present in the text of the scientific literature that is not yet available in these databases. Using tools that mine the scientific literature we are able to extract some of this potentially relevant information. In this work we show how we can use publicly available databases in conjunction with the information extracted from the scientific literature to infer the networks that are involved in specific biological processes, such as viral replication and cancer tumor growth.
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Affiliation(s)
- Sid Kiblawi
- Department of Computer Sciences, University of Wisconsin, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI, USA
| | - Amanda Henning
- Department of Oncology, University of Wisconsin, Madison, WI, USA
| | - Eunju Park
- Institute for Molecular Virology, University of Wisconsin, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | | | - Michael Gould
- Department of Oncology, University of Wisconsin, Madison, WI, USA
| | - Paul Ahlquist
- Institute for Molecular Virology, University of Wisconsin, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
- Howard Hughes Medical Institute, University of Wisconsin, Madison, WI, USA
| | - Mark Craven
- Department of Computer Sciences, University of Wisconsin, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
- * E-mail:
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4
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Pacini C, Koziol MJ. Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0073. [PMID: 29685977 PMCID: PMC5915717 DOI: 10.1098/rstb.2017.0073] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2017] [Indexed: 02/07/2023] Open
Abstract
It is widely known that epigenetic modifications are important in regulating transcription, but several have also been reported in alternative splicing. The regulation of pre-mRNA splicing is important to explain proteomic diversity and the misregulation of splicing has been implicated in many diseases. Here, we give a brief overview of the role of epigenetics in alternative splicing and disease. We then discuss the bioinformatics methods that can be used to model interactions between epigenetic marks and regulators of splicing. These models can be used to identify alternative splicing and epigenetic changes across different phenotypes. This article is part of a discussion meeting issue ‘Frontiers in epigenetic chemical biology’.
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Affiliation(s)
- Clare Pacini
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
| | - Magdalena J Koziol
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK .,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
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5
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DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity. Proc Natl Acad Sci U S A 2018; 115:E4294-E4303. [PMID: 29654148 PMCID: PMC5939057 DOI: 10.1073/pnas.1711365115] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Single-cell high-throughput technologies enable the ability to identify combination cancer therapies that account for intratumoral heterogeneity, a phenomenon that has been shown to influence the effectiveness of cancer treatment. We developed and applied an approach that identifies top-ranking drug combinations based on the single-cell perturbation response when an individual tumor sample is screened against a panel of single drugs. This approach optimizes drug combinations by choosing the minimum number of drugs that produce the maximal intracellular desired effects for an individual sample. An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.
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6
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Carlin DE, Paull EO, Graim K, Wong CK, Bivol A, Ryabinin P, Ellrott K, Sokolov A, Stuart JM. Prophetic Granger Causality to infer gene regulatory networks. PLoS One 2017; 12:e0170340. [PMID: 29211761 PMCID: PMC5718405 DOI: 10.1371/journal.pone.0170340] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 10/26/2017] [Indexed: 01/09/2023] Open
Abstract
We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.
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Affiliation(s)
- Daniel E. Carlin
- University of California San Diego, Department of Medicine, La Jolla, CA, United States of America
| | - Evan O. Paull
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
| | - Kiley Graim
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
| | - Christopher K. Wong
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
| | - Adrian Bivol
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
| | - Peter Ryabinin
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
| | - Kyle Ellrott
- Oregon Health Sciences University, Department of Biomedical Engineering, Portland, OR, United States of America
| | - Artem Sokolov
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
- * E-mail: (JMS); (AS)
| | - Joshua M. Stuart
- University of California Santa Cruz, Department of Biomolecular Engineering, Santa Cruz, CA, United States of America
- * E-mail: (JMS); (AS)
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7
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Pirkl M, Diekmann M, van der Wees M, Beerenwinkel N, Fröhlich H, Markowetz F. Inferring modulators of genetic interactions with epistatic nested effects models. PLoS Comput Biol 2017; 13:e1005496. [PMID: 28406896 PMCID: PMC5407847 DOI: 10.1371/journal.pcbi.1005496] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 04/27/2017] [Accepted: 04/03/2017] [Indexed: 12/27/2022] Open
Abstract
Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gene pair can be explained by the action of a third gene that modulates the interaction. We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that describe interactions between regulators on downstream genes and proteins. We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. In an application to data from deletion mutants of kinases and phosphatases in S. cerevisiae we show that epistatic NEMs can point to modulators of genetic interactions. Our approach is implemented in the R-package 'epiNEM' available from https://github.com/cbg-ethz/epiNEM and https://bioconductor.org/packages/epiNEM/.
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Affiliation(s)
- Martin Pirkl
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Madeline Diekmann
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Niko Beerenwinkel
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Holger Fröhlich
- Bonn-Aachen International Center for IT (B-IT), University of Bonn, Bonn, Germany
- UCB Biosciences GmbH, Monheim, Germany
| | - Florian Markowetz
- University of Cambridge, Cancer Research UK Cambridge Institute, Cambridge, United Kingdom
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8
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Pirkl M, Hand E, Kube D, Spang R. Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models. Bioinformatics 2016; 32:893-900. [PMID: 26581413 PMCID: PMC5939970 DOI: 10.1093/bioinformatics/btv680] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 10/19/2015] [Accepted: 11/11/2015] [Indexed: 11/21/2022] Open
Abstract
MOTIVATION Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. RESULTS We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. AVAILABILITY AND IMPLEMENTATION R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. CONTACT martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Martin Pirkl
- Statistical Bioinformatics Department, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg and
| | - Elisabeth Hand
- Department of Haematology and Oncology, University Medical Centre of the Georg-August University of Göttingen, 37073 Göttingen
| | - Dieter Kube
- Department of Haematology and Oncology, University Medical Centre of the Georg-August University of Göttingen, 37073 Göttingen
| | - Rainer Spang
- Statistical Bioinformatics Department, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg and
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9
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Besson P, Driffort V, Bon É, Gradek F, Chevalier S, Roger S. How do voltage-gated sodium channels enhance migration and invasiveness in cancer cells? BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2015; 1848:2493-501. [PMID: 25922224 DOI: 10.1016/j.bbamem.2015.04.013] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 04/13/2015] [Accepted: 04/20/2015] [Indexed: 11/16/2022]
Abstract
Voltage-gated sodium channels are abnormally expressed in tumors, often as neonatal isoforms, while they are not expressed, or only at a low level, in the matching normal tissue. The level of their expression and their activity is related to the aggressiveness of the disease and to the formation of metastases. A vast knowledge on the regulation of their expression and functioning has been accumulated in normal excitable cells. This helped understand their regulation in cancer cells. However, how voltage-gated sodium channels impose a pro-metastatic behavior to cancer cells is much less documented. This aspect will be addressed in the review. This article is part of a Special Issue entitled: Membrane channels and transporters in cancers.
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Affiliation(s)
- Pierre Besson
- Inserm UMR1069 "Nutrition, Croissance et Cancer", Faculté de Médecine, Université François Rabelais de Tours, France; Faculté de Sciences Pharmaceutiques, Université François Rabelais de Tours, France.
| | - Virginie Driffort
- Inserm UMR1069 "Nutrition, Croissance et Cancer", Faculté de Médecine, Université François Rabelais de Tours, France
| | - Émeline Bon
- Inserm UMR1069 "Nutrition, Croissance et Cancer", Faculté de Médecine, Université François Rabelais de Tours, France
| | - Frédéric Gradek
- Inserm UMR1069 "Nutrition, Croissance et Cancer", Faculté de Médecine, Université François Rabelais de Tours, France
| | - Stéphan Chevalier
- Inserm UMR1069 "Nutrition, Croissance et Cancer", Faculté de Médecine, Université François Rabelais de Tours, France; Faculté de Sciences Pharmaceutiques, Université François Rabelais de Tours, France
| | - Sébastien Roger
- Inserm UMR1069 "Nutrition, Croissance et Cancer", Faculté de Médecine, Université François Rabelais de Tours, France; Faculté des Sciences et Techniques, Université François Rabelais de Tours, France
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10
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Chasman D, Ho YH, Berry DB, Nemec CM, MacGilvray ME, Hose J, Merrill AE, Lee MV, Will JL, Coon JJ, Ansari AZ, Craven M, Gasch AP. Pathway connectivity and signaling coordination in the yeast stress-activated signaling network. Mol Syst Biol 2014; 10:759. [PMID: 25411400 PMCID: PMC4299600 DOI: 10.15252/msb.20145120] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet
knowledge of the complete stress-activated regulatory network as well as design principles for
signal integration remains incomplete. We developed an experimental and computational approach to
integrate available protein interaction data with gene fitness contributions, mutant transcriptome
profiles, and phospho-proteome changes in cells responding to salt stress, to infer the
salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions
by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and
pointing to previously unknown ‘hubs’ of signal integration. We exploited these
predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of
RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related
transcripts. We find that the orthologous human network is enriched for cancer-causing genes,
underscoring the importance of the subnetwork's predictions in understanding stress
biology.
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Affiliation(s)
- Deborah Chasman
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Yi-Hsuan Ho
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - David B Berry
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Corey M Nemec
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - James Hose
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna E Merrill
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - M Violet Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jessica L Will
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Department of Biological Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Aseem Z Ansari
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark Craven
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Audrey P Gasch
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
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11
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Sadeh MJ, Moffa G, Spang R. Considering unknown unknowns: reconstruction of nonconfoundable causal relations in biological networks. J Comput Biol 2014; 20:920-32. [PMID: 24195708 DOI: 10.1089/cmb.2013.0119] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Our current understanding of cellular networks is rather incomplete. We over look important but so far unknown genes and mechanisms in the pathways. Moreover, we often only have a partial account of the molecular interactions and modifications of the known players. When analyzing the cell, we look through narrow windows leaving potentially important events in blind spots. Network reconstruction is naturally confined to what we have observed. Little is known on how the incompleteness of our observations confounds our interpretation of the available data. Here we ask which features of a network can be confounded by incomplete observations and which cannot. In the context of nested effects models, we show that in the presence of missing observations or hidden factors a reliable reconstruction of the full network is not feasible. Nevertheless, we can show that certain characteristics of signaling networks like the existence of cross-talk between certain branches of the network can be inferred in a nonconfoundable way. We derive a test for inferring such nonconfoundable characteristics of signaling networks. Next, we introduce a new data structure to represent partially reconstructed signaling networks. Finally, we evaluate our method both on simulated data and in the context of a study on early stem cell differentiation in mice.
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Affiliation(s)
- Mohammad J Sadeh
- Institute of Functional Genomics, Computational Diagnostics Group, University of Regensburg , Regensburg, Germany
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12
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Chasman D, Gancarz B, Hao L, Ferris M, Ahlquist P, Craven M. Inferring host gene subnetworks involved in viral replication. PLoS Comput Biol 2014; 10:e1003626. [PMID: 24874113 PMCID: PMC4038467 DOI: 10.1371/journal.pcbi.1003626] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 02/06/2014] [Indexed: 12/16/2022] Open
Abstract
Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at http://www.biostat.wisc.edu/~craven/chasman_host_virus/.
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Affiliation(s)
- Deborah Chasman
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Brandi Gancarz
- Luminex Corporation, Madison, Wisconsin, United States of America
- Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Linhui Hao
- Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Howard Hughes Medical Institute, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Michael Ferris
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Paul Ahlquist
- Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Howard Hughes Medical Institute, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Morgridge Institute for Research, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Mark Craven
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
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13
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Wang X, Yuan K, Hellmayr C, Liu W, Markowetz F. Reconstructing evolving signalling networks by hidden Markov nested effects models. Ann Appl Stat 2014. [DOI: 10.1214/13-aoas696] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Gibbs DL, Gralinski L, Baric RS, McWeeney SK. Multi-omic network signatures of disease. Front Genet 2014; 4:309. [PMID: 24432028 PMCID: PMC3882664 DOI: 10.3389/fgene.2013.00309] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Accepted: 12/19/2013] [Indexed: 12/12/2022] Open
Abstract
To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.
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Affiliation(s)
- David L Gibbs
- McWeeney Lab, Division of Bioinformatics and Computational Biology, Oregon Health & Science University Portland, OR, USA
| | - Lisa Gralinski
- Baric Lab, Department of Microbiology and Immunology, University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Ralph S Baric
- Baric Lab, Department of Microbiology and Immunology, University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Shannon K McWeeney
- McWeeney Lab, Division of Bioinformatics and Computational Biology, Oregon Health & Science University Portland, OR, USA ; McWeeney Lab, OHSU Knight Cancer Institute, Oregon Health & Science University Portland, OR, USA
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15
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Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, Jing X, Kaushik P, He Q, Mills G, Solit DB, Pratilas CA, Weigt M, Braunstein A, Pagnani A, Zecchina R, Sander C. Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput Biol 2013; 9:e1003290. [PMID: 24367245 PMCID: PMC3868523 DOI: 10.1371/journal.pcbi.1003290] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Accepted: 08/26/2013] [Indexed: 12/16/2022] Open
Abstract
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of the prohibitively large number of possible sets of interactions that may or may not be consistent with observations. Our new method overcomes this difficulty by adapting a method from statistical physics, called Belief Propagation, which first calculates probabilistically the most likely interactions in the vast space of all possible solutions, then derives a set of individual, highly probable solutions in the form of executable models. In this paper, we test this method on artificial data and then apply it to model signaling pathways in a BRAF-mutant melanoma cancer cell line based on a large set of rich output measurements from a systematic set of perturbation experiments using drug combinations. Our results are in agreement with established biological knowledge, predict novel interactions, and predict efficacious drug targets that are specific to the experimental cell line and potentially to related tumors. The method has the potential, with sufficient systematic perturbation data, to model, de novo and quantitatively, the effects of hundreds of proteins on cellular responses, on a scale that is currently unreachable in diverse areas of cell biology. In a disease context, the method is applicable to the computational design of novel combination drug treatments.
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Affiliation(s)
- Evan J. Molinelli
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Anil Korkut
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Weiqing Wang
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin L. Miller
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Nicholas P. Gauthier
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Xiaohong Jing
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Poorvi Kaushik
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Qin He
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Gordon Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - David B. Solit
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Christine A. Pratilas
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Department of Pediatrics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin Weigt
- Laboratoire de Génomique des Microorganismes, Université Pierre et Marie Curie, Paris, France
| | - Alfredo Braunstein
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Andrea Pagnani
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Riccardo Zecchina
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Chris Sander
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail:
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16
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Failmezger H, Praveen P, Tresch A, Fröhlich H. Learning gene network structure from time laps cell imaging in RNAi Knock downs. Bioinformatics 2013; 29:1534-40. [PMID: 23595660 DOI: 10.1093/bioinformatics/btt179] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION As RNA interference is becoming a standard method for targeted gene perturbation, computational approaches to reverse engineer parts of biological networks based on measurable effects of RNAi become increasingly relevant. The vast majority of these methods use gene expression data, but little attention has been paid so far to other data types. RESULTS Here we present a method, which can infer gene networks from high-dimensional phenotypic perturbation effects on single cells recorded by time-lapse microscopy. We use data from the Mitocheck project to extract multiple shape, intensity and texture features at each frame. Features from different cells and movies are then aligned along the cell cycle time. Subsequently we use Dynamic Nested Effects Models (dynoNEMs) to estimate parts of the network structure between perturbed genes via a Markov Chain Monte Carlo approach. Our simulation results indicate a high reconstruction quality of this method. A reconstruction based on 22 gene knock downs yielded a network, where all edges could be explained via the biological literature. AVAILABILITY The implementation of dynoNEMs is part of the Bioconductor R-package nem.
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Affiliation(s)
- Henrik Failmezger
- Computational Biology and Regulatory Networks, Max-Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, 50829 Cologne, Germany
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17
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Shih YK, Parthasarathy S. A single source k-shortest paths algorithm to infer regulatory pathways in a gene network. ACTA ACUST UNITED AC 2013; 28:i49-58. [PMID: 22689778 PMCID: PMC3371844 DOI: 10.1093/bioinformatics/bts212] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motivation: Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene. Results: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines. Availability: All data and codes are freely available upon request. Contact:srini@cse.ohio-state.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-Keng Shih
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA
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18
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Suen S, Lu HHS, Yeang CH. Evolution of domain architectures and catalytic functions of enzymes in metabolic systems. Genome Biol Evol 2012; 4:976-93. [PMID: 22936075 PMCID: PMC3468959 DOI: 10.1093/gbe/evs072] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Domain architectures and catalytic functions of enzymes constitute the centerpieces of a metabolic network. These types of information are formulated as a two-layered network consisting of domains, proteins, and reactions-a domain-protein-reaction (DPR) network. We propose an algorithm to reconstruct the evolutionary history of DPR networks across multiple species and categorize the mechanisms of metabolic systems evolution in terms of network changes. The reconstructed history reveals distinct patterns of evolutionary mechanisms between prokaryotic and eukaryotic networks. Although the evolutionary mechanisms in early ancestors of prokaryotes and eukaryotes are quite similar, more novel and duplicated domain compositions with identical catalytic functions arise along the eukaryotic lineage. In contrast, prokaryotic enzymes become more versatile by catalyzing multiple reactions with similar chemical operations. Moreover, different metabolic pathways are enriched with distinct network evolution mechanisms. For instance, although the pathways of steroid biosynthesis, protein kinases, and glycosaminoglycan biosynthesis all constitute prominent features of animal-specific physiology, their evolution of domain architectures and catalytic functions follows distinct patterns. Steroid biosynthesis is enriched with reaction creations but retains a relatively conserved repertoire of domain compositions and proteins. Protein kinases retain conserved reactions but possess many novel domains and proteins. In contrast, glycosaminoglycan biosynthesis has high rates of reaction/protein creations and domain recruitments. Finally, we elicit and validate two general principles underlying the evolution of DPR networks: 1) duplicated enzyme proteins possess similar catalytic functions and 2) the majority of novel domains arise to catalyze novel reactions. These results shed new lights on the evolution of metabolic systems.
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Affiliation(s)
- Summit Suen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
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19
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Niederberger T, Etzold S, Lidschreiber M, Maier KC, Martin DE, Fröhlich H, Cramer P, Tresch A. MC EMiNEM maps the interaction landscape of the Mediator. PLoS Comput Biol 2012; 8:e1002568. [PMID: 22737066 PMCID: PMC3380870 DOI: 10.1371/journal.pcbi.1002568] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Accepted: 05/04/2012] [Indexed: 11/18/2022] Open
Abstract
The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors.
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Affiliation(s)
- Theresa Niederberger
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Stefanie Etzold
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Michael Lidschreiber
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Kerstin C. Maier
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Dietmar E. Martin
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Holger Fröhlich
- Bonn-Aachen International Center for IT (B-IT) Algorithmic Bioinformatics, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, Germany
| | - Patrick Cramer
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Achim Tresch
- Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
- Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Institute for Genetics, University of Cologne, Cologne, Germany
- * E-mail:
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20
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Fröhlich H, Praveen P, Tresch A. Fast and efficient dynamic nested effects models. Bioinformatics 2010; 27:238-44. [DOI: 10.1093/bioinformatics/btq631] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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21
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House CD, Vaske CJ, Schwartz AM, Obias V, Frank B, Luu T, Sarvazyan N, Irby R, Strausberg RL, Hales TG, Stuart JM, Lee NH. Voltage-gated Na+ channel SCN5A is a key regulator of a gene transcriptional network that controls colon cancer invasion. Cancer Res 2010; 70:6957-67. [PMID: 20651255 DOI: 10.1158/0008-5472.can-10-1169] [Citation(s) in RCA: 200] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Voltage-gated Na(+) channels (VGSC) have been implicated in the metastatic potential of human breast, prostate, and lung cancer cells. Specifically, the SCN5A gene encoding the VGSC isotype Na(v)1.5 has been defined as a key driver of human cancer cell invasion. In this study, we examined the expression and function of VGSCs in a panel of colon cancer cell lines by electrophysiologic recordings. Na(+) channel activity and invasive potential were inhibited pharmacologically by tetrodotoxin or genetically by small interfering RNAs (siRNA) specifically targeting SCN5A. Clinical relevance was established by immunohistochemistry of patient biopsies, with strong Na(v)1.5 protein staining found in colon cancer specimens but little to no staining in matched-paired normal colon tissues. We explored the mechanism of VGSC-mediated invasive potential on the basis of reported links between VGSC activity and gene expression in excitable cells. Probabilistic modeling of loss-of-function screens and microarray data established an unequivocal role of VGSC SCN5A as a high level regulator of a colon cancer invasion network, involving genes that encompass Wnt signaling, cell migration, ectoderm development, response to biotic stimulus, steroid metabolic process, and cell cycle control. siRNA-mediated knockdown of predicted downstream network components caused a loss of invasive behavior, demonstrating network connectivity and its function in driving colon cancer invasion.
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Affiliation(s)
- Carrie D House
- Department of Pharmacology, The George Washington University Medical Center, Washington, DC, USA
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22
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Carter GW, Rush CG, Uygun F, Sakhanenko NA, Galas DJ, Galitski T. A systems-biology approach to modular genetic complexity. CHAOS (WOODBURY, N.Y.) 2010; 20:026102. [PMID: 20590331 PMCID: PMC2909309 DOI: 10.1063/1.3455183] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2010] [Accepted: 05/26/2010] [Indexed: 05/29/2023]
Abstract
Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising multiple clearly delineated linear pathways. We seek to clarify the situation by discerning the difference between genetic modules and classical pathways. We review a method to optimize the discovery of biologically meaningful genetic modules based on a previously described context-dependent information measure to obtain maximally informative networks. We compare the results of this method with the established measures of network clustering and find that it balances global and local clustering information in networks. We further discuss the consequences for genetic interaction networks and propose a framework for the analysis of genetic modularity.
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Affiliation(s)
- Gregory W Carter
- Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington 98103, USA
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23
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Wu G, Feng X, Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol 2010; 11:R53. [PMID: 20482850 PMCID: PMC2898064 DOI: 10.1186/gb-2010-11-5-r53] [Citation(s) in RCA: 474] [Impact Index Per Article: 33.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2010] [Revised: 03/16/2010] [Accepted: 05/19/2010] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. RESULTS We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. CONCLUSIONS We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases.
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Affiliation(s)
- Guanming Wu
- Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, ON M5G 0A3, Canada
| | - Xin Feng
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
- Stony Brook University, Stony Brook, NY 11794, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, ON M5G 0A3, Canada
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
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25
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Yip KY, Alexander RP, Yan KK, Gerstein M. Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data. PLoS One 2010; 5:e8121. [PMID: 20126643 PMCID: PMC2811182 DOI: 10.1371/journal.pone.0008121] [Citation(s) in RCA: 85] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2009] [Accepted: 10/13/2009] [Indexed: 11/23/2022] Open
Abstract
We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.
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Affiliation(s)
- Kevin Y. Yip
- Department of Computer Science, Yale University, New Haven, Connecticut, United States of America
| | - Roger P. Alexander
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Koon-Kiu Yan
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Mark Gerstein
- Department of Computer Science, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- * E-mail:
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26
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Fröhlich H, Sahin O, Arlt D, Bender C, Beissbarth T. Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions. BMC Bioinformatics 2009; 10:322. [PMID: 19814779 PMCID: PMC2770070 DOI: 10.1186/1471-2105-10-322] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2009] [Accepted: 10/08/2009] [Indexed: 11/10/2022] Open
Abstract
Background Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems. Results In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the ERBB signaling network in de novo trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi. Conclusion DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.
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Affiliation(s)
- Holger Fröhlich
- German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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