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Title: Unified feature association networks through integration of transcriptomic and proteomic data

Abstract

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks. We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network.more » Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm disease associated mechanisms of disease.« less

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [2];  [3]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [4];  [5]
+ Show Author Affiliations
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Biological Sciences Division
  2. Univ. of North Carolina, Chapel Hill, NC (United States). School of Medicine, Dept. of Microbiology and Immunology
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Signatures Science and Technology Division
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Biological Sciences Division; Oregon Health & Sciences Univ., Portland, OR (United States). Dept. of Molecular Microbiology and Immunology
  5. Univ. of Illinois at Urbana-Champaign, IL (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1577846
Report Number(s):
PNNL-SA-137420
Journal ID: ISSN 1553-7358
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 15; Journal Issue: 9; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

McClure, Ryan S., Wendler, Jason P., Adkins, Joshua N., Swanstrom, Jesica, Baric, Ralph, Kaiser, Brooke L. Deatherage, Oxford, Kristie L., Waters, Katrina M., McDermott, Jason E., and Jensen, Paul. Unified feature association networks through integration of transcriptomic and proteomic data. United States: N. p., 2019. Web. doi:10.1371/journal.pcbi.1007241.
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McClure, Ryan S., Wendler, Jason P., Adkins, Joshua N., Swanstrom, Jesica, Baric, Ralph, Kaiser, Brooke L. Deatherage, Oxford, Kristie L., Waters, Katrina M., McDermott, Jason E., & Jensen, Paul. Unified feature association networks through integration of transcriptomic and proteomic data. United States. https://doi.org/10.1371/journal.pcbi.1007241
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McClure, Ryan S., Wendler, Jason P., Adkins, Joshua N., Swanstrom, Jesica, Baric, Ralph, Kaiser, Brooke L. Deatherage, Oxford, Kristie L., Waters, Katrina M., McDermott, Jason E., and Jensen, Paul. Tue . "Unified feature association networks through integration of transcriptomic and proteomic data". United States. https://doi.org/10.1371/journal.pcbi.1007241. https://www.osti.gov/servlets/purl/1577846.
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@article{osti_1577846,
title = {Unified feature association networks through integration of transcriptomic and proteomic data},
author = {McClure, Ryan S. and Wendler, Jason P. and Adkins, Joshua N. and Swanstrom, Jesica and Baric, Ralph and Kaiser, Brooke L. Deatherage and Oxford, Kristie L. and Waters, Katrina M. and McDermott, Jason E. and Jensen, Paul},
abstractNote = {High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks. We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm disease associated mechanisms of disease.},
doi = {10.1371/journal.pcbi.1007241},
journal = {PLoS Computational Biology (Online)},
number = 9,
volume = 15,
place = {United States},
year = {2019},
month = {9}
}
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https://doi.org/10.1371/journal.pcbi.1007241
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Works referenced in this record:

Dynamics of Time-Lagged Gene-to-Metabolite Networks of Escherichia coli Elucidated by Integrative Omics Approach
journal, January 2011

  • Takahashi, Hiroki; Morioka, Ryoko; Ito, Ryosuke
  • OMICS: A Journal of Integrative Biology, Vol. 15, Issue 1-2
  • DOI: 10.1089/omi.2010.0074

Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis
journal, January 2012

  • McDermott, Jason E.; Diamond, Deborah L.; Corley, Courtney
  • BMC Systems Biology, Vol. 6, Issue 1
  • DOI: 10.1186/1752-0509-6-28

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function
journal, January 2008

Dual-species transcriptional profiling during systemic candidiasis reveals organ-specific host-pathogen interactions
journal, November 2016

  • Hebecker, Betty; Vlaic, Sebastian; Conrad, Theresia
  • Scientific Reports, Vol. 6, Issue 1
  • DOI: 10.1038/srep36055

Global Metabolomic Analysis of a Mammalian Host Infected with Bacillus anthracis
journal, October 2015

  • Nguyen, Chinh T. Q.; Shetty, Vivekananda; Maresso, Anthony W.
  • Infection and Immunity, Vol. 83, Issue 12
  • DOI: 10.1128/IAI.00947-15

KEGG: Kyoto Encyclopedia of Genes and Genomes
journal, January 2000

  • Kanehisa, Minoru; Goto, Susumu
  • Nucleic Acids Research, Vol. 28, Issue 1, p. 27-30
  • DOI: 10.1093/nar/28.1.27

The Landscape of Human Proteins Interacting with Viruses and Other Pathogens
journal, January 2008

Temporal Proteome and Lipidome Profiles Reveal Hepatitis C Virus-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics
journal, January 2010

Biological Cluster Evaluation for Gene Function Prediction
journal, June 2014

  • Klie, Sebastian; Nikoloski, Zoran; Selbig, Joachim
  • Journal of Computational Biology, Vol. 21, Issue 6
  • DOI: 10.1089/cmb.2009.0129

Combining affinity propagation clustering and mutual information network to investigate key genes in fibroid
journal, May 2017

  • Chen, Qian-Song; Wang, Dan; Liu, Bao-Lian
  • Experimental and Therapeutic Medicine, Vol. 14, Issue 1
  • DOI: 10.3892/etm.2017.4481

Quantitative Proteomics and Lipidomics Analysis of Endoplasmic Reticulum of Macrophage Infected with Mycobacterium tuberculosis
journal, January 2015

  • Saquib, Najmuddin Mohd; Jamwal, Shilpa; Midha, Mukul Kumar
  • International Journal of Proteomics, Vol. 2015
  • DOI: 10.1155/2015/270438

“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
journal, March 2012

Transcriptomic analysis of responses to cytopathic bovine viral diarrhea virus-1 (BVDV-1) infection in MDBK cells
journal, March 2016

A transcriptional miRNA-gene network associated with lung adenocarcinoma metastasis based on the TCGA database
journal, January 2016

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus
journal, September 2016

  • McDermott, Jason E.; Mitchell, Hugh D.; Gralinski, Lisa E.
  • BMC Systems Biology, Vol. 10, Issue 1
  • DOI: 10.1186/s12918-016-0336-6

Dengue virus life cycle: viral and host factors modulating infectivity
journal, April 2010

  • Rodenhuis-Zybert, Izabela A.; Wilschut, Jan; Smit, Jolanda M.
  • Cellular and Molecular Life Sciences, Vol. 67, Issue 16
  • DOI: 10.1007/s00018-010-0357-z

Gene Network and Proteomic Analyses of Cardiac Responses to Pathological and Physiological Stress
journal, December 2013

The Molecular Signatures Database Hallmark Gene Set Collection
journal, December 2015

Improved quality control processing of peptide-centric LC-MS proteomics data
journal, August 2011

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors
journal, November 2011

  • Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Jacobs, Jon M.
  • PROTEOMICS, Vol. 11, Issue 24
  • DOI: 10.1002/pmic.201100078

The Gonococcal Transcriptome during Infection of the Lower Genital Tract in Women
journal, August 2015

A multi-omic systems approach to elucidating Yersinia virulence mechanisms
journal, January 2013

  • Ansong, Charles; Schrimpe-Rutledge, Alexandra C.; Mitchell, Hugh D.
  • Mol. BioSyst., Vol. 9, Issue 1
  • DOI: 10.1039/C2MB25287B

Comparative lipidomics analysis of HIV-1 particles and their producer cell membrane in different cell lines: Lipidomics of HIV-1 particles and producer plasma membranes
journal, January 2013

  • Lorizate, Maier; Sachsenheimer, Timo; Glass, Bärbel
  • Cellular Microbiology, Vol. 15, Issue 2
  • DOI: 10.1111/cmi.12101

Evidence That Maternal Dengue Antibodies Are Important in the Development of Dengue Hemorrhagic Fever in Infants
journal, March 1988

  • Nimmanitya, Suchitra; Kliks, Srisakul C.; Burke, Donald S.
  • The American Journal of Tropical Medicine and Hygiene, Vol. 38, Issue 2
  • DOI: 10.4269/ajtmh.1988.38.411

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions
journal, January 2013

  • Kotze, Helen L.; Armitage, Emily G.; Sharkey, Kieran J.
  • BMC Systems Biology, Vol. 7, Issue 1
  • DOI: 10.1186/1752-0509-7-107

Novel insights into human respiratory syncytial virus-host factor interactions through integrated proteomics and transcriptomics analysis
journal, January 2016

Transcriptomic analysis of the dialogue between Pseudorabies virus and porcine epithelial cells during infection
journal, January 2008

  • Flori, Laurence; Rogel-Gaillard, Claire; Cochet, Marielle
  • BMC Genomics, Vol. 9, Issue 1
  • DOI: 10.1186/1471-2164-9-123

Microarray background correction: maximum likelihood estimation for the normal-exponential convolution
journal, December 2008

The landscape of viral proteomics and its potential to impact human health
journal, May 2016

Impact of Salmonella Infection on Host Hormone Metabolism Revealed by Metabolomics
journal, February 2011

  • Antunes, L. Caetano M.; Arena, Ellen T.; Menendez, Alfredo
  • Infection and Immunity, Vol. 79, Issue 4
  • DOI: 10.1128/IAI.01373-10

Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network
journal, August 2017

  • Chen, Xi; Wang, Qiao-Ling; Zhang, Meng-Hui
  • Experimental and Therapeutic Medicine, Vol. 14, Issue 4
  • DOI: 10.3892/etm.2017.4931

Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium
journal, February 2009

  • McDermott, Jason E.; Taylor, Ronald C.; Yoon, Hyunjin
  • Journal of Computational Biology, Vol. 16, Issue 2
  • DOI: 10.1089/cmb.2008.04TT

Separating the Drivers from the Driven: Integrative Network and Pathway Approaches Aid Identification of Disease Biomarkers from High-Throughput Data
journal, January 2010

  • McDermott, Jason E.; Costa, Michelle; Janszen, Derek
  • Disease Markers, Vol. 28, Issue 4
  • DOI: 10.1155/2010/708932

Wisdom of crowds for robust gene network inference
journal, July 2012

  • Marbach, Daniel; Costello, James C.; Küffner, Robert
  • Nature Methods, Vol. 9, Issue 8
  • DOI: 10.1038/nmeth.2016

A comprehensive collection of systems biology data characterizing the host response to viral infection
journal, October 2014

  • Aevermann, Brian D.; Pickett, Brett E.; Kumar, Sanjeev
  • Scientific Data, Vol. 1, Issue 1
  • DOI: 10.1038/sdata.2014.33

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer
journal, July 2016

Integration of omic networks in a developmental atlas of maize
journal, August 2016

Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella
journal, January 2011

  • Yoon, Hyunjin; Ansong, Charles; McDermott, Jason E.
  • BMC Systems Biology, Vol. 5, Issue 1
  • DOI: 10.1186/1752-0509-5-100

Integrated Transcriptomic and Proteomic Analysis of the Physiological Response of Escherichia coli O157:H7 Sakai to Steady-state Conditions of Cold and Water Activity Stress
journal, October 2011

  • Kocharunchitt, Chawalit; King, Thea; Gobius, Kari
  • Molecular & Cellular Proteomics, Vol. 11, Issue 1
  • DOI: 10.1074/mcp.M111.009019

The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics
journal, January 2007

Enabling network inference methods to handle missing data and outliers
journal, September 2015

  • Folch-Fortuny, Abel; Villaverde, Alejandro F.; Ferrer, Alberto
  • BMC Bioinformatics, Vol. 16, Issue 1
  • DOI: 10.1186/s12859-015-0717-7

Lipidomics reveals control of Mycobacterium tuberculosis virulence lipids via metabolic coupling
journal, March 2007

  • Jain, M.; Petzold, C. J.; Schelle, M. W.
  • Proceedings of the National Academy of Sciences, Vol. 104, Issue 12
  • DOI: 10.1073/pnas.0610634104

Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data
journal, March 2012

Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data
journal, November 2010

  • Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann; Waters, Katrina M.
  • Journal of Proteome Research, Vol. 9, Issue 11
  • DOI: 10.1021/pr1005247

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements
journal, November 2014

  • Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Datta, Susmita
  • Molecular & Cellular Proteomics, Vol. 13, Issue 12
  • DOI: 10.1074/mcp.M113.030932

Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles
journal, January 2007

minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information
journal, October 2008

  • Meyer, Patrick E.; Lafitte, Frédéric; Bontempi, Gianluca
  • BMC Bioinformatics, Vol. 9, Issue 1
  • DOI: 10.1186/1471-2105-9-461

Quantitative proteomic analysis of host—pathogen interactions: a study of Acinetobacter baumannii responses to host airways
journal, May 2015

The utility of protein and mRNA correlation
journal, January 2015

Inferring the relation between transcriptional and posttranscriptional regulation from expression compendia
journal, January 2014

Improved quality control processing of peptide-centric LC-MS proteomics data
text, January 2011

  • G., Pounds, Joel; C., Sims, Amy; O., Metz, Thomas
  • The University of North Carolina at Chapel Hill University Libraries
  • DOI: 10.17615/kpz0-8g92

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements
journal, August 2014

The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics
journal, January 2005

‘‘Guilt by Association’’ Is the Exception Rather Than the Rule in Gene Networks
dataset, January 2018

‘‘Guilt by Association’’ Is the Exception Rather Than the Rule in Gene Networks
dataset, January 2019

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