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McDermott JE, Jacobs JM, Merrill NJ, Mitchell HD, Arshad OA, McClure R, Teeguarden J, Gajula RP, Porter KI, Satterfield BC, Lundholm KR, Skene DJ, Gaddameedhi S, Dongen HPAV. Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment. J Proteome Res 2024; 23:1547-1558. [PMID: 38619923 DOI: 10.1021/acs.jproteome.3c00418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.
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Affiliation(s)
- Jason E McDermott
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, Oregon 97239, United States
| | - Jon M Jacobs
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Nathaniel J Merrill
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Osama A Arshad
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ryan McClure
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Justin Teeguarden
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Rajendra P Gajula
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Kenneth I Porter
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Brieann C Satterfield
- Sleep and Performance Research Center, Washington State University, Spokane, Washington 99202, United States
- Department of Translational Medicine and Physiology, Washington State University, Spokane, Washington 99202, United States
| | - Kirsie R Lundholm
- Sleep and Performance Research Center, Washington State University, Spokane, Washington 99202, United States
- Department of Translational Medicine and Physiology, Washington State University, Spokane, Washington 99202, United States
| | - Debra J Skene
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom
| | - Shobhan Gaddameedhi
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, United States
- Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Hans P A Van Dongen
- Sleep and Performance Research Center, Washington State University, Spokane, Washington 99202, United States
- Department of Translational Medicine and Physiology, Washington State University, Spokane, Washington 99202, United States
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2
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Sosa-Acosta P, Quiñones-Vega M, Guedes JDS, Rocha D, Guida L, Vasconcelos Z, Nogueira FCS, Domont GB. Multiomics Approach Reveals Serum Biomarker Candidates for Congenital Zika Syndrome. J Proteome Res 2024; 23:1200-1220. [PMID: 38390744 DOI: 10.1021/acs.jproteome.3c00677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The Zika virus (ZIKV) can be vertically transmitted, causing congenital Zika syndrome (CZS) in fetuses. ZIKV infection in early gestational trimesters increases the chances of developing CZS. This syndrome involves several pathologies with a complex diagnosis. In this work, we aim to identify biological processes and molecular pathways related to CZS and propose a series of putative protein and metabolite biomarkers for CZS prognosis in early pregnancy trimesters. We analyzed serum samples of healthy pregnant women and ZIKV-infected pregnant women bearing nonmicrocephalic and microcephalic fetuses. A total of 1090 proteins and 512 metabolites were identified by bottom-up proteomics and untargeted metabolomics, respectively. Univariate and multivariate statistical approaches were applied to find CZS differentially abundant proteins (DAP) and metabolites (DAM). Enrichment analysis (i.e., biological processes and molecular pathways) of the DAP and the DAM allowed us to identify the ECM organization and proteoglycans, amino acid metabolism, and arachidonic acid metabolism as CZS signatures. Five proteins and four metabolites were selected as CZS biomarker candidates. Serum multiomics analysis led us to propose nine putative biomarkers for CZS prognosis with high sensitivity and specificity.
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Affiliation(s)
- Patricia Sosa-Acosta
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
- Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Mauricio Quiñones-Vega
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
- Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Jéssica de S Guedes
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
- Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Danielle Rocha
- Fernandes Figueira Institute, Fiocruz, Rio de Janeiro 22250-020, Brazil
| | - Letícia Guida
- Fernandes Figueira Institute, Fiocruz, Rio de Janeiro 22250-020, Brazil
| | | | - Fábio C S Nogueira
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
- Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Gilberto B Domont
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
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3
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Wu R, Davison MR, Nelson WC, Smith ML, Lipton MS, Jansson JK, McClure RS, McDermott JE, Hofmockel KS. Hi-C metagenome sequencing reveals soil phage-host interactions. Nat Commun 2023; 14:7666. [PMID: 37996432 PMCID: PMC10667309 DOI: 10.1038/s41467-023-42967-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 10/27/2023] [Indexed: 11/25/2023] Open
Abstract
Bacteriophages are abundant in soils. However, the majority are uncharacterized, and their hosts are unknown. Here, we apply high-throughput chromosome conformation capture (Hi-C) to directly capture phage-host relationships. Some hosts have high centralities in bacterial community co-occurrence networks, suggesting phage infections have an important impact on the soil bacterial community interactions. We observe increased average viral copies per host (VPH) and decreased viral transcriptional activity following a two-week soil-drying incubation, indicating an increase in lysogenic infections. Soil drying also alters the observed phage host range. A significant negative correlation between VPH and host abundance prior to drying indicates more lytic infections result in more host death and inversely influence host abundance. This study provides empirical evidence of phage-mediated bacterial population dynamics in soil by directly capturing specific phage-host interactions.
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Affiliation(s)
- Ruonan Wu
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Michelle R Davison
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - William C Nelson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Montana L Smith
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mary S Lipton
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ryan S McClure
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jason E McDermott
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA
| | - Kirsten S Hofmockel
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
- Department of Agronomy, Iowa State University, Ames, IA, USA.
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4
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Ferrocino I, Rantsiou K, McClure R, Kostic T, de Souza RSC, Lange L, FitzGerald J, Kriaa A, Cotter P, Maguin E, Schelkle B, Schloter M, Berg G, Sessitsch A, Cocolin L. The need for an integrated multi-OMICs approach in microbiome science in the food system. Compr Rev Food Sci Food Saf 2023; 22:1082-1103. [PMID: 36636774 DOI: 10.1111/1541-4337.13103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 12/05/2022] [Accepted: 12/19/2022] [Indexed: 01/14/2023]
Abstract
Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop "Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research" gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety.
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Affiliation(s)
- Ilario Ferrocino
- Department of Agriculture, Forest and Food Science, University of Turin, Grugliasco, Italy
| | - Kalliopi Rantsiou
- Department of Agriculture, Forest and Food Science, University of Turin, Grugliasco, Italy
| | - Ryan McClure
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Tanja Kostic
- AIT Austrian Institute of Technology GmbH, Bioresources Unit, Tulln, Austria
| | - Rafael Soares Correa de Souza
- Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Lene Lange
- BioEconomy, Research & Advisory, Valby, Denmark
| | - Jamie FitzGerald
- Teagasc Food Research Centre, Moorepark, Fermoy, County Cork, Ireland
| | - Aicha Kriaa
- MICALIS, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Paul Cotter
- Teagasc Food Research Centre, Moorepark, Fermoy, County Cork, Ireland
| | - Emmanuelle Maguin
- MICALIS, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | | | | | - Gabriele Berg
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | - Angela Sessitsch
- AIT Austrian Institute of Technology GmbH, Bioresources Unit, Tulln, Austria
| | - Luca Cocolin
- Department of Agriculture, Forest and Food Science, University of Turin, Grugliasco, Italy
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5
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Walker AM, Cliff A, Romero J, Shah MB, Jones P, Felipe Machado Gazolla JG, Jacobson DA, Kainer D. Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data. Comput Struct Biotechnol J 2022; 20:3372-3386. [PMID: 35832622 PMCID: PMC9260260 DOI: 10.1016/j.csbj.2022.06.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.
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Affiliation(s)
- Angelica M. Walker
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | - Ashley Cliff
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | - Jonathon Romero
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | - Manesh B. Shah
- Computational and Predictive Biology, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge 37830, TN, USA
| | - Piet Jones
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, 821 Volunteer Blvd, Knoxville 37996, TN, USA
| | | | - Daniel A Jacobson
- Computational and Predictive Biology, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge 37830, TN, USA
- Corresponding authors.
| | - David Kainer
- Computational and Predictive Biology, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge 37830, TN, USA
- Corresponding authors.
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6
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Shankar P, McClure RS, Waters KM, Tanguay RL. Gene co-expression network analysis in zebrafish reveals chemical class specific modules. BMC Genomics 2021; 22:658. [PMID: 34517816 PMCID: PMC8438978 DOI: 10.1186/s12864-021-07940-4] [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: 03/30/2021] [Accepted: 08/15/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response. RESULTS Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure. CONCLUSIONS Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.
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Affiliation(s)
- Prarthana Shankar
- Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, 28645 East Highway 34, Oregon State University, Corvallis, OR, 97331, USA
| | - Ryan S McClure
- Biological Sciences Division, Pacific National Northwest Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, WA, 99352, USA
| | - Katrina M Waters
- Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, 28645 East Highway 34, Oregon State University, Corvallis, OR, 97331, USA.,Biological Sciences Division, Pacific National Northwest Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, WA, 99352, USA
| | - Robyn L Tanguay
- Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, 28645 East Highway 34, Oregon State University, Corvallis, OR, 97331, USA.
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7
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Feng S, Heath E, Jefferson B, Joslyn C, Kvinge H, Mitchell HD, Praggastis B, Eisfeld AJ, Sims AC, Thackray LB, Fan S, Walters KB, Halfmann PJ, Westhoff-Smith D, Tan Q, Menachery VD, Sheahan TP, Cockrell AS, Kocher JF, Stratton KG, Heller NC, Bramer LM, Diamond MS, Baric RS, Waters KM, Kawaoka Y, McDermott JE, Purvine E. Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC Bioinformatics 2021; 22:287. [PMID: 34051754 PMCID: PMC8164482 DOI: 10.1186/s12859-021-04197-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 05/13/2021] [Indexed: 12/25/2022] Open
Abstract
Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04197-2.
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Affiliation(s)
- Song Feng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Emily Heath
- Department of Mathematics, University of Illinois, Urbana-Champaign, IL, USA
| | - Brett Jefferson
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Cliff Joslyn
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA.,Systems Science Program, Portland State University, Portland, OR, USA
| | - Henry Kvinge
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Brenda Praggastis
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Amie J Eisfeld
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Amy C Sims
- Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Larissa B Thackray
- Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA
| | - Shufang Fan
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Kevin B Walters
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Peter J Halfmann
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Danielle Westhoff-Smith
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA
| | - Qing Tan
- Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA
| | - Vineet D Menachery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Timothy P Sheahan
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Jacob F Kocher
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Natalie C Heller
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Michael S Diamond
- Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA.,Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ralph S Baric
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katrina M Waters
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.,Department of Comparative Medicine, University of Washington, Seattle, WA, USA
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA.,Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, 108-8639, Japan.,ERATO Infection-Induced Host Responses Project, Saitama, 332-0012, Japan.,Department of Special Pathogens, International Research Center for Infectious Diseases, Institute of Medical Science, University of Tokyo, Tokyo, 108-8639, Japan
| | - Jason E McDermott
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.,Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA
| | - Emilie Purvine
- Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA.
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