Systems Virology SysBEP Host-pathogen interactions from a systems perspective: studying bacterial virulence and host response to viral infection Jason McDermott Senior Research Scientist Pacific Northwest National Laboratory Richland, WA, USA The Center for Systems Virology Team The Center for Systems Biology of Enteropathogens Team

Systems Virology SysBEP Systems Virology SysBEP Systems Biology of Infectious Disease What is Systems Biology? Salmonella host-pathogen interactions Background Type III secreted effectors at the host-pathogen interface Network analysis Systems biology in Salmonella Typhimurium Influenza and SARS-CoV host-pathogen interactions Background Network-based integration of data Systems biology to identify drivers of pathogenesis Conclusions Gaps and Future Directions 2

Systems Virology SysBEP Systems Virology SysBEP Systems Biology Approach 3 Hypothesis Experimental design Data generation Analysis/modeling Predictions Interpretation Hypothesis

Systems Virology SysBEP Systems Virology SysBEP Systems Biology of Infectious Disease What is Systems Biology? Salmonella host-pathogen interactions Background Type III secreted effectors at the host-pathogen interface Network analysis Systems biology in Salmonella Typhimurium Influenza and SARS-CoV host-pathogen interactions Background Network-based integration of data Systems biology to identify drivers of pathogenesis Conclusions Gaps and Future Directions 4

Systems Virology SysBEP Virulence Regulation in Salmonella Regulation of virulence in Salmonella Infection of macrophages essential for virulence 19 regulators with a significant impact on virulence Type III secretion system Salmonella pathogenecity island (SPI) 2 is essential for infection SPI-1 is involved in epithelial cell infection Effectors interact with host network Essential for virulence Goal 1: Identify type III effectors Goal 2: Identify virulence Salmonella genes/proteins 5

Systems Virology SysBEP Systems Virology SysBEP Host-pathogen Interface 6 Image: wikicommons

Systems Virology SysBEP Problems in Type III Secretion 7

Systems Virology SysBEP Overview of the SVM-based Identification and Evaluation of Virulence Effectors (SIEVE) Method

Systems Virology SysBEP Classification Performance of SIEVE Psy->STm ROC = 0.95 STm->Psy ROC = 0.96 Samudrala, et al PLoS Pathogens 5(4):e

Systems Virology SysBEP SIEVE Validation Using CyaA Fusions 10 McDermott, et al Infection and Immunity. 79(1):23-32 Niemann, et al Infection and Immunity. 79(1): 33-43

Systems Virology SysBEP SIEVE Extensions and Availability SIEVEserver Availability: SIEVE applied to Mannheimia haemolytica (Lawrence et al BMC Genomics. 11:535) cSIEVE: Chlamydia-specific SIEVE (Hovis, et al. under review) Identification of an RNA-coded signal for Salmonella secretion (Niemann, et al. J. Bacteriology 195(10): ) SIEVE-Ub: Ubiquitin ligase effector prediction (Chikkodougar, et al. under review) 11

Systems Virology SysBEP Systems Virology SysBEP Systems Biology of Infectious Disease What is Systems Biology? Salmonella host-pathogen interactions Background Type III secreted effectors at the host-pathogen interface Network analysis Systems biology in Salmonella Typhimurium Influenza and SARS-CoV host-pathogen interactions Background Network-based integration of data Systems biology to identify drivers of pathogenesis Conclusions Gaps and Future Directions 12

Systems Virology SysBEP Biological Networks Types of networks Regulatory networks Protein-protein interaction networks Biochemical reaction networks Association networks Network inference Statistical similarity in expression patterns Regulatory, functional, or physical interactions Abstract representation of the system and its states 13 McDermott JE, et al Drug Markers, 28(4):

Systems Virology SysBEP Yu H et al. PLoS Comp Biol 2007, 3(4):e59 Hubs  High centrality, highly connected  Exert regulatory influences  Vulnerable points Bottlenecks  High betweenness  Regulate information flow within network  Removal could partition network McDermott J, et al J. Comp. Bio. 16(2): Diamond DL, et al PLoS Pathogens. 6(1):e McDermott, JE, et al PLoS One 6(2): e McDermott J.E., et al Mol Biosystems 7(8):

Systems Virology SysBEP Systems Virology SysBEP 15 Bottlenecks in Salmonella are essential for virulence McDermott J, et al J. Comp. Bio. 16(2):

Systems Virology SysBEP What is this all good for? Prediction of new virulence factors Yoon H., et al Secretion of Salmonella virulence factors into host cytoplasm via outer membrane vesicles. BMC Systems Biology. 5:100. Ansong et al A multi-omic systems approach to elucidating Yersinia virulence mechanisms. Molecular Biosystems. 9(1): PMID: Interpreting/enhancing metabolic models Kim, et al Salmonella Modulates Metabolism During Growth under Conditions that Induce Expression of Virulence Genes. Molecular BioSystems (accepted) Interpretation of in vivo infection results Overall, et al. in preparation 16

Systems Virology SysBEP Systems Virology SysBEP Systems Biology of Infectious Disease What is Systems Biology? Salmonella host-pathogen interactions Background Type III secreted effectors at the host-pathogen interface Network analysis Systems biology in Salmonella Typhimurium Influenza and SARS-CoV host-pathogen interactions Background Network-based integration of data Systems biology to identify drivers of pathogenesis Conclusions Gaps and Future Directions 17

Systems Virology SysBEP Overview What are the causes of pathogenesis in respiratory viruses? Goal: Identify and prioritize potential mediators of pathogenesis that are common and unique to influenza and SARS Goal: Identify and prioritize potential mediators of high- pathogenecity viral infection Approach: Mouse models of infection Transcriptomics Network-based approach Topological network analysis to define targets Validation studies

Systems Virology SysBEP Systems Virology SysBEP Transcription al analysis SARS MA15 Target Gene List Hubs Bottlenecks Influenza VN1203 Common Hubs Common Bottleneck s WGCN A CLR Network inference Topological analysis KO mouse infection Wt mouse infection Pathogenesis ? Model validation Transcription al analysis Study Design

Systems Virology SysBEP Systems Virology SysBEP Ido1/Tnfrsf1b Module Kepi Module SARS-CoV-infected Wild type Mouse Inferred Network

Systems Virology SysBEP Hypotheses for Validation KO Mouse Infection SurvivalDeathNegative Phenotype: Network: Altered Negative

Systems Virology SysBEP Systems Virology SysBEP Computational Network Validation Is predicted neighborhood of targets downregulated in knock-out mice? 22

Systems Virology SysBEP Predicted targets abrogate influenza pathogenesis Tnfrsf1b (aka. Tnfr2) Predicted common regulator for influenza and SARS pathogenesis Tnf  binding Negatively regulate TNFR1 signaling, which is proinflammatory Promote endothelial cell activation/migration Activation and proliferation of immune cells 23 H5N1 infection SARS infection

Systems Virology SysBEP

Systems Virology SysBEP Additional Mouse Knock-out Results Knock-out mice infected with SARS Baric lab Total of 20 different mouse strains Knock-out mice infected with H5N1 Total of 11 different strains Both positive and negative predictions AUC 0.83

Systems Virology SysBEP Systems Virology SysBEP Systems Biology Approach 26 Hypothesis Experimental design Data generation Analysis/modeling Predictions Interpretation Hypothesis

Systems Virology SysBEP Systems Virology SysBEP Systems Biology of Infectious Disease What is Systems Biology? Network analysis Salmonella and Yersinia host-pathogen interactions Influenza and SARS-CoV host-pathogen interactions Conclusions Gaps and Future Directions 27

Systems Virology SysBEP Systems Virology SysBEP Conclusions Systems biology Completing the cycle Identification of pathogenesis/virulence genes Biological insight into pathogenesis/virulence Generation of hypotheses for further investigation Development of novel computational approaches Network approaches to target identification Data integration methods Integration of computational modeling with biological investigation Hypothesis Experimental design Data generation Analysis/modeling Predictions Interpretation Hypothesis

Systems Virology SysBEP Systems Virology SysBEP Gaps and Future Directions Education and communication improvement Modelers who understand biology What kinds of questions are important? Biologists who understand modeling What kinds of questions can be asked? Rigorous examination of target selection methods How well do we do at picking out negatives? Development of network approaches that are predictive Qualitatively Quantitatively Better integration of other data types Better methods/approaches for target validation 29

Systems Virology SysBEP Systems Virology SysBEP Acknowledgements Portions of the research were performed at the W.R. Wiley Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by US Department of Energy’s Office of Biological and Environmental Research (BER) program located at PNNL. PNNL is operated for the US Department of Energy by Battelle under contract DE-AC05-76RLO

Systems Virology SysBEP Systems Virology SysBEP Systems Biology of Enteropathogens Acknowledgements OHSU Fred Heffron-TL Afshan Kidwai Jie Li George Niemann Hyunjin Yoon JCVI-Peterson Scott Peterson-TL Marcus Jones UTMB-Motin Vladimir Motin-TL Sadhana Chauhan WSU Kate McAteer Meagan Burnet PNNL Joshua Adkins-PI Richard Smith-Co-PI Gordon Anderson-TL Charles Ansong, PM Jason McDermott-TL Thomas Metz-TL NIH/DHHS NIAID IAA Y1-AI UCSD Bernhard Palsson-TL Pep Charusanti Daniel Hyduke Josh Lerman Monica Mo James Sanford Alexandra Schrimpe-Rutledge Heather Brewer Roslyn Brown Brooke Deatherage Young-Mo Kim Matthew Monroe

Systems Virology SysBEP Systems Virology SysBEP 32 University of Washington Michael Katze Lynn Law Laurence Josset Sean Proll Stewart Chang Sarah Belisle Xinxia Peng Lauri Aicher Jean Chang Tim Owens Rich Green University of Wisconsin Yoshi Kawaoka Amie Eisfeld Gabi Neuman Chengjun Li Amy Ellis Shufang Fan University of North Carolina Ralph Baric Lisa Gralinski Amy Sims Vineet Menachery PNNL modeling Katrina Waters Jason McDermott Hugh Mitchell Susan Tilton Harish Shankaran Bobbie-Jo Webb-Robertson Melissa Matzke Systems Virology Acknowledgements This project has been funded in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN C. PNNL ‘omics Richard Smith Tom Metz Robbie Heegle Athena Schepmoes Karl Weitz Anil Shukla Maria Luna Ronald J. Moore

Systems Virology SysBEP Systems Virology SysBEP About Me About: Blog: The Mad Scientist’s Confectioner’s Club 33

Systems Virology SysBEP Systems Virology SysBEP NIH/NIAID Systems Biology Centers Systems biology projects to characterize host-pathogen interactions Salmonella and Yersinia interacting with mouse macrophages Influenza and SARS interacting with human cells and mice Tuberculosis interacting with macrophages Influenza and S. aureus Publicly available data for host-pathogen interactions Development of methods for investigating interactions 34

Systems Virology SysBEP Identification of an RNA-based secretion signal 35 Niemann, et al. J. Bacteriology 195(10):

Systems Virology SysBEP Bottlenecks in macrophage networks are targeted by pathogens 36 McDermott, J.E. et al PLoS One, 6(2): e14673

Systems Virology SysBEP Identification of a Core Response Module in Macrophages 37 McDermott JE, Archuleta M, Thrall BD, Adkins JN, Waters KM. 2011a. Controlling the response: predictive modeling of a highly central, pathogen- targeted core response module in macrophage activation. PLoS ONE 6(2): e14673.

Systems Virology SysBEP Regulatory Network Modeling of Salmonella Typhimurium Existing knowledge Mapped regulatory relationships Salmonella literature Network inference from transcriptomics Mutual information Logical influence Network inference from proteomics Logical influence CHIPseq experiments 38

Systems Virology SysBEP Salmonella regulation in multiple host cells 39 CD8+ T-cells B cells Dendritic cells Monocytes Natural killer Neutrophils CD4+ T-cells Macrophage Functions not observed  Amino acid biosynthesis (Ala, Asp, Gln, Gly, Ile, Leu, Lys, Met, Phe, Ser, Trp, Tyr, Val)  Transposase (tnpA)  Cytochrome C biogenesis (ccm operon) Functions not in macrophages  Amino acid biosynthesis (Arg, His)  Propanediol utilization-related (pdu, cbi)  Flagella (flg, flh, fli)  T3SS (pagD, pagK, ssaI, ssaP, sseA, sseB, sseI) Functions in macrophages only  Thiamine biosynthesis (thiJ, thiK, thiQ)

Systems Virology SysBEP T3SS Regulation in Macrophages 40

Systems Virology SysBEP T3SS Regulation in Neutrophils 41

Systems Virology SysBEP T3SS Regulation in CD8+ T-cells 42

Systems Virology SysBEP Computational Validation Collaborative cross mice infected with an influenza strain Low-pathogenesis and high-pathogenesis Ferris, et al. PLoS Pathog (2):e

Systems Virology SysBEP Systems Virology SysBEP Infection of KO mice Does genetic deletion of target gene affect expression of predicted downstream genes? Does genetic deletion of target gene have affect pathogenesis? 44 GeneVirus Pathogenesis Phenotype CCR5SARS-CoVAltered CCL5SARS-CoVAltered CFBSARS-CoVAltered STAT1SARS-CoVAltered Ppp1r14cSARS-CoVAltered Myd88SARS-CoVAltered Lilrb3SARS-CoVAltered TLR7SARS-CoVAltered CCR2SARS-CoVAltered CCR1SARS-CoVAltered C4BSARS-CoVAltered IL1R1H5N1Altered IL17raH5N1Altered IFNA1H5N1Altered MX1H5N1Altered C3H5N1Altered GeneVirus Pathogenesis Phenotype IndoSARS-CoVNot altered TLR2SARS-CoVNot altered CH25HSARS-CoVNot altered Ptgs2SARS-CoVNot altered NOS2SARS-CoVNot altered Tnfrsf1bSARS-CoVNot altered CXCR3SARS-CoVNot altered IndoH5N1Not altered Tnfrsf1bH5N1Not altered TNFRsf1aH5N1Not altered IL6H5N1Not altered MP1aH5N1Not altered CCL2H5N1Not altered NFkbp50H5N1Not altered