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Jason McDermott Senior Research Scientist Pacific Northwest National Laboratory Richland, WA, USA

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

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Jason McDermott Senior Research Scientist Pacific Northwest National Laboratory Richland, WA, USA

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  1. 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

  2. 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 SalmonellaTyphimurium • 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

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

  4. 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 SalmonellaTyphimurium • 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

  5. 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

  6. Host-pathogen Interface Image: wikicommons 6

  7. Problems in Type III Secretion 7

  8. Overview of the SVM-based Identification and Evaluation of Virulence Effectors (SIEVE) Method

  9. Classification Performance of SIEVE Psy->STm ROC = 0.95 STm->Psy ROC = 0.96 Samudrala, et al. 2009 PLoS Pathogens 5(4):e1000375

  10. SIEVE Validation Using CyaA Fusions McDermott, et al. 2011. Infection and Immunity. 79(1):23-32 Niemann, et al. 2011. Infection and Immunity. 79(1): 33-43 10

  11. SIEVE Extensions and Availability • SIEVEserver Availability: • http://cbb.pnl.gov/portal/tools/sieve.html • SIEVE applied to Mannheimiahaemolytica(Lawrence et al. 2010 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):2119-25) • SIEVE-Ub: Ubiquitin ligase effector prediction (Chikkodougar, et al. under review) 11

  12. 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 SalmonellaTyphimurium • 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

  13. 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 • McDermott JE, et al. 2010. Drug Markers, 28(4):253-66. 13

  14. 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. 2009. J. Comp. Bio. 16(2):169-180 • Diamond DL, et al. 2010. PLoS Pathogens. 6(1):e1000719 • McDermott, JE, et al. 2011. PLoS One 6(2): e14673. • McDermottJ.E., et al. 2011. Mol Biosystems 7(8):2407-2418 Yu H et al. PLoS Comp Biol 2007, 3(4):e59

  15. Bottlenecks in Salmonella are essential for virulence • McDermott J, et al. 2009. J. Comp. Bio. 16(2):169-180 15

  16. What is this all good for? • Prediction of new virulence factors • Yoon H., et al. 2011. Secretion of Salmonella virulence factors into host cytoplasm via outer membrane vesicles. BMC Systems Biology.5:100. • Ansong et al. 2013.A multi-omic systems approach to elucidating Yersinia virulence mechanisms.Molecular Biosystems. 9(1):44-54. PMID: 23147219 • Interpreting/enhancing metabolic models • Kim, et al. 2013. SalmonellaModulates 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

  17. 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 SalmonellaTyphimurium • 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

  18. 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

  19. Study Design Influenza VN1203 SARS MA15 Wt mouse infection Wt mouse infection Transcriptional analysis Transcriptionalanalysis WGCNA CLR WGCNA CLR Network inference Network inference Common Hubs Common Bottlenecks Hubs Bottlenecks Hubs Bottlenecks Topological analysis Topological analysis Target Gene List Transcriptionalanalysis KO mouse infection Pathogenesis? Model validation

  20. SARS-CoV-infected Wildtype Mouse Inferred Network Ido1/Tnfrsf1b Module Kepi Module

  21. Hypotheses for Validation KO Mouse Infection Survival Death Negative Negative Phenotype: Altered Altered Altered Negative Network:

  22. Computational Network Validation • Is predicted neighborhood of targets downregulated in knock-out mice? 22

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

  24. 10 5 0 -5

  25. 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

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

  27. 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

  28. Conclusions Hypothesis Hypothesis • 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 Hypothesis Experimental design Interpretation Data generation Predictions Analysis/modeling

  29. 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

  30. 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-1830. 30

  31. Systems Biology of Enteropathogens Acknowledgements • http://www.sysBEP.org PNNL Joshua Adkins-PI Richard Smith-Co-PI Gordon Anderson-TL Charles Ansong, PM Jason McDermott-TL Thomas Metz-TL • Heather Brewer • Roslyn Brown • Brooke Deatherage • Young-Mo Kim • Matthew Monroe • James Sanford • Alexandra Schrimpe-Rutledge NIH/DHHS NIAID IAA Y1-AI-8401-01 OHSU Fred Heffron-TL Afshan Kidwai Jie Li George Niemann Hyunjin Yoon WSU Kate McAteer Meagan Burnet JCVI-Peterson Scott Peterson-TL Marcus Jones UCSD Bernhard Palsson-TL Pep Charusanti Daniel Hyduke Josh Lerman Monica Mo UTMB-Motin Vladimir Motin-TL Sadhana Chauhan

  32. Systems Virology Acknowledgements • http://www.systemsvirology.org 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. HHSN272200800060C. University of Washington Michael Katze Lynn Law Laurence Josset Sean Proll Stewart Chang Sarah Belisle XinxiaPeng LauriAicher Jean Chang Tim Owens Rich Green University of North Carolina Ralph Baric Lisa Gralinski Amy Sims VineetMenachery PNNL modeling Katrina Waters Jason McDermott Hugh Mitchell Susan Tilton Harish Shankaran Bobbie-Jo Webb-Robertson Melissa Matzke PNNL ‘omics Richard Smith Tom Metz Robbie Heegle Athena Schepmoes Karl Weitz Anil Shukla Maria Luna Ronald J. Moore University of Wisconsin YoshiKawaoka Amie Eisfeld Gabi Neuman Chengjun Li Amy Ellis Shufang Fan 32

  33. About Me • Email: Jason.McDermott@pnnl.gov • About: http://www.jasonya.com/wp/about/ • Twitter: @BioDataGanache • Blog: The Mad Scientist’s Confectioner’s Club • http://www.jasonya.com/wp/ 33

  34. NIH/NIAID Systems Biology Centers • Systems biology projects to characterize host-pathogen interactions • http://www.niaid.nih.gov/labsandresources/resources/dmid/sb/ • Salmonella and Yersinia interacting with mouse macrophages • http://www.sysBEP.org • Influenza and SARS interacting with human cells and mice • http://www.systemsvirology.org • Tuberculosis interacting with macrophages • http://www.broad.mit.edu/annotation/tbsysbio/ • Influenza and S. aureus • http://www.systemsinfluenza.org • Publicly available data for host-pathogen interactions • Development of methods for investigating interactions 34

  35. Identification of an RNA-based secretion signal Niemann, et al. J. Bacteriology 195(10):2119-25 35

  36. Bottlenecks in macrophage networks are targeted by pathogens McDermott, J.E. et al. 2011. PLoS One, 6(2): e14673 36

  37. Identification of a Core Response Module in Macrophages 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. 37

  38. Regulatory Network Modeling of SalmonellaTyphimurium • Existing knowledge • Mapped regulatory relationships • Salmonella literature • Network inference from transcriptomics • Mutual information • Logical influence • Network inference from proteomics • Logical influence • CHIPseq experiments 38

  39. CD8+ T-cells B cells Dendritic cells Monocytes Natural killer Neutrophils CD4+ T-cells Macrophage Salmonella regulation in multiple host cells • Functions not observed • Amino acid biosynthesis (Ala, Asp, Gln, Gly, Ile, Leu, Lys, Met, Phe, Ser, Trp, Tyr, Val) • Transposase (tnpA) • Cytochrome C biogenesis (ccmoperon) • 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) 39

  40. T3SS Regulation in Macrophages 40

  41. T3SS Regulation in Neutrophils 41

  42. T3SS Regulation in CD8+ T-cells 42

  43. Computational Validation • Collaborative cross mice infected with an influenza strain • Low-pathogenesis and high-pathogenesis • Ferris, et al. PLoS Pathog. 2013 9(2):e1003196 http://compgen.unc.edu/wp/?page_id=99 43

  44. 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

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