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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 - PowerPoint PPT Presentation
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Systems Virology SysBEP
Host-pathogen interactions from a systems perspective: studying
bacterial virulence and host response to viral infection
Jason McDermottSenior Research Scientist
Pacific Northwest National LaboratoryRichland, WA, USA
The Center for Systems Virology TeamThe Center for Systems Biology of Enteropathogens Team
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology of Infectious Disease
What is Systems Biology?Salmonella host-pathogen interactions
BackgroundType III secreted effectors at the host-pathogen interfaceNetwork analysisSystems biology in Salmonella Typhimurium
Influenza and SARS-CoV host-pathogen interactionsBackgroundNetwork-based integration of dataSystems biology to identify drivers of pathogenesis
ConclusionsGaps and Future Directions
2
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology Approach
3
Hypothesis
Experimental design
Data generation
Analysis/modeling
Predictions
Interpretation
HypothesisHypothesisHypothesis
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology of Infectious Disease
What is Systems Biology?Salmonella host-pathogen interactions
BackgroundType III secreted effectors at the host-pathogen interfaceNetwork analysisSystems biology in Salmonella Typhimurium
Influenza and SARS-CoV host-pathogen interactionsBackgroundNetwork-based integration of dataSystems biology to identify drivers of pathogenesis
ConclusionsGaps and Future Directions
4
Systems Virology SysBEP
Virulence Regulation in SalmonellaRegulation of virulence in Salmonella
Infection of macrophages essential for virulence19 regulators with a significant impact on virulence
Type III secretion systemSalmonella pathogenecity island (SPI) 2 is essential for infectionSPI-1 is involved in epithelial cell infectionEffectors interact with host networkEssential for virulence
Goal 1: Identify type III effectorsGoal 2: Identify virulence Salmonella genes/proteins
5
Systems Virology SysBEPSystems 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 = 0.95STm->Psy ROC = 0.96
Samudrala, et al. 2009 PLoS Pathogens 5(4):e1000375
Systems Virology SysBEP
SIEVE Validation Using CyaA Fusions
10
0 20 40 60 80 100 120 140 160 180 2000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Secretion versus SIEVE score
CyaA
CyaA Activity (relative to SrfH)
SIEV
E Zs
core
McDermott, et al. 2011. Infection and Immunity. 79(1):23-32Niemann, et al. 2011. Infection and Immunity. 79(1): 33-43
Systems Virology SysBEP
SIEVE Extensions and Availability
SIEVEserver Availability:http://cbb.pnl.gov/portal/tools/sieve.html
SIEVE applied to Mannheimia haemolytica (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
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology of Infectious Disease
What is Systems Biology?Salmonella host-pathogen interactions
BackgroundType III secreted effectors at the host-pathogen interfaceNetwork analysisSystems biology in Salmonella Typhimurium
Influenza and SARS-CoV host-pathogen interactionsBackgroundNetwork-based integration of dataSystems biology to identify drivers of pathogenesis
ConclusionsGaps and Future Directions
12
Systems Virology SysBEP
Biological Networks
Types of networksRegulatory networksProtein-protein interaction networksBiochemical reaction networksAssociation networks
Network inference
Statistical similarity in expression patternsRegulatory, functional, or physical interactionsAbstract representation of the system and its states
13
McDermott JE, et al. 2010. Drug Markers, 28(4):253-66.
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. 2009. J. Comp. Bio. 16(2):169-180Diamond DL, et al. 2010. PLoS Pathogens. 6(1):e1000719McDermott, JE, et al. 2011. PLoS One 6(2): e14673.McDermott J.E., et al. 2011. Mol Biosystems 7(8):2407-2418
Systems Virology SysBEPSystems Virology SysBEP
15
Bottlenecks in Salmonella are essential for virulence
McDermott J, et al. 2009. J. Comp. Bio. 16(2):169-180
Systems Virology SysBEP
What is this all good for?
Prediction of new virulence factorsYoon 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 modelsKim, et al. 2013. Salmonella Modulates Metabolism During Growth under Conditions that Induce Expression of Virulence Genes. Molecular BioSystems (accepted)
Interpretation of in vivo infection resultsOverall, et al. in preparation
16
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology of Infectious Disease
What is Systems Biology?Salmonella host-pathogen interactions
BackgroundType III secreted effectors at the host-pathogen interfaceNetwork analysisSystems biology in Salmonella Typhimurium
Influenza and SARS-CoV host-pathogen interactionsBackgroundNetwork-based integration of dataSystems biology to identify drivers of pathogenesis
ConclusionsGaps and Future Directions
17
Systems Virology SysBEP
OverviewWhat 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 infectionApproach:
Mouse models of infectionTranscriptomicsNetwork-based approachTopological network analysis to define targetsValidation studies
Systems Virology SysBEPSystems Virology SysBEP
Transcriptional analysis
Transcriptional analysis
SARS MA15
Target Gene List
Hubs Hubs BottlenecksBottlenecks
Influenza VN1203
Common Hubs
Common Bottleneck
s
WGCNA WGCNA CLRCLR
Network inference Network inference
Topological analysis Topological analysis
KO mouse infection
Wt mouse infection
Wt mouse infection
Pathogenesis?
Model validation
Transcriptional analysis
Study Design
Systems Virology SysBEPSystems Virology SysBEP
Ido1/Tnfrsf1b ModuleKepi Module
SARS-CoV-infected Wild type Mouse Inferred Network
Systems Virology SysBEP
Hypotheses for Validation
KO Mouse
Infection
Survival Death Negative NegativePhenotype:
Network: Altered Altered Altered Negative
Systems Virology SysBEPSystems Virology SysBEP
Computational Network ValidationIs 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 pathogenesisTnfa bindingNegatively regulate TNFR1 signaling, which is proinflammatoryPromote endothelial cell activation/migrationActivation and proliferation of immune cells
23
H5N1 infection
0 1 2 3 4 5 6 770
80
90
100
110
B6TnfrsfPe
rcen
t Sta
rting
Wei
ght
SARS infection
Systems Virology SysBEP
0
5
10
-5
Systems Virology SysBEP
Additional Mouse Knock-out ResultsKnock-out mice infected with SARS
Baric labTotal of 20 different mouse strains
Knock-out mice infected with H5N1
Total of 11 different strains
Both positive and negative predictionsAUC 0.83
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology Approach
26
Hypothesis
Experimental design
Data generation
Analysis/modeling
Predictions
Interpretation
HypothesisHypothesisHypothesis
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology of Infectious Disease
What is Systems Biology?Network analysisSalmonella and Yersinia host-pathogen interactionsInfluenza and SARS-CoV host-pathogen interactionsConclusionsGaps and Future Directions
27
Systems Virology SysBEPSystems Virology SysBEP
Conclusions
Systems biologyCompleting the cycleIdentification of pathogenesis/virulence genesBiological insight into pathogenesis/virulenceGeneration of hypotheses for further investigationDevelopment of novel computational approaches
Network approaches to target identificationData integration methodsIntegration of computational modeling with biological investigation
Hypothesis
Experimental design
Data generation
Analysis/modeling
Predictions
Interpretation
HypothesisHypothesisHypothesis
Systems Virology SysBEPSystems Virology SysBEP
Gaps and Future Directions
Education and communication improvementModelers 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
QualitativelyQuantitatively
Better integration of other data typesBetter methods/approaches for target validation
29
Systems Virology SysBEPSystems 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-1830.
30
Systems Virology SysBEPSystems Virology SysBEP
Systems Biology of Enteropathogens Acknowledgements
OHSUFred Heffron-TLAfshan KidwaiJie LiGeorge NiemannHyunjin Yoon JCVI-Peterson
Scott Peterson-TLMarcus Jones
UTMB-MotinVladimir Motin-TLSadhana Chauhan
WSUKate McAteerMeagan Burnet
PNNLJoshua Adkins-PIRichard Smith-Co-PIGordon Anderson-TLCharles Ansong, PMJason McDermott-TLThomas Metz-TL
NIH/DHHS NIAIDIAA Y1-AI-8401-01
UCSDBernhard Palsson-TLPep CharusantiDaniel HydukeJosh LermanMonica Mo
James SanfordAlexandra Schrimpe-Rutledge
Heather BrewerRoslyn BrownBrooke DeatherageYoung-Mo KimMatthew Monroe
http://www.sysBEP.org
Systems Virology SysBEPSystems Virology SysBEP
32
University of WashingtonMichael KatzeLynn LawLaurence JossetSean ProllStewart ChangSarah BelisleXinxia Peng Lauri AicherJean ChangTim OwensRich Green
University of WisconsinYoshi KawaokaAmie EisfeldGabi NeumanChengjun LiAmy EllisShufang Fan
University of North CarolinaRalph BaricLisa GralinskiAmy SimsVineet Menachery
PNNL modelingKatrina WatersJason McDermottHugh MitchellSusan TiltonHarish ShankaranBobbie-Jo Webb-RobertsonMelissa 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. HHSN272200800060C.
PNNL ‘omicsRichard SmithTom MetzRobbie HeegleAthena SchepmoesKarl WeitzAnil ShuklaMaria LunaRonald J. Moore
http://www.systemsvirology.org
Systems Virology SysBEPSystems Virology SysBEP
About Me
Email: [email protected]: http://www.jasonya.com/wp/about/Twitter: @BioDataGanacheBlog: The Mad Scientist’s Confectioner’s Club
http://www.jasonya.com/wp/
33
Systems Virology SysBEPSystems Virology SysBEP
NIH/NIAID Systems Biology CentersSystems 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.orgInfluenza and SARS interacting with human cells and mice
http://www.systemsvirology.orgTuberculosis interacting with macrophages
http://www.broad.mit.edu/annotation/tbsysbio/Influenza and S. aureus
http://www.systemsinfluenza.orgPublicly available data for host-pathogen interactionsDevelopment of methods for investigating interactions
34
Systems Virology SysBEP
Identification of an RNA-based secretion signal
35
Niemann, et al. J. Bacteriology 195(10):2119-25
Systems Virology SysBEP
Bottlenecks in macrophage networks are targeted by pathogens
36
McDermott, J.E. et al. 2011. 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 knowledgeMapped regulatory relationshipsSalmonella literature
Network inference from transcriptomicsMutual informationLogical influence
Network inference from proteomicsLogical influence
CHIPseq experiments
38
Systems Virology SysBEP
Salmonella regulation in multiple host cells
39
CD
8+ T
-cel
ls
B c
ells
Den
driti
c ce
lls
Mon
ocyt
es
Nat
ural
kill
er
Neu
trop
hils
CD
4+ T
-cel
ls
Mac
roph
age
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 ValidationCollaborative cross mice infected with an influenza strainLow-pathogenesis and high-pathogenesisFerris, et al. PLoS Pathog. 2013 9(2):e1003196
43
http://compgen.unc.edu/wp/?page_id=99
Systems Virology SysBEPSystems Virology SysBEP
Infection of KO miceDoes genetic deletion of target gene affect expression of predicted downstream genes?Does genetic deletion of target gene have affect pathogenesis?
44
Gene VirusPathogenesis Phenotype
CCR5 SARS-CoV AlteredCCL5 SARS-CoV AlteredCFB SARS-CoV AlteredSTAT1 SARS-CoV AlteredPpp1r14c SARS-CoV AlteredMyd88 SARS-CoV AlteredLilrb3 SARS-CoV AlteredTLR7 SARS-CoV AlteredCCR2 SARS-CoV AlteredCCR1 SARS-CoV AlteredC4B SARS-CoV AlteredIL1R1 H5N1 AlteredIL17ra H5N1 AlteredIFNA1 H5N1 AlteredMX1 H5N1 AlteredC3 H5N1 Altered
Gene VirusPathogenesis Phenotype
Indo SARS-CoV Not alteredTLR2 SARS-CoV Not alteredCH25H SARS-CoV Not alteredPtgs2 SARS-CoV Not alteredNOS2 SARS-CoV Not alteredTnfrsf1b SARS-CoV Not alteredCXCR3 SARS-CoV Not alteredIndo H5N1 Not alteredTnfrsf1b H5N1 Not alteredTNFRsf1a H5N1 Not alteredIL6 H5N1 Not alteredMP1a H5N1 Not alteredCCL2 H5N1 Not alteredNFkbp50 H5N1 Not altered