jason mcdermott senior research scientist pacific northwest national laboratory richland, wa, usa

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

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Systems Biology Approach

3

Hypothesis

Experimental design

Data generation

Analysis/modeling

Predictions

Interpretation

HypothesisHypothesisHypothesis







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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

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Host-pathogen Interface

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Image: wikicommons


Problems in Type III Secretion

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


Classification Performance of SIEVE

Psy->STm ROC = 0.95STm->Psy ROC = 0.96

Samudrala, et al. 2009 PLoS Pathogens 5(4):e1000375


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


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)

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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

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


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


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Bottlenecks in Salmonella are essential for virulence

McDermott J, et al. 2009. J. Comp. Bio. 16(2):169-180


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

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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


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


Study Design


Ido1/Tnfrsf1b ModuleKepi Module

SARS-CoV-infected Wild type Mouse Inferred Network


Hypotheses for Validation

KO Mouse

Infection

Survival Death Negative NegativePhenotype:

Network: Altered Altered Altered Negative


Computational Network ValidationIs predicted neighborhood of targets downregulated in knock-out mice?

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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

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H5N1 infection

0 1 2 3 4 5 6 770

80

90

100

110

B6TnfrsfPe

rcen

t Sta

rting

Wei

ght

SARS infection


0

5

10

-5


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 Biology Approach

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Hypothesis

Experimental design

Data generation

Analysis/modeling

Predictions

Interpretation




What is Systems Biology?Network analysisSalmonella and Yersinia host-pathogen interactionsInfluenza and SARS-CoV host-pathogen interactionsConclusionsGaps and Future Directions

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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



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

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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.

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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


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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


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/

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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

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Identification of an RNA-based secretion signal

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Niemann, et al. J. Bacteriology 195(10):2119-25


Bottlenecks in macrophage networks are targeted by pathogens

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McDermott, J.E. et al. 2011. PLoS One, 6(2): e14673


Identification of a Core Response Module in Macrophages

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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.


Regulatory Network Modeling of Salmonella Typhimurium

Existing knowledgeMapped regulatory relationshipsSalmonella literature

Network inference from transcriptomicsMutual informationLogical influence

Network inference from proteomicsLogical influence

CHIPseq experiments

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Salmonella regulation in multiple host cells

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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)


T3SS Regulation in Macrophages

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T3SS Regulation in Neutrophils

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T3SS Regulation in CD8+ T-cells

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Computational ValidationCollaborative cross mice infected with an influenza strainLow-pathogenesis and high-pathogenesisFerris, et al. PLoS Pathog. 2013 9(2):e1003196

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http://compgen.unc.edu/wp/?page_id=99


Infection of KO miceDoes genetic deletion of target gene affect expression of predicted downstream genes?Does genetic deletion of target gene have affect pathogenesis?

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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

jason mcdermott senior research scientist pacific northwest national laboratory richland, wa, usa

Documents

host membrane

systems perspective

host response

host networkessential

case of salmonella

identifiable secretion

effector structure

bacterial virulence