modeling immunity to enteric pathogens...josep bassaganya-riera nutritional immunology &...

Josep Bassaganya-Riera Nutritional Immunology & Molecular Medicine Lab Center for Modeling Immunity to Enteric Pathogens

MMI Symposium in Computational Immunology Virginia Tech, Blacksburg, VA

Modeling Immunity to Enteric Pathogens

http://www.nimml.org

http://www.modelingimmunity.org

www.modelingimmunity.org

www.nimml.org

www.ndssl.vbi.vt.edu

Presenter

Presentation Notes

The components have become fully integrated and the lines between them are blurry within MIEP For instance, our immunology GRAs are capable of building computational models using COPASI, calibrating the models and running simulations.



http://www.ndssl.vbi.vt.edu

McGhee JR, Fujihashi K (2012) Inside the Mucosal Immune System. PLoS Biol 10(9): e1001397. doi:10.1371/journal.pbio.1001397

Mucosal Immune System

Presenter

Presentation Notes

Let me give you some facts and figures; some food for thought The gut associated immune system is the largest immune compartment (70% of the immune system). The mucosal immune system accounts for 300 square meters in humans 10 to the 12 bacteria per cubic centimiter in humans (colon) 1,000 bacterial species

Microbiome

Diet

Inflammation & Immunity

Genes RNA

Proteins

IKBKE node

IRF4 node

Rab7A

Health vs. Disease

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Presentation Notes

The research thrust of NIMML represents a Translational Medicine and HUMAN HEALTH thrust The human intestine must peacefully coexist with about 100 trillion commensal organisms while swiftly responding to pathogens that threaten its integrity

H. pylori

Modeling immune responses to Helicobacter pylori

Background • High prevalence (> 50 % world’s population) • Extreme differences in geographic distribution (socioeconomic factors)

Background Most common cause of gastritis, with associated complications: peptic,

duodenal ulcer, gastric adenocarcinoma, MALT lymphoma.

Helicobacter pylori

• H. pylori was classified as a type I carcinogen by the WHO... Should it be eradicated?

• H. pylori should be included in the list of most endangered species (M. Blaser)...and preserved as a beneficial commensal

• Inverse correlation between H. pylori prevalence and rate of overweight/obesity (Lender, 2014)

http://www.modelingimmunity.org/models/copasi-helicobacter-pylori-computational-model-archive/

Model of H. pylori infection

Th1 and Th17 effector responses contribute to gastritis in the chronic phase of infection.

Model predictions

Presenter

Presentation Notes

Madhav has talked about ENISI, I will focus on the latter two systems integrated into the Web Portal providing

Simulation of PPAR γ deletion

H. pylori Loads and Lesions Uninfected Wild Type

Myeloid cell PPARγ-deficient

STO

MAC

H

WPI

16

Presenter

Presentation Notes

H. pylori loads drop drastically after week 2 in LysMcre mice Lack of PPARγ in macrophages seems to help the clearance of bacteria Myeloid cells are critical regulators of colonization and gastric pathology during H. pylori infection

15min H. pylori co-culture

Macrophage-Hp co-cultures

HUMAN & ANIMAL STUDIES

Publicly available data

(GEO)

In-house generated NGS

data

ANALYSIS with GALAXY pipeline

Sequencing RESULTS (gene reads)

Data TREATMENT

Read Averages, Read Trimming, and Calculations of FCs and Log2

Integration of data into Ingenuity Pathway Analysis

Core analysis Identification of Canonical

Pathways Differences in expression

Network inference

Extraction of data and construction of SBML-

compliant network

GENERATION of NEW HYPOTHESES

Importation into COPASI and ENISI for Model Calibration, Simulation,

and Analysis

Presenter

Presentation Notes

You have seen this slide before and it is a work in progress. In this case I want to remind you about the process in the context of H. pylori infection

Cholesterol Biosynthesis B

A C

D

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P<0.05

A B

C 360 min

Metabolic Response

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The mitochondrial respiratory chain is responsible for oxidative phosphorylation

Innate Responses to H. pylori

Modeling Innate Responses to H. pylori

NLRX1 Sensitivity Analysis

• Local sensitivity analysis portrays relationship between NLRX1 and viral signaling cascades during intracellular H. pylori infection

• Intimate link between NLRX1 and

IFN signaling • Sensitivities suggest there may be a

role for NLRX1 in MHC class I signaling as well

Presenter

Presentation Notes

the role of NLRX1 in IFN & MHC signaling is novel and unreported the darker the red, the more impact a change in NLRX1 will exert on the corresponding molecule

MHC Class I Presentation

Control/ H. pylori J99/SS1

0 7 14 21 28 35 42 49 56

CD8+ T cell responses

NLRX1 Expression Validation in Macrophages

Wild type PPARγ-deficient

Validation in NLRX1 ko

Summary • H. pylori infection modulates two phases of

innate immune pathways that intersect with metabolism

• NLRX1 regulates host responses to H. pylori infection in macrophages

• We identified an inverse relationship between expression of PPAR γ and NLRX1 in macrophages

• Modeling was used to assess the sensitivities of our network to NLRs and their immunoregulatory mechanisms during H. pylori infection

a leading cause of enteritis & persistent diarrhea worldwide

High risk populations: – Travelers – HIV infected – Malnourished children

EAEC

Diarrheagenic Isolate Frequency Distribution

EAEC

EPEC

EHEC

ETEC

EIEC

41.1%

41.1%

AAF fimbria: primary virulence

factor attributed to mucosal adherence

Fli-C flagellin: responsible for IL-8 secretion

Dispersin: Allows dissociation from biofilm and

spread of colonization

EAEC

• Our in vivo murine model data suggested a beneficial role for Th17 cells and IL17A

• We used computational modeling to predict the effects of enhancing effector T cell populations during EAEC infection

Targeting PPARγ as an inflammatory mediator

Treg Th17

PPAR γ

• Gene expression: Upregulation of proinflammatory markers in CD4Cre+ • Histopathology: High leukocytic infiltration early during infection in CD4Cre+

followed by amelioration of colonic inflammation by day 14

EAEC T cell Model

Parameter estimation Calibration

0 1 3 5 7 10 14

Presenter

Presentation Notes

we used time course flow cytometry and gene expression (mRNA for cytokines) data to calibrate our model used malnourished EAEC infected colonic LPL data

Pharmacological blockade

GW9662 a potent PPARγ

antagonist Administration of GW9662 promoted the

upregulation of proinflammatory cytokines that correlated to significantly lower levels of EAEC in

feces early during infection

Wild Type systemCD4+ T cells during EAEC infection

0 5 10 15 200

30000

60000

90000Treg

Th1

Th17

time (days)

parti

cle

conc

entr

atio

n

Wild Type systemCytokines during EAEC infection

0 5 10 15 200.0000

0.0002

0.0004

0.0006

0.0008IL-10

TNF-α

IFN-γIL-6

IL-17

TGF-β

time (days)

parti

cle

conc

entra

tion

Colonic TNF-α Expression

0

5.0×10- 7

1.0×10- 6

1.5×10- 6

*

TNF

- α:

: β-A

ctin

[pg

cDN

A/u

g R

NA

]

Colonic IL-6 Expression

0

5.0×10- 7

1.0×10- 6

1.5×10- 6

2.0×10- 6

*

IL-6

:β

-Act

in [p

g cD

NA

/ug

RN

A]

Colonic MCP-1 Expression

0.00

0.02

0.04

0.06

0.08

0.10 *

MC

P-1

: â-

Act

in [p

g cD

NA

/ug

RN

A]

PPARγ deficient systemCD4+ T cells during EAEC infection

0 5 10 15 200

100000

200000

300000

400000

Treg

Th1

Th17

time (days)

parti

cle

conc

entra

tion

PPARγ deficient simulationCytokines during EAEC infection

0 5 10 15 200.000

0.001

0.002

0.003TNF-α

IFN-γ

IL-6

IL-17

time (days)

part

icle

con

cent

ratio

n

Colonic IL-1β Expression

0.00

0.05

0.10

0.15

*

IL-1β

:β

-Act

in [p

g cD

NA

/ug

RN

A]

Colonic IL-17 Expression

0

2.0×10- 7

4.0×10- 7

6.0×10- 7

8.0×10- 7

*

IL-1

7 : β

-Act

in [p

g cD

NA

/ug

RN

A]

malnourished 5 days post infection

malnourished 5 days post infection

A B

C D E

F G

H I

UninfectedInfected wildtypeInfected PPARγdeficient

malnourished 14 dpi

Bacterial Clearance in silico

0 5 10 15 200

5.0×100 9

1.0×101 0

1.5×101 0

2.0×101 0

EAEC: PPARγ deficientEAEC: Wild Type system

time (days)

part

icle

con

cent

ratio

n

3 4 50

1×100 4

2×100 4

3×100 4

Bacterial Load in Feces

*

Infected non-treatedInfected GW9662 treated

day post infection

CF

U/m

g fe

ces

A B

Presenter

Presentation Notes

PPAR g deficient system had enhanced effector responses

Antimicrobial Peptides

naïve T cell

RORγt Th17

TGFβ IL-6

IL-17 IL-21 Pharmacological blockade

of PPARγ beneficial Late during infection GW9662 treated mice expressed

cytokines responsible for potentiating Th17 differentiation in addition to significantly higher

levels of anti-microbial peptides.

IL-17A Neutralization abrogates benefits of PPARγ Blockade

Anti-IL-17A neutralizing antibody abrogates the beneficial effects of GW9662 in ameliorating disease based on weight loss and bacterial shedding

Presenter

Presentation Notes

Bacterial loads are significantly elevated in untreated and anti-IL17 + GW9662 groups early during infection Significant %body weight differences beginning on day 3 post infection coincide with an increased bacterial burden in untreated and anti-IL17 + GW9662-treated mice

Computational Modeling

COPASI & ENISI Tools and Models

• Host cells and bacteria are agents (108 agents) • Agents move around gut mucosa and lymph nodes • Agents in a same location are considered to be in contact • Co-evolving Graphical Discrete Dynamical System

(CGDDS): Linking mathematical theory and HPC • Contacting agents can interact:

– Agent-Agent interaction – Group-Agent interaction – Timed interaction

• Each agent represented as an automaton

ENISI Modeling Environment

Presenter

Presentation Notes

Stochasticity, spatial heterogeneity, capturing emerging behaviours

[Meier-Schellersheim’09]

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Presentation Notes

A diagrammatic representation of different biological scales and their associated modeling techniques and experimental approaches. Abbreviations: ODE: ordinary differential equation; PDE: partial differential equation; IP: immuno-precipitation; SPR: surface plasmon resonance; Y2H: yeast two-hybrid.

ENISI MSM • Tissue Scale • Cellular Scale • Chemokine Scale • Intracellular Scale

Scales Time Space Mathematical Model Software Environment

Tissue Hours-Weeks Centimeters Spatial compartments ENISI

Cellular Minutes-Days Millimeters ABM ENISI ABM

Cytokines Seconds Millimeters PDE ENISI

Intracellular Millisecond Nanometers ODE/SDE COPASI/ENISI SDE

ENISI MSM

ENISI MSM System Architecture

Intracellular Model: CD4+ T cells

• Comprehensive T cell differentiation model – 94 species – 46 reactions – 60 ODEs

• A deterministic model for in silico experiments with T cell differentiation: Th1, Th2, Th17, and Treg

• However, this model cannot represent the stochastic nature of T cell differentiation – Transcription – Translation rate ODE intracellular

model

Chemokine/Cytokine Fluid Scale

• Consists of concentration of cytokines and chemokines

• Each cytokine or chemokine has diffusion process of the form:

– L(x,y,z)=concentration of cytokine/chemokine – D=diffusion rate – γ=degradation rate

• Realized with partial differential equations (PDE)

Cytokine/Chemokine Diffusion

• Host cells and bacteria are agents • Each agent has an associated intracellular

model • Agents move around gut mucosa and

lymph nodes • Nearby agents are “in contact” • Agents in contact can interact:

– Agent-Agent interaction – Group-Agent interaction – Timed interaction

Cellular Scale: Agent Based Model

Agent Based Model

ENISI V1

• In an early version of ENISI states of an agent were represented by rule-based automaton

ENISI MSM • In current version of ENISI an agent has ODE

based intracellular model

• Participating cells are located in the GI tract.

• Cells move in the tissue sites. • Tissue Sites:

– Lumen – Epithelial Cells – Lamina Propria – Gastric Lymph Node

Tissue Scale: ABM

GI tract

Presenter

Presentation Notes

All the cells occupy some location. Cells can move in a location randomly. We have simplified the gut with 4 major compartments: Lumen, Epithelial Cells, Lamina Propria and Gastric Lymph Node Lamina propria: this layer below the epithelial cells is highly vascularized. In the small intestine, nutrient absorption is accomplished by means of this tissue. Lymph Nodes – source of T Cells and other immune cells

In silico Gut Lesion Formation • Developing visualizations of cellular movements • Lesion formation is observed in chemotaxis-based

movement models

Without Chemotaxis (Uniform Mix)

With Chemotaxis (Formation of Lesion)

VisIt Workflow

Simulation Engine (ENISI) • Generates output files

Post-Processing and *.silo creation • Generates silo files

from ENISI output

Visualize with VisIt GUI • Make plots with

various options

46

ENISI 3-D Visualizations

Sharing: ENISI Pathway Navigator • Network available at the MIEP web portal • Interactive Modeling Tool

– The user has the ability to modify parameters and experimental setup for the H. pylori model and simulate it on MIEP high performance cluster

• Statistical Results – We provide statistical results based on replicates of

ENISI simulations displaying mean and standard deviation

Results in ENISI Pathway Navigator

Presenter

Presentation Notes

Here we show the simulation results of one cell type as opposed to the entire compartment, including the annotation.

ENISI ISE Web Interface

Modeling Environment

ENISI Causal HPC-oriented cellular interaction

modeling platform

COPASI ODE-based modeling platform

Presenter

Presentation Notes

Evaluated two user interface prototypes and choose the current design User-centric design considering immunologists as the target audience Usability evaluation

MIEP Team Virginia Bioinformatics Institute Josep Bassaganya-Riera - Principal Investigator and

Center Director Jim Walke – Project Manager

Raquel Hontecillas - Immunology Lead

Barbara Kronsteiner-Dobramysl – Immunology Researcher Xiaoying Zhang – Immunology Pinyi Lu - Bioinformatics and Modeling Adria Carbo - Immunology and Modeling Kristin Eden- Immunology and Modeling Monica Viladomiu – Immunology

Irving C. Allen - Immunology Ken Oestreich - Immunology

Casandra Philipson – Immunology and Modeling

Eric Schiff, Patrick Heizer, Nathan Palmer, Mark Langowski, Chase Hetzel, Emily Fung – Interns

David Bevan- Education Lead

Virginia Bioinformatics Institute (continued) Madhav Marathe - Modeling Lead

Keith Bisset - Modeling Expert Stephen Eubank - Modeling Expert Tricity Andrew- Modeling GRA Maksudul Alam - Modeling GRA

Stefan Hoops/Yongguo Mei - Bioinformatics Leads Pinyi Lu – Bioinformatics GRA Pawel Michalak – Genomics Tools Nathan Liles - Bioinformatician Xinwei Deng – Statistical Analysis University of Virginia Richard Guerrant - Infectious Disease Expert

Cirle A. Warren - Infectious Disease Expert David Bolick - Sr. Laboratory and Research Specialist

Funding: Supported by NIAID Contract No. HHSN272201000056C

MMI Acknowledgements

• Adria Carbo • Kimberly Borkowski • David Bevan • Jim Walke • Kathy O’hara • Noah Philipson

• Rachel Robinson • Traci Roberts • Tiffany Trent • Kristopher Monger • Ivan Morozov • Josh Dunbar

Location dependent interaction rules

Tissue Sites wherein interaction occurs

Cells Cell types and Their behaviors

Mathematical Model • Each individual occupies a state

(cell-type, immunological-state, location)

• Location changes based on cell-type/immunological state creating a contact network

• State changes upon contact according to specific rules

• Uses ENISI environment

Can incorporate: • Spatial heterogeneity • Stochasticity • Phenotype emergence through

individual evolution • Moving from 104 to 108 agents

within the model

Presenter

Presentation Notes

In Step 2, a set of activity templates for households are determined, based on several thousand responses to an activity or time-use survey. These activity templates include the sort of activities each household member performs and the time of day they are performed. Thus for a city - demographic information for each person and location, and a minute-by-minute schedule of each person's activities and the locations where these activities take place is generated by a combination of simulation and data fusion techniques. This yields a {\em dynamic social contact network} represented by a (vertex and edge) labeled bipartite graph $G_{PL}$, where $P$ is the set of people and $L$ is the set of locations. If a person $p\in P$ visits a location $\ell\in L$, there is an edge $(p,\ell,label)\in E(G_{PL})$ between them, where $label$ is a record of the type of activity of the visit and its start and end points. It is {\em impossible} to build such a network by simply collecting field data; the use of generative models to build such networks is a unique feature of this work.

modeling immunity to enteric pathogens...josep bassaganya-riera nutritional immunology &...

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