Modeling Immunity to Enteric Pathogens Josep Bassaganya-Riera - - PowerPoint PPT Presentation

▶
modeling immunity to enteric pathogens
SMART_READER_LITE
LIVE PREVIEW

Modeling Immunity to Enteric Pathogens Josep Bassaganya-Riera - - PowerPoint PPT Presentation

Dec 07, 2022 138 likes •716 views

Modeling Immunity to Enteric Pathogens Josep Bassaganya-Riera Nutritional Immunology & Molecular Medicine Lab Center for Modeling Immunity to Enteric Pathogens MMI Symposium in Computational Immunology Virginia Tech, Blacksburg, VA

slide-1
SLIDE 1

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

slide-2
SLIDE 2

www.modelingimmunity.org www.nimml.org www.ndssl.vbi.vt.edu

slide-3
SLIDE 3

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

Mucosal Immune System

slide-4
SLIDE 4

Microbiome Diet Inflammation & Immunity Genes RNA Proteins

IKBKE node IRF4 node Rab7A

Health vs. Disease

slide-5
SLIDE 5
  • H. pylori

Modeling immune responses to Helicobacter pylori

slide-6
SLIDE 6

Background

  • High prevalence (> 50 % world’s population)
  • Extreme differences in geographic distribution (socioeconomic factors)
slide-7
SLIDE 7

Background

Most common cause of gastritis, with associated complications: peptic, duodenal ulcer, gastric adenocarcinoma, MALT lymphoma.

slide-8
SLIDE 8

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
  • verweight/obesity (Lender, 2014)
slide-9
SLIDE 9

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

Model of H. pylori infection

slide-10
SLIDE 10

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

Model predictions

slide-11
SLIDE 11

Simulation of PPAR γ deletion

slide-12
SLIDE 12
  • H. pylori Loads and Lesions

Uninfected Wild Type Myeloid cell PPARγ-deficient

STOMACH WPI 16

slide-13
SLIDE 13

15min H. pylori co-culture

Macrophage-Hp co-cultures

slide-14
SLIDE 14

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

slide-15
SLIDE 15

Cholesterol Biosynthesis

B A C D

slide-16
SLIDE 16

A B C

360 min

Metabolic Response

slide-17
SLIDE 17

Innate Responses to H. pylori

slide-18
SLIDE 18

Modeling Innate Responses to H. pylori

slide-19
SLIDE 19

Modeling Innate Responses to H. pylori

slide-20
SLIDE 20

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

slide-21
SLIDE 21

MHC Class I Presentation

slide-22
SLIDE 22

Control/ H. pylori J99/SS1 0 7 14 21 28 35 42 49 56

CD8+ T cell responses

slide-23
SLIDE 23

NLRX1 Expression Validation in Macrophages

Wild type PPARγ-deficient

slide-24
SLIDE 24

Validation in NLRX1 ko

slide-25
SLIDE 25

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
  • ur network to NLRs and their immunoregulatory

mechanisms during H. pylori infection

slide-26
SLIDE 26

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

slide-27
SLIDE 27

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

slide-28
SLIDE 28

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

slide-29
SLIDE 29

EAEC T cell Model

Parameter estimation  Calibration

0 1 3 5 7 10 14

slide-30
SLIDE 30

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 system CD4+ T cells during EAEC infection 5 10 15 20 30000 60000 90000 Treg Th1 Th17 time (days) particle concentration Wild Type system Cytokines during EAEC infection 5 10 15 20 0.0000 0.0002 0.0004 0.0006 0.0008 IL-10 TNF-α IFN-γ IL-6 IL-17 TGF-β time (days) particle concentration Colonic TNF-α Expression 5.0× 10- 7 1.0× 10- 6 1.5× 10- 6

*

TNF-α: : β-Actin [pg cDNA/ug RNA] Colonic IL-6 Expression 5.0× 10- 7 1.0× 10- 6 1.5× 10- 6 2.0× 10- 6

*

IL-6 : β-Actin [pg cDNA/ug RNA] Colonic MCP-1 Expression 0.00 0.02 0.04 0.06 0.08 0.10 * MCP-1 : â-Actin [pg cDNA/ug RNA] PPARγ deficient system CD4+ T cells during EAEC infection 5 10 15 20 100000 200000 300000 400000 Treg Th1 Th17 time (days) particle concentration PPARγ deficient simulation Cytokines during EAEC infection 5 10 15 20 0.000 0.001 0.002 0.003 TNF-α IFN-γ IL-6 IL-17 time (days) particle concentration Colonic IL-1β Expression 0.00 0.05 0.10 0.15 * IL-1β : β-Actin [pg cDNA/ug RNA] Colonic IL-17 Expression 2.0× 10- 7 4.0× 10- 7 6.0× 10- 7 8.0× 10- 7 * IL-17 : β-Actin [pg cDNA/ug RNA] malnourished 5 days post infection malnourished 5 days post infection

A B C D E F G H I

Uninfected Infected wild type Infected PPARγ deficient malnourished 14 dpi

Bacterial Clearance in silico

5 10 15 20 5.0× 100 9 1.0× 101 0 1.5× 101 0 2.0× 101 0

EAEC: PPARγ deficient EAEC: Wild Type system

time (days) particle concentration 3 4 5 1× 100 4 2× 100 4 3× 100 4

Bacterial Load in Feces

*

Infected non-treated Infected GW9662 treated

day post infection CFU/mg feces

A B

slide-31
SLIDE 31

Antimicrobial Peptides

naïv e T cell

RORγt Th17

TGFβ IL-6 IL-17 IL-21

Pharmacological blockade

  • f 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.

slide-32
SLIDE 32

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

slide-33
SLIDE 33

Computational Modeling

COPASI & ENISI Tools and Models

slide-34
SLIDE 34
  • 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

slide-35
SLIDE 35

[Meier-Schellersheim’09]

slide-36
SLIDE 36

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

slide-37
SLIDE 37

ENISI MSM

slide-38
SLIDE 38

ENISI MSM System Architecture

slide-39
SLIDE 39

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

slide-40
SLIDE 40

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

slide-41
SLIDE 41
  • 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

slide-42
SLIDE 42

ENISI V1

  • In an early version of ENISI states of an agent

were represented by rule-based automaton

slide-43
SLIDE 43

ENISI MSM

  • In current version of ENISI an agent has ODE

based intracellular model

slide-44
SLIDE 44
  • 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

slide-45
SLIDE 45

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)

slide-46
SLIDE 46

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

slide-47
SLIDE 47

ENISI 3-D Visualizations

slide-48
SLIDE 48

ENISI 3-D Visualizations

slide-49
SLIDE 49

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

slide-50
SLIDE 50

Results in ENISI Pathway Navigator

slide-51
SLIDE 51

ENISI ISE Web Interface

slide-52
SLIDE 52

Modeling Environment

ENISI

Causal HPC-oriented cellular interaction modeling platform

COPASI

ODE-based modeling platform

slide-53
SLIDE 53
slide-54
SLIDE 54

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

slide-55
SLIDE 55

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
slide-56
SLIDE 56
slide-57
SLIDE 57

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