Logical modelling of hematopoietic cell fate decisions Denis - - PowerPoint PPT Presentation

Logical modelling

• f hematopoietic cell fate decisions

Brussels, March 21, 2011

Denis Thieffry

Cell proliferation, differentiation

• r death...

How are decisions taken?

Hematopoietic stem cell differentiation

• How does a cell decide

which differentiation pathway to follow?

• When and to what

extend cells become committed?

• To what extend and

how is it possible to force cell to change their differentiation states?

Requirements of Transcription Factors in Haematopoiesis

Orkin & Zon (2008) Cell 132: 631-44.

Red bars indicate the stages at which hematopoietic development is blocked for different gene knockouts. Factors associated with

• ncogenesis are

emphasised in bold.

LT-HSC: long-term haematopoietic stem cell; ST-HSC: short-term haematopoietic stem cell; CMP: common myeloid progenitor; CLP: common lymphoid progenitor; MEP: megakaryocyte/ erythroid progenitor; GMP: granulocyte/ macrophage progenitor; RBC: red blood cells.

Transcription factors with proven or suspected roles in lineage commitment. Blue boxes indicate altered cell phenotypes. Lack of shading indicates that no phenotype was observed or that defects were not studied. The altered phenotypes were either a complete loss of a lineage (lack), a maturational block (matur), a functional defect ( func), decreased numbers of lineage cells (decr), or increased numbers of lineage cells (incr). Abbreviations: ZnF, zinc finger domain; HTH, helix-turn-helix domain; HLH, helix-loop-helix domain; transmem, transmembrane; HMG box, high motility group box; bZip, basic leucine zipper; RHD, Rel homology domain.

Haematopoietic cell phenotypes of mice lacking transcriptional regulators

Laiosa et al (2006) Annu Rev Immunol 24: 705-38.

Orange arrows depict lineage reprogramming upon expression of the transcription factors GATA-1, C/EBP,

• r GATA-3. Abbreviations: HSC, hematopoietic stem cell; CMP, common myeloid progenitor; CLP, common

lymphoid progenitor; MEP, megakaryocyte/erythroid progenitor; GMP, granulocyte/ macrophage progenitor. Orkin & Zon (2008) Cell 132: 631-44.

Reprogramming of Haematopoietic cells

CD4+ T-helper cell differentiation

Multiple signalling pathways Various transcriptional factors Specific expression patterns (TFs and lymphokines)

Modelling of Th activation and differentiation

Klamt et al (2006). BMC Bioinformatics 7: 56.

Boolean model focusing on signalling

• Insights regarding input/output

relationships

• Identification of intervention points

Modelling of peripheral Th1/Th2 cell differentiation

Mendoza L (2006). BioSystems 84: 101-14.

Multilevel logical model The model recapitulates the differentiation of Th0 cells into Th1, Th1* and Th2 subtypes Positive circuits <=> multiple stable states

GINsim: a software dedicated to the logical modelling of biological regulatory networks

analysis toolbox core simulator GINML parser user interface

graph analysis graph editor simulation

State transition graph

Regulatory graph

Available at http://gin.univ-mrs.fr/GINsim

Aurélien NALDI Fabrice LOPEZ Duncan BERENGIER Claudine CHAOUIYA

Naldi et al (2009) BioSystems 97: 134-9

Towards a comprehensive, modular logical model

• f the Th differentiation network

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ILR = 1 IFF IL AND ILR1 AND ILR2

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Logical modelling of the Th network

IL = 1 IFF NFAT AND proliferation AND ...

Logical modelling of the Th network

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Multiple uses

• f receptor

chains

Logical modelling of the Th network

Ternary variables Converging signals

IRF1 IL4 CGC IFNB_e IL12_e STAT3 IL12RB2 IL4R IL17 TBET IL10 IL23R GP130 IL21 STAT6 IL6_e proliferation APC IL15_e CD28 IL2 IL12RB1 IFNGR1 IFNGR STAT4 SMAD3 IL2R IL4_e IFNG IL6RA IL4RA STAT1 IFNGR2 IL15RA IKB TCR IL10_e IL15R TGFB_e IFNG_e IL10RB IL10R IL23_e IL2RA NFKB STAT5 NFAT IL27RA IL27_e IL2_e TGFBR RORGT RUNX3 IFNBR IL10RA IL21R GATA3 IL21_e IL6R TGFB IL23 IL27R IL12R FOXP3 IL2RB

13 input components, 52 internal components, 339 circuits => too large to perform simulations

Current logical model of the Th network

Naldi et al (2010) PLoS Comput Biol 6: e1000912.

Model Reduction

X T T R3 R1 R3 R1 R2 R2

✓ Keep the detailed model ✓ User defined reductions ✓ Reduction before analysis

=> New rules for targets

• f hidden nodes

✓ Iterative procedure ✓ Dynamical consistency

• No circuit deletion
• Same stable states
• Reachability may change

Naldi et al (2011). Theoretical Computer Science, in press.

RORGT IL2_e IL10_e NFAT FOXP3 STAT3 IL17 IL21_e STAT4 IL2R IL21 IL10 GATA3 proliferation APC STAT5 TGFB IL6_e TGFB_e IFNG STAT1 IL4_e IL23 IL2 STAT6 IFNG_e IFNB_e IL15_e IL2RA IL4 IL27_e TBET IL12_e IL23_e

Reduced logical model

13 input components, 21 internal components

Selected environments for simulations

APC IL2 IL4 IL6 IL10 IL12 IFNG TGFB No input APC Pro-Th1 Pro-Th1’ Pro-Th2 Pro-Th17 Pro-Treg Pro-Treg’

IL2R IL2RA IFNG IL2 IL4 IL10 IL21 IL23 TGFB TBET GATA3 FOXP3 NFAT STAT1 STAT3 STAT4 STAT5 STAT6 proliferation RORGT IL17 Support

Th0 [7] Activated Th0 [7] Th1 [7] Activated Th1 [7] Anergic Th1 [78] Anergic Th1 RORγt+ predicted Th1 RORγt+ [44,45,70] Th1 Foxp3+ [12] Anergic Th17 Th2 [7] Activated Th2 [7] Anergic Th2 [78] Th2 RORγt+ [49] Activated Treg [79] Treg RORγt+ [46–48] Th1 Foxp3+ RORγt+ predicted Th2 Foxp3+ RORγt+ predicted

Stable signatures

Asynchronous simulations in the absence of stimulation

GATA3, Tbet, Foxp3 and RORγt

Pro Th2 environment (IL4 & IL6)

GATA3, Tbet, Foxp3 and RORγt

Pro Treg environment (IL2 & TGFb | IL10)

GATA3, Tbet, Foxp3 and RORγt

Overview of the simulation results for ≠ micro-environments

Absence of stimulation Pro-Th1 IL2 & IFNg

• r IL12

APC only Pro-Th2 IL4 & IL6 Pro-Treg IL2 & TGFb

• r IL10

Pro-Th17 IL6 & TGFb

GATA3 Tbet Foxp3 RORγt

Naldi et al (2010) PLoS Comput Biol 6: e1000912.

Regulatory circuit analysis

Functional positive circuits Negative circuits

Conclusions and prospects

• Model reproducing the main reported Th subtypes (Th0, Th1,

Th2, Treg, Th17) in terms of stable states

• More stable states depending on signalling environment,

including hybrid subtypes

• Plasticity of Th subtypes depending on signalling environment

=> differentiation network

• Validate experimentally the existence of unreported hybrid cell

types and reprogramming conditions

• Extension of the model to include other factors (e.g. epigenetic

factors), pathways and cell types

• Towards a multi-cellular model
• Towards a comprehensive model for hematopoietic cell

specification

PU1 CEBPa Egr/Nab

Runx1 GATA2 SCL Fli1 PU1 GATA1 CEBPa

GATA1 FOG1

GATA1 GATA2 Runx1 PU1

GATA1 KLF1

GATA2 PU1 CEBPa PU1 EBF E2a Notch1 GATA3 E2a

EBF E2a IL7R Pax5

CEBPa PU1 Gfi1

HSC MPP MEP MegaP EryP CLP GMP Mono, Mac, DC Neut BcP TcP

KLF1 Fli1 Gf1 EBF PU1 CEBPa GATA1 Fli1

MCP EoP/BaP

FOG1 CEBPa Notch1 EGR/Nab GATA3

NK

GATA2 Fli1 SCL Notch GATA3 Notch IL7R E2a EBF Pax5 Runx1 PU1 CEBPa

PU1 GATA1 CEBPa

CMP

Nab/EGR PU1 PU1

CEBPa GATA2 GATA1 PU1

GATA1 GATA2 PU1 GATA1

Regulatory switches involved in hematopoietic cell specification

Towards an integrative logical model for hematopoietic cell specification

Use of functional genomic data (ChIP-seq, transcritptome) to complete the network => talk by Jacques van Helden

Modelling of cell fate decisions: further reading

• Calzone et al (2010). Mathematical Modelling of Cell-Fate Decision in

Response to Death Receptor Engagement. PLoS Computational Biology 6: e1000702.

• Fauré et al (2009). Modular logical modelling of the budding Yeast cell cycle.

Molecular Biosystems 5: 1787–96.

• Fauré et al (2006). Dynamical analysis of a generic Boolean model for the

control of the mammalian cell cycle. Bioinformatics 22: e124-31.

• Sahin et al (2009). Modeling ERBB receptor-regulated G1/S transition to

find targets for de novo trastuzumab resistance. BMC Systems Biology 3: 1.

• González et al (2008). Qualitative dynamical modelling of the formation of

the anterior-posterior compartment boundary in the Drosophila wing imaginal disc. Bioinformatics 24: i234-40.

• Sánchez et al (2008). Segmenting the fly embryo: logical analysis of the role
• f the Segment Polarity cross-regulatory module. International Journal of

Developmental Biology 52: 1059-75.

Contributors & supports

★ TAGC (Marseille)

• Elodie Darbo
• Adrien Fauré
• Luca Grieco
• Carl Herrmann
• Cyrille Lepoivre
• Fabrice Lopez
• Abibatou Mbodj
• Aurélien Naldi
• Denis Puthier

★ IML (Marseille)

• Elisabeth Rémy
• Duncan Berenguier

★ ULB (Brussels)

• Jacques van Helden

★ IGC (Lisbon)

• Claudine Chaouiya
• Jorge Carneiro

★ CRG (Barcelona)

• Thomas Graf