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

Logical modelling of hematopoietic cell fate decisions Denis Thieffry Brussels, March 21, 2011

Cell proliferation, differentiation or 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 Red bars indicate the stages at which hematopoietic development is blocked for different gene knockouts . Factors associated with oncogenesis 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. Orkin & Zon (2008) Cell 132 : 631-44.

Haematopoietic cell phenotypes of mice lacking transcriptional regulators 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. Laiosa et al (2006) Annu Rev Immunol 24 : 705-38.

Reprogramming of Haematopoietic cells Orange arrows depict lineage reprogramming upon expression of the transcription factors GATA-1, C/EBP, or 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.

CD4+ T-helper cell differentiation Multiple signalling pathways Various transcriptional factors Specific expression patterns (TFs and lymphokines)

Modelling of Th activation and differentiation Boolean model focusing on signalling • Insights regarding input/output relationships • Identification of intervention points Klamt et al (2006). BMC Bioinformatics 7 : 56.

Modelling of peripheral Th1/Th2 cell differentiation Multilevel logical model The model recapitulates Positive circuits <=> multiple stable states the differentiation of Th0 cells into Th1, Th1* and Th2 subtypes Mendoza L (2006). BioSystems 84 : 101-14.

GINsim : a software dedicated to the logical modelling of biological regulatory networks Aurélien NALDI Fabrice LOPEZ Duncan BERENGIER analysis toolbox Claudine CHAOUIYA core simulator State transition graph GINML parser user interface Regulatory graph graph simulation editor graph analysis Available at http://gin.univ-mrs.fr/GINsim Naldi et al (2009) BioSystems 97 : 134-9

Towards a comprehensive, modular logical model of the Th differentiation network $% $%& ( $%& ' !"# !"#" ILR = 1 IFF IL AND ILR1 AND ILR2 Yamoka et al (2004)

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Logical modelling of the Th network Multiple uses !-./$% !"3$% !"#$% of receptor chains !"#&0 !-./&, !-./&# !"#&' !"3&+ !"#&( !-./& !"3& !"#& Converging Ternary signals variables )*+*, )*+*2 )*+*1

Current logical model of the Th network IL27_e IL21_e IL23_e TGFB_e APC IFNB_e IFNG_e IL6_e IL10_e IL12_e IL4_e IL15_e IL2_e proliferation CGC IL15RA IFNGR2 GP130 IL2RA IL12RB1 IL12RB2 IL4RA IL2RB IFNGR1 IL27RA IL6RA IL10RA IL10RB CD28 TCR IFNBR IFNGR IL27R TGFBR IL12R IL15R IL6R IL21R IL23R IL10R IL4R IL2R NFAT STAT5 STAT4 STAT6 STAT1 STAT3 IKB IL21 IL23 TGFB IL17 IL10 IL4 IL2 NFKB IFNG SMAD3 IRF1 RUNX3 TBET GATA3 RORGT FOXP3 13 input components, 52 internal components, 339 circuits => too large to perform simulations Naldi et al (2010) PLoS Comput Biol 6 : e1000912.

Model Reduction ✓ Keep the detailed model R2 R1 ✓ User defined reductions ✓ Reduction before analysis X T => New rules for targets R3 of hidden nodes ✓ Iterative procedure ✓ Dynamical consistency R2 R1 - No circuit deletion T - Same stable states - Reachability may change R3 Naldi et al (2011). Theoretical Computer Science , in press.

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

Stable signatures proliferation RORGT Support GATA3 FOXP3 STAT1 STAT3 STAT4 STAT5 STAT6 IL2RA TGFB TBET NFAT IFNG IL2R IL10 IL21 IL23 IL17 IL2 IL4 Th0 [7] Activated Th0 [7] Th1 [7] Activated Th1 [7] Anergic Th1 [78] Anergic Th1 predicted ROR γ t+ Th1 ROR γ t+ [44,45,70] Th1 Foxp3+ [12] Anergic Th17 Th2 [7] Activated Th2 [7] Anergic Th2 [78] Th2 ROR γ t+ [49] Activated Treg [79] Treg ROR γ t+ [46–48] Th1 Foxp3+ predicted ROR γ t+ Th2 Foxp3+ predicted ROR γ t+

Asynchronous simulations in the absence of stimulation GATA3, Tbet, Foxp3 and ROR γ t

Pro Th2 environment (IL4 & IL6) GATA3, Tbet, Foxp3 and ROR γ t

Pro Treg environment (IL2 & TGFb | IL10) GATA3, Tbet, Foxp3 and ROR γ t

Overview of the simulation results for ≠ micro-environments Absence of APC only stimulation Pro-Th1 Pro-Th2 IL2 & IFNg IL4 & IL6 or IL12 Pro-Treg Pro-Th17 IL2 & TGFb IL6 & TGFb or IL10 GATA3 Tbet Foxp3 ROR γ t Naldi et a l (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

HSC Regulatory GATA2 Runx1 GATA2 switches involved SCL Fli1 Runx1 Fli1 SCL in hematopoietic PU1 cell specification MPP PU1 CEBPa GATA1 CEBPa CLP GATA1 PU1 PU1 EBF CMP E2a PU1 GATA1 MEP CEBPa IL7R GATA1 PU1 FOG1 Notch EBF GATA1 FOG1 CEBPa PU1 GATA2 E2a Fli1 KLF1 GMP CEBPa PU1 CEBPa Notch EBF GATA1 GATA1 KLF1 Notch1 Fli1 GATA3 Nab/EGR GATA2 Gf1 EGR/Nab Pax5 PU1 GATA3 EryP MegaP NK CEBPa Notch1 GATA2 EBF CEBPa GATA1 GATA1 GATA3 PU1 E2a PU1 GATA2 PU1 E2a CEBPa IL7R Runx1 Gfi1 Pax5 Egr/Nab PU1 EoP/BaP TcP Neut BcP MCP Mono, Mac, DC

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  of 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 •