Logical Modeling Peripheral T Cell Differen5a5on Jim Faeder - PowerPoint PPT Presentation

Logical Modeling Peripheral T Cell Differen5a5on Jim Faeder Department of Computa.onal and Systems Biology CMACS PI Mee5ng New York University October 29, 2010

Acknowledgements Faeder Lab • Department of Computa5onal and Systems Biology – Natasa Miskov‐Zivanov – John Sekar – Leonard Harris – Jus5n Hogg – Jintao Liu – Arshi Arora – Jose Tapia Morel and Kane Labs • Department of Immunology – Michael Turner – Lawrence Kane – Penelope Morel • Funding: – NSF (Expedi5ons in Compu5ng) – NIH (P01, Dendri5c Cell Vaccines)

Peripheral T cell differen5a5on • T cell subpopula5on ra5os are cri5cal for numerous immune and auto‐immune pathologies Source : Ochs et al., J Allergy Clin Immunol, 2009.

Peripheral T cell differen5a5on • T cell subpopula5on ra5os are cri5cal for numerous immune and auto‐immune pathologies • Key target for immunomodula5on therapy in cancer* B. Tumor cell TLR TLR DC DC TLR EGFR VEGFR Co-stim Co-stim Cytokine/ PD-L1 chemokine MHC class I MHC class II receptors Immune activation co-stimulation TLR adjuvants T TLR CD137 CD8+ Treg Teffectors TLR Proliferation/ CTL differentiation * Whiteside, T.L. “Inhibi5ng the Proliferation/ TCR TLR Inhibitors...”, Expert Opin. Biol. differentiation Cytokine/ Adoptive T-cell chemokine transfers MDSC Ther. (2010), 10 , 1019. receptors Apoptosis

The Journal of Immunology Dominant Role of Antigen Dose in CD4 � Foxp3 � Regulatory T Cell Induction and Expansion 1 Michael S. Turner, Lawrence P. Kane, and Penelope A. Morel 2 Naïve T cells s5mulated with low Ag doses produce a high percentage of regulatory cells, which falls off as dose is increased.

The Journal of Immunology Dominant Role of Antigen Dose in CD4 � Foxp3 � Regulatory T Cell Induction and Expansion 1 Michael S. Turner, Lawrence P. Kane, and Penelope A. Morel 2 Inverse correla5on between Foxp3+ Treg expansion and TCR signaling via Akt/mTOR/pS6.

Key Findings • Treg induc5on is determined by Ag dose • Mechanism is T cell intrinsic – Observed with both iDC and mDC – Observed with plate‐bound an5‐CD3/CD28 • Inverse correla5on between mTOR ac5va5on at 18h and Foxp3+ Treg at 7 days • No exogenous TGF‐β

Modeling Goals • Determine whether known mechanisms are sufficient to explain experimental observa5ons. • Suggest addi.onal experiments to iden5fy missing mechanisms and clarifying areas of uncertainty . • Iden5fy other early markers of the response. • Incorporate signals through other receptors  predic.ve model.

Rule‐Based Modeling of Signal Transduc5on Wiring diagram Object‐oriented model of protein � - CD28 � - CD3 � - IgG Fc Fc 21. PLC γ 1 Fab Fab Fab Fab Fab Fab E E E Gene names: PLCG1, PLC1 � � TCR/CD3 Uniprot accession number: P19174 � � � � CD28 CD28 � � ITAM ITAM ITAM2 ITAM2 Molecule type definiton: PLCG1(SH2 N,SH2 C,Y771 ∼ u ∼ p,Y775 ∼ u ∼ p,Y783 ∼ u ∼ p) PRS PRS Domain structure: M M pY199 pY111 pY123 Lck Itk ZAP-70 ZAP-70 Fyn SH2 SH2-N SH2-C PTK PRS SH3 PTK SH3 SH2 PTK SH3 SH2 PTK C C M pY237 pY240 pY273 C pY192 pY292 pY531 pY315 pY512 pY493 Ub n -Lys pY394 pY505 pY319 SLAP-130 C pY571 SHP-1 UbcH7 pY61 CD45 In the map of molecular interactions, PLC γ 1 is represented with the following graph: SH2-N PTP PTP M Cbl C SLP-76 pY113 PLC � 1 RING TKB PRS pY132 PRS RxxK SH2 LAT pY128 pY191 Vav1 C pY145 SH2-N SH2-C M pY267 C pY731 DH SH2 Cbp/PAG pY280 pY826 PRS pY783 C M pY775 pY771 pY317 GDP GTP PLC � 1 Nck1 Gads Csk Grb2 SH3-3 SH2 SH2-N SH2-C Cdc42 SH2 SH3-C Phospholipase C γ 1 is an enzyme essential for T cell activation ( 127 ). It cleaves phos- SH3-N SH2 SH2 RHO C C C pY783 C phatidylinositol 4,5-bisphosphate, generating the second messengers diacyl glycerol (DAG) C pY771 pY775 C and inositol 1,4,5-trisphosphate (IP 3 ) ( 128 ). IP 3 binds to receptors on the endoplasmic Sos1 WASp PBD PRS WH2 reticulum, leading to release of Ca 2+ ( 129 ). Itk phosphorylates PLC γ 1 on Y783, which PRS C C is important for activation ( 51,130,131 ). PLC γ 1 binds to phosphorylated LAT ( 111 ). The pY291 Hu, Chylek, and Hlavacek, in prepara5on.

Rule‐Based Modeling of Signal Transduc5on Wiring diagram � - CD28 � - CD3 � - IgG Fc Fc Fab Fab Fab Fab Fab Fab E E E � � TCR/CD3 � � � � CD28 CD28 � � ITAM ITAM ITAM2 ITAM2 B IO N ET G EN / NF SIM PRS PRS M M pY199 pY111 pY123 Lck Itk ZAP-70 ZAP-70 Fyn SH2 SH2-N SH2-C PTK PRS SH3 PTK SH3 SH2 PTK SH3 SH2 PTK a b C C M pY237 pY240 pY273 C A b pY192 pY292 a pY531 Molecule Types: B a C pY315 pY512 c pY493 Ub n -Lys pY394 pY505 pY319 SLAP-130 Reaction Volume Reaction Volume C pY571 SHP-1 A b UbcH7 pY61 CD45 A-B binding c SH2-N PTP PTP a A Reactants A b C M c Cbl C B Reactants SLP-76 Reaction Rules B a pY113 RING TKB PRS pY132 PRS RxxK SH2 LAT pY128 pY191 Vav1 C A-B unbinding pY145 A b B M a pY267 C pY731 DH SH2 A-B Reactants c Cbp/PAG pY280 pY826 PRS A b A-C binding B A b a C B M a c pY317 A Reactants c a GDP GTP C C Reactants PLC � 1 Nck1 Gads Csk Grb2 SH3-3 SH2 SH2-N SH2-C Cdc42 SH2 SH3-C a SH3-N SH2 SH2 C a RHO C C C C pY783 C C pY771 pY775 C Sos1 WASp PBD PRS WH2 PRS C C pY291 Hu, Chylek, and Hlavacek, in prepara5on.

Rule‐Based Modeling of Signal Transduc5on Wiring diagram � - CD28 � - CD3 � - IgG Fc Fc Fab Fab Fab Fab Fab Fab E E E Issues � � TCR/CD3 � � � � CD28 CD28 � � ITAM ITAM ITAM2 ITAM2 • Models are very Kme‐consuming to construct. B IO N ET G EN / NF SIM PRS PRS M M • Limited knowledge about wiring. pY199 pY111 pY123 Lck Itk ZAP-70 ZAP-70 Fyn • Lack of high‐resoluKon data. SH2 SH2-N SH2-C PTK PRS SH3 PTK SH3 SH2 PTK SH3 SH2 PTK a b C C M pY237 pY240 pY273 C A b pY192 pY292 a pY531 Molecule Types: B a C pY315 pY512 c pY493 Ub n -Lys pY394 pY505 pY319 • Lack of measured parameters. SLAP-130 Reaction Volume Reaction Volume C pY571 SHP-1 A b UbcH7 pY61 CD45 A-B binding c SH2-N PTP PTP a A Reactants A b C M c Cbl C B Reactants SLP-76 Reaction Rules B a pY113 RING TKB PRS pY132 PRS RxxK SH2 LAT pY128 pY191 Vav1 C A-B unbinding pY145 A b B M a pY267 C pY731 DH SH2 A-B Reactants c Cbp/PAG pY280 pY826 PRS A b A-C binding B A b a C B M a c pY317 A Reactants c a GDP GTP C C Reactants PLC � 1 Nck1 Gads Csk Grb2 SH3-3 SH2 SH2-N SH2-C Cdc42 SH2 SH3-C a SH3-N SH2 SH2 C a RHO C C C C pY783 C C pY771 pY775 C Sos1 WASp PBD PRS WH2 PRS C C pY291 Hu, Chylek, and Hlavacek, in prepara5on.

Rule‐Based Modeling of Signal Transduc5on Wiring diagram � - CD28 � - CD3 � - IgG Fc Fc Fab Fab Fab Fab Fab Fab E E E Issues � � TCR/CD3 � � � � CD28 CD28 � � ITAM ITAM ITAM2 ITAM2 • Models are very Kme‐consuming to construct. B IO N ET G EN / NF SIM PRS PRS M M • Limited knowledge about wiring. pY199 pY111 pY123 Lck Itk ZAP-70 ZAP-70 Fyn • Lack of high‐resoluKon data. SH2 SH2-N SH2-C PTK PRS SH3 PTK SH3 SH2 PTK SH3 SH2 PTK a b C C M pY237 pY240 pY273 C A b pY192 pY292 a pY531 Molecule Types: B a C pY315 pY512 c pY493 Ub n -Lys pY394 pY505 pY319 • Lack of measured parameters. SLAP-130 Reaction Volume Reaction Volume C pY571 SHP-1 A b UbcH7 pY61 CD45 A-B binding c SH2-N PTP PTP a A Reactants A b C M c Cbl C B Reactants SLP-76 Reaction Rules B a pY113 RING TKB PRS pY132 PRS RxxK SH2 LAT pY128 pY191 Vav1 C A-B unbinding pY145 A b B M a pY267 C pY731 DH SH2 A-B Reactants c Cbp/PAG pY280 We did not “stand and fight” this Kme. pY826 PRS A b A-C binding B A b a C B M a c pY317 A Reactants c a GDP GTP C C Reactants PLC � 1 Nck1 Gads Csk Grb2 SH3-3 SH2 SH2-N SH2-C Cdc42 SH2 SH3-C a SH3-N SH2 SH2 C a RHO C C C C Wisdom or cowardice? pY783 C C pY771 pY775 C Sos1 WASp PBD PRS WH2 PRS C C pY291 Hu, Chylek, and Hlavacek, in prepara5on.

A Simpler Approach Boolean Networks • The state of an element in the signaling network can be described by a Boolean variable , expressing that it is: – Ac5ve or present (on or ‘1’) – Inac5ve or absent (off or ‘0’) • Boolean funcKons : – Represent interac5ons between elements – The state of an element is calculated from states of other elements • The resul5ng network is a Boolean network • Long history of applica5ons to biology.

Logical Modeling Approach • Generaliza5on of Boolean – variables may have more than 2 values. • Systema5c study of the dynamics of large systems: – Depends largely on the interconnec5on structure • Does not require numerical parameters. • Discrete networks provide informa5on about: – Mul5‐sta5onarity – Stability – Oscillatory behavior • Highly relevant for obtaining qualitaKve measures – Perturba5ons – Environment – Alterna5ve wiring of the network

Boolean Network Modeling Example Biological network p 1 p 3 p 2 Proteins: p 1 , p 2 , p 3

Boolean Network Modeling Example Biological network Boolean network p 1 p 1 p 3 p 3 p 2 p 2 p 1 *= p 2 OR p 3 p 2 *= NOT p 1 AND p 3 Proteins: p 1 , p 2 , p 3 p 3 *= p 1 AND NOT p 3