A model for T cell differentiation Natasa Miskov-Zivanov University - PowerPoint PPT Presentation

PI Meeting University of Maryland April 2011 000 p 1 001 101 011 p 3 p 2 010 100 111 110 A model for T cell differentiation Natasa Miskov-Zivanov University of Pittsburgh

Acknowledgements 2  Faeder Lab:  Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh  John Sekar, James Faeder  Morel Lab:  Department of Immunology, School of Medicine, University of Pittsburgh  Michael Turner, Penelope Morel  Clarke Lab:  Computer Science Department, Carnegie Mellon University  Paolo Zuliani, Haijun Gong, Qinsi Wang, Edmund Clarke PI Meeting, April 2011

Timeline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tatistical model checker 1$!*/ New )?A K#? -L "0#8 % mode 1&!&J "&34 l Model !67 Trace statistics Simulations :&;)#- )?O $!002 &1#28&1#- New 1,02 )%39 $30- $30N experiments More 4(8!& 4(8 $ 9 51, trace 3)0 =40 :&;)#2 s? 1$!*/ 1&!&J (CA =I@ 4(8!& 4(8 $ 9 !"82 (CM5/ %&'() 4(8!& !"82 1&!&J 4(8!& 1$!*/ 1&!&J .<8-) # 4(8!& !"82 4(8 $ 9 (CM5/ 1&!&J !"#$* ! + + .<8- 4(8!& !"82 4(8 $ 9 !"#$ Kickoff NSF Meeting PI Review PI Meeting October 2009 March 2010 October 2010 April 2011 PI Meeting, April 2011

Today’s talk 4 System Methodology Antigen presenting Model design cell (APC) Model elements Influence sets Experiments Naïve (Interaction map) Expert T cell knowledge Set of discrete values Literature for each element Influence table Circuit Model design Helper T Regulatory T analysis methods (Th) cells (Treg) cells Model rules Model simulations PI Meeting, April 2011

Origins of 5 Regulatory T cells (Treg)  Treg cells mediate antigen- specific suppression of T cell activation  Play a key role in maintaining tolerance  Naïve T cells can be converted into Treg cells in the periphery  High therapeutic potential PI Meeting, April 2011

Role in cancer 6 Antigen presenting cell (APC) Tumor cell Naïve T cell Tumor secreted cytokines ( e.g ., TGF β ) Helper T Regulatory T (Th) cells (Treg) cells Release Release cytokines that cytokines stimulate the that inhibit immune the immune response response PI Meeting, April 2011

Determinants of 7 differentiation  Determine whether known mechanisms are sufficient to explain experimental observations  Foxp3 transcription factor is critical for Treg function Huehn et al. Nat. Rev. Immunol. 9 , 83-89 (2009) PI Meeting, April 2011

Challenges for Modeling 8  Large number of components and interactions  Rapidly evolving list of important components and interactions  structural uncertainty in the model  Involvement of multiple processes  signaling  gene regulation  protein expression  (cell division) PI Meeting, April 2011

Network model 9 ?D7EF?7EC@  Receptors: !"# E@GEHE7EC@ 7>?@ABCD?7EC@ $%# #*+,  T cell receptor (TCR) .<8-K 3P &'( !  Co-stimulation through CD28 # ! " #*-+ &'( ! ) .<8-) &#)  IL-2 receptor (IL-2R) Model elements  TGF β receptor =!0/ "."/ "*02 "./0 (TGF β R) 1$!*/ )?A K#? -L "0#8 % 1&!&J "&34  Transcription Influence sets !67 (Interaction map) factors: :&;)#- &1#28&1#- )?O $!002  AP-1, NFAT, NF κ B, 1,02 )%39 SMAD3, STAT5 $30- $30N 4(8!& 4(8 $ 9 51, 3)0 =40 :&;)#2  Genes:  IL-2, CD25, Foxp3 1$!*/ 1&!&J (CA =I@ 4(8!& 4(8 $ 9 !"82 (CM5/  Other important 4(8!& 1$!*/ %&'() 4(8!& !"82 1&!&J 1&!&J elements: .<8-) # 4(8!& !"82 4(8 $ 9 !"#$* ! + +  PTEN, PI3K, PIP3, PDK1, (CM5/ 1&!&J  Akt, mTORC1, mTORC2, .<8- 4(8!& !"82 4(8 $ 9 !"#$  TSC1-TSC2, Rheb, S6K1, pS6 PI Meeting, April 2011

Influence sets 10 Element Influence set Element Influence set PI3K TCR, CD28, IL-2, IL-2R AP-1 Fos, Jun Akt PDK1, mTORC2 ERK Ras Model elements mTORC1 Rheb, PKC- θ JNK Ras mTORC2 PI3K, S6K1 Fos ERK Influence sets (Interaction map) Foxp3 NFAT, AP-1, STAT5, Smad3 Jun JNK IL-2 NFAT, AP-1, NF κ B, Foxp3 NFAT Ca CD25 NFAT, AP-1, NF κ B, STAT5, Ca TCR Foxp3 STAT5 IL-2, IL-2R PDK1 PIP3 NF κ B PKC- θ , Akt TSC1-TSC2 Akt Smad3 TGF β , Akt, mTORC1 Rheb TSC1-TSC2 PIP3 PI3K, PTEN S6K1 mTORC1 Ras TCR, CD28, IL-2, IL-2R pS6 S6K1 PI Meeting, April 2011

Circuit design: Variables 11  Number of values for variables  Example: three levels for modeling TCR necessary  No antigen  Low antigen dose Model elements  High antigen dose Influence sets (Interaction map) Set of discrete values for each element PI Meeting, April 2011

Circuit design: Variables 12  Number of values for variables  Example: three levels for modeling TCR necessary  No antigen (TCR_LOW = 0, TCR_HIGH = 0)  Low antigen dose (TCR_LOW = 1, TCR_HIGH = 0) Model elements  High antigen dose (TCR_LOW = 0, TCR_HIGH = 1)  encoded with two Boolean variables Influence sets (Interaction map) Set of discrete values for each element PI Meeting, April 2011

Circuit design: Variables 13  Number of values for variables  Example: three levels for modeling TCR necessary  No antigen (TCR_LOW = 0, TCR_HIGH = 0)  Low antigen dose (TCR_LOW = 1, TCR_HIGH = 0) Model elements  High antigen dose (TCR_LOW = 0, TCR_HIGH = 1)  encoded with two Boolean variables Influence sets (Interaction map)  Example: three levels for modeling PI3K necessary  Low and high level of PI3K have different impact Set of discrete values for each on mTORC2 element PI Meeting, April 2011

Low Antigen Dose Trajectory 14 ;4K8KGEPGKBQFQB ?D7EF?7EC@ !"# ;4K8KBCRKBQFQB E@GEHE7EC@ ;(( 7>?@ABCD?7EC@ $%# #*+, .<8-K 3S &'( ! Trajectory Summary # ! " #*-+ &'( ! ) .<8-) &#) TCR =!0/ "."/ "*02 "./0 PI3K 1$!*/ )?A PTEN K#? -L "0#8 % "&34 1&!&J PIP3 !67 AKT )?O $!002 :&;)#- &1#28&1#- MTORC1 1,02 $30- $30N S6K1 )%39 4(8!& 4(8 $ 9 MTORC2 51, 3)0 =40 :&;)#2 STAT5 IL-2 (CA =I@ CD25 4(8 $ 9 1&!&J 4(8!& 1$!*/ FOXP3 !"82 (CM5/ 4(8!& 1$!*/ %&'() 4(8!& !"82 1&!&J 1&!&J value = ON_HIGH value = ON_ LOW 4(8!& !"82 4(8 $ 9 !"#$* ! + + (CM5/ 1&!&J value = OFF 4(8!& !"82 4(8 $ 9 !"#$ PI Meeting, April 2011

High Antigen Dose Trajectory 15 >8101IGQI1DRHRD AF;GHA;GEB !"# >8101DES1DRHRD GBIGJG;GEB >(( ;@ABCDEFA;GEB $%# #*+, ./0-1 23 &'( ! Trajectory Summary # ! " #*-+ &'( ! ) ./0-) &#) TCR ?!54 "."4 "*57 ".45 PI3K 6$!*4 PTEN )AC 1#A -M "5#0 % "&28 6&!&L PIP3 !:; AKT )AP $!557 =&>)#- &6#70&6#- MTORC1 S6K1 6,57 $25- $25O )%2< 8(0!& 8(0 $ < MTORC2 96, STAT5 2)5 ?85 =&>)#7 IL-2 CD25 (EC ?KB 8(0 $ < 6&!&L 8(0!& 6$!*4 FOXP3 !"07 (EN94 8(0!& 6$!*4 %&'() 8(0!& !"07 6&!&L 6&!&L value = ON_HIGH value = ON_ LOW 8(0!& !"07 8(0 $ < !"#$* ! + + (EN94 6&!&L value = OFF 8(0!& !"07 8(0 $ < !"#$ PI Meeting, April 2011

Circuit design: Influence tables 16 Example 1: Example 2: 2-level mTORC1 3-level PI3K, 2-level mTORC2 Rheb PI3K 0 1 0 1 2 PKC- Θ S6K1 0 0 1 0 0 1 1 1 0 1 1 0 0 1 Example 3: 3-level Foxp3 STAT5,mTOR 00 01 02 10 11 12 20 21 22 NFAT, Smad3 00 0 0 0 0 1 2 0 1 2 01 0 0 0 0 0 1 0 1 or 0 1 02 0 0 0 0 0 0 0 0 0 10 0 1 2 1 2 2 1 or 2 2 2 11 0 0 1 0 1 1 0 or 1 1 1 12 0 0 0 0 0 0 0 1 or 0 1 20 1 2 2 2 2 2 2 2 2 21 0 1 1 1 1 1 1 1 2 22 0 0 0 0 0 0 0 1 1 * : Rule 1, * : Rule 2 PI Meeting, April 2011

Example 1: 2-level mTORC1 17 Rheb 0 1 Model elements PKC- Θ mTORC1’ = Rheb and PKC- θ 0 0 0 1 0 1 ‘and’ rule means both are Influence sets necessary for activation (Interaction map) Rheb 0 1 Set of discrete PKC- Θ mTORC1’ = Rheb values for each 0 0 1 element 1 0 1 Influence tables Rheb 0 1 PKC- Θ mTORC1’ = Rheb or PKC- θ 0 0 1 ‘or’ rule means either one Model rules 1 1 1 is sufficient for activation PI Meeting, April 2011