thermodynamic modeling explains the regulation of cyp1a1 expression in the liver

C O M P U TAT I O N A LM O D E L L I N G I NM E D I C I N E

Pascal Schulthess

04.03.2016

Thermodynamic modeling explains the regulation of CYP1A1 expression in the liver Thesis defense

C O M P U TAT I O N A LM O D E L L I N G I NM E D I C I N E

Pascal Schulthess

04.03.2016

Thermodynamic modeling explains the regulation of CYP1A1 expression in the liver Thesis defense

Exposure to dioxin through food uptake

welt.de, wikipedia.org, beyondthedish.wordpress.com

CYPs biotransform toxins

Cytochrome P450s (CYPs)

‣ major enzymes in detoxification

‣ harbor broad substrate specificity

‣ increase hydrophilicity of compounds to ease their excretion in bile or urine

wikipedia.org

Dioxin

CYP1A1

Dioxin induces zonated expression of CYP1A1

Braeuning, A. & Schwarz, M. Biol. Chem. 391, 139–148 (2010) Braeuning, A., Köhle, C., Buchmann, A. & Schwarz, M. Toxicol. Sci. 122, 16–25 (2011).

rela

tive

mR

NA

exp

ress

ion [a

. u.]

0

3

6

9

100

150

200

250

300

Dioxin – +

CYP1A1primary mousehepatocytes

n=5

Dioxin induces zonated expression of CYP1A1

CYP1A

WT

P

C

Braeuning, A. & Schwarz, M. Biol. Chem. 391, 139–148 (2010) Braeuning, A., Köhle, C., Buchmann, A. & Schwarz, M. Toxicol. Sci. 122, 16–25 (2011).

rela

tive

mR

NA

exp

ress

ion [a

. u.]

0

3

6

9

100

150

200

250

300

Dioxin – +

CYP1A1primary mousehepatocytes

n=5

Dioxin induces zonated expression of CYP1A1

CYP1A

WT

P

C

β-ca

teni

n k.

o.

P

C

Braeuning, A. & Schwarz, M. Biol. Chem. 391, 139–148 (2010) Braeuning, A., Köhle, C., Buchmann, A. & Schwarz, M. Toxicol. Sci. 122, 16–25 (2011).

rela

tive

mR

NA

exp

ress

ion [a

. u.]

0

3

6

9

100

150

200

250

300

Dioxin – +

CYP1A1primary mousehepatocytes

n=5

Dioxin and β-catenin signaling converge on CYP1A1 promoter

AhR

Dioxin

Arnt

Dioxin and β-catenin signaling converge on CYP1A1 promoter

AhR

Dioxin

Arntβ-catenin

TCF

Wnt

Dioxin and β-catenin signaling converge on CYP1A1 promoter

AhR

Dioxin

Arntβ-catenin

TCF

Wnt

Dioxin and β-catenin signaling converge on CYP1A1 promoter

AhR

Dioxin

Arnt

CYP1A1

F E D T C

β-catenin

TCF

Wnt

Dioxin and β-catenin signaling converge on CYP1A1 promoter

How are the two signals integrated?

AhR

Dioxin

Arnt

CYP1A1

F E D T C

β-catenin

TCF

Wnt

Dioxin and β-catenin signaling converge on CYP1A1 promoter

How are the two signals integrated?

How do binding sites cooperate?

AhR

Dioxin

Arnt

CYP1A1

F E D T C

β-catenin

TCF

Wnt

1. Introduction of the modeling framework

2. Model of synthetic promoter

3. Model of wild-type CYP1A1 promoter

Outline

1. Introduction of the modeling framework

2. Model of synthetic promoter

3. Model of wild-type CYP1A1 promoter

Outline

Modeling approaches for gene expression

Level of detail

(Un)directed graphsBayesian networksBoolean networks

⁝

(Nonlinear) ODEsPDEs⁝

Thermodynamicstate ensembles

Mathematical model combines signaling and promoter logic

+

Thermodynamic model Signaling model

AhR

β-catenin

Dioxin

1

2

Arnt

εBP

εAPεAB

KPKAKB

Thermodynamic state ensemble model of gene expression

KP

Sherman MS, Cohen BA, PLoS Comput Biol 8(3):e1002407 (2012) Bintu L, et al., Curr Opin Genet Dev 15(2):116–124 (2004)

Thermodynamic state ensemble model of gene expression

KP

Sherman MS, Cohen BA, PLoS Comput Biol 8(3):e1002407 (2012) Bintu L, et al., Curr Opin Genet Dev 15(2):116–124 (2004)

Thermodynamic state ensemble model of gene expression

KP

εAP

KA

Sherman MS, Cohen BA, PLoS Comput Biol 8(3):e1002407 (2012) Bintu L, et al., Curr Opin Genet Dev 15(2):116–124 (2004)

Thermodynamic state ensemble model of gene expression

KP

εAP

KA

εBP

εAB

KB

Sherman MS, Cohen BA, PLoS Comput Biol 8(3):e1002407 (2012) Bintu L, et al., Curr Opin Genet Dev 15(2):116–124 (2004)

1. Introduction of the modeling framework

2. Model of synthetic promoter

3. Model of wild-type CYP1A1 promoter

Outline

Model of synthetic promoters1× C 2× C 3× C

4× C 5× C 6× C

1× D 2× D 3× D

CYP1A1

F E D T C

Model of synthetic promoters1× C 2× C 3× C

4× C 5× C 6× C

1× D 2× D 3× D

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]

Dioxin [nM]

5101520

5101520

0 0.5 5 50 250

5101520

0 0.5 5 50 250 0 0.5 5 50 250

CYP1A1

F E D T C

Model of synthetic promoters1× C 2× C 3× C

4× C 5× C 6× C

1× D 2× D 3× D

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]

Dioxin [nM]

5101520

5101520

0 0.5 5 50 250

5101520

0 0.5 5 50 250 0 0.5 5 50 250

Exp.

CYP1A1

F E D T C

Model of synthetic promoters1× C 2× C 3× C

4× C 5× C 6× C

1× D 2× D 3× D

model parameterssignaling 3

C constructs 13 (28)D constructs 1 (9)

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]

Dioxin [nM]

5101520

5101520

0 0.5 5 50 250

5101520

0 0.5 5 50 250 0 0.5 5 50 250

Exp.

CYP1A1

F E D T C

Model of synthetic promoters1× C 2× C 3× C

4× C 5× C 6× C

1× D 2× D 3× D

model parameterssignaling 3

C constructs 13 (28)D constructs 1 (9)

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]

Dioxin [nM]

5101520

5101520

0 0.5 5 50 250

5101520

0 0.5 5 50 250 0 0.5 5 50 250

Exp.Fit

CYP1A1

F E D T C

Model parameters unravel TF interactions

Binding Energy [kBT]

-20 -4 -6 -8

Model parameters unravel TF interactions

Only first TF interacts with polymerase

Binding Energy [kBT]

-20 -4 -6 -8

Model parameters unravel TF interactions

Only first TF interacts with polymerase

Only nearby TFs cooperate

Binding Energy [kBT]

-20 -4 -6 -8

Model parameters unravel TF interactions

Only first TF interacts with polymerase

Only nearby TFs cooperate

Binding Energy [kBT]

-20 -4 -6 -8

19bp 49bp

156bp 292bp

Model parameters unravel TF interactions

Only first TF interacts with polymerase

Only nearby TFs cooperate

Binding Energy [kBT]

-20 -4 -6 -8

19bp 49bp

156bp 292bp

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]

Dioxin [nM]

2

6

10

14

0 0.5 5 50 250

2

6

10

14

0 0.5 5 50 250

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Reduced induction for 4+ binding sites

Sequestration responsible for reduced induction

0.1

0.2

0

0.5

1

1.5

0

mean number of occupied binding sites

probability that first binding site is occupied

5

10

15

20

maximal induction

Prediction of dioxin and β-catenin signaling integration

Dioxin [nM]

β-ca

t Act

ivity

[%]

ExperimentPrediction3x C

100

28

93

6753

00 0.5 5 50 250

2015105Relative Luciferase Activity [a.u.]

Prediction of dioxin and β-catenin signaling integration

Dioxin [nM]

β-ca

t Act

ivity

[%]

ExperimentPrediction3x C

100

28

93

6753

00 0.5 5 50 250

2015105Relative Luciferase Activity [a.u.]

Prediction of dioxin and β-catenin signaling integration

10093675328

0 0.5 5 50 250

Dioxin [nM]

β-ca

t Act

ivity

[%]

ExperimentPrediction3x C

100

28

93

6753

00 0.5 5 50 250

2015105Relative Luciferase Activity [a.u.]

Prediction of dioxin and β-catenin signaling integration

Integration of the two signals follows AND gate logic

10093675328

0 0.5 5 50 250

Dioxin [nM]

β-ca

t Act

ivity

[%]

ExperimentPrediction3x C

100

28

93

6753

00 0.5 5 50 250

2015105Relative Luciferase Activity [a.u.]

Conclusion for synthetic promoter

How do binding sites cooperate?‣ only first binding site interacts with polymerase ‣ only nearby binding sites cooperate

How are the two signaling pathways integrated?‣ integration follows an AND gate logic

Conclusion for synthetic promoter

How do binding sites cooperate?‣ only first binding site interacts with polymerase ‣ only nearby binding sites cooperate

How are the two signaling pathways integrated?‣ integration follows an AND gate logic

Conclusion for synthetic promoter

How do binding sites cooperate?‣ only first binding site interacts with polymerase ‣ only nearby binding sites cooperate

How are the two signaling pathways integrated?‣ integration follows an AND gate logic

Does the same hold true for the

wild-type CYP1A1 promoter?

1. Introduction of the modeling framework

2. Model of synthetic promoter

3. Model of wild-type CYP1A1 promoter

Outline

Model of wild-type CYP1A1 promoterT CT

WT

TD TE

TF CTD CTE CTF TDE

TDF TEF TD CE

TDF C TF CE

TDFEDF CE

Model of wild-type CYP1A1 promoterT CT

WT

TD TE

TF CTD CTE CTF TDE

TDF TEF TD CE

TDF C TF CE

TDFEDF CE

Dioxin [nM]

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]1

3

5

7

0 0.5 5 50 250

1

3

5

7

1

3

5

7

1

3

5

7

0 0.5 5 50 2501

3

5

7

0 0.5 5 50 250

0 0.5 5 50 250 0 0.5 5 50 250

0 0.5 5 50 250

1

3

5

7

Model of wild-type CYP1A1 promoterT CT

WT

TD TE

TF CTD CTE CTF TDE

TDF TEF TD CE

TDF C TF CE

TDFEDF CE

Experiment

Dioxin [nM]

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]1

3

5

7

0 0.5 5 50 250

1

3

5

7

1

3

5

7

1

3

5

7

0 0.5 5 50 2501

3

5

7

0 0.5 5 50 250

0 0.5 5 50 250 0 0.5 5 50 250

0 0.5 5 50 250

1

3

5

7

Model of wild-type CYP1A1 promoterT CT

WT

TD TE

TF CTD CTE CTF TDE

TDF TEF TD CE

TDF C TF CE

TDFEDF CE

Experiment

model parameterssignaling 3

thermodynamic 16 (21)

Dioxin [nM]

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]1

3

5

7

0 0.5 5 50 250

1

3

5

7

1

3

5

7

1

3

5

7

0 0.5 5 50 2501

3

5

7

0 0.5 5 50 250

0 0.5 5 50 250 0 0.5 5 50 250

0 0.5 5 50 250

1

3

5

7

Model of wild-type CYP1A1 promoterT CT

WT

TD TE

TF CTD CTE CTF TDE

TDF TEF TD CE

TDF C TF CE

TDFEDF CE

Experiment Fit

model parameterssignaling 3

thermodynamic 16 (21)

Dioxin [nM]

Rel

ativ

e Lu

cife

rase

Act

ivity

[a.u

.]1

3

5

7

0 0.5 5 50 250

1

3

5

7

1

3

5

7

1

3

5

7

0 0.5 5 50 2501

3

5

7

0 0.5 5 50 250

0 0.5 5 50 250 0 0.5 5 50 250

0 0.5 5 50 250

1

3

5

7

Model parameters unravel TF interactions

Binding Energy [kBT]0 -4 -8 -12 -16 -20-18-14-10-6-2

synthetic promoter wild-type CYP1A1 promoter

Model parameters unravel TF interactions

Binding Energy [kBT]0 -4 -8 -12 -16 -20-18-14-10-6-2

synthetic promoter wild-type CYP1A1 promoter

Model parameters unravel TF interactions

More and stronger TF-RNAP interactions

Binding Energy [kBT]0 -4 -8 -12 -16 -20-18-14-10-6-2

synthetic promoter wild-type CYP1A1 promoter

Model parameters unravel TF interactions

β-catenin is important interaction partner

More and stronger TF-RNAP interactions

Binding Energy [kBT]0 -4 -8 -12 -16 -20-18-14-10-6-2

synthetic promoter wild-type CYP1A1 promoter

Prediction of dioxin and β-catenin signaling integration

Dioxin [nM]

β-ca

teni

n Ac

tivity

[%]

ExperimentPrediction

WT

0 0.5 5 50 250

10093675328

0 0.5 5 50 250

10093

6753

28

0

RLA

[a.u

.]

54321

3x C

100

28

93

6753

0

20

15

10

5 RLA

[a.u

.]

Prediction of dioxin and β-catenin signaling integration

Dioxin [nM]

β-ca

teni

n Ac

tivity

[%]

ExperimentPrediction

WT

0 0.5 5 50 250

10093675328

0 0.5 5 50 250

10093

6753

28

0

RLA

[a.u

.]

54321

3x C

100

28

93

6753

0

20

15

10

5 RLA

[a.u

.]

Prediction of dioxin and β-catenin signaling integration

Dioxin [nM]

β-ca

teni

n Ac

tivity

[%]

ExperimentPrediction

WT

0 0.5 5 50 250

10093675328

0 0.5 5 50 250

10093

6753

28

0

RLA

[a.u

.]

54321

3x C

100

28

93

6753

0

20

15

10

5 RLA

[a.u

.]

Prediction of dioxin and β-catenin signaling integration

Gradual AND gate enables finer regulation

Dioxin [nM]

β-ca

teni

n Ac

tivity

[%]

ExperimentPrediction

WT

0 0.5 5 50 250

10093675328

0 0.5 5 50 250

10093

6753

28

0

RLA

[a.u

.]

54321

3x C

100

28

93

6753

0

20

15

10

5 RLA

[a.u

.]

Conclusion for wild-type CYP1A1 promoter

How do binding sites cooperate?‣ more complex interaction patterns ‣ increased importance of TF-RNAP interactions ‣ β-catenin is an important interaction partner

How are the two signaling pathways integrated?‣ integration follows an AND gate logic ‣ gradual signal integration enables a finer regulation of

promoter response

Conclusion for wild-type CYP1A1 promoter

How do binding sites cooperate?‣ more complex interaction patterns ‣ increased importance of TF-RNAP interactions ‣ β-catenin is an important interaction partner

How are the two signaling pathways integrated?‣ integration follows an AND gate logic ‣ gradual signal integration enables a finer regulation of

promoter response

Conclusion for wild-type CYP1A1 promoter

How do binding sites cooperate?‣ more complex interaction patterns ‣ increased importance of TF-RNAP interactions ‣ β-catenin is an important interaction partner

How are the two signaling pathways integrated?‣ integration follows an AND gate logic ‣ gradual signal integration enables a finer regulation of

promoter response

Model qualitatively predicts hepatic zonationEx

perim

ent

Pred

ictio

n

Dioxin [nM]

PC

PC

PC

P

CP

C

P

CP

C

Model qualitatively predicts hepatic zonationEx

perim

ent

Pred

ictio

n

Dioxin [nM]

PC

PC

PC

P

CP

C

P

CP

C

Conclusion

The interactions at the signaling level, together with the TF cooperativity in the CYP1A1 promoter enable the

spatial expression pattern observed in vivo.

Schulthess P, et al. (2015) Signal integration by the CYP1A1 promoter - a quantitative study. Nucleic Acids Research 43(11):5318–5330.

β-cateninDioxin

Acknowledgements

C O M P U TAT I O N A LM O D E L L I N G I NM E D I C I N E

Nils BlüthgenManuela Benary Torsten Gross Bertram Klinger Johannes Meisig Mattias Rydenfelt

Jörn Schmiedel Anja Sieber Besray Ünal Florian Uhlitz Franziska Witzel

Albert BraeuningLuise Kreft Alexandra Löffler Michael Schwarz Silvia Vetter