.

March 24, 2015

Natal van Riel, Eindhoven University of Technology, the Netherlands

Dept. of Biomedical Engineering

Systems Biology and Metabolic Diseases

n.a.w.v.riel@tue.nl

@nvanriel

Metabolic Syndrome

• Cluster of metabolic abnormalities

• obesity

• hypertension

• insulin resistance

• high TG

• low HDL-C

Rask-Madsen et al. (2012) Arterioscler Thromb Vasc Biol,

32(9):2052-2059

dyslipidemia

Systems Medicine of Metabolic Syndrome

• Resolving the disturbed dynamics and mechanisms

underlying the high triglyceride and low HDL

cholesterol phenotype and insulin resistance

• In patients with the metabolic syndrome and its

associated co-morbidities like cardiovascular disease,

type 2 diabetes, and fatty liver disease

• Through combining basic pre-

clinical and clinical research,

network analysis and

computational modeling

/ biomedical engineering PAGE 325-3-2015 www.resolve-diabetes.org

A mouse model for Metabolic Syndrome

• APOE*3-Leiden.CETP mouse (Cholesteryl ester transfer protein)

model for human-like lipoprotein metabolism

• high-fat diet (HFD / 60% energy)

obesity, hyperglycemia, impaired

glucose tolerance, insulin resistance

• HFD + 0.4% cholesterol

dyslipidemia, hepatic steatosis,

atherosclerosis

Westerterp et al. Arterioscler Thromb Vasc Biol. 2006; 26(11):2552-9

Yanan Wang, Yvonne Rozendaal

Mechanism-based model

• Differential equations

• Integrated system dynamics

• Prediction

Tiemann et al., PLOS Comput Biol 2013

Data analysis in longitudinal studies

• Mathematical parameters inferred from data

• Estimation of unobserved metabolic parameters

• At unobserved time points

• Data: black bars and white dots

• Model: the darker the more

likely

• Variability in data

differences in

accuracy of

mathematical

parameters

quantification of

uncertainty in

predictions

• ADAPT: Analysis of Dynamic

Adaptations in Parameter

Trajectories

Modelling and monitoring of intervention

• Liver X Receptor (LXR, nuclear receptor),

induces transcription of multiple genes

modulating metabolism of fatty acids,

triglycerides, and lipoproteins

• LXR agonists increase plasma high density

lipoprotein cholesterol (HDLc)

• LXR as target for anti-

atherosclerotic therapy?Levin et al, (2005) Arterioscler

Thromb Vasc Biol. 25(1):135-42

LDLR-/-

RXR: retinoid X receptor Calkin & Tontonoz 2012

Modelling and monitoring of intervention

• Treated with T0901317 for 1, 2, 4, 7, 14, and 21 days

• Hypothesis 1: increase in HDLc is the result of increased

peripheral cholesterol efflux to HDL

Grefhorst et al. Atherosclerosis, 2012, 222: 382– 389

0 10 200

100

200Hepatic TG

Time [days]

[um

ol/g]

0 10 200

1

2

3Hepatic CE

Time [days]

[um

ol/g]

0 10 200

2

4

6Hepatic FC

Time [days]

[um

ol/g]

0 10 200

50

100Hepatic TG

Time [days]

[um

ol]

0 10 200

0.5

1

1.5Hepatic CE

Time [days]

[um

ol]

0 10 200

2

4Hepatic FC

Time [days]

[um

ol]

0 10 200

1000

2000

3000Plasma CE

Time [days]

[um

ol/L]

0 10 200

1000

2000

3000HDL-CE

Time [days]

[um

ol/L]

0 10 200

500

1000

1500Plasma TG

Time [days]

[um

ol/L]

0 10 206

8

10

12VLDL clearance

Time [days]

[-]

0 10 20100

200

300

400ratio TG/CE

Time [days]

[-]

0 10 200

5

10

15VLDL diameter

Time [days]

[nm

]

0 10 200

1

2

3VLDL-TG production

Time [days]

[um

ol/h]

0 10 201

2

3Hepatic mass

Time [days]

[gra

m]

0 10 200

0.2

0.4DNL

Time [days]

[-]

Analysis: HDL cholesterol

Analysis: increased excretion of cholesterol

Observation: increased HDLc

• SR-B1 (Scavenger Receptor-B1)

• Protein activity:

Reduced presence of SR-B1 in liver

membranes contributes to induction of HDLc

• HDL excretion and uptake flux

are increased

Tiemann et al., PLOS Comput Biol 2013

SR-B1 protein content is decreased in

hepatic membranes

Sr-b1 mRNA

expression not

changed

model: decreased

hepatic capacity to

clear cholesterol

Hepatic steatosis

• Hypothesis 2: LXR-induced hepatic steatosis is caused by an

increase in de novo lipogenesis (DNL)

Liver section of mice

treated 4 days with LXR

agonist T0901317

Oil-Red-O staining for

neutral fat

hepatic steatosis

0 10 200

100

200Hepatic TG

Time [days]

[um

ol/g]

0 10 200

1

2

3Hepatic CE

Time [days]

[um

ol/g]

0 10 200

2

4

6Hepatic FC

Time [days]

[um

ol/g]

0 10 200

50

100Hepatic TG

Time [days]

[um

ol]

0 10 200

0.5

1

1.5Hepatic CE

Time [days]

[um

ol]

0 10 200

2

4Hepatic FC

Time [days]

[um

ol]

0 10 200

1000

2000

3000Plasma CE

Time [days]

[um

ol/L]

0 10 200

1000

2000

3000HDL-CE

Time [days]

[um

ol/L]

0 10 200

500

1000

1500Plasma TG

Time [days]

[um

ol/L]

0 10 206

8

10

12VLDL clearance

Time [days]

[-]

0 10 20100

200

300

400ratio TG/CE

Time [days]

[-]

0 10 200

5

10

15VLDL diameter

Time [days]

[nm

]

0 10 200

1

2

3VLDL-TG production

Time [days]

[um

ol/h]

0 10 201

2

3Hepatic mass

Time [days]

[gra

m]

0 10 200

0.2

0.4DNL

Time [days]

[-]

Increased hepatic FFA influx is the initial

contributor to hepatic TG accumulation

• [13C]16-palmitate infusion

Hijmans et al. (2014) FASEB J.

SFA = saturated fatty acid

C16:0 palmitate

C18:0 stearate

MUFA = monounsaturated fatty acid

C16:1 palmitoleate

C18:1 oleate

Agenda WG 4 session• 15.45-16.00 WG4 Natal Van Riel, Eindhoven University of

Technology

“Modelling and monitoring of intervention”

• 16.00-16.15 WG4 Peter Allegrini, Novartis (TBC)

“Imaging and interventions”

• 16.15-16.30 WG 4 3 min presentation from working group

participants

• Peter McCourt, University of Tromsø

• Carly Taylor, ETH Zurich

• Enrico Dall'Ara, University of Sheffield

• 16.30-17.00 Round table discussion on imaging and modelling

technologies

13