COUNTERSTRIKE (COUNTERacting Sarcopenia with proTeins and exeRcise – Screening the CALM cohort for lIpoprotein biomarKErs) kick off seminar

1. COUNTERSTRIKE kick-off seminar
April 22, 2015 Copenhagen
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

2. Lipoprotein metabolism
• 3 types of lipoproteins
• Chylomicrons
• Very low density lipoproteins
(VLDL), apoB
• High density lipoproteins (HDL),
apoA
• A continuum of particles of
different size, different
composition of TG,
cholesterol and CE
• With distinct apo-
lipoproteins
• Lipoprotein distribution (LPD)
codetermines metabolic and
cardiovascular disease risks
0 10 20 30 40 50
FPLC(arbitrary
units)
Fraction number
VLDL
IDL/LDL
HDL

3. Plasma lipoprotein cholesterol profiles
• Fast protein liquid chromatography
Rigotti et al, 1997, PNAS 94: 12610-12615 PLTP
Jiang et al, J. Clin. Invest.
103:907–914 (1999).scavenger receptor class B type I (SR-B1)
plasma phospholipid transfer protein (PLTP)

4. Concept
• Fasted condition, no chylomicrons

5. Concept
• Particle size and heterogeneity
• Fasted condition, no chylomicrons
selective uptake
CE index
Triglycerides
Cholesteryl ester
Sips, et al. (2014) PLoS Comput Biol 10(5): e1003579.

6. Relating TG and CE content (model) to particle
diameter
• …and diameter to fraction number (FPLC)

7. Processes in the model
• ApoB-containing lipoprotein
metabolism (VLDL, LDL)
• ApoA-containing lipoprotein
metabolism (HDL)
CETP
PLTP
PLTP: phospholipid transfer protein
CETP: cholesteryl ester transport protein

8. Model-based data analysis
• Integration of model and data
• Inference of model parameters
(parameter estimation)
• Dealing with imperfect data
(noisy, missing, inconsistent)
• Data for model development
and calibration
• Independent data to validate
model predictions

9. In silico cholesterol FPLC profiles of
transgenic mice
SR-B1 -/-
PLTP -/-
 WT
 PLTP +/-
 PLTP -/-
LDLr -/-
WT LDLr -/-

10. 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

11. Pharmaceutical intervention
• Extending the model
• Hypotheses
• E1: extra cholesterol accumulation in
HDL
• E2: extra HDL lipoprotein uptake
• E3: additional large nascent HDL
/ biomedical engineering PAGE 1122-4-2015
Grefhorst et al. (2002) J Biol
Chem 277(37):34182

12. Quantitative distinction between hypotheses:
fluxes
• Although the three different models yield equivalent lipoprotein
profiles, there are clear differences in the predictions of lipid
fluxes and lipoprotein metabolism
Sips, et al. (2014) PLoS Comput Biol 10(5): e1003579.

13. Modelling progressive changes in
metabolic fluxes

14. 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 20
0
100
200
Hepatic TG
Time [days]
[umol/g]
0 10 20
0
1
2
3
Hepatic CE
Time [days]
[umol/g]
0 10 20
0
2
4
6
Hepatic FC
Time [days]
[umol/g]
0 10 20
0
50
100
Hepatic TG
Time [days]
[umol]
0 10 20
0
0.5
1
1.5
Hepatic CE
Time [days]
[umol]
0 10 20
0
2
4
Hepatic FC
Time [days]
[umol]
0 10 20
0
1000
2000
3000
Plasma CE
Time [days]
[umol/L]
0 10 20
0
1000
2000
3000
HDL-CE
Time [days]
[umol/L]
0 10 20
0
500
1000
1500
Plasma TG
Time [days]
[umol/L]
0 10 20
6
8
10
12
VLDL clearance
Time [days]
[-]
0 10 20
100
200
300
400
ratio TG/CE
Time [days]
[-]
0 10 20
0
5
10
15
VLDL diameter
Time [days]
[nm]
0 10 20
0
1
2
3
VLDL-TG production
Time [days]
[umol/h]
0 10 20
1
2
3
Hepatic mass
Time [days]
[gram]
0 10 20
0
0.2
0.4
DNL
Time [days]
[-]

15. Mechanism-based model
• Differential equations
• Interconnected system (network)
• Model parameters have biological meaning

16. Data integration
• Estimation of unobserved metabolic parameters
• At unobserved time points
1. Metabolite concentrations
-Hepatic free cholesterol (FC)
-Hepatic cholesteryl ester (CE)
-Hepatic triglyceride (TG)
-Plasma free fatty acids (FFA)
-Plasma TG
-Plasma total cholesterol
-HDL cholesterol
-VLDL (very low density lipoprotein) TG/C ratio
-Nascent VLDL particle diameter
2. Fluxes
-VLDL-TG production
-Hepatic cholesterol synthesis
-VLDL catabolism/clearance from the plasma
Tiemann et al. (2013) PLOS Comput Biol. 9(8):e1003166

17. ADAPT: Analysis of Dynamic Adaptations in Parameter Trajectories
• Model parameters inferred from data
• Mathematical model + ADAPT computation connects and
describes the data accurately
• 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

18. Analysis: HDL cholesterol
Analysis: increased excretion of cholesterol
Observation: increased HDLc

19. • 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
mRNA of cholesterol efflux transporters
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

20. 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 20
0
100
200
Hepatic TG
Time [days]
[umol/g]
0 10 20
0
1
2
3
Hepatic CE
Time [days]
[umol/g]
0 10 20
0
2
4
6
Hepatic FC
Time [days]
[umol/g]
0 10 20
0
50
100
Hepatic TG
Time [days]
[umol]
0 10 20
0
0.5
1
1.5
Hepatic CE
Time [days]
[umol]
0 10 20
0
2
4
Hepatic FC
Time [days]
[umol]
0 10 20
0
1000
2000
3000
Plasma CE
Time [days]
[umol/L]
0 10 20
0
1000
2000
3000
HDL-CE
Time [days]
[umol/L]
0 10 20
0
500
1000
1500
Plasma TG
Time [days]
[umol/L]
0 10 20
6
8
10
12
VLDL clearance
Time [days]
[-]
0 10 20
100
200
300
400
ratio TG/CE
Time [days]
[-]
0 10 20
0
5
10
15
VLDL diameter
Time [days]
[nm]
0 10 20
0
1
2
3
VLDL-TG production
Time [days]
[umol/h]
0 10 20
1
2
3
Hepatic mass
Time [days]
[gram]
0 10 20
0
0.2
0.4
DNL
Time [days]
[-]

21. Increased hepatic FFA influx is the initial
contributor to hepatic TG accumulation
• [13C]16-palmitate infusion
Hijmans et al. (2015) FASEB J. 29(4):1153-64

C16:0 palmitate
C18:0 stearate
C16:1 palmitoleate
C18:1 oleate
saturated fatty acid monounsaturated fatty
acid

22. Conclusions (2)
• LXR activation in C57Bl/6J mice leads to complex time-dependent
perturbations in cholesterol and triglyceride metabolism
HDL cholesterol metabolism
• Peripheral cholesterol efflux to HDL and hepatic HDLc uptake increase over
time
• Reduced presence of SR-B1 in liver membranes despite an increment in
hepatic HDLc uptake
Hepatic triglyceride metabolism
• Input and output fluxes to liver TG are massively upregulated and a minor
imbalance between input and output fluxes causes steatosis
• Increased hepatic FFA influx is the initial contributor to hepatic TG
accumulation

23. Acknowledgements
• Peter Hilbers
• Christian Tiemann
• Joep Vanlier
• Yvonne Rozendaal
• Fianne Sips
• Bert Groen
• Jan Albert Kuivenhoven
• Maaike Oosterveer
• Brenda Hijmans
Systems Biology of Disease Progression -
ADAPT modeling
http://www.youtube.com/watch?v=x54ysJDS7i8

24. / biomedical engineering