Disturbed energy balance regulation and links to human metabolic diseases such as obesity – general introduction, discussion of phenotyping test assays for body composition

Jan Rozman

Helmholtz Zentrum München

Fatty acid oxidation

Mitochondrial metabolism

Porphyrin metabolism

Purine or pyrimidine metabolism

Metabolism – chemical transformations to provide energy

and matter for life sustaining structures and processes

Major categories of inherited metabolic disorders

rare diseases – widespread diseases

Steroid metabolism

Mitochondrial function

Peroxisomal function

Lysosomal storage disorders

Carbohydrate metabolism

Amino acid metabolism

Urea Cycle Defects

Organic acid metabolism

Energy transformation

Basics

SI unit energy: Joule [J] 4.1868 J is the energy amount required to heat up 1 g of water from 14.5°C to 15.5°C at standard conditions (1013 hPa barometric pressure).

Energy turnover of a 70 kg man: 11300 kJ.

SI unit „power“: Watt [W] Power is defined as energy per time (1 Watt = 1 Joule/s). Power of a 70 kg man: 131 W (11300 kJ *1000 / 86400).

Heterotrophy and oxidative processes

source: www.hcrowder.com

Sun

Max Kleiber: Fire of Life (1961)

Max Kleiber (1893 –1976), Swiss

agricultural biologist, born and

educated in Zurich, Switzerland.

Thesis The Energy Concept in the

Science of Nutrition. Research at

UC Davies. Kleiber contributed

fundamental concepts to better

understand principles of metabolic

regulation (Kleiber’s Law)

Obesity and diabetes are serious public

health problems

Energy balance regulation and disease

adapted from:

Several human diseases are related to a positive

energy balance e.g. obesity and its sequelae

from: WHO 2013 (http://apps.who.int/bmi/index)

Energy balance regulation

Rozman et al. 2014

Overweight and obesity – a showcase

Body mass development of a 67 y/o man

In 1998: BMI 23.2 in 2015: BMI 28.9 (plus of 19.6 kg in 17 years)

Partly, daily body mass recordings together with major abnormalities in life

style factors (food intake, stress, etc.)

with kind permission of B. Meier

Precise energy regulation:

.... an average person actually gains 11 kg between the ages of 25 and 65; during this period he/she eats roughly 20 tons of food; his/her weight change corresponds to an average daily error of only 350 mg of food, as compared with the exact amount of food required for energy balance .

Reginald Passmore (c.f. Hervey, Nature 1969)

Energy balance regulation

Rozman et al. 2014

Implications of the „scaling problem“ on metabolic phenotyping

Rough estimate of the ratio of daily energy flux and total body energy

content:

Humans: 1:70 (10 MJ : 700 MJ)

Mice: 1:7 (40 kJ : 280 kJ)

In smaller animals fluctuations of daily energy flux have a higher

impact on energy regulation compared with larger animals:

Advantage of mouse models

Implications of the „scaling problem“ on metabolic phenotyping

Rough estimate of the ratio of daily energy flux and total body energy

content:

Humans: 1:70 (10 MJ : 700 MJ)

Mice: 1:7 (40 kJ : 280 kJ)

In smaller animals fluctuations of daily energy flux have a higher

impact on energy regulation compared with larger animals:

Advantage of mouse models

Kleiber‘s Law

How can we measure energy balance?

dietary energy

assimilated

energy

metabolizable

energy

energy storage (fat)

feces

urine

glucose fatty acids amino acids

basal metabolism

activity thermogenesis

diet induced

thermo-

genesis

growth

bomb calorimetry &

FT/IR spectrometry

metabolic cages clinical chemistry

body composition analysis

indirect calorimetry &

activity monitoring

metabolic cages

chemical analysis &

FT/IR spectrometry

monitoring of body temperature

Read outs for energy flux

Purpose of primary metabolic phenotyping:

Detection of disturbed regulation of energy balance in mice in search of new animal models for human metabolic disorders.

Let‘s start with obesity!

Difference in body weight (g)

-20 -15 -10 -5 0 5 10 15 20

Dnmt1Abcb4_2

IFIT-2VMP1 KO cond_het

TOM40AEY069

Traf7ADAR2

Tyk2Camp1

Cln3_delta_ex7/8_mutEmp3

LucPLKMFAP4

EPD0089_4_F11mir34 koBCH002

EUC FP00052B10_1Cln3_delta_ex7/8_het

EUC FP00100C08MEGAPBAP005RNtre2

MVD013HdhQ111

COX4-2 KoALI034

EUC BL6-FP00042B07IRP2

Fermt2EPD0028_5_G01

LrbaTmem45b

AEY17EPS8L2

EPD0156_1_B01 EPD0105_5_E01

HST012_hetNox4

Trdmt1Fra2

Cited4 KO condEPD0051_2_D09

PRDM11Csemp1

EmoryPept1

SAP007Afamin_2

FLG/HRN1Them 2

Abcb4_1HST012_mut

NfyaBAP002BAP004BAP012HST001 A

Difference in body weight (g)

-20 -15 -10 -5 0 5 10 15 20

SAP007IRP2

Afamin_2Abcb4_2

TOM40Csemp1

Nox4Cln3_delta_ex7/8_het

EUC FP00100C08MFAP4

Abcb4_1EPD0051_2_D09EPD0028_5_G01

Tyk2VMP1 KO cond_mut

Cln3_delta_ex7/8_mutPept1Fra2

EPS8L2mir34 ko

Mir221 KO convS248A KI_mutS248A KI_het

RNtre2Traf7

EPD0089_4_F11BAP005

NfyaHdhQ111

Dnmt1Trdmt1

Tmem45bEUC BL6-FP00042B07

Camp1MEGAP

PRDM11EUC FP00052B10_1

EPD0105_5_E01IFIT-2

Fermt2EPD0156_1_B01

Emp3COX4-2 Ko

ADAR2MVD013

PTBP2Lrba

ALI034FLG/HRN1

BAP012AEY069AEY17

HST012_hetHST012_mut

Cited4 KO condEmory

BAP002LucPLKHST001 B

male female

unpublished data

Body mass

Body mass is an easy to obtain parameter

at least it should be – calibration of electronic

balances, control time of the day, treatment of mice,

correct identification, etc.

Important parameter – first indication that growth or

energy allocation are affected

Important confounder for many other parameters

like energy expenditure, etc.

0

5

10

15

20

25

0 5 10 15 20 25 30 35

Body mass (g)

Lean

mass (

g)

m 44 wt

m 44 mut

0

5

10

15

0 10 20 30 40

Body mass (g)

Fat

mass (

g)

m 44 wt

m 44 mut

Good to know - big or fat?

NMR

Micro CT

lean visc

sc

Prim

ary

phenoty

pin

g

Secondary

phenoty

pin

g

unpublished data

Primary Screen Results

Body mass and body composition phenotypes

Males Females

Parameter Reduced Increased cohorts Reduced Increased cohorts

Body mass 19 (31.1%) 2 (3.3%) 61 16 (27.1%) 5 (8.5%) 59

Lean mass 13 (26.5%) 1 (2.0%) 49 10 (21.7%) 6 (13.0%) 46

Fat mass 13 (26.5%) 4 (8.2%) 49 7 (15.2%) 2 (4.3%) 46

Body composition analysis normalized to body mass

Lean mass 2 (4.1%) 4 (8.2%) 49 2 (4.3%) 5 (10.9%) 46

Fat mass 4 (8.2%) 1 (2.0%) 49 6 (13.0%) 2 (4.3%) 46

Analysis based on 1655 mice from 73 different wildtype ~ mutant cohorts (data: M. Willershäuser)

Energy balance regulation

Energy assimilation

Food intake Separation of spillage and feces Bomb calorimetry

Workflow

day 1 day 8

Mo Tu We Thu Fr Sa Mo Su Tu We Thu Fr Sa Su Mo

Food ad libitum

pill

Tu We

Make sure enough material is collected (> 2 g of feces)

Quality control

Energy balance regulation

Indirect calorimetry

Indirect calorimetry in humans

from: www.cosmed.com

Clinical application

• Therapy of adiposity

• substrate utilization and

metabolic flexibility

• control of wasting

syndrome in cancer

patients

• control of energy balance

during post surgery

therapy or total parenteral

nutrition

Also important in large-scale cohort studies

monitoring metabolic functions and disease

(e.g. obesity), addressing basal research

questions (contribution of BEIGE/BRITE

cells to daily energy expenditure,

contribution of microbiota to metabolic

rate).

Challenges & Aims: Explain Sources of Variation in Metabolic Rate to Finally Identify

Factors Playing a Role in Energy Balance Regulation.

PhenoScale: Development and refinement of new metabolic and behavioural phenotyping assays

www.phenoscale.com

Partners:

Medical Research Council – Mammalian Genetics Unit – Harwell, UK (Coordinator Roger Cox)

National Research Council, CNR – Institute of Cell Biology, IBC – Monterotondo, Italy

Helmholtz Zentrum München – German Mouse Clinic, GMC – Munich, Germany

The Italian Institute of Technology, IIT – Genova, Italy

TSE Systems GmbH - Bad Homburg, Germany

•Measurement: 21 hours

•Food and water ad libitum

change

T-5 T0 T12 T16

Light on Light on Light off

T-5

measurement

HMGU 19:00 CET

drafted by J Rozman (HMGU) & M. F. Champy (ICS)

further contributions from all EUMODIC partners

Careful evaluation of the protocol during PhenoScale

Metabolic rate

unpublished data

Are 21 hours sufficient for a robust measurement?

20

40

60

80

100

120

14:00 19:00 00:00 05:00 10:00 15:00 20:00 01:00 06:00 11:00 16:00 21:00

Time (hrs:min)

Oxyg

en

co

nsu

mp

tio

n (

ml/h

)

DAY 1 DAY 2

20

40

60

80

100

120

Day 1 Day 2

Ox

yg

en

co

ns

um

pti

on

(m

l/h

)

n =

14

r2 =

Comparison of 14 mice over more than

two days

small differences in the temporal pattern

(e.g. adaptation)

mean VO2 not different but highly

correlated (r2 = 0.87)

unpublished data

• SOP is rather flexible and takes into account capacity limitations (possible to do only males).

• Duration is flexible (minimum 21 hours, acclimation can be added).

• Equipment differs between centers. • Calorimetry still seems to be a bottleneck in the pipeline. • Data analysis challenging because of complex nature of

the results (display on www.mousephenotype.org needs improvement).

Read outs of the indirect calorimetry test

• Oxygen consumption VO2 [ml O2*h-1*animal-1]

• Carbon dioxide production VCO2 [ml CO2*h-1*animal-1]

• Respiratory exchange ratio RER [VCO2/ VO2]

• Lipid and carbohydrate oxidation rates [mg min-1]

• Heat production HP [mW*animal-1]

• Food consumption and water uptake [g and ml]

• Physical activity (locomotor activity [cm 15 min-1] and rearing [counts 15 min-1])

• Body mass [g]

Read outs are evaluated in the literature meaningful to characterize metabolic functions in our experience identify interesting new phenotypes

Several high ranking papers address how metabolic data should be analysed (e.g. Tschoep et al, Speakman papers, Kayala/Schwarz group).

Metabolic rate plotted versus body mass

Body mass (g)

0 10 20 30 40 50 60

VO

2 (

ml O

2*h

-1)

0

20

40

60

80

100

120

140

160

WT and MUT all lines

WT line 05

MUT line 05

Analysis by linear regression models

including genotype, sex and body mass:

Body mass V

O2

VO2 ~ sex * genotype + body mass

Read outs of the indirect calorimetry test

Hypometabolism in Myoz1 (IMPC)

The reduction in energy turnover

likely exceeds the reduction in

physical activity.

Further studies are conducted to

dissect primary and secondary

effects on energy balance

regulation.

Update GMC indirect calorimetry in IMPC

Project Sex Wildtype Mutant Sum

EUMODIC females 168 218 386

males 201 243 444

(sub-total 369 461 830)

IMPC females 371 640 1011

males 392 646 1038

(sub-total 763 1286 2049)

Total 1132 1747 2879

Data available (reporting date 22/04/2015): • 99 mutant lines IMPC • 36 mutant lines EUMODIC 2879 mice in total For pilot evaluation only mean oxygen consumption (VO2 mean over 21 hours) was used. First time, cross-project analysis of gene effects on metabolic regulation.

Histogram

cohort size [n]

0 5 10 15 20 25 30

Count

[n]

0

10

20

30

40

50

60

Count

Meta analysis of indirect calorimetry data

EUMODIC IMPC

SM-MARS-8A, 8 cages, Sable Systems, Las Vegas, US

PhenoMaster, 32 cages, TSE Systems GmbH, Bad Homburg, Germany

Main source of variation: body mass

Amazingly good fit of the two data sets from EUMODIC and IMPC. We modelled mean VO2 in a linear regression model to obtain residual and adjusted VO2.

Adjusted mean oxygen consumption seems to be independent of body mass.

But is related to rearing and physical activity

Residual and adjusted VO2: independent of BM

Mean oxygen consumption in EUMODIC & IMPC

VO2

unadjusted

Body mass

.

Mutant lines

Mean oxygen consumption in EUMODIC & IMPC

VO2

unadjusted

Body mass

.

Mutant lines VO2 residual .

Mutant lines

0

5

10

15

20

25

30

35

0,7

500

0,7

750

0,8

000

0,8

250

0,8

500

0,8

750

0,9

000

0,9

250

0,9

500

0,9

750

1,0

000

1,0

250

1,0

500

1,0

750

1,1

000

1,1

250

1,1

500

1,1

750

1,2

000

1,2

250

1,2

500

Freq

uen

cy [

co

un

ts]

Out of 134 mutant lines form IMPC

and EUMODIC analysed here:

• 10 were hypometabolic (7.5%)

• 3 hypermetabolic (2.2%)

In almost 10% of the mutant line

effects on metabolic regulation could

be detected. The additional data (e.g.

locomotor activity) will help to

distinguish between primary and

secondary effects on energy turnover.

mean +/- 1 S.D. of

controls (n=1132

mice)

New gene candidates involved in metabolic regulation?

Not only monitoring of metabolic rate, but also energy

allocation and on-line monitoring of metabolic functions (e.g. in

combination with challenges like HFD, food deprivation,

treadmill, or running wheel)

Additional value – further read outs of the indirect calorimetry

• Oxygen consumption VO2 [ml O2*h-1*animal-1]

• Carbon dioxide production VCO2 [ml CO2*h-1*animal-1]

• Heat production HP [mW*animal-1]

• Food consumption and water uptake [g and ml]

• Physical activity - locomotor activity, rearing, link between metabolic & behavioral phenotyping, biorhythms (?), sleep (?)

• Respiratory exchange ratio RER [VCO2/ VO2], metabolic flexibility

• Lipid and carbohydrate oxidation rates [mg min-1], substrate utilization, in vivo monitoring of metabolic functions

Additional value – further read outs of the indirect calorimetry

control mutant

A

ctivity

S

ubstr

ate

use

RE

R

[cm

/20

min

]

[g/m

in]

[VC

O2

/VO

2]

Time [h:min]

One step further:

Indirect calorimetry – Substrate oxidation

C6H12O6 + 6 H2O + 6 O2 6 CO2 + 12 H2O H0 = -2813 kJ mol-1

CH3(CH2)14COOH + 23 O2 16 CO2 + 16 H2O H0 = -10025 kJ mol-1

Glucose oxidation: RER = 6 CO2 / 6 O2 = 1.0

oxicaloric equivalent =

2813 kJ mol-1 / 6 mol O2 = 468.8 kJ mol O2-1 = 20.9 kJ liter-1 O2

Palmitate oxidation: RER = 16 CO2 / 23 O2 = 0.70

oxicaloric equivalent =

10025 kJ mol-1 / 23 mol O2 = 435.9 kJ mol O2-1 = 19.5 kJ liter-1 O2

Net glucose oxidation: c [g/min] = 0.746 VCO2 – 3.21 VO2 Net lipid oxidation: f [g/min] = 1.67 VO2 – 1.67 VCO2

Calculations do not consider nitrogen metabolism and are only valid for standard conditions

RER

1.00

0.70

© J Rozman HMGU

Substrate utilization depending on energy expenditure

AKR/J and SWR/J mice, fed

CD, 11-12 weeks old, n=29-

30, by indirect calorimetry for

24 h.

(submitted manuscript: Kless,

Rink, Rozman & Klingenspor

2016)

More lipid oxidation in SWR/J.

Different pattern in substrate

utilization.

Energy balance regulation – a showcase

-25 -20 -15 -10 -5 0 5 10 15 20 25

KTA041

ABE17 hom

Sepp hom

ABE17 het

AGA002

A008A01

Popdc2

Ali18

ATE2

Ali35

HST009

ABE012

ABE 1

ATE001

Medane1

Drasic

SIP

Mag

Dea3

ABE 2

NCAM

R1-KO

EYL

Delta 1

Insl 5

PK

Ptdsr

DKK3

M076C04

DLG3

NADH

Glut8

Cin85

Tp53

Miz1

Ali027

Fin13

Vimentin

EPS15

p0071

SUMO 1

Neuch het

UBB+1/3413

CIN85

FoxP2

DMBT1

Elastin

Sepp het

FFM1017

Mchr1

Ptpg

Lrba

Arl4

DNAseX

ALI22

Nbea

ESCP

Neurobeachin heterozygous mice are

moderately obese:

What is known about the gene?

member of BEACH domain family of proteins

role intracellular targeting of membrane proteins

peripheral & central neurons as well as in

endocrine cells

essential for synaptic neurotransmission

In humans, the gene was supposed to be

associated with autism

Nbea has not been associated with obesity

before

Neurobeachin: A new candidate gene for obesity?

Nbea

Energy balance regulation

Rozman et al. 2014

Nbea +/- were moderately overweight, have increased body fat at 12 weeks of

age, and were slightly more susceptible to feeding a high fat diet (60en% fat)

Olszewski et al. (PLoS Genetics 2012)

Only subtle effects in behavioural and neurological screening, clinical

chemistry and gene expression. No obvious link to energy regulation.

Even differences in body mass and body composition are moderate.

Olszewski et al. (PLoS Genetics 2012)

Nbea heterozygotes

• overeat when offered an energy dense

and palatable diet

• overeat after fasting

• overconsume palatable caloric liquids

• but not palatable non-caloric liquids

• are leptin sensitive

• hyper-responsive to the anorexigenic

opioid receptor antagonist naltrexone

(NTX)

In depth analysis of

physiological functions -

specific feeding

paradigms:

Links to human studies:

Olszewski et al. (PLoS Genetics 2012)

Comparison of Nbea+/- and WT

mice under different feeding

paradigms

Ad libitum-fed - differential

expression of dynorphin (DYN)

mRNA in the hypothalamus.

16-h food deprivation differently

affected expression of DYN,

proopiomelanocortin (POMC),

opioid-like receptor-1 (ORL1) and

corticotropin releasing hormone

(CRH) in the hypothalamus

Differential expression of feeding-related genes

Monitoring of energy balance – indirect calorimetry

The exact determination of food intake, energy assimilation and energy

expenditure in mice 8 weeks of age (early onset of obesity)

Olszewski et al. (PLoS Genetics 2012)

Ad libitum access to

food and water

Home cage conditions

Single caged mice

Determination of energy balance

Olszewski et al. (PLoS Genetics 2012)

Polymorphisms in the Nbea gene are linked to human

obesity

Collaborators at the National Childhood Obesity Centre, Karolinska

Institute, Stockholm, Sweden detected a linkage of polymorphisms in the

Neurobeachin gene with human obesity in two clinical studies.

- adult man (normal weight and obese) older than 60 years of age

- overweight adolescents

Olszewski et al. (PLoS Genetics 2012)

The new metabolic phenotype linked to Nbea heterozygotes was detected in

the primary screen.

In depth second-line phenotyping provided a functional explanation for the

development of the phenotype (hyperphagia).

A direct link to human obesity was found in clinical studies.

Nbea is a new candidate gene for human adiposity

Acknowledgement

Molekulare Ernährungsmedizin

Technische Universität München

Martin Hrabé de Angelis (Director)

GMC Coordination Team

Helmut Fuchs (sci.-tech. Head)

Valerie Gailus-Durner (sci.-adm. Head)

Energy Metabolism Monja Willershäuser, Martin Kistler Ann-Elisabeth Schwarz, Anna Dewert, Brigitte Hermann

Nicole Ehrhard

Clinical Chemistry Birgit Rathkolb Kateryna Butuzova

Elfi Holupirek

Eckhard Wolf, LMU München

Martin Klingenspor

Hannelore Daniel

Diabetes Susanne Neschen