The Future of Metabolic Phenotyping Using data bandwidth to maximize N, analytical flexibility and...

The Future of Metabolic Phenotyping: Using data bandwidth to maximize N, analytical flexibility and reproducibility

Sponsored by:

John Lighton, PhDSable Systems International

Andy HentonInsideScientific

Jennifer A. Teske, PhDUniversity of Arizona

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The Future of Metabolic Phenotyping: Using data bandwidth to maximize N, analytical flexibility and reproducibility

John Lighton, PhD

President & Chief Scientist, Sable Systems International

Some history…

• I’m a comparative physiologist

• Ph.D. 1987, UCLA under George Bartholomew

• Respiratory physiology & energetics

• Attracted to extreme technical challenges

• > 80 papers using diverse metabolic measurement methods

• Founded Sable Systems International in 1987

• “By Scientists, For Scientists”

“John Lighton has probably done more to

modernize and consolidate the field of

whole-animal respirometry than any single

person. And now he has written a book that

explains all.”

– Theodore Garland, UCR

Technical Challenges?

Designed for Research

“…a precision of 0.3 ppm was obtained…”

(against a background of 209,400 ppm)

Application of a Differential Fuel-Cell Analyzer for Measuring

Atmospheric Oxygen Variations

BRITTON B. STEPHENS

Earth Observing Laboratory, National Center for Atmospheric Research, Boulder, Colorado

The Challenge… from the biomedical community

1. Make a higher throughput metabolic phenotyping system… (faster)

2. Using the techniques you’ve learned

3. Without cage seals & desiccants

4. With better accuracy, resolution & repeatability

5. While reducing animal stress

6. While increasing animal safetyand…

7. With expert technical support!

METABOLIC SCREENING

Our growing global user base…

Back to Basics

• VO2 = FR(ΔO2)/(1 - FiO2)

• What limits data resolution & bandwidth?

- Cage time constant (TC) = cage volume / FR

- E.g. 5 L / 0.5 L min-1 = 10 minutes

- Require ~5 TC for equilibrium

• To reduce TC, we must increase FR

• But that reduces ΔO2 !

• Usual ΔO2 target is ~0.5%, limiting > of FR

• Rethinking needed! Here come 6 rethinkings…

FRi FRe

RETHINK (1)

“ In order to detect relevant changes in metabolism of a small mammal, the analyzers should be able to detect changes in Vol%O2 or Vol%CO2 at a relative precision of 0.01% ”

– Meyer, Reitmeyer & Tschöp, 2015

Sophisticated analyzers can resolve 0.0001% - a new standard is available

Specifically designed for cutting-edge flow-through respirometry- allows much higher flow rates

Appropriate scientific gas analyzer technology

Fuel cell technology, when applied properly, is the clear leader in low noise, high resolution & speed

15 second dwell time – can reduce further if needed

Shows extraction of cage 4 excurrent O2

data from a 4-cage multiplexed system

Incurrent O2

measurement

• High analyzer resolution allows higher FR

• This << Time Constant of chamber

• High analyzer speed << cycle time (animal 1 to animal 1 in a multiplexed system ~ 2 minutes!)

• Now, finally, cycle time can be less than chamber TC – even with shorter TC!

RETHINK (2)

Result: drastic temporal & data resolution improvement= HIGHER BANDWIDTH

RETHINK (3)

• Desiccant systems are unreliable, unnecessary & slow down gas analysis

• Simple physics: Dalton’s Law of Partial Pressures allows desiccant elimination

IFF water vapor pressure & barometric pressure are measured with high resolution

• Developed WVP analyzer for my research that can resolve 1 Drosophila easily

Effect of Barometric PressureIncurrent O2 is constant, yet measured

incurrent O2 is variable!

Data courtesy of Vanderbilt MMPC

Does mathematical WV dilution comp. actually work?

After WV Dilution Correction(eqn. in Lighton, 2008)

After WV Dilution and BP Correction

Hell yes

Data courtesy of Vanderbilt MMPC

After WV Dilution Correction After WV Dilution and BP Correction

The result of mathematical WV compensation…

The speed penalties, reliability problems & inefficiencies of desiccators, including chemical, thermal and permeable-membrane ARE NOW ELIMINATED

BONUS: Extra data channel of whole-animal water flux rates!

• Collect ALL raw data from ALL analyzers, flow generators, cage sensors (intake, body mass, position) every second, making no changes to the data

• Create agnostic Deep Data Field -- High Bandwidth and continuous recording of all raw data

• NO pre-set decisions from the scientist necessary for routine data acquisition

RETHINK (4)

Result: Data analysis is uniquely flexible

Light Field Camera = Raw Light Data

Legacy Systems…

PROMETHION

You’ve Got Data

LEGACY:16 cages x 5 min x 20 variables/animal = 320 var (max!)/300 sec= ~1 variable/second

PROMETHION:1 measurement/sec from all analyzers & sensors in 16-cage system = 350-450 variables/second

RETHINK (5)

• All data transfer is digital

• Mission-critical, high speed error-correcting network optimizes simplicity, reliability and…

RESOLUTION!

• Exclusively Pull Mode = high FR!, Bedding OK

• No seals to leak, minimum dead volume

• Lowest possible stress, best animal welfare

• Best reproducibility

RETHINK (6)

What about acclimation…?

Body mass increases from start of run

EE remains similar; factorial scope approx. 2

Activity pattern remains similar

Data: 21 gram C57BL/6J transferred straight from communal housing into Promethioncage…

Wait…That Didn’t Look Right?

Typical Legacy Resolution…

Source: Advertising brochure

• 5 minute cycle time

• REE? AEE?• Note correlation

quality between activity and VO2

• Note super-unity RER during scotophase

Promethion Resolution

• Clean activity data (1 cm beam spacing)

• Excellent correlation between activity & EE

• Unambiguous active & resting EE

• Realistic RQs

Tight correlation between EE and activity -Fast response from high bandwidth

Excellent resolution of resting EE (CV ~2-3% with no averaging)

Excellent resolution of active EE quantifies precise energetic cost of activity; factorial scope approx. 2

Accurate RQs (measure WV & correct mathematically)

I don’t recognize the data…?

combination of fast cycle time & fast TC unique to Promethion

How to Use the Resolution

• This example: Promethion-C, 1 sample/sec from all cages in the system

• Superb temporal & data resolution

…e.g., determine cost of transport

Correlate distance run on wheel and energy expended while running

measure of coordination & musculoskeletal condition

Usually requires a treadmill… Lighton & Gillespie, 1987

• Very repeatable• Tighter regression

than with most treadmill runs

• No stress for animals orpersonnel

• Time-efficient

But not with Promethion

Continuous Measurement – Promethion-C

METABOLIC RATE SAMPLED AT 1 Hz…120x - 2700x faster than multiplexed systems

Vs. Legacy “Continuous”

METABOLIC RATE SAMPLED AT 0.016 Hz… 60x slower than Promethion-C

Vs. “Continuous”… Analyzing the same Gas Stream

Red = legacy systemBlue = Promethion-C

system

ACTH & Cortisol spike? BAT?

Detail of EE in body mass habitat

• Cool-Down Period Lasts ~15 Min

• Variable Low Activity Duration Correlates with Low EE

• ANY Movement is Detectable in Habitat

• EE Rises PRIOR to Activity (Leaving Habitat)

Productivity

How is high bandwidth related to productivity?

Increase data resolution & replicability

Increase analytical flexibility & publishability for researchers

Decrease re-runs, repeats, re-bookings, wasted time & funds

Perfect for labs and high-demand MP cores

Support & training by Ph.D.-level personnel

Our emphasis – not on us, but on YOU AND YOUR SCIENCE!

Quantifying components of total energy expenditure (TEE): integrating data across multiple platforms

Jennifer A. Teske, PhD

Assistant Professor,University of Arizona,

Department of Nutritional Sciences

Promethion-C Indirect calorimetry, Sable Systems Int’l EEG/EMG by telemetry, Data Sciences International

Infrared physical activity sensors, Sable Systems Int’l

Integrating equipment to quantify TEE:

Promethion-C Indirect calorimetry, Sable Systems Int’l EEG/EMG by telemetry, Data Sciences International

Infrared physical activity sensors, Sable Systems Int’l

Integrating equipment to quantify TEE:

Components of total energy expenditure

Energy expenditure during…

• REM sleep: calories during REM sleep

• Non-REM sleep: calories during NREM sleep

• Rest:1. Metabolic rate during the early light cycle when not moving based on IR-beam sensors

2. Calories when wake and not moving based on IR-beam sensors

• Physical activity: sum of calories when moving based on IR-beam sensors

Quantifying components of energy expenditure

Time Stamp Physical Kcal/hr Sleep/wake Temperature Activity counts EE due to EE due to EE due to EE due to

Activity (m/s) stage (Celsius) (Counts/min) PA Rest/QW NREM REM

10:54:20 AM 0.00266 3.340 active wake 37.60 18 3.340

10:54:21 AM 0.00172 3.338 active wake 37.60 18 3.338

10:54:22 AM 0.00272 3.342 active wake 37.58 18 3.342

10:54:23 AM 0.00274 3.351 active wake 37.57 18 3.351

10:54:24 AM 0.00248 3.359 active wake 37.65 18 3.359

10:55:10 AM 0.00000 2.715 active wake 37.50 0 2.715

10:55:11 AM 0.00000 2.723 active wake 37.59 0 2.723

10:55:12 AM 0.00000 2.732 active wake 37.62 0 2.732

10:55:13 AM 0.00000 2.744 active wake 37.63 0 2.744

10:55:14 AM 0.00000 2.756 active wake 37.57 0 2.756

10:52:20 AM 0.00000 2.377 quiet wake 36.66 0 2.805

10:52:21 AM 0.00000 2.375 quiet wake 36.65 0 2.805

10:52:22 AM 0.00000 2.373 quiet wake 36.66 0 2.805

10:52:23 AM 0.00000 2.371 quiet wake 36.66 0 2.805

10:52:24 AM 0.00000 2.369 quiet wake 36.65 0 2.805

12:10:10 PM 0.00000 2.404 NREM sleep 37.48 0 2.404

12:10:11 PM 0.00000 2.399 NREM sleep 37.48 0 2.399

12:10:12 PM 0.00000 2.393 NREM sleep 37.47 0 2.393

12:10:13 PM 0.00000 2.386 NREM sleep 37.47 0 2.386

12:10:14 PM 0.00000 2.379 NREM sleep 37.47 0 2.379

12:06:20 PM 0.00000 2.440 REM sleep 37.46 0 2.440

12:06:21 PM 0.00000 2.440 REM sleep 37.47 0 2.440

12:06:22 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:23 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:24 PM 0.00000 2.442 REM sleep 37.46 0 2.442

EE: energy expenditure, QW: quiet wake, NREM: non-rapid eye movement sleep, REM: rapid eye movement sleep

10:54:20 AM 0.00266 3.340 active wake 37.60 18 3.340

10:54:21 AM 0.00172 3.338 active wake 37.60 18 3.338

10:54:22 AM 0.00272 3.342 active wake 37.58 18 3.342

10:54:23 AM 0.00274 3.351 active wake 37.57 18 3.351

10:54:24 AM 0.00248 3.359 active wake 37.65 18 3.359

10:55:10 AM 0.00000 2.715 active wake 37.50 0 2.715

10:55:11 AM 0.00000 2.723 active wake 37.59 0 2.723

10:55:12 AM 0.00000 2.732 active wake 37.62 0 2.732

10:55:13 AM 0.00000 2.744 active wake 37.63 0 2.744

10:55:14 AM 0.00000 2.756 active wake 37.57 0 2.756

10:52:20 AM 0.00000 2.377 quiet wake 36.66 0 2.805

10:52:21 AM 0.00000 2.375 quiet wake 36.65 0 2.805

10:52:22 AM 0.00000 2.373 quiet wake 36.66 0 2.805

10:52:23 AM 0.00000 2.371 quiet wake 36.66 0 2.805

10:52:24 AM 0.00000 2.369 quiet wake 36.65 0 2.805

12:10:10 PM 0.00000 2.404 NREM sleep 37.48 0 2.404

12:10:11 PM 0.00000 2.399 NREM sleep 37.48 0 2.399

12:10:12 PM 0.00000 2.393 NREM sleep 37.47 0 2.393

12:10:13 PM 0.00000 2.386 NREM sleep 37.47 0 2.386

12:10:14 PM 0.00000 2.379 NREM sleep 37.47 0 2.379

12:06:20 PM 0.00000 2.440 REM sleep 37.46 0 2.440

12:06:21 PM 0.00000 2.440 REM sleep 37.47 0 2.440

12:06:22 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:23 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:24 PM 0.00000 2.442 REM sleep 37.46 0 2.442

• Score 15-s epochs of EEG and EMG for the appropriate sleep-wake state (active wake, quiet wake, NREM sleep, or REM sleep)

• Align physical activity (from Sable infrared sensors), energy expenditure (from Sable Promethion-C) and sleep/wake (scored from DSI)

• May include temperature and subjective activity counts data from DSI

EE due to PA

10:54:20 AM 0.00266 3.340 active wake 37.60 18 3.340

10:54:21 AM 0.00172 3.338 active wake 37.60 18 3.338

10:54:22 AM 0.00272 3.342 active wake 37.58 18 3.342

10:54:23 AM 0.00274 3.351 active wake 37.57 18 3.351

10:54:24 AM 0.00248 3.359 active wake 37.65 18 3.359

10:55:10 AM 0.00000 2.715 active wake 37.50 0 2.715

10:55:11 AM 0.00000 2.723 active wake 37.59 0 2.723

10:55:12 AM 0.00000 2.732 active wake 37.62 0 2.732

10:55:13 AM 0.00000 2.744 active wake 37.63 0 2.744

10:55:14 AM 0.00000 2.756 active wake 37.57 0 2.756

10:52:20 AM 0.00000 2.377 quiet wake 36.66 0 2.805

10:52:21 AM 0.00000 2.375 quiet wake 36.65 0 2.805

10:52:22 AM 0.00000 2.373 quiet wake 36.66 0 2.805

10:52:23 AM 0.00000 2.371 quiet wake 36.66 0 2.805

10:52:24 AM 0.00000 2.369 quiet wake 36.65 0 2.805

12:10:10 PM 0.00000 2.404 NREM sleep 37.48 0 2.404

12:10:11 PM 0.00000 2.399 NREM sleep 37.48 0 2.399

12:10:12 PM 0.00000 2.393 NREM sleep 37.47 0 2.393

12:10:13 PM 0.00000 2.386 NREM sleep 37.47 0 2.386

12:10:14 PM 0.00000 2.379 NREM sleep 37.47 0 2.379

12:06:20 PM 0.00000 2.440 REM sleep 37.46 0 2.440

12:06:21 PM 0.00000 2.440 REM sleep 37.47 0 2.440

12:06:22 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:23 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:24 PM 0.00000 2.442 REM sleep 37.46 0 2.442

Quantifying components of energy expenditureEE due to Rest/QW

• Multiple ways to calculate

• Traditional: Metabolic rate during the early light cycle

• Promethion-C:

• Metabolic rate during the early light cycle when not moving based on IR-beam sensors

• Calories when wakeand not moving based on IR-beam sensors

10:54:20 AM 0.00266 3.340 active wake 37.60 18 3.340

10:54:21 AM 0.00172 3.338 active wake 37.60 18 3.338

10:54:22 AM 0.00272 3.342 active wake 37.58 18 3.342

10:54:23 AM 0.00274 3.351 active wake 37.57 18 3.351

10:54:24 AM 0.00248 3.359 active wake 37.65 18 3.359

10:55:10 AM 0.00000 2.715 active wake 37.50 0 2.715

10:55:11 AM 0.00000 2.723 active wake 37.59 0 2.723

10:55:12 AM 0.00000 2.732 active wake 37.62 0 2.732

10:55:13 AM 0.00000 2.744 active wake 37.63 0 2.744

10:55:14 AM 0.00000 2.756 active wake 37.57 0 2.756

10:52:20 AM 0.00000 2.377 quiet wake 36.66 0 2.805

10:52:21 AM 0.00000 2.375 quiet wake 36.65 0 2.805

10:52:22 AM 0.00000 2.373 quiet wake 36.66 0 2.805

10:52:23 AM 0.00000 2.371 quiet wake 36.66 0 2.805

10:52:24 AM 0.00000 2.369 quiet wake 36.65 0 2.805

12:10:10 PM 0.00000 2.404 NREM sleep 37.48 0 2.404

12:10:11 PM 0.00000 2.399 NREM sleep 37.48 0 2.399

12:10:12 PM 0.00000 2.393 NREM sleep 37.47 0 2.393

12:10:13 PM 0.00000 2.386 NREM sleep 37.47 0 2.386

12:10:14 PM 0.00000 2.379 NREM sleep 37.47 0 2.379

12:06:20 PM 0.00000 2.440 REM sleep 37.46 0 2.440

12:06:21 PM 0.00000 2.440 REM sleep 37.47 0 2.440

12:06:22 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:23 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:24 PM 0.00000 2.442 REM sleep 37.46 0 2.442

• Calories when in NREM sleep based on scored EEG/EMG from DSI and no movement based on IR-beam sensors from Sable Promethion-C.

EE due to NREM sleep

10:54:20 AM 0.00266 3.340 active wake 37.60 18 3.340

10:54:21 AM 0.00172 3.338 active wake 37.60 18 3.338

10:54:22 AM 0.00272 3.342 active wake 37.58 18 3.342

10:54:23 AM 0.00274 3.351 active wake 37.57 18 3.351

10:54:24 AM 0.00248 3.359 active wake 37.65 18 3.359

10:55:10 AM 0.00000 2.715 active wake 37.50 0 2.715

10:55:11 AM 0.00000 2.723 active wake 37.59 0 2.723

10:55:12 AM 0.00000 2.732 active wake 37.62 0 2.732

10:55:13 AM 0.00000 2.744 active wake 37.63 0 2.744

10:55:14 AM 0.00000 2.756 active wake 37.57 0 2.756

10:52:20 AM 0.00000 2.377 quiet wake 36.66 0 2.805

10:52:21 AM 0.00000 2.375 quiet wake 36.65 0 2.805

10:52:22 AM 0.00000 2.373 quiet wake 36.66 0 2.805

10:52:23 AM 0.00000 2.371 quiet wake 36.66 0 2.805

10:52:24 AM 0.00000 2.369 quiet wake 36.65 0 2.805

12:10:10 PM 0.00000 2.404 NREM sleep 37.48 0 2.404

12:10:11 PM 0.00000 2.399 NREM sleep 37.48 0 2.399

12:10:12 PM 0.00000 2.393 NREM sleep 37.47 0 2.393

12:10:13 PM 0.00000 2.386 NREM sleep 37.47 0 2.386

12:10:14 PM 0.00000 2.379 NREM sleep 37.47 0 2.379

12:06:20 PM 0.00000 2.440 REM sleep 37.46 0 2.440

12:06:21 PM 0.00000 2.440 REM sleep 37.47 0 2.440

12:06:22 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:23 PM 0.00000 2.441 REM sleep 37.46 0 2.441

12:06:24 PM 0.00000 2.442 REM sleep 37.46 0 2.442

• Calories when in REM sleep based on scored EEG/EMG from DSI and no movement based on IR-beam sensors from Sable Promethion-C.

EE due to REM sleep

Sleep ad libitum

(control)

Sprague-

Dawley

Sleep deprived

(8h/d for 9d)

Validation that exposure to pre-recorded environmental noise reduces sleep

52Mavanji, Teske, Kotz, Billington. 2013. Obesity. Partial sleep deprivation by environmental noise increases food intake and body weight in obesity-resistant rats.21(7):1396-405. Data represented as mean +/- S.E.M, N = 8/group. *P<0.01, **P<0.001 as compared to respective baseline. NREM: non-rapid eye movement sleep, REM:

rapid eye movement sleep

Sleep/wake stage% tim

Undisturbed (control (C))

Partial sleep deprivation

Wake REM SWS

Does acute sleep deprivation affect TEE?

NREMREMWake

Sleep/wake stage% tim

Undisturbed (control (C))

Partial sleep deprivation

Wake REM SWSNREMREMWake

Sleep ad libitum

(control)

Sprague-

Dawley

Sleep deprived

(8h/d for 9d)

Validation that exposure to pre-recorded environmental noise reduces sleep

53Mavanji, Teske, Kotz, Billington. 2013. Obesity. Partial sleep deprivation by environmental noise increases food intake and body weight in obesity-resistant rats.21(7):1396-405. Data represented as mean +/- S.E.M, N = 8/group. *P<0.01, **P<0.001 as compared to respective baseline. NREM: non-rapid eye movement sleep, REM:

rapid eye movement sleep

Does acute sleep deprivation affect Total EE?

Does exposure to environmental noise affect energy expenditure?

Study Design: effect of acute environmental noise on energy expenditure

54Data represented as mean +/- S.E.M, N = 7/group. *P<0.05.

• 10-d acclimation to Sable food and water containers

• 3-day acclimation in Sable Promethion-C chambers

• 2500mL/min flow rate

• Food and water ad libitum

• 12-h noise exposure on day 5

• 36-h recovery phase after noise exposure

55Data represented as mean +/- S.E.M, N = 7/group. *P<0.05. SD: sleep deprivation

• 12-h noise exposure causes significantly greater weight gain

• Weight gain remained elevated after noise exposure

Treatment

ht gain

sleep ad lib

SD recoverysleep ad lib

Sleep Treatment (24 h)

Change in

ad lib SD recover

Treatment

E (kca

light & dark cycle

ad lib SD recover

RecoverSDAd lib

Treatment

56Data represented as mean +/- S.E.M, N = 7/group. *P<0.05. SD: sleep deprivation

Treatment24h b

ht gain

sleep ad lib

Change in

ad lib SD recover

Treatment

E (kca

light & dark cycle

ad lib SD recover

• 12-h noise exposure causes significantly greater weight gain

• Weight gain remained elevated after noise exposure

• Reduced total energy expenditure during and after noise exposure

Treatment

ht gain

sleep ad lib

Change in

ad lib SD recover

Treatment

E (kca

light & dark cycle

ad lib SD recover

RecoverSDAd lib

TreatmentRecoverSDAd lib

Treatment

Acute sleep deprivation reduces energy expenditure during sleep

Data represented as mean +/- S.E.M, N = 7/group. *P<0.05. SD: sleep deprivation

Acute sleep deprivation reduces energy expenditure during rest and physical activity

Data represented as mean +/- S.E.M, N = 7/group. *P<0.05. SD: sleep deprivation

• DSI EMG leads implanted in neck musculature

• 1Energy expenditure during rest: when awake but no movement based on Sable IR-beams andDSI EMG activity counts

• 2Energy expenditure during reset: when awake but no movement based on IR-beams only.

How does central orexin A increase total EE?

SLEEP, Vol. 38, No. 9, 2015 1367 Orexin-A in the VLPO Modulates Behavior—Mavanji et al.

well characterized. These results show that local injection of

orexin-A in the VLPO produces a behavioral profile similar

to that observed after orexin-A injection into other wake-

promoting nuclei. Microinjection of orexin-A in the VLPO

enhances wakefulness, SPA, SPA-induced energy expenditure

and total energy expenditure without any feeding effect. Fur-

thermore, we showed that blockade of both orexin receptors

in the VLPO reduces orexin-A stimulated wakefulness and

SPA, while blockade of OX2R alone partially reduced orexin-

A stimulated SPA. Together these data suggest that the VLPO

may be a critical site of convergence for orexin-A mediation of

vigilance states and energy balance regulation.

The VLPO contains both orexin receptor subtypes,34,35 and

there are reciprocal connections with orexinergic nuclei and the

VLPO.32,36–38,40–42,59 Neuroanatomical and functional39,44 data

indicate that the VLPO promotes sleep, at least partially, by

inhibiting orexin neurons. This suggests that orexin can inhibit

sleep-promoting neurons in the VLPO to maintain wakeful-

ness and also that the VLPO may modulate orexin-A stimu-

lated behavior in a push-pull relationship29,60 similar to that of

other arousal-promoting centers such as the tuberomammil-

lary nucleus.61 In addition, locally in the VLPO, orexin affects

galaninergic/GABAergic interneurons.42,62

Our work and that of others suggest that orexin-A increases

SPA and elevates energy expenditure in a dose-dependent and

cumulative manner, which promotes obesity resistance.14,23,63,64

Here, we observed significantly greater SPA, SPA-induced en-

ergy expenditure and total energy expenditure in response to

orexin-A in the VLPO during the 0–2 h post-injection time

period. One novelty of this finding lies in the time-locked rela-

tionship between SPA and energy expenditure in both orexin-

A-treated and non-treated rats. This can be seen from the

matching of long intervals of SPA with peaks of total energy

expenditure and from the decline in total energy expenditure

towards that of resting metabolic rate during times of low SPA.

This is the first demonstration that orexin-A in the VLPO stim-

ulates SPA-related energy expenditure, and that increases in

whole body energy expenditure after orexin-A administration

in the VLPO are directly coupled to SPA-induced energy ex-

penditure.56,64 Together these data imply that pharmacological

interventions to enhance orexin activity in the VLPO may be

effective in combating obesity by increasing physical activity.

In contrast to the effect of orexin-A on vigilance states, SPA,

and energy expenditure, VLPO administration of orexin-A

failed to augment either acute or 24-h food intake. Orexin-

A administered in the VLPO did not increase food intake,

which is in agreement with previous findings that orexin-A

Figure 6—Study 5. Orexin-A in the ventrolateral preoptic area (VLPO)

has no effect on (A) acute (e.g. 0–1, 0–2 or 0–4 h post-injection) or (B)

chronic food intake (e.g. 0–24 h post-injection). n = 18. Data represented

as mean ± SEM. Note different scaling on y-axes.

Dose of orexin-A (pmol)

0–1 h 0–2 h 0–4 h

62.5012

.5 125

0–24 h

Figure 7—Study 6. Representative examples of the time course of spontaneous physical activity (SPA, right y-axis in red) and total energy expenditure

(TEE, left y-axis in blue) after injection of (A) vehicle-artificial cerebrospinal fluid or (B) orexin-A, beginning 2-h post-injection. Microinjection of orexin-A into

the ventrolateral preoptic area (VLPO) significantly increases (C) distance traveled, (D) TEE and (E) SPA-induced energy expenditure relative to vehicle

injection. n = 4. Data represented as mean ± SEM. Brackets indicate bars that are significantly different from each other (*P < 0.05) in panels C D, and E.

Note different scaling on the y-axes.

Time (min)

travele

30 60 90 1200

Time (min)

Orexin-A

30 60 90 1200

0 125 0 125

0–1 h 0–2 hTime (h):

0 125 0 125

0–1 h 0–2 hTime (h):

0 125 0 125

0–1 h 0–2 hTime (h):

Mavanji, Perez-Leighton, Kotz, Billington,

Parthasarathy, Sinton, Teske, JA. Promotion of

wakefulness and energy expenditure by orexin A

in the ventrolateral preoptic area. Sleep. 38(9):

1361-1370. Data represented as mean +/- S.E.M,

Brackets indicate bars that are significantly

different from each other (P < 0.05) N = 4.

0 62.5

Dose orexin A (pmol / 0.5 uL)

re (kca

0 62.5

re (kca

0 62.5

te (kca

0 62.5

re (kca

EE: energy expenditure, NREM: non-rapid eye movement sleep, REM: rapid eye movement sleep

Does orexin A increase components of Total energy expenditure?

Orexin A increases several components of total EE

0 62.5

re (kca

l/h) *

0 62.5

re (kca

0 62.5

te (kca

0 62.5

*• Energy expenditure

during physical activity: when awake and moving based on Sable IR-beams

• Energy expenditure during rest (when awake but no movement based on Sable IR-beams andDSI EMG activity counts): total and resting metabolic rate

0 62.5

re (kca

l/h) *

0 62.5

re (kca

0 62.5

te (kca

0 62.5

re (kca

l/h) *

0 62.5

re (kca

0 62.5

te (kca

0 62.5

Does chronic sleep deprivation reduce physical activity-related EE to favor weight gain?

1d 2d 3d 4d 5d 6d 7d 8d 9d

Orexin Aresponse

Sleep deprivation by environmental noise (8h/d for 9d)

N= 7-8 per study. 62.5 or 125pmol/0.5 μL (American Peptide, Sunnyvale, CA). 1Data Sciences International, 2Promethion-Continuous, Sable Systems International

Chronic sleep deprivation

1. Time spent1 in wake, NREM and REM sleep

2. Distance traveled2

3. Total energy expenditure2

4. Energy expenditure during physical activity2

Endpoints for Studies:

before after0

Acute SD

before after0

Chronic SD

before after0.0

Acute SD

E (kca

before after-0.5

Acute SD

activity

l/h) *

before after0.0

Chronic SD

E (kca

l/h) *

before after0.0

Chronic SD

ity (kca

A. B. C. D.

E. F. G. H.

before after-100

Acute SD

before after-80

Chronic SD

The response to orexin A was attenuated after sleep deprivation

62EE = energy expenditure. *p < .05 as compared to 0 dose of orexin A. Data represent mean ± SEM. N = 7-8/group.

Dose OXA (pmol)

• Before sleep deprivation: Validated that orexin-A in the ventrolateral preoptic area increases wake time, total EE and the EE due to physical activity and reduces sleep time.

Dose OXA (pmol)Dose OXA (pmol) Dose OXA (pmol)

Before

University of Arizona

Christopher M. Sinton, Ph.D. Jennifer Barbee, M.A. Giuliano Sciani, B.A. Martina Sepulveda

Minneapolis VA Health Care System

Charles J. Billington, MD.Catherine M. Kotz, Ph.D. Vijay Mavanji, Ph.D. Martha Grace, B.A.

Universidad Andres Bello, Santiago, Chile

Claudio E. Perez-Leighton, Ph.D.

Funding:

Department of Veterans Affairs RR&D (F7212W to J.Teske)

United States Department of Agriculture (ARZT-1360220-H23-150 J.Teske)

Minnesota Obesity Center NIH/NIDDK NORC (P30-DK050456)

UMR Graphic Standards March 2011

Introduction ................................ ................................ ................................ ......... Page 2

Expectations ................................ ................................ ................................ ........ Page 2

E-Standards……………………………………………………………………………..Page 2

Driven to Discover Standards ................................ ................................ .............. Page 3

Wordmark Standards ................................ ................................ .......................... Page 3

Other UMR Campus Symbols ................................ ................................ ............. Page 6

U of M Colors ................................ ................................ ................................ ...... Page 8

UMR Images Library……………………………………………………………………Page 8

Online Resources ................................ ................................ ................................ Page 9

UMR Communications Contact Information ................................ ........................ Page 9

Thank You!For additional information on the Promethion line of metabolic phenotyping systems, please visit:

http://www.sablesys.com/

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