Automated home cage behavioral monitoring is receiving increasing attention from the scientific community because of its benefits with regards to translational research, data replicability and animal welfare. In this webinar, Kenneth Dyar (Helmholtz Diabetes Center) and Joanna Moore (GSK) discuss how home cage monitoring can be used to reduce animal stress, optimize methodology and guide physiology and animal behavior research. Dr. Kenneth Dyar Passive locomotor activity monitoring for real-time circadian study design Circadian clocks are fundamental determinants of physiology, behavior and health. For skeletal muscle, the circadian clock promotes insulin sensitivity and orchestrates rhythms of glucose, lipid, and amino acid metabolism. Physical activity synchronizes circadian clocks by altering body temperature and through distribution of various hormones and metabolites. Research suggests that misalignment of the ‘muscle clock’ plays an important pathophysiological role in metabolic disease. In this webinar, Dr. Kenneth Dyar highlights some examples of how the DVC system can be used for locomotor activity monitoring in order to evaluate circadian alignment before, during or after various dietary and pharmacological interventions. Dr. Joanna Moore Using home-cage monitoring to determine the impact of timed mating on male mouse welfare The use of sterile male mice to induce pseudopregnancy in female mice assigned for the implantation of embryos is a vital component in the production of Genetically Altered Animals (GAA). This process involves swapping a genetically sterile male’s female companion for a new female. In this presentation, Dr. Joanna Moore discusses the use of home cage activity monitoring to evaluate the potential impact of this procedure on the welfare of male mice and how the impact of this intervention may be reduced. All animal studies were ethically reviewed and carried out in accordance with the Animals (Scientific Procedures) Act 1986 and the GSK Policy on the Care, Welfare and Treatment of Animals. Key topics will include… - Using home cage activity as a readout for animal welfare - Using locomotor activity to optimize methodology and validate study design in real-time - Pre-study screening of cohorts for outliers

1. Kenneth Dyar, PhD
Senior Scientist
Helmholtz Diabetes Center
Munich, Germany
Case Studies in Home Cage Monitoring:
Rodent Behavior, Circadian Biology and
Animal Welfare
Joanna Moore, PhD,
FIAT, R.An.Tech
Investigator
GSK

2. Case Studies in Home Cage Monitoring:
Rodent Behavior, Circadian Biology and
Animal Welfare
Kenneth Dyar and Joanna Moore present applications
of automated home cage activity monitoring and
discuss how it can be used to improve animal welfare,
optimize study design and drive animal behavior and
physiology research.

3. Kenneth Dyar, PhD
Senior Scientist
Helmholtz Diabetes Center
Munich, Germany
Passive locomotor activity
monitoring for real-time
circadian study design
Copyright 2020 K. Dyar and InsideScientific. All Rights Reserved.

4. 24-hr Gene Expression in Skeletal Muscle: What Is It Good For?
Numberofgenes
SOL! TA!
300!
600!
900!
1200!
-2! +2!
Numberofcircadiangenes
Soleus
Tibialis
Anterior
TA!
Ctrl! mKO!
SOL!
Ctrl! mKO!
300!
600!
900!
-2! +2!
Skeletal muscle circadian genes
expression
level
day night
day night

5. Regulation of circadian genes in skeletal muscle

6. Physiological and
behavioral circadian
rhythms in humans
*All these rhythmic
processes are controlled
by circadian clocks
0 6 12 18 0 Time, hours
Locomotor
Activity

7. Transcriptional logic of the core circadian oscillator
modified from Preitner et al,
Cell. 110(2):251‐260 (2002)

8. Transcriptional logic of the core circadian oscillator
β
BMAL1 targets
REV-ERB targets
activated “clock-controlled genes”
repressed “clock-controlled genes”

9. Entrainment of circadian clocks in peripheral tissues
Entrainment occurs via
neural cues
(Sympathetic Nervous
System) &
circulating factors
(hormones,
metabolites)
modified from Kondratova & Kondratov
Nat Rev. Neurosci 13(5):325‐335 (2012)

10. Entrainment of circadian clocks in peripheral tissues
lipids
retinoids
estrogens
glucocorticoids
heme
modified from Teboul et al., J Appl Physiol (1985). 107(6):1965‐1971 (2009)

11. Chronic circadian misalignment causes disease

12. Chronic circadian misalignment causes disease

13. Kohsaka et al, Cell Metabolism 2007; (6) 414–421
Chow Diet High Fat Diet
Chow (RC)
High Fat (HF)
High Fat Diet Lengthens Free-Running Locomotor Activity Period in Mice

14. Chow High Fat Diet
Chow High Fat Diet Chow (RC)
High Fat (HF)
Kohsaka et al, Cell Metabolism 2007; (6) 414–421
24-hr Activity and Feeding Rhythms Altered by High Fat Diet

15. Dyar et al, Cell 2018 (174), 1571–1585
24-hr Metabolite Rhythms Are Altered by High Fat Diet

16. Chow High Fat Diet
Chow
HighFat
Body Composition
Liver Steatosis
Liver
Liver
Histology
Fatty Liver
Liver
Histology
Body mass
Chow
HighFat
Hatori et al. Cell Metabolism 2012 (15) 848–860

17. Time-Restricted Feeding Attenuates Metabolic Disturbances of High Fat Diet
Hatori et al. Cell Metabolism 2012 (15) 848–860

18. High Fat Diet (FA)
Fatty Liver
Liver
Histology
High Fat Diet + TRF (FT)
Normal Liver
Liver
Histology
Hatori et al. Cell Metabolism 2012 (15) 848–860

19. 24-hr Locomotor Activity
Time-Restricted Feeding Increases Locomotor Activity and Performance
Time on Accelerating Rotarod
Hatori et al. Cell Metabolism 2012 (15) 848–860

20. 0
10
20
30
40
bodyweight (g) fat mass (g) lean mass (g)
Start of Experiment
0
10
20
30
40
bodyweight (g) fat mass (g) lean mass (g)
End of Experiment
Ad Libitum feed (chow)
Caloric Restriction (chow)
Caloric Restriction: Time-Restricted Feeding Taken to the Extreme
Start 12 Weeks of Age

21. 3 consecutive days
n=30
Average +/-sem
Activity Week 1: both groups fed Ad Libitum
Animal Locomotion index [Percentage (x100) %]

22. Activity Week 2: 10% CR
Animal Locomotion index [Percentage (x100) %]
3 consecutive days
n=30
Average +/-sem

23. Anticipated locomotor activity rhythm under
caloric restriction; reduced peak at end of dark phase
Activity Week 8: 40% CR, 4th week
Animal Locomotion index [Percentage (x100) %]
3 consecutive days
n=30
Average +/-sem

24. n=30
10% CR
20% CR
30% CR
Caloric restriction increases locomotor activity ~10%
Cumulative Activity over 58 days
Animal Locomotion index [Percentage (x100) %]
40% CR

25. EcoMRIAd Libitum
Caloric Restriction
Activity over 58 days

26. morning cage change
Activity over 58 days
Ad Libitum
Caloric Restriction

27. 0 6 12 18
0
10
20
30
40
Zeitgeber time (h)
Specificb2-ARbindingsites
(fmol/mgprotein)
Circadian expression of b2-adrenergic receptors
in quadriceps muscle
ZT6
ZT18
* longitudinally monitor
gain/loss of lean/fat mass
Chronic Treatment Protocol
*4weeks daily injection
Bioavailabilityof
EndogenousReceptor
Functional day/night differences in ligand sensitivity?

28. Example Slide Header
Activity monitor rack
Leddy light boxes
to impose 24-hr
lighting schedule

29. 06:00 to 18:00 12:00 to 24:00 24:00 to 12:00
red arrow=
start of shifted
light schedule
green arrow=
echoMRI
Leddyout
Leddyin
12 hrs lights on:
no treatment NOON MIDNIGHTTreatment Group:
Battery charge
(East to West Jetlag) (West to East Jetlag)

30. 1) Time matters in health and disease
time of day, feeding time, activity time, time to adapt, treatment time
2) Locomotor activity rhythms are a useful metric to define relative circadian
alignment, ensure data quality, and facilitate proper study design
and data interpretation
3) Real-time monitoring of locomotor activity during an experiment gives
feedback about the current circadian state, and a general overview
of relevant factors that can inform rational study design and corrective
measures to avoid potential pitfalls
Conclusions

31. UC Irvine/UT Houston
Paolo Sassone-Corsi
Kristin Eckel-Mahan
Pierre Baldi
University of Kbhvn
Henriette Pilegaard
Anders Gudiksen
Rasmus Biensø
Salk Institute
Ron Evans
Xuan Zhao
VIMM
Stefano Schiaffino
Bert Blaauw
Vanina Romanello
Marco Sandri
Helmholtz Munich
Henriette Uhlenhaut
Dominik Lutter
Anna Artati
Jerzy Adamski
Matthias Tschöp
University of Graz
Thomas Eichmann
University of Trieste
Gianni Biolo
Sara Mazzucco
Thanks!!!

32. Using home-cage
monitoring to determine
the impact of timed
mating on male mouse
welfare
Joanna Moore, Giulia Del Panta,
Eloisa Brook and Hilary Lancaster
Joanna.l.moore@gsk.com

33. Ethical statement
All animal studies were ethically reviewed and carried out in accordance with the
Animals (Scientific Procedures) Act 1986 and the GSK Policy on the Care, Welfare
and Treatment of Animals.

34. – The production of pseudo pregnant females within our
animal facilities includes the use of sterile male *Prm1
mice.
– They are typically housed with companion females.
– At timed mating the companion is swapped for a naïve
female.
– After 24 hours the naïve female is removed and the original
companion put back with the male.
Introduction
*Prm1 Genetically Sterile Protamine-1 (Prm1) EGFP Transgenic mouse obtained under licence from Dr Pawel Pelczar, University of Zurich, Switzerland
Haueter, S., Kawasumi, M., Asner, I., Brykczynska, U., Cinelli, P., Moisyadi, S., Bürki, K., Peters, H.F.M. A., and Pelczar, P, (2010) Overexpression of Prm1‐EGFP fusion
protein in elongating spermatids causes dominant male sterility in mice. 2010. Genesis, 48 (3) 151-160. doi.org/10.1002/dvg.20598

35. – We cannot be certain by visual assessment what the impact
of the naïve female is on cage activity.
– An unusually high increase in cage activity for a sustained
period could have an effect on mouse welfare (Pernold et al,
(2019).
– We used an established home-cage monitoring system to
determine how much disruption is created to the activity
pattern during timed mating.
Introduction
Pernold et al. (2019) Towards large scale automated cage monitoring – Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a
non-disrupting capacitive-based technique. PLOS ONE. [Accessed 15 Sept 2019]

36. Our overall aim was to show whether cage activity
significantly increases when a companion female is
replaced with a naïve female.
Study objectives:
– To determine if the naïve female causes a disruption in
the cage activity.
– How long it takes for activity to return to pre swap
activity.
Objectives and Aim

37. What did we learn from the Digital Ventilated Cage system?
Working with big data

38. Introduction
Working with DVC data
38
– This presentation focuses on one of the
early studies we completed with the Digital
Ventilated Cage system (DVC).
– With so much data generated it is
important to construct your scientific
question prior to the start of the study.
– When there is more than one animal in the
cage, it is important to note the cage is the
experimental unit.
The DVC is a non intrusive tracking system which
sends data from the rack to a cloud storage via a
Master computer.

39. Introduction
Position of the 12
electromagnetic sensors to
continuously track and
monitor spontaneous mouse
activity (Iannello, 2019).
Iannello (2019) Non-intrusive high throughput automated data collection from the home cage. Heliyon, [Accessed 18 March 2020]
Working with DVC data
– The data is the animal activity index measured for all twelve
electrodes per minute.
– The animal activity index measures how much each electrode is
activated, i.e. how often the mouse moves over each electrode.
– The summary measure over the cage is the Average Activity Index.
– It is a normalized measurement assuming values between 0% and
100%.

40. Introduction
Working with the DVC data
40
– It is important to have a reasonable understanding of the pre swap activity of the mice
being studied to determine what activity period will give the most accurate answer to the
scientific question.
– After initially reviewing the mouse activity pattern, we noticed that the mice that were
housed on the DVC during this study had sporadic activity across the day and night
periods, this may be related to the social interaction within pairs.
– Data from each cage had a lot of variability over time, so we selected two-hour time
intervals rather than a whole 24hr period.

41. Methods

42. Methods
– The study ran over ten days and the experimental
unit was the cage.
– We included 20 cages of established proven sterile
Prm1 males with their companion females and 10
naïve CD1 weight matched females.
– Mice were housed in GM500 individually ventilated
cages on the Digital Ventilated Cage system (DVC),
(Tecniplast S.p.A. Italy).
Subjects and equipment

43. Methods
– Each cage contained a mouse Igloo (LBS),
cardboard fun tunnel (LBS) and aspen chew block
with Lignocel wood bedding (IPS). Paper shred
(Datesand) and a half a Lignocel Large Wood Wool
Disc (IPS).
– Mice were housed under a light cycle of 14 light:10
dark with a ten minute dusk and dawn period.
– Food and water was provide Ad Libitum.
Subjects and equipment

44. Methods
.
Date of activity Activity to be completed
Friday 6th December All selected cages were cage changed.
Monday 9th December The cages were randomised by companion female weight to Group One companion
females.
Thursday 12th December
Between 07:00-08:00hrs
A note of the time the last cage was replaced on the rack was made.
 Group One: the cage was removed from the rack, the companion female replaced
with a naïve female
 Group Two: the cage was removed from the rack, each female was removed from
the cage for a five seconds and returned to the home-cage
Monday 16th December All naïve females from Group One were replaced with the original companion female.
Study ends.

45. Methods
Activity sampling
– We focused on the activity at the following four Two-Hour Time Intervals (THTI):
08:00-10:00hrs, 10:00-12:00hrs, 12:00-14:00hrs and 20:00-22:00hrs.
Two-hour time interval (THTI)
change from pre-swap activity
Pre female replacement
THTI activity on Thursday –
Average THTI activity
(Saturday-Wednesday)
Post female replacement
THTI activity on Thursday –
Average THTI activity
(Friday – Sunday)

46. Results

47. Results
Heat maps give a visual view of mouse activity
47
Lights on Lights off
Heatmap showing activity across study days.
Black boxes indicate cage interventions; (1) Cage change, (2) Female swap.
(1)
(1)
(2)
(2)
Group One: female swapped
Group Two: female returned to male

48. Results and discussion
This analysis focuses on the pre-female replacement
– The pre-swap activity was generally
similar between both groups.
– There was an increase in activity for
Group 1 (companion female
replaced) during the 10:00-12:00hrs
interval on Thursday compared to
previous days, and group 2 on the
same day.
Replacement
day
Group One: female swapped
Group Two: female returned to male

49. Results and discussion
This analysis focuses on the pre-female replacement
‒ We conducted a T-Test on the change
from pre-female replacement
measurement for each time interval
comparing the average activity of the
previous days in the same time
interval.
‒ There was a significant increase in
activity (P=0.0198) between the
groups during the 10.00-12.00hrs
time interval.
Group One: female swapped
Group Two: female returned to male

50. Results and discussion
This analysis focuses on the post-female replacement
– The activity for Group 1
(companion female replaced)
decreased between 10am-12pm
after 48 hours.
Replacement
day
Group One: female swapped
Group Two: female returned to male

51. Results and discussion
‒ A T-Test was conducted as
before, at each of the THTI.
‒ There was a significant
increase in activity between
the groups during the
10:00-12:00hrs time interval
(P=0.0036),
and 12:00-14:00hrs
(P=0.023).
This analysis focuses on the post-female replacement
Group One: female swapped
Group Two: female returned to male

52. Discussion

53. Discussion
What can we additionally infer from this study?
– The mice in this study had a varied activity pattern
which may be linked to them being mixed sex pairs.
– During our initial studies the increase in activity
caused by the female being replaced was still visible
even when carried out at the same time as cage
changing (Unpublished data).

54. Discussion
What can we additionally infer from this study?
– This increase in activity suggests there has been a
disruption on the normal activity of mice as a result
of female replacement.
– The decrease in activity between 20:00-22:00hrs
from Group One compared to Group Two (after
female replacement) is likely due to mice being
more active during the light phase and possibly
fatigued during the dark phase.

55. Discussion
What can we infer from this study?
– Both male and female mice may be fatigued after initial
mating.
– Cage activity may return to pre swap activity sooner if
the female is replaced nearer the end of the working
day.
– Home Cage Monitoring produces activity data that we
cannot obtain through observations alone.

56. Including overall conclusion
Further work

57. Conclusion and further work
What further work could be done to give us more information?
– Test to determine if time mating in the late afternoon
would decrease the disruption in cage activity.
– Does the activity increase if a naïve female is added
to the cage but the companion left in the cage too?
– Investigating whether the increase in activity was
mirrored when the companion female is returned to
the male on the same day.
– Would we get a similar peak in activity if we
introduced two unfamiliar female mice?

58. Conclusion and further work
What further work could be done to give us more information?
– It would be interesting to see if the same conclusions
were found under reverse lighting.
– Use ultra sound noise monitoring to test the
vocalisations made by the mice during the peak
activity period.
– Video data would enable us to be confident about the
behaviour of mice after the females are swapped.

59. Overall Conclusions
Home cage activity monitoring gives us a unique
insight in terms of how the work we do can impact
the welfare of animals, thus giving us an opportunity
to refine our husbandry processes, scientific studies
and care to further meet their needs.

60. Thank you to the following people who helped with
this project:
Fabio Iannello, Tecniplast S.p.A
Guido Gottardo, Tecniplast S.p.A
Scott Carnell, Tecniplast UK
David Wille, Research Statistics, GSK
Steven Barrett, Research Statistics, GSK
Steve Wilson and team, IVSD, GSK
Kay Dowse, IVSD, GSK
Helen Murphy, LAM, GSK
Acknowledgements
Joanna.l.moore@gsk.com

61. Kenneth Dyar, PhD
Senior Scientist
Helmholtz Diabetes Center
Munich, Germany
Joanna Moore, PhD,
FIAT, R.An.Tech
Investigator
GSK
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