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Diversity Outbred Mouse A population based model Michael DeVito, Ph.D. Acting Chief National Toxicology Program Laboratory Division of the National Toxicology Program NIEHS
Diversity Outbred Mouse A population based model
Michael DeVito, Ph.D.
Acting Chief
National Toxicology Program Laboratory
Division of the National Toxicology Program
NIEHS
• How to incorporate into NTP toxicology project strategies and future health assessments.
• NTP workshop of on Stocks and Strains (2005)
• Recent NTP experience from the assessment of benzene in the DO mouse and high fat diet in the DO mouse.
• Population-Based Rodent Resources for Environmental Health Sciences Workshop held at NIEHS (2015)
• Outcomes from internal discussions between NTP staff at NIEHS
Thoughts on utility of population based models
• Model of genetic diversity with each mouse having unique genotype
• Derived from 144 partially inbred CC mice (F4-F12)
• Genetic variation is uniformly distributed with multiple allelic variants.
• Resource for high resolution genetic mapping.
• Approximately 45 million SNPs
What is the Diversity Outbred Mouse
• Improved assessment of population variability in hazard estimation
• Mode of Action assessment
– Identify genetic basis for a response
– Species concordance of the potential mode of action (MOA)
• Suspected cases of toxicities in humans that were not predicted by traditional rodent models
Why a population based model mouse study?
• Population mouse study design are more like that of a human “prospective cohort study” rather than a traditional toxicological assessment in a specific defined mouse strains.
– Highly variable genetics
– No two test subjects are alike
– Even two groups of subjects are not really alike
– Matching characteristics of a control cohort is a key design element
– The best “control” is likely the subject itself, pre-exposure
A design realization
• Aging studies – Koh et al (2014)
• Behavior
– Novelty related behaviors; locomotor; anxiety; nociception
– Logan et al 2013; Recla et al 2014; Dickson et al 2015)
• High fat diet
– Clinical chemistry (Svenson et al 2012)
• Toxicity studies
– Benzene (French et al., 2014)
– Green Tea (Church et al 2014
• Body weight size and growth (Mouse phenome database)
How have the DO mice been used?
• 4 week benzene inhalation exposure.
• Male DO mice 3-4 weeks old
• 0, 1, 10 and 100 ppm 6hr/d, 5d/week.
• 2 cohorts 75 mice/group/cohort.
• Cohorts 4 months apart
• Measured Peripheral Blood and Bone Marrow micronucleated reticulocytes
• Compared DO results to B6C3F1 (Farris et al 1996).
The effects of benzene in the Diversity Outbred Mice (French et al., 2014)
• No significant difference between the population response across cohorts.
• BMC analysis suggests that the DO have a 10 fold lower BMC than the B6C3F1 mice.
• Data indicates that a polymorphism in Sult3a1 resulting in greater expression and is a marker for resistance.
The effects of benzene in the Diversity Outbred Mice (French et al., 2014)
• Population based BMC calculation based on both sensitive and resistant subpopulations.
– Should we drop the resistant subpopulation from the analyses and use that for the BMC?
• Gene Polymorphism vs Pathway Polymorphism
– Sult3a1 has no homologous gene in humans.
– The Sult3a1 polymorphism suggests variability in metabolism would be important to evaluate in humans.
– This approach becomes more challenging when applying to toxicodynamic responses.
• Did not prove that the polymorphism is in the Sult3a1 gene. The data is strongly suggestive, but not definitive.
• Did not have power to identify genes that make for a more sensitive phenotype.
The Effects of Benzene in the DO Mice (cont.)
Resistant
Responders
Sensitive
0 mg/kg
50 mg/kg
50 mg/kg
50 mg/kg
Sensitive Resistant Responder Vehicle
DO Mouse Model Idiosyncratic Liver Injury Epigallocatechin gallate (Church et al 2014)
Confirmation of mouse genetic
associations in humans for green tea
extract
Gene
Symb
ol
SNP
ID
(Arr
ay)
Gene Name
Chro
moso
me
Positi
on
P value
for
clinical
associat
ion
Risk/
Prote
ctive
allele
Effect
PER3
exm
107
62
period circadian
clock 3 1
78872
34 0.004937 T/C
Missens
e (R/W)
MFN2
exm
159
28
mitofusin 2 1 12069
692 0.0067 A/G
Missens
e (I/V)
VPS1
3D
exm
164
80
vacuolar protein
sorting 13
homolog D (S.
cerevisiae)
1 12343
493 0.043064 A/T
Missens
e (R/S)
Table 1. Confirmation of candidate quantitative trait genes in 15 clinical EGCG case samples.
Mitofusin 2, involved in mitochondrial regulation and maintenance, may contribute to susceptibility to EGCG-induced liver injury by herbal supplement use.
Church et al. FCT. 2015
Power simulations demonstrate the relationship between power, sample size, and percentage
variance explained.
Daniel M. Gatti et al. G3 2014;4:1623-1633
©2014 by Genetics Society of America
• Can we get an idea of whether a factor of 10 for population variability is reasonable for a chemical under study?
• How many DO mice do we need to have the same power as a typical subchronic study?
Do we need to identify the polymorphism or gene in every study?
• Male and female rats (Harlan SD) and mice (B6C3F1)
• 3-5 dose levels plus controls
• 10 animals/treatment group
• Body weight/body weight gain
• Organ weight
– liver, thymus, left and right kidney, left and right testis, left and right epididymis, left and right ovary, heart, and lungs
• Histopathology (approximately 41 tissues)
• Hemotology (rats and mice)
• Clinical Chemistry (rats only)
Typical NTP 90 day study design
Effects of High Fat Diet in DO Mice
• 150 male DO mice at 6 weeks of age
– 75 on control diet (10 kcal% fat) D12450J (Research Diets, New Brunswick, NJ)
– 75 on High Fat diet (60 kcal %) D12492 (Research Diets, New Brunswick, NJ)
• Singly housed
• Food and water ad lib
• Resting Blood Glucose
– Prestart and Week 1, 5, 9 and Tsac (week 13) tail prick
• Insulin and Leptin – week 1 by retroorbital bleed and at Tsac.
• Oral glucose tolerance – week 12
• Sperm count and morphology
End points Evaluated/Collected
• End of 13 weeks on the diet
– Organs
• Left and right epididymis (weighed individually) Left processed for fresh sperm analysis; right frozen in liquid nitrogen and stored at or below -70°C.
• Left and Right testis right was collected in modified Davidson’s solution; left testis was weighed, frozen in liquid nitrogen and stored at or below -70°C.
• Liver weighed and stored in Liquid N2
• Left and right kidney (weighed individually), Right kidney was placed into 10% NBF; left kidney was frozen in liquid nitrogen and stored at or below -70oC.
• Abdominal fat was divided into two sections, frozen in liquid nitrogen and stored in two vials at or below -70oC for gene expression and/or protein analysis.
• Brain weighed
• Skeletal muscle posterior right thigh was dissected away from the femur and cut in half, with one half placed on a card and collected in 10% NBF and the remaining half frozen in liquid nitrogen and stored at or below -70oC.
• Tail frozen
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0
1 0
2 0
3 0
4 0
B o d y w e ig h t g a in
In d iv id u a l A n im a ls
Bo
dy
we
igh
t (g
)
C o n tro l D ie t
H ig h F a t D ie t
Variability in weight gain after 14 weeks on a high fat diet in the DO mice
B6C3F1 mice gain approximately 14-17 g in a subchronic study
Variability in Sperm characterization in the DO Sperm Concentration
1 3 5 7 9
11
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75
0
5
10
15
20
25
30
35
High Fat Diet
Control Diet
Individual Animals
M/m
l
Motility
1 3 5 7 9
11
13
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29
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65
10
20
30
40
50
60
70
80
90
High Fat DietControl Diet
Individual Animals
Pe
rce
nt
66 fold
8 fold
How does the DO compare to the B6C3F1?
Startin
g body
wgt
Term
inal
body
wgt
liver
/bod
y wgt
r kid
ney/bod
y wgt
r tes
tes/bo
dy w
gt
L epi
didym
us/bod
y wgt
Spe
rm c
ount
0
10
20
30
40
50
60
70
Coefficient of VariationC
oeff
icie
nt
of
Vari
an
ce DO
B6C3F1
In general CV is 3-8 times higher in DO than B6C3F1
How does the DO compare to the B6C3F1?
Power Calculations
Endpoint Number of DO mice to have same
power as traditional subchronic study
Body Weight 27
Liver Weight 33
Kidney weight 22
Testis weight 32
Epididymis weight 16
Relative liver weight 23
Relative kidney weight 20
Relative testis weight 29
Relative epididymis weight 34
Sperm Counts 150
Sperm Motility 32
• Traditional short term toxicology studies use n=5-20 per control and exposure group per strain
• Sample size of anywhere from 20-30 per exposure group may be needed for a “pilot studies” to estimate population variability in response
• Subsequent studies to identify QTLs may require 400 or more per exposure group and follow up studies to verify the QTLs
Design considerations
• Given the large group sizes required agents should ideally have a known or anticipated effect of concern.
• Known significant “population exposure” to justify early use of a population based resource.
• Is it an initial “discovery” research tool for screening of agents of unknown effect or only for chemicals with known toxicity?
What agents/test articles should be studied.
• The endpoint should ideally be a continuous variable.
• Some expectation of a population variability
• “Within strain” variability of the agent-response should be low;
• Toxicodynamic/time course stability
– a highly dynamic response may lead false negative “non-responders” due to small differences in time course
– a very steep dose response may lead to false negative “non-responders” due to small shifts in potency.
• Non invasive assessments will allow ‘paired” analysis.
Endpoint considerations.
• A model is only as good as our understanding of the model
– How well do we understand the DO model?
• The developmental basis of adult disease.
– How do we use the DO in developmental toxicity studies?
• Will the DO just pick up differences in dose, or will it pick up completely different responses?
• How well do we need to characterize variability
– Do we need to “prove” we found the SNP?
– Do we need a more pragmatic approach to characterizing variability?
Closing Thoughts
• NTP/NIEHS
– Benzene Study
• John E. French, Daniel L. Morgan, Grace E. Kissling, Keith R. Shockley, Gabriel A. Knudsen, Deborah King, Kristine L. Witt, Lars C. Pedersen
– NTP Working Group
• Alex Merrick, Nigel Walker, June Dunnick, Scott Auerbach, Paul Foster
– High Fat diet
• Grace Kissling, Greg Travlos, Keith Shockley
• Jackson Labs
• Daniel M. Gatti, Steven C. Munger, Karen L. Svenson, and Gary A. Churchill
• Alion Science and Technology
– Herman Price
• University of Arkansas for Medical Sciences
– Alison Harrill
• ILS
– Kim G. Shepard (benzene)
– Susan Borgohoff (High Fat)
– Herman C. Price
Acknowledgements
