Artificial Selection for Rat Models of
Complex Disease Risks:
An Evolutionary Strategy
Steven L. Britton
Lauren Gerard Koch
University of Michigan
Starting in 1982 I was influenced by the
quantitative geneticist John Rapp.
The Pioneer of Mammalian Genomics.
He stimulated me to think about animal models of
complex diseases.
I studied all models but could not grasp the
mechanistic logic.
They seemed too simplistic.
Streptozotocin = diabetes?
Coronary occlusion = heart failure?
Knock in/knock out?
Mutagenetics?
John P. Rapp
Studied the genetics of Dahl
salt sensitive rats.
Initial idea: use artificial selection to create a low and high form
for disease risks.
But what trait would tell us the most about disease?
I went searching……….and made rules.
The model must:
1) emulate an important clinical phenotype(s)
2) be polygenic
3) respond to positive and negative health environments
4) be explained by fundamental scientific principles.
Wanted the approach to be explanatory and predictive.
An idea emerged in ~1988
I envisioned a connection between disease and evolution that
might have mechanistic value
Disease
Energy Metabolism
Evolution +
Clinical
Associational
Theoretical
Base
base
Clinical Associational Base
A small literature demonstrated a strong statistical linkage between
disease risks and low capacity for energy transfer. On this basis I
formulated the:
The Energy Transfer Hypothesis
“Variation in energy transfer metabolism is a central mechanistic
determinant of the divide between disease and health.”
Preventive Medicine (1987)
1992: I formulated that 2way artificial selection for
low and high exercise
capacity would test the
energy transfer hypothesis.
Founder
n
Population
capacity
That is, would disease risks
segregate with selection for
low capacity for energy
transfer?
If true, it would also yield
mechanism-based
contrasting models.
This was the predictive type
of approach I sought.
LOW
HIGH
High
Low
Disease
Disease
Risk
Risk
An idea emerged
In about 1988, I envisioned a connection between
disease and evolution that might have mechanistic value
Disease
Energy Metabolism
Evolution
Clinical
Explanatory
Theoretical
Associational
Base
base
We sought a principle-based explanation for the Energy Transfer
Hypothesis.
In a very non-linear path we connected ideas from:
Evolution
Energy Metabolism
Earth’s oxygen history
Directed us towards using evolution
and thermodynamics.
Jack Baldwin Hans Krebs
The evolution of metabolic cycles
Jack E. Baldwin & Hans Krebs
Nature 1981
Two messages in this paper:
1. Life evolves along the transfer of energy.
2. More complexity equates with more energy transfer.
(You don’t get something for nothing)
Biochemical Pathways
Energy transfer capacity evolved
simultaneously with all other features.
Krebs Cycle
Glucose
Pyruvate
Acetyl CoA
Cholesterol
From Alberts, et al, Molecular Biology of the Cell
Oxygen metabolism is special in the
universe for energy transfer
~15x more efficient than glycolysis
And…….
Oxygen metabolism operates at the high end of the energy
spectrum.
Electronegativity is the capacity to accept an electron.
Oxygen is very electronegative.
THE RELATIVE ELECTRONEGATIVITY OF ATOMS
LINUS PAULING
Journal of the American Chemical Society
Volume 54, p. 3570-3582, 1932
Pauling scale: 0.7 lowest
3.98 = highest
Pauling Electronegativity Scale (0.7 to 3.98)
Lowest
Fr
La
K
Sr
Ce
Th
Na
Y
Li
K
Pr
Ac
Pa
Nd
Pm
Sm
Gd
Dy
Zr
Er
Tm
Yb
Lu
Ta
0.7
0.79
0.82
0.82
0.89
0.89
0.93
0.95
0.98
1
1.1
1.1
1.1
1.12
1.13
1.14
1.17
1.2
1.22
1.22
1.23
1.24
1.25
1.27
93 elements on earth
Cm
W
U
Bk
Cf
Es
Fm
Md
No
Lr
Rf
Db
Mg
Nb
Ca
Am
Pu
Re
Np
Sc
Cr
Be
Mo
1.28
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.31
1.33
1.36
1.36
1.38
1.5
1.5
1.54
1.55
1.57
1.6
Al
Bi
Ti
Cu
V
In
Sn
Zn
Mn
Fe
Si
Ni
Ru
Ir
Co
Cd
Sb
Pb
Rn
Ga
At
B
I
1.61
1.62
1.63
1.65
1.66
1.69
1.78
1.81
1.83
1.88
1.9
1.9
1.9
1.9
1.91
1.93
1.96
2
2
2.01
2.02
2.04
2.05
Xe
Tc
As
P
H
Rh
Ag
Pt
Au
Pd
Hg
Po
Os
Tl
C
Se
S
Ba
Cs
Kr
N
Cl
O
F
2.1
2.16
2.18
2.19
2.2
2.2
2.2
2.2
2.2
2.28
2.28
2.33
2.36
2.54
2.55
2.55
2.58
2.6
2.66
2.96
3.04
3.16
3.44
3.98
Highest
Energy transfer
is basically an
electron shuttle
between
oxygen and
carbon.
photosynthesis:
electrons to
carbon
respiration:
electrons to
oxygen
As a heuristic, we synthesized bio-complexity and earth’s
oxygen history to a one page picture.
1. The rise of oxygen over the past 205 million years and the evolution of large
placental mammals.
Paul Falkowski, Science (2005).
2. The oxygenation of the atmosphere and oceans.
Heinrich Holland, Philos Trans R Soc Lond B Biol Sci (2006).
3. Why O2 is required by complex life on habitable planets and the concept of planetary
"oxygenation time?"
David Catling et al, Astrobiology (2005).
4. A molecular time scale of eukaryote evolution and the rise of complex multicellular
life.
Blair Hedges, et al, BMC Evolutionary Biology (2004)
Koch & Britton, J. Physiology, 2008
Single cell organisms only
250
200
150
100
Atmospheric oxygen (mm Hg)
Number of cell types
Placental animals
Great oxidation event
3.7 First living cells
4.6 Earth formation
2.5 Oxygenic photosynthesis
3.3 Anoxygenic photosynthesis
(glycolysis)
single cells
Aerobic respiration widespread
Anaerobic
+ aerobic:
multicellular
complexity
Multicellular organisms
There are no complex
multicellular organisms
that are purely glycolytic
Anaerobic only:
300
Single & multicellular organisms
50
0
5
4
3
2
Billions of years ago (Ga)
1
0
Koch & Britton, J. Physiology, 2008
Founder Population
In 1996 we started
artificial divergent
selection for energy
transfer capacity.
Generation 1
Generation 2
Low capacity runner = LCR
High capacity runner = HCR
Generation 3
LOW
HIGH
Generation 4
Exercise Capacity Can Be
Operationally Divided Into Two
Components.
Current
= Intrinsic + Adaptational
Phenotype
Sedentary
Acquired by
training
Energy transfer capacity was estimated from
a speed-ramped treadmill run to exhaustion
30
Speed-Ramped Protocol
Start = 10 m/min
Increased 1 m every 2 min
Rat equivalent of
the Bruce Protocol
Speed (m/min)
25
N:NIH as
founder
population
20
15
10
5
Assessed
for Intrinsic
capacity
Not
trained
capacity
-5
0
5
10
15
20
25
Duration of Run (min)
30
35
~ 14,000 rats
HCR
Lauren Koch
LCR
1996
2014
Yu-yu Ren, Katherine Overmyer, Nathan Qi, Mary Treutelaar, Lori Heckenkamp, Molly
Kalahar, Lauren Koch, Steven Britton, Charles Burant, Jun Li , PLOS ONE, 2013
Yu-yu Ren
University of Michigan
Human Genetics
Jun Li
Heritability
The proportion of total running performance that is due to
the additive effects of genes is about 40% in each line.
[h2 or narrow sense heritability]
Heritability estimates for maximal running distance (G1-G28)
Line
SOLAR*
WOMBAT*
HCR
0.44 ± 0.02
0.45 ± 0.08
LCR
0.39 ± 0.02
0.41 ± 0.06
*Modern variants of: Average information algorithm and Spatial matrix Residual
Maximum Likelihood (ASreml) software
Longevity &
Aging
(Koch)
Susceptibility
to cancer
(Thompson)
 Phosphorylating
respiration
(Harper)
 Hippocampal
neurogenesis
(Williams)
Alzheimer’s-like
neurodegeneration
(Russell)
LCR relative to HCR
display
 Post-surgical
cognitive
decline (Maze)
 Fatty liver
disease
(Thyfault)
 Activity (NEAT)
(Novak)
Ventricular
fibrillation
(Hoydal)
 Metabolic
flexibility
(Burant/Evans)
 Intra-cerebral
hemorrhage
(Keep & Hua)
 Metabolic
syndrome
(Wisloff)
High runners live longer
Survival Curves (n= 23 LCR n= 23 HCR)
24
months
34.7
months
(From Lauren Koch, et. al, Circulation Research, 2011)
VO2max predicted time of
death both between
strains and for each rat
within strain.
Exercise Capacity Can Be Divided Into Two
Components.
Exercise Capacity = Innate + Adaptive
Rat Model #2
Low Response Trainers - LRT
High Response Trainers - HRT
In 1999 the HERITAGE Family Study provides initial
information about the large inter-individual variation in
response to exercise training.
Δ VO2max (ml/min)
V)VO2max
High
responders
Low
responders
Twin and familial studies show a significant genetic component.
Bouchard et al., J. Appl. Physiol. 87(3), 1999
Wide variation for response to training in
N:NIH genetically diverse rats
Founder
n=152
1000
 DIST, meters
800
600
400
200
Population mean = +140 m
0
-200
-400
-600
25
50
75
100
Percentile
Koch et al., Physiol Gen. 2013
Selection produced populations of
Low Response Trainers (LRT ) and
High Response Trainers (HRT).
Generation 15
n=178
1000
 DIST, meters
800
600
400
Mean for HRT = +223 m
8 min longer
200
0
2.5 min less
-65 m = Mean for LRT
-200
-400
-600
25
50
75
100
Percentile
Koch et al., Physiol Gen. 2013
Blunted Cardiomyocyte
Remodeling Response in
Exercise-Resistant Rats
JACC VO L. 6 5 , N O . 1 3 , 2 01 5
AP RIL 7 , 2 01 5:1 3 7 7 – 84
Wisloff et al., 2015
-In the large senseWe do not have explanation for how (apparently) disparate clinical
conditions associate with low aerobic exercise capacity.
For interpretation we borrow ideas from Hans Krebs, Peter Mitchell,
and Ilya Prigogine about non-equilibrium thermodynamics and
entropy (order from disorder).
-Superficially-
Krebs
┼
Evolution
Prigogine
Mitchell
┼
Energy metabolism
Energy dissipation
Condensed statement:
1) evolution was underwritten by obligatory energy dissipation mechanisms (entropy).
2) emergence of complexity was coupled to the high energetic nature of oxygen
metabolism.
These statements form the basis for the Aerobic Hypothesis:
“Variation in aerobic energy metabolism is a central mechanistic determinant
of the divide between disease and health.”
-Selection for low and high aerobic capacity was an unbiased test of this hypothesis-
Interpreting through Ilya Prigogine is our new challenge
Nobel Prize: Chemistry 1977
"for his contributions to understanding energy dispersal and
complexity”
The LCR-HCR rat model system is currently
being explored at about 40 institutions.
Canada
Extramural Active
Hepple: U Calgary
Germany
McCleland: McMaster U
Doenst &
Harper: U Ottawa
Schwarzer
Tarnopolsky: McMaster U
Jena U
United States
McEwen: Rockefeller
Lee Jones: Duke U.
Leng: Johns Hopkins U
Levine: Mayo Clinic
Ritman: Mayo Clinic
Goodyear: Harvard
Lessard: Harvard U
Warden: Indiana U
Fleshner: U of Colorado
Wagner: U Cal San Diego
Russell: U Maryland
Toney: U. Texas, San Ant
Gonzalez: U Kansas
Swallow: U. South Dakota
Lust: Eastern Carolina U
Dicarlo: Wayne State U
Lujan: Wayne State U
Thyfault: U Missouri
Sowers: U Missouri
Najjar: U Toledo
Bina Joe: U Toledo
Novak: Kent State U
Dishman: U. Georgia
Metzger: U Minnesota
Warden: Indiana U
Lumeng: Indiana U
Thompson: Colorado State
Bouchard: Pennington
Maze: U California S.F.
Brazil
Brum:
U Sao Paulo
Collaborators (Primary Investigators)
Norway
Ellingsen &
Wisloff:
Norwegian U.
Finland
Hungary
Kainulainen:
Radak
U Jyvaskyla
Semmelweis U
Finland
Israel
Aviram
Technion U
United Kingdom
Kemi: U. Glasgow
GL Smith: U. Glasgow
Thomas: Novartis
Stevenson: Novartis
Burniston: John Moores U
Greenhaff: U Nottingham
Timmons: Loughborough U
Geoff Pollot: U London
Australia
Sweden
.
Cannon: Karolinska Hawley: Australian
Terrando: Karolinska Catholic University
end