Abstract
Enlarged brains of homeotherms bring behavioural advantages, but incur
high energy expenditures for the animal. The ‘Expensive Tissue’ (ET)
hypothesis links the evolution of the enlarged brain to increased
cognitive abilities (CA) that improved foraging performance, social
interactions and allowed for reduction in size of the energetically
demanding gut. We tested the directionality of the evolutionary
trade-off between brain, gut and CA using experimental evolution model
consisting of lines of laboratory mice subjected to artificial selection
on basal (BMR) or maximum (VO2max) aerobic metabolism -
traits that are implicated in evolution of homeothermy and CA. High BMR
mice had bigger guts, but not brains. Yet, they performed better in
cognitively demanding tasks and had higher neuronal plasticity than
their counterparts. The data indicate that evolutionary increase of CA
was initially associated with brain plasticity and fuelled by an
enlarged gut, which was not traded off for brain size, as the ET posits.
Introduction
Many studies suggest that the observed variation in in brain size is
ecologically adaptive and maintained by selective trade-offs (e.g.,
Kotrschal et al. 2015, Pontzer et al . 2016, Sayol et al. 2018).
Since increased brain size imposes disproportional metabolic costs, it
is likely that those trade-offs involve other metabolically expensive
organs. The ‘Expensive Tissue’ hypothesis (ET) posits that
encephalisation was primarily possible thanks to ‘financing’ metabolic
costs of brain maintenance by reducing the size of energetically
demanding gut parts (Aiello & Wheeler 1995). Such reduction was in turn
possible by increased cognitive abilities that allowed for more
efficient foraging for food of better quality.
The ET scenario is difficult to test because of the lack of
palaeontological record, that could be used to analyse the presumed
brain-gut trade-off. Evolutionary plausibility of this trade-off can
therefore only be tested if it is a more general evolutionary principle
applicable to extant animals characterized by positive association
between enlarged brains and enhanced cognitive abilities. The existence
of the brain-gut trade-off has been questioned in a thorough comparative
analysis of brain size and internal organ mass in 100 mammalian species,
including 23 primates (Navarrete et al. 2011). The trade-off is
also difficult to reconcile with a positive association between brain
size and basal metabolic rate, BMR (Isler & van Schaik, 2006) - a
measure of aerobic metabolism reflecting in large part metabolic costs
of maintenance of the gut (Konarzewski & Diamond, 1995). Furthermore,
brain size and cognitive abilities are positively correlated with
aerobic exercise capacity essential for sustaining such important
activities as reproduction or escape from predators (Koteja 2004;
Chrząścik et al . 2014; Książek et al. 2009) that must
ultimately be fuelled by the gut; these results are also incompatible
with the brain-gut trade-off.
To date, studies on the ET hypothesis predominantly used comparative
methods (Navarrete et al. 2011). A stronger test of the ET and
its associations with cognitive abilities is provided by artificial
selection experiments, because they allow for inferences about causal
relationships (Garland & Rose 2009). The most pertinent animal model of
this kind was developed by Kotrschal et al ., who demonstrated the
brain-gut trade-off in guppies (Poecilia reticulata ) artificially
selecting for relative brain size (but see Healy & Rowe 2013). However,
life history and physiology of fish is far removed from that of
homeotherms (Rose et al. 1993), therefore its relationship with
selection on encephalisation in, for example, mammals is questionable.
Furthermore, brain size alone does not provide sufficient information to
determine cognitive abilities (Healy & Rowe 2007).
Here, for the first time, we tested the directionality of evolutionary
trade-offs between the size and function of the brain and other
energetically expensive organs in a mammalian model of experimental
evolution. We used line types of laboratory mice subjected to artificial
selection on divergent rates of basal (BMR) or maximum aerobic
metabolism (VO2max, Książek et al. 2004;
Gębczyński & Konarzewski 2009)– traits widely accepted as
pre-requisites for the evolution of homeothermy and large brain size
(Benett & Ruben, 1979; Lovegrove, 2017).
Materials and Methods
Animals
We used female mice from two concurrent selection experiments carried
out at the Faculty of Biology, University of Bialystok. In the first
experiment we divergently select mice for high/low body mass-corrected
Basal Metabolic Rate (BMR) quantified according to the procedure
outlined below. In this experiment we maintain two non-replicated line
types: high BMR (H-BMR) and low BMR (L-BMR) line type, whose divergence
is sufficiently large to be confidently attributed to the results of
selection, rather than to genetic drift (Sadowska et al. 2017).
Here we used females of generation F52 and F53.
We also used female mice of generation F37 and F38 from the second
selection experiment, in which we established eight genetically isolated
Swiss-Webster laboratory mice lines. In four of the lines, forming the
Peak Metabolic Rate (PMR) line type, mice were selected for
VO2max quantified as the highest body-mass-corrected
oxygen consumption averaged over 2 min of a 5 min swim in a 25 °C water.
The other four lines form the randomly bred (RB), control line type
(Gębczyński & Konarzewski 2009). Throughout the experiment animals were
fed a standard diet (12.8 kJ g−1 metabolisable energy, 17.0 kJ g−1
caloric value manufactured by Labofeed, Kcynia, Poland; for detailed
composition see (Sadowska et al. 2017).
All procedures were approved by the by the Local Ethical Committee on
Testing Animals, (permit no. 42/2011, 11/2013, 21/2013, 194/2016).
Measurements of Basal Metabolic Rate
We simultaneously used two positive pressure, open circuit respirometry
systems fitted to two Sable Systems FC-1B oxygen analysers. In each
system the outside atmospheric air was pushed through a column of
Drierite to remove water vapour and then forced through a copper coil
submerged along with metabolic chambers (each 350 cc in volume) in a
water bath stabilized at 32 °C (a temperature that is within our
animals’ thermoneutral zone) to equalize and control the temperature.
The air stream was then divided to three independent streams, each fed
at 400 mL min-1 to a separate mass flow controller
(Sierra Instruments, Monterey, CA or ERG1000, Warsaw, Poland) and forced
through individual metabolic chambers, and further through a
computer-controlled channel multiplexer (Sable Systems, Las Vegas, NV).
Air was thereafter scrubbed of CO2 Carboabsorb AS, BDH
Laboratory Supplies), dried one more time (Drierite), subsampled at the
rate of 75 mL min-1, and fed to an oxygen analyzer.
BMR was calculated Withers’ equation no. 4a (22) and defined as the
lowest stable reading that did not vary by more than 0.01% of oxygen
concentration for at least 4 min.
Measurements of cognitive abilities
Following BMR measurements the mice were tested in an automated learning
apparatus, an IntelliCage system, from TSE Systems, Germany (Galsworthyet al . 2005; Knapska et al . 2006). The IntelliCage
consists of a large standard cage 20.5 cm high, 40 cm × 58 cm at the top
and 55 cm × 37.5 cm at the base. The cage is equipped with four operant
learning chambers fitted into the corners of the housing cage. Access
into the chamber is only possible through a tube with a built-in
transponder codes reader (antenna) that restricts access to the learning
chamber to only a single mouse at a time. Each corner, equipped with
proximity sensor, contains two openings permitting access to drinking
bottles. An automatically operated door controls access to liquid.
Poking a nose into the openings (nosepoke response) activates an
infra-red beam-break response detector. Each visit to the operant
chamber, as well as each nosepoke and the amount of water consumed
(number and duration of licks) is recorded for each individual animal.
The cage control unit permits the access to particular bottles according
to schedules individually pre-programmed for each mouse. The cage is
equipped with a sleeping shelter in the centre, with a feeder placed on
its top providing food ad libitum . Except for the technical
breaks and cage exchange (once a week), the mice were not disturbed.
A week before the experiment the mice were sedated and injected with a
glass-covered microtransponder (11.5mm length, 2.2mm diameter; DataMars)
with a unique code recognized by sensors installed in the learning
chambers. After the transponder procedure, subjects were moved from the
housing facilities to the experimental rooms. The animals were then
transferred to three IntelliCage systems, each housing 10-12 mice
randomly drawn from the stocks of their parental lines.
Mice housed in each of the IntelliCages were maintained in a 12:12 light
schedule and subjected to a 15 day protocol divided into four phases:
simple adaptation, nosepoke adaptation, and place preference learning
and reward-motivated discrimination learning (Fig. 2A).
During simple adaptation phase (days 1-4), all doors in the learning
chambers remained open and access to water was unrestricted. During the
nosepoke adaptation phase (days 5-7), all doors were closed and opened
only when an animal pokes its nose (nosepoke response) into one of the
two openings placed inside learning chambers. When an animal removed the
snout from the opening, the door
closed automatically. During the simple adaptation and nosepoke
adaptation phase each of 8 bottles contained tap water (days 1-7, Fig.
2A). During the place preference learning phase (days 8-10) access to
the drinking bottles was restricted to only one of the IntelliCage
learning chambers for each mouse.
The corner with water access was assigned randomly, to no more than 3
mice. Such procedure minimized social modulation of learning (Kiryket al. 2011). During reward-motivated discrimination learning tap
water in one bottle in the corner was replaced by 10% sucrose solution,
which is strongly preferred by mice (Days 11-15). Animals had a choice
between nosepoking (operant response) to the bottle containing tap water
or to the bottle containing a reward (sweetened water) placed in the
same conditioning corner. They had to remember location of the reward to
perform the correct response. The number of visits, nosepokes and tube
licks were recorded automatically by the computer controlled IntelliCage
system in 12-h time intervals. All raw data were then analyzed by PyMICE
- Python library for mice behavioral data analysis (Kowalski et
al. 2016).
Morphometrics
Following measurements of cognitive abilities, animals were killed by
cervical dislocation and dissected. Brain, heart, liver and kidneys were
excised, blotted from excess fluids and weighed to an accuracy of 0.001
g.
LTP measurements
Naive animals were anaesthetized with isoflurane and decapitated. The
brains were instantly removed and placed in cold artificial
cerebrospinal fluid ACSF (NaCl 117 mM, MgSO4 1.2 mM, KCl
4.7 mM, CaCl2 2.5 mM, NaHCO3 25 mM,
NaH2PO4 1.2 mM, 10 mM glucose, bubbled
with carbogen) and both hemispheres were cut into 400 μm coronal slices
with a vibratome (LeicaVT1000S). Slices containing hippocampus were
placed in a recording interface chamber (Harvard Apparatus) to recover
for at least 1.5 h before the start of recordings. The slices were
continuously perfused with carbogenated CSF at 33°C. Field excitatory
postsynaptic potentials (fEPSPs) were recorded using a glass pipette
filled with 20 mMNaCl (impendence 1.0–3.0 MΩ) from the stratum
radiatum in CA1 area of the hippocampus (Fig. 2). To evoke fEPSP,
Schafer collateral-commissural afferents were stimulated every 30 s
(test pulses at 0.033 Hz, 0.1 ms) with bipolar metal electrodes (FHC,
USA). The intensity of test stimuli were adjusted to obtain fEPSP with
slopes of one-third of the maximal response. After at least 15 min. of
stable baseline, LTP was induced tetanically (three trains of 100 Hz, 1
s stimulation, separated by 3 min). After the end of the tetanic
stimulation, a test pulse was subsequently applied for at least 90 min.
Recordings were amplified (EX4-400 Dagan Corporation, USA), digitized
(POWER1401, CED, UK) and slopes of fEPSP analyzed on-line and off-line.
For analysis of LTP, the response slopes were expressed as a percentage
of the average response slopes during the baseline period prior to LTP
induction.
Statistical analyses
Data on BMR and masses of internal organs were analysed by means of
ANCOVA with line type affiliation as a fixed factor and body mass as a
covariate. In this analysis and the behavioural analyses described
below, replicated lines were nested within line types as the random
factor of the model (4 replications in the RB and PMR line types,
respectively, but 1 line for H-BMR and L-BMR line types, respectively,
as they were not replicated; 10 lines in total). The respective error
mean square for 10 lines was used as denominator of the F statistics
testing the effect of line affiliation. Hence, the df for the
between line type comparisons was 3 (for the F numerator) and 6 (for
denominator). Likewise, the df for pairwise t-test comparisons
between the line types was 6.
Repeated measures analysis of covariance (ANCOVA) was used to analyse
the between-line type differences in total numbers of visits to all four
corners summed over four continuous 12 h periods of observation,
covering the last 24 h of place preference learning, and first 24 h of
reward-motivated discrimination learning.
For the same period of time we analysed the number of visits to the
bottles located in a corner assigned to a given animal. During first two
12 h periods both bottles in the corner contained water, and
subsequently, for the next two 12 h periods of reward-motivated
discrimination learning, one bottle was filled with 10% sucrose
solution. The numbers of correct responses (i.e. nosepokes to the bottle
with sucrose) were corrected for (1) the dark and the light phase of the
experimental period (nested within the effect of time coded as a fixed
factor) (2) numbers of nosepokes to the bottle with tap water located in
the same corner coded as a covariate. Initially, we also controlled for
possible differences between batches of animals (coded as a random
variable) maintained together in the IntelliCage system. However, since
this effect was never significant at p = 0.05, we dropped it from final
analyses. We used an analogously structured model to analyse the number
of licks on the bottles containing tap or sweetened water.
Data on LTP were analysed by means of repeated measures ANOVA with line
type affiliation as a main factor. In this analysis we compared the LTP
slopes between the H-BMR line types along with one, randomly drown RB
line as the outgroup. All statistical analyses were carried out by means
a mixed model extension of a general linear model (SAS/STAT® 14.1 User’s
Guide).
Result and Discussion
Mice of the used line types did not differ with respect to body mass.
Yet, high BMR (H-BMR) mice were characterized by conspicuously higher
BMR then mice of all other line types (Table 1, Fig. 1A). Their
metabolically expensive internal organs (liver, heart and kidneys) were
also larger than in mice of other line types (Fig. 1B-D). Yet, their
brains were not significantly larger (Fig. 1E). Thus we did not observe
the brain-gut trade-off as predicted by the ET hypothesis.
To compare learning abilities of the line types we trained mice in
IntelliCages, an automated system that allows for individual assessment
of activity and learning of group-housed mice (Knapska et al.2013). In an initial acclimatization period, mice were able to access
water in any of the four corners of the IntelliCage – each corner had
two separate bottles with tap water that the mouse could choose between.
During the place preference learning, water access for each mouse was
restricted to one of the four corners. Next, in the reward-motivated
discrimination learning, one of the bottles was filled with a reward -
10% sucrose solution (Fig. 2A).
To assess reward-motivated discrimination learning we measured the
number of nosepokes that opened access to the bottle with sucrose
solution (correct responses). In comparison to the previous phase of the
training, all mice increased the number of nosepokes to the bottle that
now contained the reward. However, high BMR mice accessed the reward
more often than their low BMR, VO2max and randomly bred
counterparts (Table 2, Fig. 2B). The results indicate that the high BMR
mice learned the rewarded response faster than the other animals. To
test whether the improved learning could be attributed to changes in
thirst or taste discrimination, the number of licks from the bottles
that contained sucrose solution was analysed. We did not observe any
differences between the line types in the amount of sweetened water
consumed (Fig. 2D). Further, because differences in general activity
could potentially influence the obtained results, we compared the
numbers of visits to all corners during the reward-motivated
discrimination learning phase and the adaptation phase. The rate of
visiting corners did not differ between the line types (Table 2),
excluding the possibility that the differences in learning could be
explained by changes in general activity.
To gain insight into the potential neuronal mechanism underlying
observed differences in learning we used long-term potentiation (LTP), a
classical model for investigation of activity-dependent synaptic
plasticity. We compared effects of repeated high-frequency stimulation
of Schaeffer collaterals that make excitatory synapses onto pyramidal
cells in the CA1 region of the hippocampus, the brain structure crucial
for spatial memory formation. We compared the slope of LTP in the H-BMR
mice, L-BMR mice and the animals from one of the randomly bred
(non-selected) lines as the outgroup. In line with the behavioural
results, the H-BMR mice manifested significantly increased neuronal
plasticity (F2,24= 18.4 p<0.001, Fig. 2C).
It is important to note that throughout our experiment mice were fed the
same diet, so the partial tenet of the ET hypothesis—compensation of
the reduced gut by increased food quality could not be tested. Yet, at
least in non-mammalian animal models BGTO is likely to occur even
without a shift in quality of consumed food, as demonstrated by
Kotrschal et al . (2013) Also, as we demonstrated elsewhere
(Książek et al. 2009) high BMR mice possess a considerable
digestive safety margins, which would have left them an ample potential
for gut size reduction envisaged by the brain-gut trade-off.
The costs of increased brain size and CA can be satisfied by (i)
reallocation of resources towards brain growth and maintenance from
other sinks (other energetically expensive organs, as proposed by the ET
hypothesis (Aiello & Wheeler, 1995) or physiological traits such as
immunocompetence (Kotrschal et al. 2016); or by (ii) increasing
total energy intake, which may allow to cover the costs of cognitive
abilities without the need for reduction of other structures and
functions, including digestive abilities. Overall an increase of energy
intake is the hallmark of the evolution of endothermy (Polymeropouloset al. 2018), particularly linked with the need to fuel
reproduction (Koteja 2000). The high BMR mice are characterized by both
increased energy intake and reproductive allocation (Chrząścik et
al. 2014) and increased mass of the gut (Table 1). This points to (i)
and suggests that the selection for enhanced CA does not need to involve
brain-gut trade-off as an initial step toward the evolution of enhanced
CA. The more likely evolutionary scenario would involve initial
selection for increased overall energy intake, which would necessitate
an increased gut size and BMR (Healy & Rowe 2007). This selection may
have involved an initial increase in neuronal efficiency, if more
efficient neurons were metabolically cheaper than an increase of the
number of neurons and/or in their size (Herculano-Houzel 2011). Such
smarter, but not necessarily bigger brains allowed for foraging on
better quality food. Subsequently other trade-offs (such as gut
reduction) may have occurred in some lineages, such as proto-human apes,
allowing for brain size increases.
Acknowledgments
We acknowledge valuable comments by Leszek Kaczmarek and Jan Kozłowski
and experimental assistance provided by A. Gębczyński, B. Lewończuk, M.
Lewoc, S. Płonowski, and J. Sadowska. Miłka Piszczek greatly helped to
edit the paper. Funding: This work was supported by National
Research Center - NCN 2015/17/B/NZ8/02484 grant. Authors
contributions: A.G., E.K. and M.K. conceived of the idea and designed
the experiments. All authors participated in the experiments and data
analysis. A.G., E.K. and M.K. prepared the manuscript. Competing
interests: The authors declare no competing financial interests.Data and materials availability: All raw data are available
from Dryad repository (doi:10.5061/dryad.bk3j9kd78).
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