Post on 14-Nov-2018
UNIVERSIDADE DE LISBOA
FACULDADE DE CIÊNCIAS
DEPARTAMENTO DE BIOLOGIA ANIMAL
Regulation of body weight in small rodents: a trade‐off
between starvation and predation?
Rita Isabel Caetano Monarca
Tese orientada pela Professora Maria da Luz Mathias e pelo Professor John
R. Speakman, especialmente elaborada para a obtenção do grau de doutor
em Biologia (Ecologia).
2015
This study was funded by Fundação para a Ciência e a Tecnologia (FCT) through a PhD
fellowship – SFRH/BD/47333/2008.
This thesis should be cited as follows:
Monarca, R.I., 2015. Regulation of body weight in small rodents: a trade‐off between
starvation and predation?. Ph.D. Thesis. University of Lisbon. Portugal.
PRELIMINARY NOTES
According to the Post‐graduate Studies Regulation (Diário da República, 2ª série, nº 57, 23 March
2015) this dissertation includes papers published or submitted for publication and the candidate,
as co‐author. As doctoral candidate I was responsible for the scientific planning, sampling design,
data collection, statistical analyses and writing of all manuscripts. The advisors of the thesis were
deeply involved in the conceptual framework and in all the stages of the studies.
Papers format was made uniform to improve text flow.
Lisboa, Junho de 2015
Rita I. Monarca
NOTA PRÉVIA
Na elaboração desta dissertação foram usados artigos publicados, ou submetidos para publicação,
em revistas científicas indexadas. De acordo com o previsto no Regulamento de Estudos Pós‐
Graduados da Universidade de Lisboa, publicado no Diário da República, 2.ª série, n.º 57, de 23
de Março de 2015, a candidata esclarece que participou na concepção, obtenção dos dados,
análise e discussão dos resultados de todos os trabalhos, bem como na redacção dos respectivos
manuscritos.
A dissertação, por ser uma compilação de publicações internacionais, está redigida em Inglês.
A formatação do texto foi uniformizada de modo a tornar a leitura mais fluída.
Lisboa, Junho de 2015
Rita I. Monarca
Index
ACKNOWLEDGMENTS .................................................................................................... I
RESUMO ...................................................................................................................... III
ABSTRACT .................................................................................................................... IX
CHAPTER 1 .................................................................................................................... 1
GENERAL INTRODUCTION
1.1. SCIENTIFIC FRAMEWORK ............................................................................................. 3
1.1.1. ENERGY – GENERAL PRINCIPLES ....................................................................................... 3
1.1.2. INTERACTION BETWEEN ENERGY COMPONENTS ................................................................... 7
� MODELS EXPLAINING ENERGY HOMEOSTASIS ............................................................................. 7
1.1.3. SIGNALLING OF ENERGY BALANCE ................................................................................... 11
1.1.4. FACTORS INFLUENCING ENERGY BALANCE ......................................................................... 13
� PREDATION ....................................................................................................................... 14
� LIMITED FOOD RESOURCES ‐ STARVATION ............................................................................... 16
1.1.5. DYSREGULATION OF ENERGY BALANCE – OBESITY .............................................................. 17
1.2. AIMS AND APPROACH .............................................................................................. 23
1.3. THESIS ORGANISATION ............................................................................................. 25
1.4. REFERENCES .......................................................................................................... 26
CHAPTER 2 .................................................................................................................. 35
PREDATION RISK AND STARVATION
2.1 ABSTRACT .............................................................................................................. 37
2.2. INTRODUCTION ...................................................................................................... 38
2.3. MATERIAL AND METHODS ........................................................................................ 40
2.4. RESULTS ............................................................................................................... 49
2.5. DISCUSSION .......................................................................................................... 59
2.6. CONCLUSIONS ........................................................................................................ 66
2.7. ACKNOWLEDGEMENTS ............................................................................................. 66
2.8. REFERENCES .......................................................................................................... 66
CHAPTER 3 .................................................................................................................. 71
GROWTH AND DEVELOPMENT
3.1. ABSTRACT ............................................................................................................. 73
3.2. INTRODUCTION ...................................................................................................... 74
3.3. METHODS ............................................................................................................. 76
3.4. RESULTS ............................................................................................................... 80
3.5. DISCUSSION .......................................................................................................... 86
3.6. CONCLUSION ......................................................................................................... 92
3.7. REFERENCES .......................................................................................................... 93
CHAPTER 4 .................................................................................................................. 99
PHYSICAL ACTIVITY AND BEHAVIOUR
4.1. ABSTRACT ............................................................................................................101
4.2. INTRODUCTION ............................................................................................................ 102
4.3. METHODS .................................................................................................................. 103
4.4. RESULTS ..................................................................................................................... 108
4.5. DISCUSSION ................................................................................................................ 115
4.6. REFERENCES ................................................................................................................ 118
CHAPTER 5 ................................................................................................................ 123
GENERAL DISCUSSION
1) DO ANIMALS UNDER PERCEIVED RISK OF PREDATION ADJUST THEIR BODY WEIGHT? ...................... 126
2) WHICH PHYSIOLOGICAL AND BEHAVIOUR FACTORS ARE CHANGED TO PRODUCE SUCH ADJUSTMENTS? ..... 127
3) WHAT SIGNAL THE ENERGY BALANCE AND IMBALANCE TO PRODUCE SUCH ADJUSTMENTS? ............ 130
IMPLICATIONS AND FUTURE DIRECTIONS ..................................................................................... 131
REFERENCES ......................................................................................................................... 134
I
ACKNOWLEDGMENTS
Taking the final steps of long journey, there are several people who crossed my way and
whom I am greatly indebted.
At institutional level, I would like to thank “Fundação para a Ciência e Tecnologia” for
funding my work through a PhD grant.
To my supervisors for accepting the challenge of accompanying along this journey. To
Professor Maria da Luz Mathias, who many years ago put some faith in me by accepting
me into the “small mammal group” and who across the years have been getting used to
my simple ways of seeing the world, sometimes called minimalistic. My sincere gratitude
to Professor John Speakman, for all his guidance, helping cross several bumps along the
way and for making all these energetics related topics much more familiar.
In Lisbon, to my colleagues Joaquim, Sofia, Ana Cerveira, Cristiane, Isabel, Flávio,
Margarida and Ana Quina, for their constant help, words of support and scientific input.
Ana Cerveira, Joaquim and Sofia, many thanks for the labour hours helping me on the field
work and maintaining the animals when I was away. I hope that in a near future our out
of the box ideas for research projects will see the light of day. Patricia Napoleão, thank
you for helping me with the first steps into the biochemistry lab.
In Aberdeen, to the energetics research group, Aqeel, Mia, Rachel, Madu, Quinn, Sharon,
David, Catherine, and Lobke, many thanks for your support. A special word for Lobke, for
all the help on the constant attempts of getting the study up and running.
In Beijing, to the lab colleagues, Teresa, Li‐Li, Deng Bao, Yanchao, Xinyu, Mengqin,
Chaoqun, Guanlin, I would like to thank you all for the way I was welcome into the group,
you were never refused me any help dealing with lab issues or every day issues of a non‐
speaker of the local language. To Professor De‐Hua Wang, thank you very much for
II
proving part of the equipment to perform the experiments. To Teresa and Panqian, thanks
for the really fun moments we shared during our city adventures.
A special thanks to Paula Lopes for the amazing help on putting together the first
foundations of this project, and for every now and then quick word of advice.
To worldwide spread but always close friends Andreia, Sofia, Guida, Hugo, Paulo, Raul e
Inês(es), feels good to know I can count on you.
To my mother who always supported my choices and to my core family thank you for the
encouragement along this journey.
Tiago, thank you so much for all the encouragement, support, patience and love, to make
it through the times apart and always be there for me, I hope that finally we can spend
more time together.
III
RESUMO
Os mecanismos envolvidos na regulação do peso do corpo, assim como as pressões
evolutivas que controlam estes mecanismos, têm sido largamente discutidos. Diversas
teorias têm proposto uma panóplia de modelos explicativos, que incluem diversas
componentes, como sobrevivência em períodos de escassez de recursos, sucesso
reprodutor, alterações de comportamento ou ausência de co‐adaptação às actuais dietas.
No entanto, o funcionamento destes mecanismos e a interacção dos factores que os
influenciam permanece em grande parte desconhecida.
A desregulação do balanço energético pode originar situações de excesso de peso e em
casos extremos conduzir à obesidade. Durante o século XX, a incidência de obesidade
aumentou nas sociedades ocidentais de forma epidémica. Na base desta desregulação
existe uma forte componente genética, mas também uma forte influência ambiental. A
capacidade de armazenar energia na forma de gordura, pode representar uma vantagem
adaptativa visto conferir um grau de protecção contra períodos em que os recursos
alimentares são escassos e que podem incorrer em eventos de fome.
O modelo clássico de regulação de peso propõe que o peso corporal seja pré‐determinado
internamente, dependendo exclusivamente de factores individuais. Sempre que o peso
do corpo varia acima ou a abaixo deste valor pré‐estabelecido são desencadeados
mecanismos que actuam de modo a restabelecer o peso corporal inicial. Recentemente,
outros modelos têm surgido, nomeadamente o modelo de duplo ponto de intervenção,
que propõe não a existência de um valor pré‐determinado, mas a existência de um
IV
intervalo, com um limite inferior e um limite superior, entre os quais o peso pode variar
livremente. Quando o peso atinge o limite superior, ou o limite inferior do intervalo, são
activados mecanismos que restabelecem o peso do corpo para o intervalo pré‐
estabelecido.
Tem sido demonstrado que os pequenos mamíferos possuem mecanismos de regulação
do peso do corpo muito eficientes. O acesso permanente a alimento ou a modificação da
alimentação para dietas muito ricas em gordura raramente resultam em aumentos de
peso que possam ser classificados como obesidade. No entanto, os factores que
condicionam estes mecanismos são ainda desconhecidos. O risco de predação, assim
como o risco de fome têm sido indicados como dois factores que condicionam os limites
de variação do peso do corpo, mantendo‐o dentro do intervalo pré‐determinado.
Neste trabalho, utilizando pequenos mamíferos como modelos de estudo, analisou‐se a
possível existência de um equilíbrio entre o risco de predação e o risco de fome, testando‐
se experimentalmente a hipótese segundo a qual o desaparecimento da pressão de
predação conduzirá à proliferação do fenótipo associado à acumulação de gordura.
Segundo esta hipótese, os genes responsáveis pela eficiente acumulação de reservas de
gordura terão sido mantidos no pool genético por um fenómeno de deriva genética, em
humanos, depois da eliminação do risco de fome e predação, das populações ancestrais,
devido ao desenvolvimento da agricultura e armas, estes genes deveriam ter sido
eliminados, por terem perdido o seu valor adaptativo.
Para a avaliação dos efeitos do risco de predação, pequenos mamíferos foram submetidos
a estímulos sonoros que simulam a presença de um predador, e as suas respostas
V
fisiológicas e comportamentais foram analisadas. Esta metodologia foi aplicada a duas
espécies de pequenos roedores, o rato‐do‐campo Apodemus sylvaticus e à estirpe
C57BL/6 de ratinhos de laboratório. Pretendeu‐se investigar se o risco de predação afecta
o peso do corpo dos animais e de que modo se processa este efeito, nomeadamente em
termos de consumo de comida, alterações de padrões de comportamento, metabolismo,
termorregulação e actividade física. Pretendeu‐se também analisar se os mecanismos
envolvidos na regulação do balanço energético são sinalizados através de mensagens
hormonais que actuam a nível do sistema nervoso central.
Os resultados obtidos mostram uma significativa influência do risco de predação sobre o
peso do corpo, por outras palavras, os animais submetidos a um tratamento que simula a
presença de um predador reduziram o seu peso corporal ou a taxa de ganho de peso
quando submetidos a dietas com elevado teor de gordura. Esta alteração é feita através
da variação tanto da quantidade de energia ingerida, como da quantidade de energia
gasta, processos que podem funcionar de forma independente ou em simultâneo. A
redução da quantidade de energia ingerida baseia‐se principalmente na diminuição da
quantidade de comida consumida e não na alteração das taxas de digestibilidade e
absorção. O aumento da energia despendida ocorre através da alteração dos padrões de
atividade física e comportamento, ou seja, os animais favorecem comportamentos que
aumentam a actividade física e que são mais dispendiosos do ponto de vista energético,
como moveram‐se dentro da caixa, em oposição a manterem‐se imóveis ou em descanso
como método de poupança de energia. A taxa de metabolismo basal, que muitas vezes é
associada a poupança de energia, em animais que estão adaptados a ambientes extremos
VI
como desertos ou ambientes subterrâneos, não é alterada em consequência do risco de
predação.
A exposição estocástica a períodos de fome induziu reduções acentuadas do peso do
corpo, que foram recuperadas assim que a disponibilidade de comida foi restabelecida. A
compensação do peso perdido foi feita através do aumento da comida ingerida, e da
alteração de comportamento, sendo adoptados comportamentos que reduzem o gasto
energético. Ao contrário do previsto, após a exposição a vários períodos de fome, os
animais não sobrecompensaram a massa corporal perdida, ou seja, o peso corporal é
restabelecido para valores semelhantes ao período de referência, anterior aos eventos de
fome, não sendo estabelecido um novo limite de regulação. Segundo o previsto pelo
modelo de duplo ponto de intervenção, esperava‐se que o aumento da frequência de
períodos de fome reestabelecesse o nível mínimo de reservas para um limite superior, ao
de referência, de modo a assegurar a sobrevivência em próximos eventos de escassez.
Os dados revelaram também algumas diferenças no que se refere à resposta de machos
e fêmeas, tendo as fêmeas exibido uma resposta evidente ao elevado risco de predação,
principalmente quando expostas a dietas com alto teor de gordura. A resposta à
exposição a uma dieta rica em gordura, após um período inicial de dieta pouco rica em
gordura, também é diferente entre machos e fêmeas, tendo as fêmeas demonstrado
maior resistência ao aumento de peso que os machos. Estas diferenças poderão estar
relacionadas com a influência do tamanho do corpo em questões de dominância e
agressividade, e com o investimento na reprodução.
VII
Os níveis de corticosterona foram alterados em resposta ao aumento do risco de
predação, mas os níveis de leptina circulante são explicados principalmente pelas reservas
de gordura acumuladas. No entanto, os animais submetidos a períodos de fome
registaram elevados níveis de leptina circulante, o que é uma resposta inesperada face ao
observado em humanos após dietas de redução de peso, em que o abandono das dietas
para perda de peso é atribuído à manutenção de baixos níveis de leptina, após a dieta.
Estes resultados suportam a importância do papel da pressão de predação sobre a
regulação do peso do corpo, e demonstram que estas alterações podem ser alcançadas
através do recurso a uma combinação de processos fisiológicos e comportamentais, que
resultam em redução do consumo de energia ou aumento do seu gasto.
A investigação dos mecanismos que regulam as variações de peso, nomeadamente a sua
componente ambiental é fundamental para a compreensão de disfunções como a
obesidade e doenças associadas, nomeadamente na forma como vários fenótipos são
expressos sob diferentes condições ambientais. No entanto, a conhecimento da base
molecular e genética, destes mecanismos, é essencial para o desenvolvimento de novos
medicamentos e formas de terapia que permitam prevenir e controlar estas disfunções.
Palavras‐chave: Regulação de peso; Obesidade; Risco de predação; Fome.
IX
ABSTRACT
The mechanisms and evolutionary background of body weight regulation have been
largely discussed in the last years. Several models were put forward to explain the
energetic imbalances that lead to disorders as obesity, linking environmental and genetic
factors, and their effects over body weight. Unlike humans, small wild mammals have a
strong body weight regulation system, being the risk of predation one of the factors
suggested to explain the non‐prevalence of overweight animals within natural
populations. Accordingly, carrying large fat reserves influences the ability to escape
predators, being the risk of predation the main factor influencing their upper limit for
body weight regulation. On the other hand the risk of starving, due to carrying reduce fat
storages during famine periods is likely to guide the lower boundary for body weight
regulation, a trade‐off between these two pressure factors is suggested to modulate body
weight in small mammals. The predications of this model were tested using wood mice
(Apodemus sylvaticus) and C57BL/6 mice by experimentally manipulating the risks of
starvation and predation. Physiological and behavioural responses were tested to
investigate the response of mice to elevated risks of starvation and predation. Results
showed that reductions in body weight, and body weight gain, can be induced by
manipulating the predation risk. The variations were mostly explained by reduction of
food intake, and increase in energy expenditure through alteration of physical activity and
behaviour. Resting metabolic rate and thermogenic capacity were not affected. Males and
females responded differently to high fat diets, females showed stronger homeostasis
mechanisms. Starvation periods were compensated by overfeeding and reduction of
activity during the recovery period but did not induce the increase of the fat storages
above pre‐starvation. Corticosterone levels signed the perceived risks of predation and
circulating leptin was correlated with the levels of fat storage. These observation strongly
support the role of predation risk over the regulation of body weight, showing the
influence of environmental components over the setting of body weight regulation limits.
Keywords: Predation‐Starvation trade‐off; Weight regulation; Body weight; Obesity
CHAPTER 1
General Introduction
Chapter 1 – General Introduction
3
General Introduction
1.1. Scientific framework
1.1.1. Energy – General principles
Energy is considered a key currency for all the biological processes. Ultimately, energy
is one of the most essential resource for animals life, therefore acquiring the amount of
food to supply their daily need of energy is a basic goal of every animal.
The first law of thermodynamics states that energy can be transferred and transformed,
but cannot be created or destroyed (Blaxter 1989). When applied to animal physiology,
this process can be summarised by the following equation, all terms expressed as energy
per unit of time:
Energy Intake = Energy Expenditure + Energy Storage
Energy Intake primarily refers to the chemical energy obtained through consumed food
and fluids; Energy Expenditure refers to performed work, and energy lost through radiant,
conductive, and convective heat, and also evaporation. Energy Storage is the rate of
change in body’s macronutrient stores (Hall et al. 2012). The balance between energy
intake, energy expenditure and energy storage can be referred as energy homeostasis.
The Energy Intake components are macronutrients (proteins, carbohydrates and fat) and
micronutrients (vitamins, minerals and trace elements). Micronutrients are part of the
diet, but their contribution to the total energy ingested is not significant. Once ingested,
Chapter 1 – General Introduction
4
the absorption of macronutrients is variable and incomplete, typically urine accounts for
2‐3% (Grodzínski & Wunder 1975) of total energy input, faecal loss is variable, depending
on the individual variance, diet quality and intestinal factors, red pandas can lose 80% of
energy input through faeces (Wei et al. 2000) and grey seals about 8% (Ronald & Keiver
1984). The metabolized energy is given by the difference between the total energy
content of the consumed food and the energy content of faeces and urine. The
transformed macronutrient can then be used in the biological processes or be stored.
The rate of Energy Expenditure is variable across the day and during the life span.
Accordingly, maintenance, growth, reproduction, lactation, running, and other processes,
all require energy but the amounts of fuel may be variable.
The energy expenditure budget can be divided in three main components: i) Resting/Basal
metabolism, ii) Thermal effect of food, iii) Thermo regulatory metabolism and iv) Activity
metabolism.
i) Resting metabolic rate represents approximately two‐thirds of total human energy
expenditure (Cunningham 1991), and 40% of small mammals total energy expenditure
(reviewed by Speakman 2000). Basal metabolic rate can be defined as the minimal rate
necessary to sustain basic physiological processes, as respiration, heart rate and cellular
repair, measured in an inactive, post‐absorptive, non‐reproductive animal, at an
environmentally neutral temperature. This standardised estimation of metabolic rate
requires several criteria to be accomplished, however in many cases these assumptions
are not easy to achieve (McNab 1997).
Chapter 1 – General Introduction
5
Basal metabolic rate requires animals to be inactive at thermoneutrality, thus is not
measured on many studies, due to the difficulties of certifying that animals are post
absorptive. Thus measurements of resting metabolic rate are often used. They may be
slightly higher than basal metabolic rate, due to the thermal effect of food. According with
Johnstone et al. (2005), about 63% of resting metabolic rate variation can be explained by
the fat free mass, 2% by the individual variation of subjects, 6% by the body fat content,
2% by age, 0.5% by analytic error and 26,6% of the variation is unexplained.
ii) The Thermal effect of food also commonly referred as Diet induced thermogenesis,
and specific dynamic action (review by Secor 2009), is the energy associated to the
digestion and processing of food, in other words, it is the energetic cost of digesting,
assimilating and transporting nutrients from ingested food to cells and tissues. It is
assumed to be a fixed percentage of total energy intake, but may be affected by the meal
size and composition (Jonge & Bray 1997). Although, a small part of the thermal effect of
food consists of regulated heat production to dissipate excessive energy intake (Lowell &
Spiegelman 2000).
iii) In homeotherms the maintenance of a stable body temperature depends upon the
balance between heat production and heat loss ‐ Thermal regulation. The heat loss
through the body surface is proportional to the difference between ambient temperature
and surface temperature. Therefore the energy allocated to the maintenance of body
temperature is highly influenced by the ambient temperature. As the temperature falls
below core temperature, the heat demands for thermoregulation increases. Basal
metabolic rate, thermal effect of food and physical activity, all contribute to supply this
Chapter 1 – General Introduction
6
energy demand. If these sources do not supply the required energy, specific extra heat is
generated to maintain a stable body temperature.
Humans, mainly in developed countries, were able to modify their environmental
conditions to permanently balance the ambient temperature and their heat production
(Garland et al. 2011; Speakman & Keijer 2012). These conditions do not necessarily apply
to wild animals, hence the energy allocated to balance the body temperature significantly
increases. The production of heat necessary to increase the body temperature may
require shivering or non‐shivering. The non‐shivering thermogenesis includes the heat
generation using the brown adipose tissue, not involving muscular contractions. Non‐
shivering thermogenesis and brown adipose tissue are commonly the focus of studies
regarding seasonal acclimatisation of small mammals, and recently also human physiology
due to its possible anti‐obesity role (Cannon & Nedergaard 2004; Seale, Kajimura &
Spiegelman 2009).
iv) Physical activity can be defined as the thermic effect of any bodily movement that
goes beyond the basal metabolic rate (Caspersen, Powell & Christenson 1985). It
comprises the energy expenditure required for skeletal muscles to produce movement,
including minimal movement, physical exercise and powerful muscular work. Physical
activity and exercise are not synonymous. Physical activity includes exercise as well as
other activities which involve corporal movement. In humans, there is often a partition
between exercise and non‐exercise activity (Levine, Schleusner & Jensen 2000; Hollowell
et al. 2009). Energy costs with physical activity are correlated with body weight and resting
metabolic rate (Schoeller & Jefford 2002), but is one of the most variable energy
expenditure components.
Chapter 1 – General Introduction
7
1.1.2. Interaction between energy components
The three components of the energy equation interact through a series of processes
that favours homeostasis, responding to the energy available. As so, if the energy
expenditure increases, animals must increase their food intake to compensate the
discrepancy or use the energy stored. Consequently, energy is stored if the food intake
exceeds the energy expenditure.
The main components of the energy balance equation may vary across time, accordingly
with the stage of development; typically energy storage is positive during growth and
development resulting in the increase of body weight. During adult life, body weight is
usually stable, and energy storage approaches zero.
Models explaining energy homeostasis
The regulation system underpinning energy input and expenditure has not been fully
established. Multiple theories have been put forward to explain energy homeostasis; each
model is the product of a different context and therefore they focus on different aspects
of body weight regulation: the set point model is mostly based on physiological and
genetic determination, whereas the settling point regulation model is built around the
effects of social, nutritional environment. The dual point intervention model is suggested
as an alternative model including weak physiological regulation within two boundaries
Chapter 1 – General Introduction
8
that may vary across individuals and accommodates both environmental and molecular‐
physiological views points.
a) Set point model (Lipostatic model)
Kennedy (1953) was among the first to propose that the regulation of body fat involves
an internal “lipostat” that maintains body fat hence overall weight at a predetermined
level. It was then suggested that fat would produce a signal that once sensed by the brain
would inform about the levels of fat. If these levels were above or below the target body
fat, the brain would activate mechanisms to increase or reduce the energy expenditure
and bring the fat levels to the target point (Figure 1). The discovery of leptin many years
later (Zhang et al. 1994) gave strong molecular support to the model suggested by
Kennedy, by exposing the presence of a hormonal signal that informs the brain about the
fat status of the body, as reviewed by Friedman and Halaas (1998). Leptin is a hormone
primarily produced by the adipose tissue and interacts with brain areas known to be
related with energy homeostasis (Mercer & Speakman 2001; Bellinger & Bernardis 2002).
Figure 1 – Classic set‐point model of body weight regulation: Compensatory
mechanisms are applied when body mass is below or above the set point of
regulation.
Chapter 1 – General Introduction
9
b) Settling point model
One of the statements of the set‐point model is that it denies the role of environmental,
behaviour and economic factors, resuming the mechanism is strictly due to physiological
components. According to the settling point model (Wirtshafter & Davis 1977), the fat
storage in a body can be compared to the water levels in a water reservoir, in analogy like
a lake (Speakman et al. 2011) (Figure 2).
Figure 2 ‐ Body weight regulation according to the settling point model, here
illustrated as a lake. Input of water is rain falling in the hill, the output of water
is proportional to the depth of water in the outflow, as indicated by the size of
the arrows. A ‐ As the volume of inflow rain increases, the depth of outflow
raises; B ‐Rain fall represents energy input, energy expenditure is compared to
the outflow; depth of outflow represents the settling point, and the energy
storage is illustrated by the amount of water in the reservoir (lake) (adapted
from Speakman et al. (2011)).
Chapter 1 – General Introduction
10
A natural equilibrium in maintained in the lake, due to the extra rain inflow, the water
levels rises until the outflow equals the inflow. Regarding body weight, fat storages
represent the lake, energy intake is represented by the rain and energy expenditure by
the water outflow. In such system, there is little active regulation in order to predefine
body weight, the interaction of several environmental factors (e.g. improved housing,
food abundance) results in a dynamic equilibrium (“settling point”) dependent on the
individual constitution and interaction between energy input and expenditure (Dubois et
al. 2012). The settling point model denies any role for the physiological regulation of body
fatness and weight.
c) Dual intervention point model
Contrarily to the suggested by the classic “lipostat” model of Kennedy (1953), the dual
intervention point model argues that body weight is set within a range limited by an upper
limit point and a lower limit point (Levitsky 2002; Speakman 2007). If body weight rises
above the upper limit or decreases below the lower limit, the individual enables
physiological mechanisms that change energy balance to maintain the body weight within
the pre‐set range. Between these boundaries the physiological regulation is poor (Figure
3). The nature of the intervention points is still unclear, they might depend on a
combination of genetic and environmental factors acting together. A strong evolutionary
background has supported this model, the upper intervention point defined by the risk of
predation and the lower intervention point established by the risk of starvation
(Speakman 2007, 2008). The upper limit point of intervention is mostly supported by data
Chapter 1 – General Introduction
11
obtained in animal studies, given that since 2 million year ago modern humans have
developed tools, weapons and strategies to avoid predation.
Figure 3 ‐ The dual intervention point model. Over time body weight varies,
when the intervention points are reached physiological feedback controls that
operate in order to reduce or increase the body mass.
1.1.3. Signalling of energy balance
Insulin was the first hormonal signal to be related with the regulation of energy
balance. It is secreted by beta cells in the pancreas and enters in central nervous system
via the circulation. Insulin is considered an “adiposity signal” that supresses food intake,
and promotes lipid storage (Assimacopoulos‐Jeannet et al. 1995). The discovery of leptin,
a hormone secreted by the adipose tissue, provided additional information about the
signals sent to brain (Zhang et al. 1994). Both insulin and leptin have been involved in the
regulation of body weight (Figure 4) (Varela & Horvath 2012), as their levels are
proportional to the amount of body fat (Bagdade, Bierman & Porte 1967; Considine et al.
1996), and their delivery on the brain is directly related to their circulating levels (Schwartz
et al. 1996).
Chapter 1 – General Introduction
12
Two neuronal populations NPY/AGRP and POMC/CART, located in the arcuate area of the
hypothalamus (Hahn et al. 1998), express specific neuropeptides known to be involved in
energy homeostasis and related with food intake. Both leptin and insulin receptors are
expressed at the NPY and POMC neurons at the hypothalamus (Cowley et al. 2001; Könner
et al. 2007). NPY/AGRP are inhibited by leptin, hence activated by low levels of leptin,
food intake is stimulated by fasting due to the activation of NPY. On the other hand, POMC
neurons are stimulated by leptin and inhibited by NPY (Cowley et al. 2001). Insulin
signalling in the brain is essential for glucose homeostasis, POMC insulin sensitive neurons
may be involved in response to positive energy balance through promoting fat storage in
white adipose tissue (Hill et al. 2010).
Figure 4 ‐ Leptin and insulin secreted by white adipose tissue (WAT) and the
pancreas, respectively, inform the hypothalamus about the energy status of
the organism, regulating several peripheral functions. Arc‐ arcuate nucleus;
AgRP‐ agouti‐related protein; InsR‐ insulin receptor; LepR‐b‐ leptin receptor b;
POMC‐ proopiomelanocortin; VMH‐ ventromedial hypothalamus. (adapted
from Varela & Horvath (2012)).
Chapter 1 – General Introduction
13
1.1.4. Factors influencing energy balance
Factors affecting the energy balance are linked to two main pillars, genetics and
environment. Both factors interact at different levels, linking and affecting several
components of energy expenditure and intake and the overall energetic balance, as
represented in Figure 5.
Figure 5 ‐ Linkages among genetics and environmental effects over energy
balance, through several co‐existent mechanisms (adapted from Speakman
2004).
Several loci have been identified as related with energy balance. Studies with identical
twin suggest that 60 to 70% of body mass index variance is due to genetic differences
(Allison et al. 1996; Segal et al. 2009). It has also been demonstrated that genes can
influence the food intake (Rankinen & Bouchard 2006), weight gain (Bouchard et al. 1990),
physical activity (Mustelin et al. 2012) and basal metabolic rate (Rønning et al. 2007).
Chapter 1 – General Introduction
14
The influence of environment over energy balance components are mostly due to
alteration of behaviour, and its interaction with other components. Two main factors
causing imbalance of energy homeostasis are the availability of food and physical activity.
In humans, the constant food availability, their dense energetic content and portion size
are likely to influence energy intake, whilst the required physical activity is reduced mostly
by the characteristics of the neighbourhood namely existence of paths for walking and
cycling, traffic and street safety (Lee et al. 2012), television viewing (Hernández et al.
1999) and also car use (Wen et al. 2006).
Several small mammal species, as mice and voles, use environmental cues, such as day
length and temperature to trigger seasonal variations on their body mass and adiposity,
making them attractive models for the study of body weight variation as these conditions
can easily be simulated in laboratory (Bartness, Demas & Song 2002; Zhao, Chen & Wang
2010). Even though had been identified that small mammals have a strong body weight
regulation (El‐bakry, Plunkett & Bartness 1999; Peacock & Speakman 2001), keeping their
weights in a dynamic equilibrium (Levitsky 2002).
Predation
When focusing on small wild animals, the environmental components incorporates
factors, such as the risk of being predated, that have lost adaptive value to humans since
the development of defensive tools and discovery of fire, and evolution of social
behaviour.
Chapter 1 – General Introduction
15
Predation represents an important element of the overall interaction between species
and their survival demands, being a selective key force on the evolutionary and adaptive
processes involved (Abrams 2000). Nevertheless, the effects of perceived risk of predation
have been demonstrated to play a more important role shaping the preys traits than
direct consumption (Preisser, Bolnick & Benard 2005; Creel & Christianson 2008) (Figure
6).
The risk of predation in known to affect morphology (McCollum & Leimberger 1997;
Brönmark, Lakowitz & Hollander 2011), physiology (Slos & Stoks 2008) and behaviour
(Abramsky et al. 2004; Verdolin 2006) including aggression, movement patterns (Sih &
McCarthy 2002) and foraging (Brown et al. 1988; Vásquez 1994). Responses to predation
risk, i.e. activation of defence mechanisms are usually assessed by a reduction in other
fitness components such as reproduction or growth (Harvell 1990; Ylönen & Ronkainen
1994).
Chapter 1 – General Introduction
16
Figure 6 – Pathways of predation effect over prey population dynamics
(adapted from Creel & Christianson 2008).
Limited food resources ‐ Starvation
All animals at some time during their life may face events of limited food resources that
ultimately could lead to mortality. Starvation refers to the biological state wherein a
postabsorptive animal, otherwise willing is unable to eat, as the result of extrinsic
limitations on food resources (McCue 2010).
The frequency and duration of starvation events are highly variable, the predictability of
such events is also variable, it occurs seasonally or randomly according with unpredictable
Chapter 1 – General Introduction
17
factors. Animals are constantly spending energy to maintain their basic functions,
however they are not constantly acquiring energy, meaning that at any point they must
rely on physiological processes to survive based on the energy internally stored, resulting
in a disrupted energy balance during starvation periods. The ability to tolerate starvation
varies across different animal groups, small birds and mammals may tolerate only one day
of starvation (Mosin 1984; Cherel, Robin & Maho 1988), in contrast to fish (Olivereau &
Olivereau 1997), amphibians (Grably & Piery 1981), and large mammals as polar bears
(Atkinson & Ramsay 1995) that are often able to endure several months without feeding.
Large energy reserves may extend the toleration to starvation, likewise small animals are
more susceptible to food deprivation than larger animals.
Reduction of body mass associated with starvation requires prioritizing given the high
demands of maintaining tissues with high turnover rates, which takes some species to
catabolize tissues to save resources during starving periods, like the gastrointestinal track
(Battley et al. 2000; Ferraris & Carey 2000). Liver and adipose tissue are equally mobilized
(Merkle & Hanke 1988; Lamosova, Mácajová & Zeman 2004)but usually at slower rates.
Skeletal muscles is less likely to be catabolize given its role in locomotion. Brain tissue,
heart and reproductive organs are seldom catabolized during starving periods.
1.1.5. Dysregulation of energy balance – Obesity
Obesity can be explained as a prolonged imbalance between energy intake and energy
expenditure, where the food input exceeds the energy output and the difference is stored
as fat. The accumulation of energy for future needs, in the form of fat reserves, can be
Chapter 1 – General Introduction
18
found across almost all animal species, from Caenorhabditis elegans (Jones & Ashrafi
2009), to crustaceans, fish (Brockerhoff & Hoyle 1967) and birds (Meissner 2009).
In recent years, obesity has become a major public health problem in modern human
societies (James 2008), revealing ineffective mechanisms of body weight regulation. In
the mid 1970s, obesity became an issue, and has been recognized by the world health
organization as a disease, since the 1990s. It started by affecting mostly developed
societies, but has extended now to developing countries, its recognition as public health
issue is unquestionably accepted.
The origins of obesity have been associated with a combination of genetic (Aguilera, Olza
& Gil 2013) and environmental factors (Hill & Peters 1998), but the prevalence of obesity
is still being debated.
Theories explaining Obesity
Obesity is associated with a series of metabolic dysfunctions that ultimately will reduce
the fitness. Given this scenario is expected that the genes responsible for the overweight
phenotype to be quickly eliminated from the genetic pool, reducing the prevalence of
obesity. However, this has not been the case, suggesting that this apparently
unfavourable phenotype may uncover some selective advantages.
Several theories have emerged to explain the prevalence of obesity at the light of human
evolution, attempting to integrate genetic and environmental causes:
The Thrifty gene hypothesis was first suggested by Neel (1962), as a response to the
prevalence of diabetes medical condition, and later on expanded to the overweight
Chapter 1 – General Introduction
19
disorder. This hypothesis relies on the fact that genes promoting an efficient food process
and fat deposition (Prentice, Hennig & Fulford 2008), during periods of abundant
resources, would be advantageous during periods of reduced availability of resources,
allowing their holders to survive possible famine periods. A second advantage of these
genes is that they would sustain fertility during famine (Norgan 1997), given the role of
fat on reproductive function. Neel’s theory can be easily applied to ancestral times when
hominids needed to actively hunt and forage for food, however after the development of
agriculture, the advantage provided by the “thrifty genes” were lost because food became
always available. The periods of famine became improbable; therefore the phenotype
favouring large fat reserves has a negative impact on the body condition, being difficult
to understand how it was favoured by natural selection.
Arising from the challenges of the thrifty genes hypothesis, the Thrifty phenotype
hypothesis has been discussed by several authors coping the mismatch between fetal
development and the environment on childhood/adulthood. According with Hales and
Barker (1992), early poor nutrition imposes allocation of resources, thus some tissues like
the pancreas have reduced investment. Another viewpoint (Wells 2007) is that the
intrauterine environment signs the nutritional environment of the mother, forecasting the
environment of the childhood, however the mismatch of these predictions may take to
adverse effects of adult phenotype.
The discussion around the prevalence obesity is still open. Recently Sellayah et al. (2014)
suggested a theory based on ancestral exposure to environmental conditions.
Accordingly, modern obesity results from a differential exposure of hominids to
environmental factors. The susceptibility to obesity is variable across ethnicity (Caprio et
Chapter 1 – General Introduction
20
al. 2008; Albrecht & Gordon‐Larsen 2013), would be explained by the differential
exposure of ancestrals to distinct climate conditions. Therefore, descents of migrants
(who left Africa 50000 years ago) to colonize cold climates carry genes involved in
thermoregulation and cold adaptation, providing high metabolic rates and resistance to
obesity.
In particular, Baschetti (1998) has argued that metabolic dysfunction on population like
Native American and Pacific Islanders can be explained by the exposure to diets that were
unknown. These genetically unknown foods were introduced to the populations recently,
therefore they have not adapted to them.
In modern societies, diets can be deficient in some critical macro and micronutrients. Diet
characteristics can therefore stimulate the overconsumption of food to compensate their
nutritional content, and eventually lead to weight gain. The protein leverage hypothesis
(Simpson & Raubenheimer 2005; Sørensen et al. 2008) suggests that the consume of
proteins is prioritised over lipids and carbohydrates, hence feeding on a diet with deficient
protein content may require increased food intake to reach the whole protein
requirement.
In a social perspective, Belsare et al. (2010) have suggested a theory based on the
aggression control. The two major causes of aggression are the need of food and
reproduction. Aggression itself is energetically costly, therefore satiated individuals are
likely to reduce their levels of aggression. Hence, a full stomach, high levels of blood
glucose or fat stored, should cue for aggression control. Reduction of aggression is
Chapter 1 – General Introduction
21
followed by a disinvestment in muscle and bone structure, especially in human urban
lifestyle.
Contrarily to the “Thrifty” genotype hypothesis, the “Fertility first hypothesis” (Corbett,
McMichael & Prentice 2009) states that the thrifty phenotypes have prevailed due to
effects over fertility and not due to starvation. The increased adiposity, insulin insensitivity
and increased anabolic steroid levels, when is exposed to unlimited food supplies as
modern sedentary life style, is maladaptive and through several mechanisms conferred
Polycystic Ovary Syndrome (Holte 1998), causing elevated levels of infertility.
More recently, Speakman (2007) suggests that contrarily to what is argued by the thrifty
gene hypothesis, obesity does not have an adaptive role in the evolutionary process.
Accordingly, the Drifty phenotype hypothesis argues that during famine periods,
mortality affects mostly very young and elder people, due to disease effect and not
actually starvation (Mokyr & Cormac 2002). Therefore the age pattern of mortality does
not compromise the reproductive success of the individuals of breeding age, thus genes
predisposing to obesity were maintained in the genetic pool by genetic drift, or absence
of selection and not because they were positively selected (Speakman 2008). The random
mutations that allowed body weight increase were not removed from the genetic pool,
but were maintained due to genetic drift, explaining why some people are able to keep
their body weight normal despite the environmental conditions. Moreover, if the thrifty
gene hypothesis was correct early hominids were expected to gain weight and grow fat,
in periods of resource abundance, which has not been reported in any study.
Chapter 1 – General Introduction
22
A particular scenario of the drifty phenotype hypothesis is the Predation release
hypothesis, it takes into account the pressure of predation risk, and suggests that early
hominids have been subjected to stabilizing selection for body fatness, with obesity
selected against the risk of predation. As other animals, Humans were once under severe
risk of predation (Berger 2006), nevertheless since 2 million years, predation has been
released due to the increase in body size, intelligence and development of tools
(Speakman 2007).
However, the predation pressure has not been released in other mammals, like small wild
mammals who are known to have strong body weight regulation mechanisms (El‐bakry et
al. 1999; Peacock & Speakman 2001; Hambly & Speakman 2005). Small mammals carrying
large fat depots would survive longer without food, but the carrying of fat may increase
the risk of mortality by reducing its ability to escape from predators because they are less
able to run away, while lean animals can escape into refugees that predators cannot
access (Sundell & Norrdahl 2002), besides the maintenance of such fat reserves requires
longer time foraging which increase the exposure to predators.
Such regulatory system is primarily mediated by the photoperiod (Bartness et al. 2002),
but individuals adjust their responses to local variations, as temperature, resource levels
and predatory risk. Animals should trade‐off their foraging efforts and vigilance status in
relation to the temporal variation of the predatory risk (Lima & Bednekoff 1999). A strong
regulatory system for body weight have been described on small wild mammals. Studies
modifying their diets did not cause them to gain weight (Peacock & Speakman 2001), as
animals are able to keep their weights in a dynamic equilibrium (Levitsky 2002). They can
Chapter 1 – General Introduction
23
overfeed after a restriction period, or increase physical activity and reduce energy intake
when they have free access to food (Hambly & Speakman 2005).
1.2. Aims and approach
The main aim of this thesis is to experimentally test the predictions of the “predation
release hypothesis”, accordingly to analyse whether the body weight of small mammals
is modulated by the risk of predation and by the risk of starvation.
In this context, I aim to give an overall view of the energy balance of small rodents in a
scenario of increased risk of predation, analysing energy intake, expenditure and storage,
in order to identify physiological and behavioural mechanisms activated to cope with the
alterations of body weight induced by environmental factors, such as predation risk, food
composition and availability, as well as the influence of other factors as sex and growth
rate.
Therefore, the key questions for this thesis are:
1) Do animals under perceived risk of predation adjust their body weight?
2) Which physiological and behaviour factors are changed to produce such
adjustments: food intake, digestibility, metabolic rate, physical activity, behaviour
or combinations of the above?
3) What signals the energy balance and imbalance to produce such adjustments?
Chapter 1 – General Introduction
24
The overall approach developed for this thesis involved the use of wild rodents, as well as
a laboratory model, to analyse different traits and several components of energy balance.
The Wood mouse Apodemus sylvaticus was selected as a model wild rodent, given that it
is a common and widespread species, categorised as least concern at the IUCN red list
(Schlitter et al. 2008) and a common prey species for several predator species such as
carnivores and birds (Bontzorlos, Peris & Vlachos 2005; Lanszki, Zalewski & Horvath 2007).
Animal captures were performed in Portalegre – Portugal (N39º17' W 7º26'), where a
total of 38 individuals where trapped during a 6 weeks fieldwork campaign. The animals
captured where used to test the use of sound cues as method to simulate predation risk,
and also as founders of a laboratory colony in the animal facilities in the Faculty of
Sciences ‐ University of Lisbon.
The wood mouse colony was established and maintained between June 2011 and July
2012, producing 163 animals from 34 litters. Animals born in the laboratory colony were
used on experiments to test the effects of predation risk and fat enriched diets over
growth rates and also for exposure to stochastic periods of starvation.
Animal capture was authorized by the Institute of Nature Conservation and Biodiversity,
Lisbon, Portugal and experimental procedures followed the European guidelines for the
use of animals for scientific purposes (86/609/EEC).
Influence of predation risk over physical activity and behaviour were tested between
January and April 2013, at the Institute of Genetics and Developmental Biology of the
Chinese Academy of Sciences – Beijing. The experimental procedure included
Chapter 1 – General Introduction
25
implantation of telemetry devices in laboratory mice, C57BL/6 strain, to permanently
monitor their physical activity and body temperature, and to assess behavioural changes
through the use of a set a behaviour tests. The C57BL/6 mouse is widely use in obesity
research, it is known to respond to high fat diets by developing diet induced obesity and
some associated disorders (Lin et al. 2000; Collins et al. 2004). This study was given ethical
approval by the ethical review board of the Institute of Genetics and Developmental
Biology, Chinese Academy of Sciences.
1.3. Thesis organisation
The dissertation is organised in five chapters. Chapter 1 includes a general introduction
that sets the state of art and the scientific framework for the addressed questions. The
main objectives are outlined, followed by a brief description of the approach used to
achieve such aims and organisation of the thesis. Chapter 2 and 3 are composed by
scientific papers submitted to international peer reviewed journals, and Chapter 4
includes a manuscript to be submitted.
Chapter 2 ‐ Behavioural and physiological responses of wood mice (Apodemus sylvaticus)
to experimental manipulations of predation and starvation risk (published in Physiology
and Behavior) ‐ focuses on the role of perceived risk of predation and starvation on the
determination of upper and lower limits of body weight, using the analysis of energy
intake and assimilation, energy expenditure through resting metabolic rate and physical
activity, and the chemical signs involved in theses mechanisms. This chapter approaches
the main question by two opposite sides, giving a complete overview of the main problem.
Chapter 1 – General Introduction
26
First it analyses the effect of predation risk, as a factor setting the upper limit of body
weight, and secondly it tests the role of starvation defining the lower intervention point
of body weight.
In Chapter 3 – Effects of predation risk and high fat diet on body weight regulation of
growing Wood mouse (Apodemus sylvaticus) ‐ Investigated the influenced of perceived
risk of predation on the growth and development of sexual dimorphism of weaned pups,
whose body weight and food intake were monitored over a period of 100 days. The
influence of exposure to fat enriched diet at different life stages was also investigated.
Chapter 4 ‐ Predation risk modulates diet induced obesity in male C57BL/6 mice (Accepted
for publication in Obesity) – analyses the role of behaviour and physical activity
components on the energy homeostasis thought the use of implanted telemetric devices
and behavioural assessment tests.
In Chapter 5 the main results are summarized and discussed, integrating the different
approaches and main findings of the previous chapters. The implications and prospects
for future research are also discussed.
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CHAPTER 2
Predation risk and Starvation
Monarca, R.I., Mathias, M.L. & Speakman, J.R. (2015) Behavioural and physiological
responses of wood mice (Apodemus sylvaticus) to experimental manipulations of
predation and starvation risk. Physiology & Behavior, 149, 331–339.
Chapter 2 – Predation and Starvation
37
2. Behavioural and physiological responses of wood mice
(Apodemus sylvaticus) to experimental manipulations of
predation and starvation risk
2.1 Abstract
Body weight and the levels of stored body fat have fitness consequences. Greater levels
of fat may provide protection against catastrophic failures in the food supply, but they
may also increase the risk of predation. Animals may therefore regulate their fatness
according to their perceived risks of predation and starvation: the starvation‐–predation
trade‐off model. We tested the predictions of this model in wood mice (Apodemus
sylvaticus) by experimentally manipulating predation risk and starvation risk. We
predicted that under increased predation risk individuals would lose weight and under
increased starvation risk they would gain it. We simulated increased predation risk by
playing the calls made by predatory birds (owls: Tyto alba and Bubo bubo) to the mice.
Control groups included exposure to calls of a non‐predatory bird (blackbird: Turdus
merula) or silence. Mice exposed to owl calls at night lost weight relative to the silence
group, mediated via reduced food intake, but exposure to owl calls in the day had no
significant effect. Exposure to blackbird calls at night also resulted in weight loss, but
blackbird calls in the day had no effect. Mice seemed to have a generalised response to
bird calls at night irrespective of their actual source. This could be because in the wild any
bird calling at night will be a predation risk, and any bird calling in the day would not be,
because at that time the mice would normally be resting, and hence not exposed to avian
Chapter 2 – Predation and Starvation
38
predators. Consequently, mice have not evolved to distinguish different types of call but
only to respond to the time of day that they occur. Mice exposed to stochastic 24h
starvation events altered their behaviour (reduced activity) during the refeeding days that
followed the deprivation periods to regain the lost mass. However, they only marginally
elevated their food intake and consequently had reduced body weight/fat storage
compared to that of the control unstarved group. This response may have been
constrained by physiological factors (alimentary tract absorption capacity) or behavioural
factors (perceived risk of predation). Overall the responses of the mice appeared to
provide limited support for the starvation‐predation trade‐off model, and suggest that
wood mice are much more sensitive to predation risk than they are to starvation risk.
2.2. Introduction
Obesity has become one of the major public health problems in western societies
(James 2008). This has led to a need for a better understanding of the mechanisms that
underpin the regulation of body weight and fatness (Speakman 2013, 2014a). A balance
between energy intake and energy expenditure is necessary to maintain a stable body
weight. It is well established that the central nervous system (CNS) regulates food intake
and energy expenditure in response to neuronal, hormonal and nutrient signals (Schwartz
et al. 2000; Morton et al. 2006). However, the exact mechanisms by which these signals
are integrated and hence regulate the overall levels of adiposity remain elusive.
There have been several theoretical models that have attempted to describe the
underlying mechanisms involved in the regulation of body fatness. Among the earliest of
Chapter 2 – Predation and Starvation
39
these was the ‘lipostatic set point’ model. This model suggests that the size of body fat
depots is sensed by a ‘lipostat’, which adjusts food intake and energy metabolism to
maintain the body and fat masses at a set‐point (Kennedy 1953). This model requires a
signal from the body that indicates to the brain the levels of body fat and a ‘set point’ in
the brain that allows an appropriate response to these peripheral fat level – increasing
them if they are too low, by a stimulation of intake and inhibition of expenditure, and
decreasing them if they are too high by the reverse processes. Although the hormone
leptin, produced primarily in white adipose tissue (Zhang et al. 1994; Friedman & Halaas
1998b) has been often suggested to be the peripheral fat signal in this model, a central
location for the ‘set point’ has never been identified. Moreover, this model is in conflict
with observations of the patterns of changes in animal and human body weight
(Wirtshafter & Davis 1977; Berthoud 2006) not least of which is the obesity epidemic itself
(Speakman 2014a).
An alternative interpretation suggests that body weight and fat mass are not regulated by
a set‐point, but rather are free to vary dependent on environmental factors, but are
constrained to fall between upper and lower intervention points, above and below which
animals (and humans) intervene physiologically to bring their body weight and fatness
back into the acceptable range (Wirtshafter & Davis 1977; Levitsky 2002; Speakman 2007).
In humans the upper intervention point may be located at different positions in different
individuals, explaining why some individuals become obese when exposed to
environments with readily available food supplies, but others are able to regulate their
body weights at normal levels (Speakman 2008). Based on a wealth of data from small
mammals and birds e.g. (Lima 1986; Cuthill et al. 2000), it has been suggested that the
Chapter 2 – Predation and Starvation
40
lower intervention point may be defined by the risk of starvation, while the upper
intervention point may be defined by the risk of predation (Speakman 2007, 2008).
The starvation‐predation trade‐off model predicts that an increased risk of predation
would decrease the upper intervention point, and animals would tend to regulate their
weight at lower levels. Conversely, an increase in starvation risk would increase the lower
intervention point, and animals would regulate their body weights and fat mass at higher
levels. We aimed in the present study to experimentally manipulate both predation risk
and starvation risk to test these predictions.
2.3. Material and Methods
Animal capture was authorized by the Institute of Nature Conservation and
Biodiversity, Lisbon, Portugal (licence ICNB 231/2010/CAPT); experimental procedures
were conducted in the University of Lisbon facilities by an expert in laboratory animal
science accredited by the Portuguese Veterinary Authority (1005/92, DGV‐Portugal,
following FELASA category C recommendations), according to the European guidelines
(86/609/EEC).
2.3.1. Predation risk
Adult wood mice Apodemus sylvaticus used in the predation experiment were captured
near Portalegre – Portugal (N 39º17' W 7º26'), using Sherman traps, and brought to the
lab in the University of Lisbon where they were housed in individual cages, under
Chapter 2 – Predation and Starvation
41
controlled light (12D:12L) and temperature (20ºC), a small plastic box was provided for
nesting and shelter. Animals were fed ad libitum, with commercial pellets for laboratory
mice (Maintenance diet ‐ Scientific Animal Food Engineering, France), and had free access
to drinking water. After a three week acclimation period, animals were randomly assigned
into experimental groups. A total of 29 animals, 13 males and 16 females, were used in
the tests. Due to the limited number of individuals, each animal was included in 2 ̶ 3 of the
five experimental groups and the order of the treatments was randomly selected, as
follows: control (silence), blackbird sound during the night, owl sound during the night,
owl sound during the day and blackbird sound during the day. After each testing period
animals were allowed a recovery period of two weeks, to avoid interference of previous
treatment over the trials. During the recovery time, housing and maintenance conditions
were as described above and mice were not handled, except for the weekly bedding
change. Exposure to owl calls during the daytime was tested in 22 animals; exposure to
owl calls at night was tested in 18 animals; exposure to blackbird calls during the daytime
was tested on 18 individuals: and exposure to these calls in the day was tested on 10
animals. Finally 10 animals were measured without exposure to any bird calls.
Predation Risk Treatment
Elevated predation risk was simulated by exposing the animals to a playback call lasting 2
min from nocturnal predators observed at the location in the field where the mice had
been captured. The 2 minute call period started at 11 pm and was repeated every 2 h until
7 am the next day. Playback sounds from barn owl Tyto alba and eagle‐owl Bubo bubo
were obtained from commercial digital recordings of bird songs (European bird calls; Jean
Chapter 2 – Predation and Starvation
42
C. Roché, Kosmos Verlag, Stuttgart, Germany). Speakers were placed 3 m away from the
mice, and the sound level was adjusted by human ear, to guarantee that sound was heard
by the animals. A second group was exposed to a 2 minute long playback of calls from the
blackbird Turdus merula, a non‐threatening bird species, also observed at the mouse
capture site. The exposure procedure and setup were the same as described above for
the predation risk group.
The third and fourth treatment groups were exposed to the broadcasting procedure
described above, using owl and blackbird calls, but the exposures were made during
daytime. The daytime playback calls were broadcasted during a 2 minute period, every
2h, starting at 11 am until 7 pm. Animals in a further control group were kept in silence
during the entire period of the experiments. The period of exposure to the treatment
lasted 5 days.
Body weight
Mice were weighed daily between 3pm and 6pm each day. Since mice were exposed
to repeated treatments they could not be dissected at the end of each experimental
period to obtain direct measurements of body fatness.
Food intake and Dry mass absorption efficiency
Food intake and digestibility were quantified in mice from the five experimental
groups. At the beginning of the test, each animal was weighed and placed in a clean
Chapter 2 – Predation and Starvation
43
individual cage (30 cm x 20 cm). The cage floor was covered with absorbent paper, and
food was placed in excess in the food hopper. After 24h, the food left in the hopper was
weighed, as well as any food remains on the cage floor. Faeces were collected, weighed
and dried at 80ºC, until the weight was stabilized (72 h). The procedure was repeated
for the 5 consecutive days of the experiment, between 3pm and 6pm each day. Apparent
dry mass absorption efficiency was calculated as the difference between food intake and
faecal output divided by food intake e.g. (Mueller & Diamond 2001; Gebczyński et al.
2009).
Faecal corticosterone levels
On the sixth day, after the trials had finished, animals were placed in a clean cage and
fresh faeces were collected for measurement of corticosterone levels. Faeces were stored
in absolute ethanol at ‐30ºC, until being processed. The faecal concentration of
corticosterone peaks about 6 – 12 h after a stressful event (Ylönen et al. 2006), thus faeces
were collected between 10 am and 1 pm for animals in the control group and exposed to
the sound treatment during night time, and between 5 pm and 8 pm for animals exposed
to the sound treatments during daytime.
Hormone extraction was performed following a modified method of Goymann (Goymann
et al. 1999). Briefly, ~0.3g of faeces (Sartorius) was added to 4ml of methanol, and
pulverized using a small pallet knife. The mixture was then vortexed for 1 h at 500 rpm,
followed by 1 h of centrifugation at 6000 rpm. The supernatant was then transferred to
another tube and diluted with the buffer solution from an EIA Kit. Corticosterone levels
Chapter 2 – Predation and Starvation
44
were determined using Enzyme Immunoassay (EIA) commercial kits (ADI‐900‐097, Assay
Designs).
Resting metabolic rate
Oxygen consumption was measured, after 6 days of experimental treatment, using an
open‐flow respirometry system (Servomex, series 4100) as previously described (Duarte
et al. 2010). Briefly mice were placed in a cylindrical chamber, and dried atmospheric air
was pumped into the chamber at a flow rate of 500ml/min. Ambient temperature was set
at 29ºC, in the thermoneutral zone (Haim, McDevitt & Speakman 1995). Each animal was
continuously monitored, during 2 h for two consecutive days, during the daytime when
wood mice are normally inactive (Wolton, 1983). No food or water was available inside
the chamber. Measurements of oxygen concentration were digitised approximately 35x
per second and the accumulated data averaged over 30s intervals (mean of approximately
1000 measurements). These 30s averages were then saved. Resting metabolic rate was
estimated as the average value of the five lowest consecutive readings (equivalent to 2min
and 30s in the chamber) (Hayes et al., 1992), and the average of the measurements made
on consecutive days was used for further analysis. VO2 was calculated after Depocas and
Hart (1957)as VO2=V2 (F1O2–F2O2)/(1–F1O2), where V2 is the flow rate measured after the
metabolic chamber, and F1O2 and F2O2 are the oxygen concentrations before and after
the metabolic chamber. All the values were corrected to standard temperature and
pressure (STPD). Baseline values of atmospheric oxygen were corrected by a 30 minute
measurement prior to each run.
Chapter 2 – Predation and Starvation
45
2.3.2. Starvation risk
Animals and experimental design
The 30 mice used in this study were the first generation descendants of the mice used
in the experiment on predation risk. After weaning at 21 days of age, animals were
separated from their mothers, and housed in individual cages, with wood shavings for
bedding, a small plastic box and shredded paper for enrichment. Ambient temperature
was controlled at 20ºC and the light cycle was 12L:12D. Food and water were provided ad
libitum, and the animals were fed on commercial chow pellets (Scientific Animal Food
Engineering, France).
When the animals reached 12–14 weeks old, they were allocated into one of two groups:
the control group (6 males and 6 females) and the stochastic starvation group (9 males
and 9 females). Animals in the control group were fed daily with 50g of chow pellets. Every
day uneaten food on the feeder and cage floor was collected and weighed and the initial
amount was replaced into the feeder. The procedure was repeated for a total of 18 days.
Body weight was also monitored on a daily basis. At days 7 and 14 the faeces were
collected, dried for 48h at 80ºC and weighed. Animals in the starvation group were fed
following the procedure described above, the first 3 days were established as baseline,
and then they were submitted to a set of stochastic food deprivation days. The probability
of having a starvation day was pre‐established as 0.28 corresponding to a total of 4 days
within 15 remaining days of the study. Each starvation day was followed by a feeding day;
starvation days corresponded to days 6, 9, 13 and 15. In the starvation days all the food
was removed from the feeder and replaced 24 h later. For the starvation group, faeces
were collected at days 4, 7 and 14. After collection, faeces were dried for 24 h at 80ºC,
Chapter 2 – Predation and Starvation
46
and dry mass was measured using a 4 figure balance (Sartorius). Dry mass absorption
efficiency was calculated as the difference between food intake and faecal output divided
by food intake e.g. (Gebczyński et al., 2009; Mueller and Diamond, 2001).
Plasma assays
After the 18 days, all the animals were starved for 4 h before the collection of
approximately 1ml of blood by cardiac puncture, followed by sacrifice through cervical
dislocation. Samples were collected between 4pm and 6pm, blood was immediately
centrifuged for 10 min at 20.000 rpm. Plasma was then stored at ‐80ºC until processing.
Plasma leptin levels were measured using the ELISA based method, with commercially
available kits (Millipore Corporation, USA ‐ mouse leptin kit). After blood collection,
animals were dissected, and full body fat was collected and weighed.
Behavioural observations
Animals were video recorded over an 8 hour period on multiple occasions (4 h of light and
4 h of dark). The 8 hour period coincided with two different times of day. In the afternoon
cameras were set 2 h before the dark phase, and allowed to operate for 4 h, while in the
morning cameras were set 2 h before the light phase, and allowed to operate for 4 h. As
mouse vision is limited at wave lengths of light below 630nm (Jacobs et al., 1999), dark
phase records were made under red light illumination (Phillips, Infrared PAR38).
Chapter 2 – Predation and Starvation
47
The control group was observed three times, on days 2, 4 and 9. The starvation group was
observed 5 times, including free feeding days, starvation days, and refeeding days. Within
each of the recorded films, each animal was observed for 5 random periods of 10 min,
and the time spent on each of categorized behaviours was registered. Dominant
behaviours were classified into four categories: grooming, feeding, resting and general
activity, following the ethogram previously described for mice (see Speakman & Rossi
1999; Speakman et al. 2001 for details). General activity included walking, climbing the
cage and all general movements. Feeding included eating chow, drinking and occasional
coprophagia. Resting was considered when the animal was sleeping or sitting, was not
moving in the cage and was not grooming. Grooming behaviour was registered when the
animals were not moving and included licking the fur and tail and scratching with any limb.
2.3.3. Statistical Analyses
Predation risk
All the data are expressed as mean ± S.E. Mixed modelling followed by pairwise
comparisons was used to compare body weight, food intake and apparent absorption
efficiency variation across the 5 experimental days; treatment and day of measurement
were included in the model as fixed factors. Body weight was included as a covariate, for
the analysis of food intake variation. Individual ID was included in the models as a random
factor, to account for repeated measures. The influence of treatment on RMR and
cumulative food intake was analysed through mixed modelling, setting body weight as
covariate, and individual ID as a random factor due to the repeated measurement of
individuals across treatments. One‐way ANOVA was used to compare corticosterone
Chapter 2 – Predation and Starvation
48
levels on the last day of treatment, using group as a fixed factor. Due to the wide range of
body weight across individual animals (14–62g), body weight variation was analysed as
change relative to day zero of the study. Given the unbalanced number of males and
females across the treatment groups, and the reduction of statistical power, sex was not
included in the models as an independent predictor.
Starvation risk
All the data were expressed as mean ± S.E. linear mixed modelling was used to test the
body weight and food intake differences between groups and sexes, over the 18 days of
the study, and individual ID was included as a random factor to account for the repeated
measures. On the analysis of food intake, body mass was included as a covariate in the
model. One‐way analysis of variance (ANOVA) with post hoc Tukey tests were used to test
group differences in body weight and food intake, between time points when group
differences were significant over the entire time course.
Differences in cumulative food intake and dry mass absorption efficiency were tested
using one‐way analysis of variance (ANOVA) with pos hoc Tukey tests. Analysis of
covariance (ANCOVA) was performed to test differences in plasma leptin levels and body
fat between treatment groups, and body fat mass and body weight were used as
covariates accordingly. Dominant behaviours were registered with the Etholog 2.2.5
software (Ottoni, 2000) and analysed using a GLM model followed by Tukey post hoc tests.
All data were analysed using SPSS 19.0 for Windows.
Chapter 2 – Predation and Starvation
49
2.4. Results
2.4.1. Predation risk
Animals exposed to the sounds during the night, both owl and blackbird, significantly
reduced their body mass, when compared with the control ‘silence’ group (p<0.001). Owl
calls during the night caused an average of 8% (S.E=1.2; p<0.001) reduction in the body
weight, and the blackbird calls a 6% (S.E=1.6; p<0.001) reduction (Figure 1). Body weight
variation between silent controls, and animals exposed to either owl or blackbird calls
during the day, was not significantly different (p=0.659; p=0.293). The variation in the
body weight was mostly explained by time (day of experiment effect: F5,32=8.188;
p<0.001), treatment (F4,32=30.031; p<0.001), and the interaction between day of
measurement and treatment (F20,32=2.386; p=0.001).
Time (days)
0 1 2 3 4 5
Bo
dy
Ma
ss (
%)
80
85
90
95
100
105
ControlPredator day timePredator night timeNon Predator day timeNon Predator night time
Figure 1 – Changes in body mass (mean ± S.E.) relative to baseline in response
to exposure to predator and non‐predator sounds played during the day or at
night across the 5 days of the study. The control group was exposed to silence.
Chapter 2 – Predation and Starvation
50
The average food intake on the first day of the study was 3.18g across all the groups.
Variations in food intake were mostly explained by day of measurement (F4,28=4.322;
p=0.002), treatment (F4,28=14.979; p<0.001), body mass (F1,28=10.077; p=0.002) and the
interaction between day of measurement and treatment (F1,28=2.157; p=0.006). Animals
in the control group (3.38±0.153g) and animals exposed to the treatment during the day
(predator: 3.49±0.129g; non predator: 3.730±0.133g) had higher food intake than mice
submitted to the treatment during night time (predator: 3.21±0.131g; non predator:
2.82±0.148g) (Figure 2).
Time (days)
1 2 3 4 5
Ad
just
ed
Fo
od
Inta
ke (
g)
0
1
2
3
4
5
ControlPredator day timePredator night timeNon Predator day timeNon Predator night time
Figure 2 – Variation of food intake (g) (adjusted for BM=28,29g; mean ± S.E.),
in response to exposure to predator and non‐predator sounds played during
the day or at night across the 5 days of the study. The control group was
exposed to silence.
Differences in cumulative food intake after the 5 days of treatment were explained by
different treatments between groups (F4,8=5.435; p=0.001), and the effect of body mass
Chapter 2 – Predation and Starvation
51
(F1,66=14.059; p<0.001). Cumulative food intake was lower in mice submitted to the
treatment during the night (predator: 15.6±0.79g; non‐predator: 13.6±1.0g) and greater
in animals in the control group (16.7±1.0g) and exposed to the treatment during the day
(predator: 17.6±0.8g; non‐predator: 18.4±0.8g) (Adjusted for BM=28.4g).
Apparent dry mass absorption efficiency (control= 81.7±0.39; predator day= 80.5±0.24;
predator night = 80.4±0.63; non‐predator day = 81.3±0.32; non‐predator night=
81.7±0.61) showed no significant differences over the 5 days of the study (F4,27=1.171,
p=0.323), and no influence due to the treatment (F4,27=0.935, p=0.444). Resting metabolic
rate was predominantly influenced by body weight (F1,8=47,681, p<0.001), and was not
affected by the experimental treatment (F4,8=1.021, p=0.403) (Figure 3).
Body Mass (g)
10 20 30 40 50 60
RM
R (
VO
2 m
l/min
)
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
Control Predator day timePredator night time Non Predator day time Non Predator night time
Figure 3 – Effects of body mass (g) on resting metabolic rate (ml O2/min) for all
the measured individuals. Experimental treatment had no significant effect on
the resting metabolic rate (F4,8=1.021, p=0.403).
Chapter 2 – Predation and Starvation
52
Corticosterone levels varied significantly with the treatment group (ANOVA: F5,49=7.692;
p<0.001). Levels were highest in the groups exposed to the playback during daytime, of
both owl (257.9±53.90 ng/ml) and blackbird (155.0±22.3 ng/ml) sounds (Tukey p=0.308).
In contrast, exposure to sounds at night (owl: 47.4±12.85 ng/ml; blackbird: 34.0±14.2
ng/ml) did not result in elevated corticosterone levels relative to the silent control group
(41.0±14.1 ng/ml) (Tukey p=0.166).
2.4.2. Starvation risk
Body weight
The body weight variation was mostly explained by day of measurement (F18,78=2.659,
p<0.001) and the interaction between treatment and day of measurement (F18,78=9.204
p<0.001), and the effect of sex is not significant (F1,78=0.71, p=397). During the first six
days of the experiment, prior to the starvation periods, animals in the two groups did not
differ significantly in body weight (control, 29.1±0.55g; starvation, 29.5±0.45g; ANOVA
F1,26=0.198, p=0.657). Animals in the starving group significantly decreased their body
weight on the deprivation days (Table I). The first and second starvation days resulted in
a reduction of 8% in the body weight; on the third and fourth starvation days the animals
lost 12% of their body weight (Figure 4).
Chapter 2 – Predation and Starvation
53
Table I – Mean ± S.E. of body mass (g) and ANOVA results, on starvation (day
6, 9, 13 and 15) and refeeding (day 7,10, 14 and 16) days of the study.
Control Starvation ANOVA
Starvation days
Day 6 28.52 ±1.87g 26.82±1.62g F1,26=42.905; p<0.001
Day 9 27.85 ±1.81g 26.21±1.55g F1,26=6.482; p=0.017
Day 13 28.25 ±1.74g 25.02±1.50g F1,26=110.353; p<0.001
Day 15 27.81 ±1.67g 24.20±1.45g F1,26=78.354; p<0.001
Refeeding days
Day 7 28.44±1.38g 27,27±1.69 F1,26=18.453; p<0.001
Day 10 27.77±1.39g 26,57±1.67 F1,26=2.833; p=0.104
Day 14 27,97±1.39g 26,91±1.53 F1,26=57.716; p<0.001
Day 16 27,78±1.42g 27,08±1.63 F1,26=126.303; p<0.001
Days of treatment
0 2 4 6 8 10 12 14 16 18 20
Bo
dy
Ma
ss (
%)
60
70
80
90
100
110
120
ControlStarvation
Figure 4 – Effects of stochastic exposure to starvation on the body mass
variation of the wood mice, across the 18 days of the study.
Chapter 2 – Predation and Starvation
54
Except for the day following the second 24h starvation period (day 10: GLM F1,26=2.8,
p=0.104), the body weight on the first refeeding days was also significantly reduced
relative to the control animals. On average the body weight took 2 ̶ 3 days to recover to
control levels after the 24h starvation period. At the end of the experimental period, body
fat of the control group (1.7±0.23g) was higher than in the starved animals (1.58±0.198g).
These differences were mostly explained by total body weight (ANCOVA: F1,25=23.3,
p<0.001), and also by an effect of the treatment (F2,25=3.4, p=0.048).
Food intake
The analysis of food intake revealed that its variation is mostly explained but the
interaction of treatment across days of the study (F17,75=70.214, p<0.001), effects due to
sex (F1,75=0.798, p=0.377) and body mass (F1,75=3.602, p=0.061) did not significantly affect
the food intake. During the 6 initial days, when all animals were fed ad libitum, the two
groups did not differ in the average quantity of food consumed (control, 3.9 ±0.15g/day;
starvation, 4.0±0.12g/day; ANOVA F1,147=0.038, p=0.846) (Figure 5).
Chapter 2 – Predation and Starvation
55
Days of treatment
0 2 4 6 8 10 12 14 16 18 20
Adj
uste
d F
ood
inta
ke (
g)
0
1
2
3
4
5
6
7
ControlStarvation
Figure 5 – Effects of stochastic exposure to starvation on the food intake (g)
variation over the 18 days of the study (adjusted for BM= 27.85g)
Over the first day of refeeding (day 6), after the first starving period, there were no
significant differences in the food intake between the control group and starvation group
(control, 4.3±0.28g; starvation, 4.4±0.23g; ANOVA F1,28=0.063, p=0.804). Food intake on
the first day of refeeding (day 10), after the second deprivation day (day), was also not
significantly different between the two groups (control, 4.1±0.29g; starvation, 4.9±0.24g;
ANOVA F1,28=3.921, p=0.058). However, on the first refeeding day, after the third (day 14)
and fourth (day 16) days with no access to food, food intake in the starvation group was
elevated compared with the control group (day 14: control, 4.5±0.24g; starvation,
5.4±0.20g; ANOVA F1,27=8.476, p=0.007: day 16: control, 4.1 ±0.23g; starvation,
5.8±0.197g; ANOVA F1,26=30.848, p<0.001).At the end of the baseline period of 5 days,
cumulative food intake (Figure 6) was not significantly different between the two groups
(control, 19.4 ±1.51g; starvation, 19.9±1.23g; ANOVA F1,28=0.043, p=0.836). Cumulative
Chapter 2 – Predation and Starvation
56
food intake on day 12 (after 2 starvation events), was also not significantly different
between the two groups (control, 49.5±2.71g; starvation, 43.1 ± 2.213g; ANOVA
F1,28=2.214, p=0.079), but showed a trend (p >0.05 <.1) towards being greater in the
control group. On the last day of the study, cumulative food intake of the control group
was significantly higher than the starvation group (control, 77.0±3.67g; starvation, 62.8 ±
3.00g; ANOVA F1,28=8.94, p=0.006). The shortfall in intake of the starved group relative to
the controls over the 18 day study was 14.2g of food, almost equal to the daily intake
during baseline (3.94g/day) multiplied by the number of starvation days (=4) (3.94 x 4 =
15.8g). Therefore overall the intermittently starved mice did not compensate their food
intake during the days that they had food available for the days when food was absent
(Figure 6).
Figure 6 – Cumulative food intake (g) during the exposure to the stochastic
starvation treatment. Day 5 (feeding day), day 12 (after two starving events),
and day 18 (after four starving events).
Day 5 Day 12 Day 18
Cum
ula
tive
foo
d in
take
(g
)
0
20
40
60
80
100
Control GroupStarvation Group
Chapter 2 – Predation and Starvation
57
Apparent dry mass absorption efficiency was not significantly different between the
groups (control=0.804±0.004; refeeding=0.815±0.005: F1,71=0.016; p=0.899). Moreover
within the starvation group, the starvation/refeeding treatment did not significant affect
apparent dry mass absorption efficiency (F1,71=1.680; p=0.199).
Behaviour and Activity
General activity was significantly reduced on the starvation day and the first refeeding
day in the starved animals, compared with the control data (p=0.001 and p=0.004
respectively). The difference in general activity levels between the starvation day and the
first refeeding day was not significant (p=0.202). Time spent resting was significantly
increased on starvation (p=0.004), and refeeding days (p=0.005) compared with control
days. Resting time did not differ between starving and refeeding days (p=0.999). The
differences in the time spent grooming were also significantly different between the
control period and starving days (lower when starving: p=0.016), but not different
between starving and refeeding days or between control and refeeding days. Time spent
feeding was not significantly increased on the refeeding days compared with control days
(p=0.41) (Figure 7).
Chapter 2 – Predation and Starvation
58
Figure 7 – Effects of the stochastic starvation treatment on the time spent in
different behaviour categories (general activity, grooming, feeding and
resting), on the starvation days and refeeding days.
Circulating leptin
Circulating leptin levels were related to body weight (ANCOVA F1,11= 5.428; p<0.04),
body fat content (ANCOVA F1,11=14.171; p<0.003) and independently treatment (ANCOVA
F2,11=6.303; p<0.015) (Figure 8). Levels of circulating leptin for the control group (6.2±2.6
ng/ml) were significantly lower than for animals exposed to the starvation treatment
(10.5±1.8 ng/ml).
General Activity Grooming Feeding Resting
% T
ime
sp
ent
on
ea
ch b
eha
vio
ur (
%/8
h)
0
20
40
60
80
100
ControlFasting DayRefeeding
Chapter 2 – Predation and Starvation
59
Figure 8 – Variation of plasma leptin levels (ng/ml) against body fat (g) for the
control group and starved animals, measured on the final day of the study.
2.5. Discussion
2.5.1. Predation risk
According to the predation‐starvation trade‐off hypothesis, under increased risk of
predation wood mice were predicted to decreasing their fat reserves (Speakman, 2007),
thus decrease their body weight. Consistent with this prediction, when mice were
exposed to owl calls at night they significantly reduced their body weight relative to mice
kept in silence. However, contrasting our expectations the mice also showed a significant
reduction in body weight when exposed at night to calls of a non‐predatory bird
(blackbird).
Moreover, when exposed to either owl or blackbird sounds in the daytime, they did not
reduce their body weight. The different effects of the sounds broadcasted during daytime
Chapter 2 – Predation and Starvation
60
and nighttime, are possibly associated with the animal activity periods. Wood mice are
mostly active during the night (Corp et al., 1997; Wolton, 1983). Diurnal activity has been
documented rarely and usually associated with the breeding season. Birds which naturally
are active at night, where these mice live, are mostly potential predators. The mice may
therefore have evolved to respond to any bird calling at night, and in contrast have
similarly evolved to ignore any bird calling in daytime. An alternative explanation was that
during the day the mice were asleep and did not hear the calls, but at night they were
awake and were stressed by any noise causing them to reduce intake and lose weight.
However, this interpretation was at odds with the corticosterone data (see below) which
clearly indicated that the mice could hear the calls in the daytime and if anything were
more stressed by them that heard calls at night. They just did not translate this stress into
altered food intake or body weights.
These data suggest a generalisation of the response and the categorisation of the stimulus
(reviewed by Shettleworth (2001)). Large investments in predation avoidance may
compromise other fitness components, such as reproduction (Lima and Dill, 1990), and
the mechanism of discriminating traits may include an intensive training and learning
process (Kurt and Ehret, 2010; Lederle et al., 2011); therefore generalisation may be a
mechanism of saving resources. In addition, with respect to predation stimuli, the
opportunity for a learning process to occur may be limited, given that predator attacks
are often fatal (Vermeij, 1982). Hence most responses to predators may be innate, as has
been demonstrated for several taxa (Griffin et al., 2000). According to the ‘Predator
Recognition Continuum Hypothesis’ (Ferrari et al., 2007), a combination of innate and
learned recognition mechanisms is probably involved. Kindermann (2009) has
Chapter 2 – Predation and Starvation
61
demonstrated that predator‐naïve rodents (mice, rats, and gerbils) do not discriminate
their respond towards auditory cues of predator and non‐predator birds. Since the mice
in this experiment were wild caught from sites where all three of the bird species that we
used are found, it is possible that they had been previously exposed to these calls in the
wild before the experiments began, this factor was not controlled, hence we cannot rule
out either a naïve or learned response to calls occurring at night.
The body weight responses of the mice suggested that mice were unable to discriminate
between the owl calls and the blackbird calls, and instead reacted only to the cue of
generalised bird calls, combined with the time of day that they were played. The data
from the corticosterone assays supports the hypothesis that the mice were unable to
distinguish between broadcasted sounds of different species, since the corticosterone
levels were equally elevated during the diurnal period, independent of the type of bird
call, and for exposure to calls at night were also not different between the sources of the
calls. In an earlier study, faecal corticosterone levels in bank voles (Myodes glareolus)
were elevated in response to weasel (Mustela nivalis) faeces and this was presumed to be
part of the mechanism underpinning the reduction in body weight of these small rodents
in response to predation risk (Tidhar et al., 2007). Similarly rats showed elevated
corticosterone in blood when exposed to a live cat (Felis catus) compared to a stuffed
model (Blanchard et al., 1998) rabbits (Oryctolagus cuniculus) showed elevated
corticosterone in blood when exposed to fox (Vulpes vulpes) as opposed to sheep (Ovis
aries) faeces (Monclús et al., 2005) and domestic mice had higher levels of cortisol when
exposed to owl sounds compared to silence or human voice sounds (Eilam et al., 1999).
Since the faecal corticosterone in the wood mice studied here was not elevated in the
Chapter 2 – Predation and Starvation
62
groups that lost weight, relative to the control mice that did not lose weight, this suggests
that elevated corticosterone was not part of the mechanism underlying the altered body
weight changes reported here. Furthermore, lag time between hormonal levels in the
blood, and the signal on faeces is species‐specific and highly dependent on animal gut
function (Harper and Austad, 2000; Palme et al., 1996) which, as shown by Touma (2003)
is variable across the day. Therefore we consider that our method of perceived risk
assessment may have underestimated the stress induced by the predation treatments,
given that the levels of metabolites measured were highly affected by the circadian cycle.
It is unlikely that reduced corticosterone levels are due to acclimation to the testing
procedure, predation defense is highly repeatable (Dammhahn and Almeling, 2012;
Dosmann and Mateo, 2014), and habituation is not likely to cause fear extinction
(Takahashi et al., 2005).
The reduction in body weight was correlated with a reduction in food intake. There were
no differences in dry mass absorption efficiency and no change in resting metabolic rate
beyond that anticipated from the reduced body weight. Although the data that we
collected cannot rule out a contribution by elevated physical activity levels, the main
mechanism for enabling the negative energy balance, to effect the reduction in body
weight, was to reduce food intake. This strategy makes sense because it is probably during
foraging that wood mice are most susceptible to avian predation risks. Hence reducing
food intake probably has the dual benefit of directly reducing predation risk, at the same
time as forcing a negative energy balance which reduces body weight, thereby generating
secondary anti‐predation benefits. Indeed the main benefit of losing weight in terms of
Chapter 2 – Predation and Starvation
63
predation risk may be the reduced energy demands (Figure 3) which reduce the required
foraging time.
2.5.2. Starvation risk
Although the responses of the wood mice to predation risk were largely in line with the
predictions of the predation‐starvation trade‐off model the responses to experimental
elevations in starvation risk were largely at odds with the predictions. We anticipated that
in response to intermittent and unpredictable starvation events the mice would increase
their levels of stored fat, enabling them to cope with subsequent starvation events
without the need to enable behavioural or physiological responses, but simply by drawing
on stored fat reserves. In fact, when exposed to starvation the mice altered their
behaviour significantly reducing the costly components of their behaviour (activity and
grooming) and increasing the less costly elements (resting and sleeping). Once food
became available again they did not substantially elevate their food intake to offset the
shortfall in their intake but instead had only a modest or insignificant increase in intake,
matched with a continued suppression of activity to get themselves back into positive
energy balance and increase in weight. This strategy meant that it took 2‐3 days to recover
their body weight after the starvation events, and they did not show any elevation in body
weight above that of the controls. This was mirrored by the fact that at the end of the 18
day experiment the starvation group actually had lower fat reserves on average than the
control animals – the complete opposite of the predictions of the starvation‐predation
trade‐off hypothesis.
Chapter 2 – Predation and Starvation
64
There have been many previous studies of the responses of other small mammals to
periods of calorie restriction (reviewed in Speakman & Mitchell 2011) or to every other
day feeding protocols. The responses of the animals to these previous manipulations are
not directly comparable to the data generated here, the key element of which was the
stochastic nature of the food deprivation events. Nevertheless, three previous studies
have concerned the responses of domestic strains of mice to stochastic food deprivation.
Swiss mice increased their food intake dramatically, and decreased their energy
expenditure, on days between 24h starvation events (Cao et al., 2009). However, overall
after 4 weeks, similar to the findings here, their overall body weight was decreased.
However, the same research group found over 4 weeks of treatment, with 3 starvation
and 4 non‐starvation days per week, that the elevated food intake on the feeding days
was sufficient to maintain the body weight both in the same mouse strain and in striped
hamsters (Zhao and Cao, 2009). In C57Bl/6 mice, in response to stochastic intermittent
starvation, there was also a significant increase in food intake on the days between
starvation events, such that over a 42 day experiment the overall intake was not reduced
in the intermittently starved group (Zhang et al., 2012). Since these latter mice also
enabled behavioural (reduced activity) and physiological (reduced body temperature)
responses to the manipulation, at some points during the experiment they elevated their
fatness above that of the control group, in line with the starvation‐predation model
predictions.
The responses of the wood mice studied here differ substantially from these previous
studies. There was virtually no compensatory response in food intake. Hence, body weight
was restored predominantly by reduced expenditure (reduced activity). Consequently
Chapter 2 – Predation and Starvation
65
they ended up with lower, rather than elevated fat reserves. One potential reason for this
response is that the mice were constrained in their alimentary tract capacity and unable
to process greater levels of food intake (Drent and Daan, 1980; Koteja, 1996; Peterson et
al., 1990). We cannot eliminate this possibility that the absence of a food intake response
was due to a physiological constraint. A second reason, however, is that wood mice may
be acutely sensitive to predation risks, and choose not to increase their food intake levels
on the refeeding days, because this would expose them to elevated predation risk.
Separating between these explanations was beyond the scope of the current work.
Circulating leptin levels in intermittently starved C57BL/6 mice were dependent only on
the levels of body fatness (Zhang et al., 2012). This is consistent with many studies
showing a relation of leptin to stored fat (Friedman and Halaas, 1998). In wood mice the
levels of leptin also were dependent on fatness, but in contrast to previous work were
independently elevated among the mice that had been intermittently starved. This
response was unexpected because reductions in food intake, for example, during caloric
restriction, generally lead to reductions in circulating leptin levels which forms a primary
stimulus to overconsume when released from restriction (Friedman and Halaas, 1998;
Hambly et al., 2012; Morton et al., 2006; Rosenbaum and Leibel, 1998). The fact that wood
mice did not show this response to intermittent starvation in their leptin levels, is
consistent with the fact they also did not show post starvation hyperphagia.
Understanding the mechanisms by which these mice elevate leptin levels after food
restriction, and hence avoid post restriction hyperphagia, may be important because in
humans the reduced levels of leptin when dieting are presumed to be part of the primary
response causing people to break their diets (Hopkins et al., 2014; Keim et al., 1998).
Chapter 2 – Predation and Starvation
66
2.6. Conclusions
The current data provide limited support for the starvation‐predation trade‐off model
for understanding levels of stored body fat/body weight. Wood mice responded in a
manner that was complex and not entirely as expected, but could be interpreted as
consistent with the model if it is assumed that their generalised response to calls at night
was a generalisation to the predation risk of any bird in the wild that calls at night.
However, when faced with elevated starvation they did not show the anticipated
responses. This may in part be because these mice are substantially more susceptible to
predation than to starvation, and their potential responses to starvation were
compromised by the need to maintain a low level of predation risk.
2.7. Acknowledgements
This work was supported by European Funds through COMPETE and by National Funds
through the Portuguese Science Foundation (FCT) within project PEst‐
C/MAR/LA0017/2013 and PhD fellowship (SFRH/BD/47333/2008).
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CHAPTER 3
Growth and Development
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3. Effects of predation risk and high fat diet on body weight
regulation of growing Wood mouse (Apodemus sylvaticus)
3.1. Abstract
Nutrition in the early stages of life can alter organ function and thereby affect the
adult predisposal for disease, explaining in part why obese children are commonly also
obese adults. Obesity can be explained by a combination of both genetic and
environmental factors. In recent years models incorporating an environmental
component have gained relevance, compared with the classic lipostatic set‐point
approach. The risk of predation has been suggested as one of the factors explaining the
common absence of obese individuals among the populations of small mammals. Non‐
lethal effects of predation can induce phenotypic plasticity, allowing prey to cope with the
risks of being predated due to increased exposure during foraging, balance against the
risk of starving due to the insufficient fat reserves. In this study, weaned wood mice were
fed on high fat or control diet and submitted to high risk of predation (or not), simulated
by the broadcasting of owl calls during the night period. We hypothesized that the risk of
predation would play a role on the regulation of body weight by activating mechanisms
that would include modulation of energy intake and expenditure. Furthermore, we tested
the whether the risk of predation reduced the impact of exposure to fat enriched diets,
by comparing the growth trajectories of animals on two feeding regimes, and different
timings of exposure to the diet. Results showed that animals exposed to the predation
treatment gained less body mass, however variations in food intake and RMR did not
explain the observed differences. Feeding on high fat diet resulted in individuals with
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higher body weight, females reached their adult size earlier than males, and were more
responsive to the predation treatment, revealing stronger mechanisms of energy
homeostasis. This suggested that in males larger body size may be related with social
dominance and be involved in their reproductive success. Our data suggest that
predisposition to gain body fat due to the exposure to high fat diet in early stage of
growth, may be affected by the age of the initial exposure, partially supporting the
expected weight increase due to early over‐nutrition.
3.2. Introduction
While obesity is widely recognised to be a problem of energy imbalance (Hall et al
2012) the causes of such imbalance are still unclear. A combination of both genetic and
environmental factors have been used to explain its prevalence. In recent years models
incorporating environmental components, such as behaviour, have gained relevance
(Levitsky 2002; Speakman 2007), relative to the classic “lipostat” set‐point approach
where body weight regulation was presumed to be dependent only on individual
physiological attributes (Kennedy 1953). The risk of predation has been suggested as one
of the environmental factors influencing the regulation of body weight, explaining virtual
absence of overweight and obese animals among the populations of small mammals
(Speakman 2007). This is potentially because small mammals carrying large fat reserves
are unable to run as fast evading predation or to hide from predators in small burrows
(Sundell & Norrdahl 2002). Moreover the time required for foraging for resources, and
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exposure to predators, is higher for fatter individuals, potentially further increasing their
exposure to predators.
It has been suggested that the risk of predation induces changes in behaviour, foraging
activity (Jedrzejewski, Rychlich & Jedrzejewski 1993; Eilam 2005), morphology (Brönmark,
Lakowitz & Hollander 2011) and physiological reactions (Slos & Stoks 2008). Heikkila
(1993) demonstrated that predation risk can influence the development and maturity of
voles. Until sexual maturity, energy is mostly allocated to growth and development,
assuming scenarios of limited resources, and demanding survival effort. On the other
hand, over‐nutrition in early life represents an obesity risk factor, and can induce a series
of alterations such as hyperinsulinaemia, hyperphagia, hyperleptinaemia and
hypertension (López et al. 2007; Martins et al. 2008; Rodrigues et al. 2009), that overall
are linked with the obesity related diseases. Factors acting in the early stages of life
(Gluckman & Hanson 2004; Elahi et al. 2009), and personal history, are as risky for the
development of adult metabolic diseases, as adult environment and life‐style, which
explains in part why obese children are commonly obese adults (Serdula et al. 1993).
In this study we investigated the role of long term exposure to predation risk combined
with a high fat diet, on the regulation of post weaning body weight. The wood mouse
Apodemus sylvaticus (Linnaeus, 1758) was chosen as model given that it is a common
prey of several species of nocturnal raptors (Bontzorlos, Peris & Vlachos 2005), reptiles
(Santos et al. 2007) and carnivores (Lanszki, Zalewski & Horvath 2007). In this species
therefore coping with predation is an important fitness trait.
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We hypothesized that predator risk plays role setting the limits of body weight and
regulating the diet induced obesity. Moreover, we expected that the predation risk
treatment would trigger compensation mechanisms to maintain energy homeostasis,
through imbalance of energy intake and expenditure, that ultimately would be reflected
in body weight. Additionally we investigated the effects of high fat nutrition introduced at
different life stages, and whether risk of predation would compensate the effects of over‐
nutrition in early age, reversing the diet induced body weight increase.
3.3. Methods
All experimental procedures were conducted by an accredited expert in laboratory
animal science by the Portuguese Veterinary Authority (1005/92, DGV‐Portugal, following
FELASA category C recommendations), according to the European guidelines
(86/609/EEC).
Animal housing and Predation risk treatment
Wood mice (Apodemus sylvaticus) were first generation descendants from wild parents
caught in Portalegre – Portugal, in a cork oak forest. After birth, animals were kept with
their parents and siblings until they were 20 days old. On day 20 after birth (weaning),
each individual was separated from their mother and litter and placed in a clean individual
cage (30x20 cm). Each cage was supplied with absorbent paper covering the floor, and
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acting as nesting material, and a small plastic box for enrichment. Animals were kept in
rooms under controlled light (12L: 12D), and temperature conditions (20ºC). Throughout
the experimental period, food and water were provided ad libitum. The cages were
cleaned every 3 days, bedding material was renewed, uneaten food on the hopper and
cage floor was weighed and replaced by approximately 60g of the experimental diets.
The predation risk treatment involved the broadcasting of playback calls from nocturnal
raptors Barn Owl (Tyto alba) and Eurasian Eagle‐Owl (Bubo bubo) (European bird calls;
Jean C. Roché, Kosmos Verlag, Stuttgart, Germany). The owl calls were broadcasted for 2
minutes, every 2 hours, during the night time (9 pm to 7 am). To avoid litter effects,
animals from the same litter were allocated to a different combination of diet and
predatory risk (details below). Wood mice litters have typically 4‐6 pups (Zizkova & Frynta
1996), allowing us to separate the animals evenly for the groups.
Experiment 1 ‐ Long term effects of predation risk on diet induced obesity
Thirty seven animals (18 females and 19 males) were kept in a silent room, and 37
individuals (19 females and 18 males) were exposed to experimental conditions of
predation risk simulation (details above). In each room, animals were fed on one of two
diets: high fat (45% fat; 19.9 kJ/g ‐ D12451 ‐ Research Diets New Brunswick, NJ, USA) or
low fat (3% fat; 12.1 kJ/g ‐ A04 ‐ Scientific Animal Food Engineering, France). Animals were
weighed and food intake was determined based on uneaten food left in the feeder every
3 days. Animals were monitored over 100 days, until they were 120 days old. At the
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experimental end‐point resting metabolic rate was measured by open flow respirometry
(details below) and mice were subsequently killed and dissected.
Experiment 2 – Effects of early nutrition vs diet induced obesity
On the weaning day (day 20 since birth), 24 wood mouse were separated into two
groups (12 control + 12 predation risk) fed on low fat diet for 70 days. Animals in the
control group were maintained in a silent room, and animals in the predation risk group
were submitted to the predation risk treatment described above. After a 70 day period,
animals were started on a high fat diet. Following the procedure described before, body
mass, and food intake were measured for 30 days every 3 days until the study end‐point
at day 100. At the end of the trial, resting metabolic rate was measured (as above), and
the animals were then killed and dissected.
Resting Metabolic Rate
At the end of the 100 days period, resting metabolic rate of all the animals in the study
was measured using open‐circuit respirometry device. Briefly, Oxygen consumption was
measured, using an open‐flow respirometry system (Servomex, Xentra series 4100). Mice
were placed in a perspex chamber of approximately 1 dm3. After placing the animal in the
chamber, dried atmospheric air was pumped into the chamber at a flow rate of
500ml/min. Ambient temperature was set at 29ºC, within thermoneutral zone for
Apodemus sylvaticus (Haim, McDevitt & Speakman 1995). Each animal was monitored
twice, during 2 hours for two consecutive days, during the light period when wood mice
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are less active, the average of these two measurements was used for following analysis.
No food or water was available inside the chamber, and the animals were not starved
before entering in the chamber. Measurements of oxygen concentration in the excurrent
airstream were recorded at 30s intervals, CO2 was not removed from the air pumped into
the chamber to maximize the accuracy of the measures (Koteja 1996). Resting metabolic
rate was estimated as the average value of the five lowest consecutive readings
(equivalent to 2min30s in the chamber). VO2 was calculated after Depocas and Hart (1957)
as VO2=V2 (F1O2–F2O2)/(1‐F1O2), where V2 is the flow rate measured after the metabolic
chamber, and F1O2 and F2O2 are the oxygen concentrations before and after the metabolic
chamber. All the values were corrected to standard temperature and pressure (STPD).
Baseline values of atmospheric oxygen were corrected by a 30 minutes measurement
prior to each run.
Plasma assays
After the respirometry measurements (102‐105 days after the beginning of the
experiment) approximately 1ml of blood was collected, from each mouse, by cardiac
puncture and animals were sacrificed by cervical dislocation. All animals were killed
between 16h and 18h. Animals were dissected, subcutaneous, and visceral fat depots
were removed and wet weight was recorded. To account the effect of feeding into the
circulating levels of metabolites, animals were starved 4 hours prior to the blood
collection. After collection, blood was centrifuged at 10000 rpm for 10 minutes; plasma
was collected and kept at ‐80ºC until processed. Plasma leptin levels were measured using
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ELISA based method with commercially available kits (Millipore Corporation, USA ‐ Mouse
leptin kit).
Statistical Analysis
All the values are expressed as mean ± S.E. A mixed model was used to test the effects of
sex, treatment and day of measurement on the body mass and energy intake. Individual
ID was included in the model, nested within group, as a random factor to account for
repeated measures. When significant this ANOVA procedure was followed by pairwise
comparisons to test between‐group differences for the different time points. Effects on
RMR were tested using analysis of covariance (ANCOVA) considering treatment group as
a fixed factor, and body mass as covariate (following recommendations in Tschöp et al.
(2012)). To correct for the influence of body fatness in the plasma metabolites levels,
these were also tested using analysis of covariance, considering body fat as covariate. P
level <0.05 was taken as significant for all the statistic tests.
3.4. Results
Experiment 1
On the first day of the study, all the animals had approximately the same body mass
11.5±0.16g (Figure 1).
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Figure 1 ‐ Body mass variation (Mean ± S.E.) across the study. A – Females; B‐ Males.
The variation of body mass was mostly explained by the day of measurement
(F34,282=299.509; p<0.001), by the effect of sex (F1,282=57.354; p<0.001), and by the
interaction of sex, day of measurement and treatment (F139,282=7.780; p<0.001). Females
in the control group (no fat and no predation risk) increased their body weight to
26.3±2.29g, representing a gain of 128% when feeding on low fat diet, and to 28.3±1.89g
(gain of 145%) when feeding on high fat diet (no predation). In contrast the body weight
achieved by females feeding on a low fat diet but under predation risk was only 117%
(25.0±1.31g) when feeding on low fat diet and 135% (27.5±1.53g) when feeding on high
fat diet. Males in the control group feeding on low fat diet with no predation risk increased
their body mass to 219% to 36.7±2.70g and to 37.9±3.37g (230%) when feeding on high
fat diet. When submitted to the predation risk treatment, males feeding on low fat diet
gained 198% of their starting body mass (to 34.3±2.26g), and on high fat diet under
predation risk increased their body mass to 36.1±2.50g (237%).
The significant factors explaining the differences in the energy intake were the day of
measurement (F32,266=19.190; p<0.001), sex (F1,266=9.9904; p=0.003), treatment
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(F3,266=9.654; p=0.003), the interaction between treatment and day of measurement
(F96,266=1.324; p=0.02), and the interaction between treatment, sex and day of
measurement (F131,266=1.345; p=0.008). The energy intake was higher in animals feeding
on high fat diet (Control: 194.0±9.3 kJ; Predation: 216.7±10.2 kJ) than animals feeding on
low fat diet (Control: 158.3±6.4 kJ; Predation: 167.7± 6.4 kJ), accordingly animals on the
predation risk treatment also showed elevated energy intake compared with controls
(Figure 2).
Figure. 2 – Energy intake (Mean ± S.E.) across the study. A – Females; B‐ Males.
Analysis of the resting metabolic rate did not reveal any significant differences due to
treatment effect (F3,75=1.297; p=0.282), and the differences in RMR were mostly explained
by the body mass (F1,75=1.122; p=0.016) (Figure.3). Body fat was included as covariate to
test different differences in plasma leptin levels, and this was the main factor explaining
the variation of the plasma leptin (F1,22= 42.619; p<0.001) (Figure. 4). The differences
between treatment groups were not statistically significant (F3,22=0.991; p=0.415).
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Figure 3 – Resting metabolic rate (ml.O2‐min‐1) against body mass of animals
on feeding on low and high fat diet and on predation risk treatment.
Figure 4 – Plasma leptin levels (ng/ml) against body fat of animals on low fat
diet and high fat diet.
Chapter 3 – Growth and Development
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Experiment 2
The body mass variation after the introduction of the high fat diet was explained by sex
(F1,90=69.665; p<0.001), day of measurement (F10,90=32.972; p<0.001), interaction of
treatment group and day of measurement (F30,90= 3.410; p<0.001), and the interaction of
day of measurement, sex and treatment (F43,90= 2.412; p<0.001). In females, the change
of treatment had no influence on body mass (F3,46=0.324: p=0.808) but in males the
variation of treatment across time significantly affected their body mass (F30,46=3.627;
p>0.001). Males increased their body mass in response to the high fat diet, but females
did not show a significant response (Figure 5).
Figure 5 ‐ Variation of body mass during 30 days, of females (A) and males (B)
introduced to high fat diet at weaning day and 70 days post‐weaning.
Differences in the energy intake were mostly due to the day of measurement
(F10,90=2.929; p=0.001), and the interaction of treatment and day of measurement
(F30,90=1.505;p=0.045). The energy intake was elevated on the first days after the
introduction of the high fat diet and individuals on the predation risk treatment had
Chapter 3 – Growth and Development
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elevated energy intake when introduced to high fat diet (p=0.05), on controls this
alteration was not observed (p=0.40 (Figure 6).
Figure 6 ‐ Variation of energetic intake, during 30 days, of females (A) and
males (B) introduced to high fat diet at weaning and 70 days post‐weaning.
Figure 7 ‐ Resting metabolic rate (ml.O2‐min‐1) against body mass of animals
feeding on high fat diet since weaning and animals feeding on high fat diet
since day 70.
Chapter 3 – Growth and Development
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Feeding on high fat diet since weaning did not modify the resting metabolic rate (Figure
7) when compared with animals started on this diet after 70 days (F3,56=1.887; p=0.142).
The variation in RMR was not significantly related to variations in body mass (F1,56=1.470;
p=0.230).
Circulating levels of leptin (Figure 8) were mostly associated with the amount of body fat
(F1,25=51.453; p<0.001). Variation due to the diet and predation treatment are not
statistically significant (F3,25=1.515; p=0.235).
Figure 8 – Plasma leptin levels (ng/ml) against body fat of animals on high fat
diet since weaning and started on the diet after 70 days.
3.5. Discussion
The wood mouse was used as a model, to study the combined effects of high fat diet
and predation risk on the determination of body weight, starting at weaning. Some
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previous studies have shown that over‐nutrition in early life increases obesity
susceptibility by reprogramming energy homeostasis (Rodrigues et al. 2009; Glavas et al.
2010). Moreover the allocation of energy in the early stages of life is more variable than
in adulthood and can be divided between growth and development of physical functions,
for instance to improve reproductive performance, or skeletal growth.
Sex biased growth
Our data showed a reduced influence of the predation risk over the variation of body
weight, compared with the effect of the high fat diet. It is usually accepted that being
small means being vulnerable (Arendt 1997), juveniles are commonly more vulnerable to
predation than adults (Meri et al. 2008) given that they lack the sensory skills and
apparatus to identify the threat and escape. In this study the shape of the growth curve
was different between males and females, independently of the diet or predation
treatment. Females reached their mature body size and virtually stop growing at day 30
(40 days after birth). In contrast in males, the growth rate was continuous during the study
span; such differences are also observed in rats (Slob & Van der Werff Ten Bosch 1975;
Cortright & Koves 2000).
These different growth patterns between males and females produce a clear sexual size
dimorphism, with males ultimately reaching higher body masses than females. Sexual
dimorphism has been reported in several rodent species (Lima, Bozinovic & Jaksic 1997;
Scharff et al. 1999; Jackson & Aarde 2003), and has been associated and explained by the
species mating system (Schulte‐Hostedde 2007). Our data suggests that in the wood
mouse the sexual dimorphism is established by males extending the time of post weaning
Chapter 3 – Growth and Development
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growth, as described for Mastomys species (Jackson & Aarde 2003) and rats (Slob & Van
der Werff Ten Bosch 1975). In some species, males are bigger from birth (Smith & Leigh
1998), which is not the case of the wood mouse, or have faster growth rates compared
with females (Clair 1998; Badyaev 2002). Our data appear consistent with the latter from
6 days post‐weaning onwards. Wood mice adults size is reached when their body mass
rises above 20g (Fernandez, Evansa & Dunstone 1996; Rosário & Mathias 2004). The
effects of treatment and diet on the body weight are more notorious when animals
reached adult size (20g), possibly because during early stages of development individuals
are characterized by feeding drive and insensitivity to environmental cues, contrary to
later stages when homeostasis circuits are fully developed (Crespi & Unkefer 2014).
Sex differences in energy homeostasis and fat storage have been reported for several
species (Cortright & Koves 2000; Valle et al. 2005; Shi & Clegg 2009). Importantly females
are generally considered more efficient in the use of resources than males, being able to
activate mechanism to save energy during periods of scarce resources availability. The
energy allocation of males is directed towards reproductive success. In the wood mouse,
males with higher body mass, were described to produce larger offsprings (Bartmann &
Gerlach 2001). This association is not reported for females suggesting that in the wood
mouse the same sex‐biased resource allocation may occur as described for the house
mouse (Perrigo & Bronson 1985). In the house mouse, males allocate energy for traits that
will benefit their social status and dominance, improving the chances of mating, while
females have other motivations for the use of their resources. Such differences in energy
allocation may explain why males apparently have limited body weight regulation: large
size improves their fitness, contrarily to the assumptions of the predation release
Chapter 3 – Growth and Development
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hypothesis (Speakman 2007). Analysis of the relationship between predation risk and
body weight may require taking into consideration factors as the reproductive system,
given that in polygynous systems being big may be advantageous for males. Consistent
with these arguments females on the predation treatment had reduced body weight
compared with controls.
Diet effects
Contrary to the report by Díaz and Alonso (2003), the wood mice in our study
responded positively to the high energetic content of the food by increasing their body
weight and fat reserves, when compared with the animals fed on low fat diet. The high fat
diet enhanced the response to the predation risk in females, but not males. Sex biased
energy allocation is expected in small mammals due to the higher investment of females
in reproductive traits, such as lactation, more importantly females are considered more
efficient in the use of resources than males.
Unexpectedly, the body mass variation was not associated with a reduction on the food
intake, moreover, compared with controls the energy intake was increased in almost all
the animals under predation risk treatment (except for females on the low fat diet).
Enhanced energy consumption of the animals feeding on enriched fat diets may be
explained by a deficit in some essential aminoacids relative to other nutrients or minerals
(Illius & Jessop 1996), though given the formulation of these diets, such deficit it is unlikely
to occur. Imbalanced diets may take animals to increase their food intake in order to
obtain the same amount of nutrients. These differences often cause animals increase lipid
Chapter 3 – Growth and Development
90
synthesis and gain weight due to the elevated energy input (Kyriazakis, Emmans &
Whittemore 2010). Proteins are commonly ignored when considering gains of body
weight, however Simpson & Raubenheimer (2005) suggested that the appetite for
proteins may drive to excessive energy intake, explaining overweight.
The energy balance equation states that energy input equals energy expenditure plus
storage. Accordingly, we looked into other energy expenditure components such as the
resting metabolic rate, as a candidate to explain the elevated energetic consumption at
the same time as a stable body mass. RMR represents about 37% of the total energy
expenditure of the wood mouse (Corp 1994), however we did not find variations in the
oxygen consumption.
In tadpoles of the Rana temporaria oxygen consumption increased during short term
exposure to predation risk but declined after long term exposure (Steiner & Buskirk 2009).
Data on rats (Huppertz‐kessler et al. 2011) shows that post natal stress induces changes
in brain tissue and a reduction in body weight. However, the referred study stresses
animals by a combining handling, cold and noise, as it is not possible to isolate the effect
of each stress factor, the accurate interpretation of the results may be compromised. The
short term exposure to predatory cues led to changes in heart and metabolic rates
(Hawkins, Armstrong & Magurran 2004; Steiner & Buskirk 2009). Our long term exposure
protocol showed that the predatory risk did not affect the resting metabolic rate of the
animals, however immediate effects after the exposure to the sound treatment cannot
be excluded. Moreover, given the inconclusive observations, other candidate factors
among both physiological and behavioural causes should be evaluated in future.
Chapter 3 – Growth and Development
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Early nutrition
The influence of high fat nutrition in early life stages is taken as a risk factor for the
development of obesity, leptin resistance and associated diseases (Bowman et al. 2004;
Kahn, Hull & Utzschneider 2006; Van Gaal, Mertens & De Block 2006). The introduction of
high fat diet to males fed on low fat diet since weaning increased the gain of body mass,
reaching a new level similar to males that were fed the high fat diet since weaning. For
females, the introduction of high fat diet did not trigger a gain in body mass, which
supports the previous statement that females reach their adult body size earlier than
males. It also suggests that the body weight regulation mechanism of females was more
able to respond to the potential increase in energy intake by the high fat diet.
Several studies have focussed on the influence of high fat diet consumption by mothers,
during pregnancy and lactation, on the energy homeostasis and obesity predisposition of
their offspring when adult (e.g. Srinivasan et al. 2006; Bayol, Farrington & Stickland 2007;
Tsuduki et al. 2013). However, the effects of post weaning nutrition are poorly studied.
We address this question by introducing high fat diets to pups at different stages of their
development: immediately after weaning or 70 days post‐weaning. Our results suggested
that at these stages the high fat diet does not influence the overall predisposition for
weight gain, which was contrary to the expectation. Overall, the exposure to high fat diet
resulted in considerably more weight gain compared with the chow fed animals, and
therefore in significant body weight differences due to the diet effect. Early exposure to
over‐nutrition is likely to induce leptin resistance (Glavas et al. 2010) and consequence
susceptibility to develop obesity in adulthood. The timing of the exposure to the fat diet
Chapter 3 – Growth and Development
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may explain such influence. Some studies have demonstrated that maternal exposure to
high fat diet may cause alterations in the cognitive and behavioural dysfunctions in the
offspring (Niculescu & Lupu 2009; Peleg‐Raibstein, Luca & Wolfrum 2012; Mendes‐da‐
Silva et al. 2014). In particular Niculescu (2009) reported effects on hypothalamus
development, which is an important brain structure associated with energy homeostasis
(Schwartz et al. 2000). During gestation, and after birth the neuronal networks
responsible for the body weight regulation are still incomplete, the Neuropeptide Y and
Proopiomelanocortin expression are highly susceptible to environmental conditions,
which may induce an incorrect programming of regulation complex (Plagemann et al.
1999; Plagemann 2006). Our study focussed on post weaning, at this stage the
hypothalamus is less vulnerable, and it is also during this period that leptin enrols a
function on energy expenditure (Mistry, Swick & Romsos 1999), given that earlier, leptin
is unable to inhibit food intake and consumed energy is mostly allocated to growth and
development (Ahima & Hileman 2000). Nevertheless, high fat diet is likely to supress
neurogenesis of “energy‐balance neurons” (Mcnay et al. 2012), leading to premature
aging of the homeostasis system compromising its further function. However, these
differences are apparently sex‐specific (Lee et al. 2014), explaining why females were able
to maintain their energy balance when exposed to high fat diet 70 days after weaning.
3.6. Conclusion
Our data supports the hypothesis that the predation risk is involved in the regulation
of body weight and setting of its upper limits. However, we were unable to identify the
mechanism responsible for increased energy expenditure and consequent reduction of
Chapter 3 – Growth and Development
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body weight. We also suggest that other factors are likely to influence the limits of body
weight, such as the species social system, which implicates being highly species‐
dependent.
Moreover, in the light of the predation risk hypothesis large body size constrains the
species survival, however being big may provide social and reproductive advantages that
overall benefits fitness. Therefore, the positive impact of the high body weight may go
beyond the survival during starvation periods but also with increase the individual fitness
by improving the social status and dominance.
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CHAPTER 4
Physical Activity and Behaviour
Monarca, R.I., Mathias, M.L., Wang. D.H. & Speakman, J.R. (2015) Predation risk
modulates diet induced obesity in male C57BL/6 mice. Obesity.(in press)
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4. Predation risk modulates diet induced obesity in male C57BL/6
mice
4.1. Abstract
Objective: In this study we aimed to examine the behavioural and physiological changes
induced by experimentally varying the risk of predation, in male mice fed on a high fat
diet. In particular, we aimed to assess if the risk of being predated modulates the body
weight gain, providing an ecological context for the obesity resistance observed in many
species of small mammals.
Methods: Body weight, food intake, physical activity and core body temperature of 35
male C57BL/6 mice were monitored for twenty days, whilst feeding on high fat diet. A
third of the animals were exposed to elevated risk of predation through exposure to the
sounds of nocturnal predatory birds and these were compared to animals exposed to a
neutral noise or silence.
Results: Male mice exposed to predation risk had significantly lower weight gain than the
neutral or silent groups. Reduced of food intake and increased physical activity were the
main proximal factors explaining this effect. The risk of predation also induced changes in
boldness.
Conclusions: Our study provides evidence supporting the role of predation risk on body
weight gain of small mammals.
Chapter 4 – Physical Activity and Behaviour
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4.2. Introduction
Obesity has been explained as an imbalance between energy intake and expenditure,
influenced by a complex interplay of genetic, social and environmental factors (Prentice
2001; Song, Lee & Sung 2014). Observations on many wild small mammal species have
suggested that contrary to humans, they have a strong regulation mechanism that allows
them to avoid the risk of becoming obese (El‐bakry, Plunkett & Bartness 1999; Peacock &
Speakman 2001). One explanation for this strong regulatory system is that body weight
fluctuates according to a dual intervention point mechanism (Levitsky 2002; Speakman
2007; Speakman et al. 2011). The dual point intervention model suggests that there are
upper and lower limit points of regulation (Levitsky 2002; Speakman 2007, 2014).
Between these points mass may vary freely but when the intervention point is reached
animals enable physiological mechanisms to avoid further weight gain or weight loss. This
allows them to maintain their body weight within a limited range. The risk of predation
has been suggested as a factor setting the upper limit point of intervention, given that an
animal carrying large fat reserves may have an elevated risk of being predated (Sundell &
Norrdahl 2002). In contrast, the risk of starvation and the impact on reproduction
(Tataranni et al. 1997), due to insufficient energy reserves, have been proposed as
regulators of the lower limit point of intervention. One hypothesis for the diversity in
human adiposity in modern societies is that the virtual absence of predation risk for 2
million years has led to genetic drift in the genes that control the upper intervention point
(Speakman 2007).
Inbred C57BL/6 mice are sensitive to diet induced obesity when fed a high fat diet and
have been a popular model for the study of obesity (Zhang et al. 2012b; Yang et al. 2014).
Chapter 4 – Physical Activity and Behaviour
103
In this study, we aimed to evaluate whether experimentally altering the risk of predation
modulates weight gain of C57BL/6 mice, induced by feeding on high fat diet. Second, we
investigated the behavioural and physiological adjustments in response to the exposure
to elevated risk of predation, that might underpin this effect, through monitoring multiple
parameters including the food intake, physical activity, body temperature and assessing
behavioural changes by observing their exploratory behaviour and boldness.
4.3. Methods
Animals and predation treatment
Thirty five male mice C57BL/6 were purchased from a commercial supplier (Vital River
Ltd, Beijing, China), aged 10‐12 weeks old and individually housed in standard mouse
cages (30 ×15 ×20 cm). Wood shavings and shredded paper were provided for bedding
and environmental enrichment. Light conditions in the housing room were set as 12L: 12D
(lights on at 7am). Animals had free access to food and water for the whole experimental
period. First, mice were maintained on a 10% kcal/fat diet (D12450B ‐ Research Diets,
New Brunswick, USA), for transmitter implantation and recovery period (details below),
lasting a total of two weeks. After this period mice were started on high fat diet, 45%
kcal/fat diet (D12451 ‐ Research Diets, New Brunswick, USA), and randomly assigned into
one of three groups: control, predation and neutral (respectively 13, 9 and 13 individuals
per group). Body weight and food intake were monitored on a daily basis, for all the
animals, between 6 and 7 p.m. Each group was monitored for 20 days; the differential
treatments started on the 8th day. Animals in the predation group were submitted to a
Chapter 4 – Physical Activity and Behaviour
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treatment simulating the presence of predators near the cages, and risk of being
predated. The treatment consisted of a 2 minute playback of owl calls (Tyto alba and Bubo
bubo) accessed from commercially available compilations of bird songs (European bird
calls; Jean C. Roché, Kosmos Verlag, Germany), broadcasted every 2 hours, for a total of 6
events per night, starting 1 hour after lights switched off. The neutral treatment was
composed of playback of the recorded sound of a human voice reading a scientific paper
(Neel, 1962 ‐ Diabetes Mellitus: A "Thrifty" Genotype Rendered Detrimental by
"Progress"?) in English, monotonically during 2 minutes, played every 2 hours, in a total
of 6 events per night. This neutral treatment exposed animals to a sound that did not
represent a threat of predation. All the sounds were played using a computer connected
to audio speakers. Speakers were placed 2 meters way from the cages, and sound volume
was regulated by human ear to guarantee that it was heard by the animals. Animals in the
control group were kept in a silent room for the entire period of the experiment.
Physical activity and core body temperature
Two weeks before the experiment a sub‐set of animals were implanted with telemetry
transmitters to monitor body temperature and physical activity (Model PDT‐4000 E‐
Mitter, Mini‐Mitter, Bend, OR, USA). The transmitter was inserted intraperitoneally.
Animals were anesthetized with a mixed flow of Isoflurane and Oxygen, allowing us to
make a small incision of approx. 1 cm in both the ventral skin and peritoneal wall. After
the insertion of the transmitter, the two layers were sutured independently. The total
surgical procedure took 15‐20 minutes, per mouse. After the surgical procedure, mice
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were given a recovery period of one week. Receiver pads (ER‐4000 Receiver, Mini‐Mitter,
Bend, USA) were installed below the mouse cages, receiving the information from the
transmitters, data were them collected by a windows based software (VitalView™: Mini‐
Mitter, Bend, USA), at 15 seconds intervals. Due to the limited number of receiver pads
available, the number of monitored mice for these parameters was 7 per group.
Corticosterone levels
Corticosterone levels have been previously implicated as a mechanism linking elevated
predation risk to food intake and weight regulation in rodents (Eilam et al. 1999).
Corticosterone was measured on the last day of the experiment, after 12 days exposed to
the predation risk treatment. Given that faecal concentration of corticosterone peaks 6 –
12 hours after a stressful event (Ylönen et al. 2006), each animal was placed in a clean
cage, between 10am and 3pm, and fresh faeces were collected and stored in 100%
ethanol at ‐30ºC, until being processed. Hormone extraction was performed following a
modified method of Goymann (1999). Briefly, ~0.05g of faeces (Sartorius) were added to
1ml of 80% methanol, and pulverized using a small pallet knife. The blend was then
vortexed for one hour at 500 rpm, followed by 20 minutes of centrifugation at 2500g. The
supernatant was then transferred to another tube and diluted with a buffer solution.
Corticosterone levels were measured using Enzyme Immunoassay (EIA) commercial kits
(Cayman Chemical Item no. 500655).
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Behaviour analysis
Dominant behaviours
Animals were randomly video recorded over the 12 days experimental period. Each
mouse was monitored for a total period of 10 hours, starting 2 hours before dark phase,
cameras were set at 5pm, and left for 10 hours. Dark phase recording were made using
infrared night vision mode from the video cameras (JVC, GZ‐MG20). Within each recording
the animals were observed for 5 random periods of 10 minutes, and their dominant
behaviours were listed.
Dominant behaviours were classified into four categories: grooming, feeding, resting and
general activity, according with the ethogram previously described for this species
(Speakman & Rossi 1999). General activity included walking, climbing the cage and all
general movements; Feeding included eating the pellets provided, occasional coprophagy
and drinking. Resting behaviour was considered when the animal was sleeping or sitting
not moving in the cage or grooming. Grooming was registered when the animals were
seen licking the fur or tail, scratching with any limb and not moving in the cage. Time spent
on each behaviour was registered with ETHOWATCHER® software using Real‐time
ethography mode (Crispim Junior et al. 2012).
Exploration and Boldness
Individual reaction towards a novel situation, e.g. new and unknown environment, is
commonly used as an index of animals general behaviour and to unravel fitness related
traits (Walsh & Cummins 1976; Réale et al. 2007). The open field test was used to assess
these behavioural components. Trials were conducted after 12 days on the treatment.
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The tests were performed after “dusk” during the normal circadian active period, between
8pm and 12pm. The dimensions of the open field arena were 50×50x40 cm, constructed
with plastic covered plywood. A video camera was positioned above the apparatus, to
record the behaviour of the animals using infrared sensitive night mode; the testing room
was illuminated by a red bulb. For the test, each animal was placed in the same corner of
the arena, using a plastic jar to transport each mouse for the home cage to the arena to
minimise handling. Each mouse was allowed to explore the apparatus freely for a 10
minutes trial period. During the tests the experimenter was not in the room. Between
trials, the apparatus was cleaned using 70% ethanol. Each animal was tested once.
The videos were analysed using ETHOWATCHER® in activity analysis mode (Crispim Junior
et al. 2012), and the following aspects were extracted: Total distance travelled; % of time
resting; % of time in the central area of arena (625 cm2, 12.5 cm away from the walls).
Statistics
All the data were expressed as means ± S.E. General Linear Modelling with repeated
measures was used to test the body weight variation and food intake across the days of
the study, including treatment as a fixed factor, and body‐weight as covariate when
testing food intake variation. The effect of treatment on concentrations of faecal
corticosterone were analysed using One‐way analysis of variance (ANOVA), followed by
post hoc Tukey test, setting treatment group as a fixed factor. Physical activity and core
body temperature data were averaged for each hour, and analysed during the baseline
and treatment periods, for variation over the 24h period and daily across the study. GLM
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modelling with repeated measures procedure was used, considering repeated measures
accordingly along 24 hours or days of study, setting treatment group as fixed factor,
followed by post hoc Tukey test.
Dominant behaviours were analysed through multivariate GLM model followed by Tukey
post hoc tests. Open‐field data were analysed through GLM model, including total
distance travelled, % of time resting and % of time in the central area of the arena as
variables and treatment group as factor. This procedure was followed by post‐hoc Tukey
tests to examine the differences between treatment groups. All data were analysed using
SPSS 22.0 for Windows. Statistical significance was set at p = 0.05.
4.4. Results
To analyse the effects of predation risk on the body weight, mice were first fed with
high fat diet for 8 days. During this baseline period, body weight increased in all three
groups (day effect: F7,224= 72.656; p<0.001) but did not differ significantly between the
groups (F2,32= 0.423; p<0.653). After starting the predation risk treatments the variation
in body weight was also mostly affected by the day of measurement (F12,384=100.403;
p<0.001), but there was also a significant effect of the treatment (F1,32= 5.194; p=0.011)
and the interaction between treatment and day of measurement (F24,384=3.800; p<0.001).
Over the 12 days of sound treatment, animals in both control group and neutral groups
increased their body weight by 10% (Tukey: p=0.902), consistent with the baseline rate of
increase. However, animals under the predation treatment increased their body weight
by only 4% (Tukey: predation vs control p=0.013; predation vs neutral p=0.032) (Figure 1).
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Figure 1 – Body weight variation (%) (Mean±S.E) in male C57BL/6 mice
throughout the baseline and predation treatment days.
The analysis of food intake (Figure 2) revealed that during the baseline period the
variation in food intake was mostly correlated with variations in the body weight
(F1,30=15.479; p<0.001). Day of measurement (F6,180=0.778; p<0.588) and treatment group
(F2,30=1.592; p=0.220) had no significant effects. Throughout the treatment period, the
food intake in the predation group was reduced (2.72±0.11g), when compared with
animals in the control group (3.36±0.08g) and neutral group (3.54±0.09g) (F2,30=16.034;
p<0.001). During this period body weight (F1,30=2.850; p=0.102), and day of measurement
(F12,360=0.709; p=0.743), were not significantly related to food intake.
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Figure 2 – Food intake (g) (Mean±S.E) variation in male C57BL/6 mice
throughout baseline and treatment periods.
Analysis of corticosterone levels showed a significant difference between the treatment
groups (F3,18=20.408; p<0.001). Post hoc tests revealed (p=0.005) that corticosterone
levels were elevated in both the neutral (544.1±51.1 ng/ml) and predation groups
(323.4±136.2 ng/ml) relative to the control animals (95.5± 55.9 ng/ml).
Physical activity and core body temperature
Animals exhibited similar patterns of circadian physical activity. Briefly, mice were
active during the dark phase and inactive during the light phase. Two peaks in physical
activity occurred during the light changes, at 7 pm “dusk” and 7 am “dawn”
(F23,391=39.193; p<0.001). Throughout the baseline period (Figure 3A) animals showed
inconsistent activity variation during the dark period which resulted in a small but
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statistically significant group effect, specifically between control group and neutral
treatment group (F2,17=6.185 p=0.01; Tukey p=0.007). During the treatment period (Figure
3B), the physical activity pattern slightly changed, resulting in significant effects due to
the treatment (F2,17=13.886 p<0.01), animals in the predation treatment group increased
their activity during the dark period compared with controls (Tukey p=0.001) and neutral
group (Tukey p=0.001).
Figure 3 ‐ Physical activity of male C57BL/6 over 24 hour periods (mean±S.E).
Light period: 7h to 19h; Dark period: 19h to 7h. A ‐ Physical activity during the
baseline period. B ‐ Physical activity during the predation risk treatment period.
Daily activity (Figure 4) was primarily affected by the day of measurement during the
treatment period (F12,204= 8.349; p<0.01) but not during the baseline (F6,102= 1.047; p<0.4).
During the treatment, daily activity was 42% higher in the group submitted to the
predation treatment (4.19±0.18 counts/h) compared to controls (2.94±0.19 counts/h) and
22% higher than the neutral group (3.46±0.2 counts/h). Between the baseline period and
treatment the animals in the predation group increased their activity by 23%, control and
neutral group reduced activity by 10% and 20% respectively.
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Figure 4 ‐ Daily activity (Mean ± S.E. for 24hours) of male C57BL/6 mice across
baseline and treatment periods.
Figure 5 ‐ Core body temperature of male C57BL/6 mice, over 24 hour period.
A – Core body temperature during the baseline period. B ‐ Core body
temperature during the predation risk treatment period.
Variation in core body temperature of all the animals followed a circadian pattern that
peaked between 6pm an 7pm, and between 5am and 6am, about one hour before dusk
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and dawn respectively (Figure 5). The core body temperature was generally about 1ºC
higher during the dark phase. The time of measurement was the main factor associated
with the variation of core body temperature during the baseline period (F23,391=72.685;
p<0.001) and the treatment period (F23,391=157.731; p<0.001). The treatment did not
affect the daily core body temperature variation (F2,17=0.085; p=0.919; Control:
37.0±0.11ºC; Neutral: 37.1±0.12 ºC; Predation: 37.1±0.11 ºC). During the predatory risk
treatment, the core body temperature was not affected by the treatment effect
(F2,17=0.060; p=0.942; control: 36.8±0.08 ºC; neutral: 36.8±0.08 ºC; predation: 36.6±0.08
ºC). Rather the variation was mostly correlated with the hour of measurement
(F23,391=157.731; p<0.001) (Figure 6).
Figure 6 ‐ Daily variation of core body temperature of male C57BL/6 mice
measured for 24 hour periods across the baseline period and 12 treatment
days.
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Behaviour observations
The time spent on each of the main behaviours categories (Figure 7) was not different
between treatment groups (F6,40=0.716; p=0.639). Animals spent 39.4± 3.1% of time on
general activity, 33.7± 4.0% resting, 10.3± 1.8% of time feeding and 16.6± 1.5% of time
grooming.
Figure 7 ‐ Effects of experimental treatment over the time spent on each
category of dominant behaviours (Mean± S.E. %time/10h) (General activity,
resting, feeding and grooming).
The analysis of boldness and exploration variables revealed that the treatment had a
significant association with the % time resting (F2,32=3.501, p<0.042) and the % of time in
the centre of the arena (F2,32=14.866, p<0.001), but not with the total distance travelled
(F2,32=1.627, p=0.212) (Figure 8). However, the apparent tendency was not validated by
the post‐hoc Tukey tests, when analysing the % of time resting (Tukey: Control vs
Predation p=0.076; Control vs Neutral p=0.085; Neutral vs Predation p=0.973). Animals in
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the group submitted to elevated predation risk spent significantly more time at the central
area of the arena (11.7±1.7%) than both control and neutral groups (Tukey: p<0.001 and
p=0.001; Control: 3.7±0.6%; Neutral: 5.3±0.9%).
Figure 8 – Boldness and exploration behaviour parameters (Mean ±S.E.) (Total
distance travelled, % of time immobile and % of time in the centre of the area).
4.5. Discussion
In this study we analysed the effect of the perceived risk of predation on energy
balance of male C57BL/6 mice, by inducing weight gain through feeding on high fat diet.
These animals are sensitive to diet induced obesity but the propensity to gain weight is
variable between individuals (Zhang et al. 2012b). Our data showed that the rate of body
weight gain was consistent between animals that were exposed to a neutral noise (human
speech) or to no noise at all. However, body weight gain was significantly reduced in
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animals submitted to the predation risk treatment, strongly supporting the original
hypothesis that predation risk is a modulating factor over weight control. Analysing the
parameters that influenced energy balance, the reduced rate of gain in body weight of
the predation treatment group was explained by a combination of lower food intake and
increased physical activity, particularly during the dark phase.
Since we only studied young males we do not know if this response would also be
observed in females, or in older individuals. In fact loss of hearing in older C57BL/6 mice
(Willott & Turner 1999) may render them unresponsive to sound cues, suggesting the
response may be specific to younger animals.
Previous studies have also found impacts of perceived predation risk on foraging
behaviour and food intake in several species (Brown 1988; Lima 1998; Gentle & Gosler
2001). Our study showed a clear reduction of food intake when mice were exposed to the
predator calls. Noise and other stress sources have been demonstrated to cause reduction
of body weight gained on rats (Alario et al. 1987) and mice (Finger, Dinan & Cryan 2011).
Because we used a neutral noise treatment, which did not cause a reduction in weight
gain relative to mice kept in silence, we can rule out the possibility that the retarded
weight gain was a generalised stress effect due to noise. Previous work has suggested that
a mediating mechanism by which predator risk may influence energy balance and hence
body weight is via an increase in stress hormones. Indeed we found that corticosterone
levels were elevated in the mice exposed to predator calls relative to the mice kept in
silence. However, the levels of corticosterone were also increased in mice exposed to the
neutral sounds, which did not have retarded weight gain. Hence it seems unlikely that
these increased levels of corticosterone mediated the weight reduction effect. Rather, it
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suggests that mice may be able to distinguish between sounds, and discriminate if they
represent a predation threat necessitating modulation of energy balance and body
weight.
These observations contradict the suggestion of a generalisation process made by
previous studies (Getschow et al. 2013), which suggest that animals may have a
generalized response towards auditory cues, not distinguishing if the source represents a
threat. Nevertheless, recognition of predation cues has been discussed to comprise both
innate and learnt components, being often dependent upon experience (Apfelbach et al.
2005).
The imbalance between energy intake and expenditure was reinforced by the variation in
activity patterns. The predation risk caused a general increase of the physical activity in
the animals, supported by the behaviour observations. This effect was unexpected
because freezing and reduced physical activity is a commonly used strategy by prey
species to avoid detection by predators, as reported for fish (Johansson & Andersson
2009), larval frogs (Anholt, Werner & Skelly 2000) and voles (Jedrzejewski, Rychlich &
Jedrzejewski 1993). In contrast, increased activity or ‘ fleeing’ is used mostly after being
spotted by the predator, to reduce the distance between predator and prey (Eilam 2005).
Moreover, this variation occurred during the naturally active period, meaning that the
sound disturbance did not disrupt the circadian cycle, but involved modification of the
level of activity within the active phase of the cycle. We should also take into
consideration the diet component given that fat enriched diets have been described to
cause alterations on the circadian clock (Kohsaka et al. 2007; Bravo et al. 2014) and induce
depressive and anxiety‐like behaviours (Mizunoya et al. 2013). The data obtained from
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the open field test also resulted in unexpected observations. According to Eilam (Eilam
2003), animals avoid the open area in the centre of the arena and seek the area close to
the walls, which provides a more secure environment, which reflects observations made
in natural patches (Abramsky et al. 2004). Our data suggests an increased boldness in the
animals under elevated risk of predation. This may appear counterintuitive as predation
avoidance strategy but boldness has been associated with the capacity to make quick
decisions (Mamuneas et al. 2015), therefore it may have adaptive role, being beneficial in
a high risk environment (Sih, Bell & Johnson 2004).
Another feature of energy balance that should not be neglected is the body temperature.
Lowering body temperature is among the mechanisms used by animals to save energy, as
exemplified in hibernation, torpor (Geiser 2004) and caloric restriction studies (Zhang et
al. 2012a). However in our study, we did not find significant variations in body
temperature associated with the experimental treatment and weight gain.
In summary, the current data supports the role of the predation risk in the regulation of
body weight, modulating obesity levels by reducing food intake and promoting energy
expenditure.
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CHAPTER 5
General Discussion
Chapter 5 – General Discussion
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5. General Discussion
The dysregulation of the balance between energy input and expenditure is the main cause
of obesity. A combination of environmental and genetic factors have been recognized to
influence such imbalance of energy homeostasis. However, the evolutionary factors
explaining the origin, prevalence and success of obesity phenotypes are still under
discussion.
It is generally accepted that body weight is regulated, as supported by several studies
(MacLean et al. 2006; Hambly et al. 2012). However the factors involved in setting body
weight limits are still unclear. According to the predation release hypothesis (Speakman
2007), small mammals are able to maintain their body weight within certain limits. It has
been suggested that the upper limit is set by the risk of predation, given that larger
individuals would require more energy to maintain their body, therefore spend more time
foraging and exposed to predators. Moreover, animals carrying larger fat depots may not
be able to hide in small burrows and may have their running and escaping capacities
compromised. On the other hand, the risk of starvation due to carrying reduced energy
stores has been suggested as the factor underpinning the lower limit of body weight.
Following the predictions of the predation release hypothesis, this thesis aims to test
whether the risk of being predated influences the body weight of small mammals (Wood
mouse Apodemus sylvaticus and Laboratory mice – C57BL/6), in addition to elucidate the
physiological and behaviour factors that are modified to support such alterations and
Chapter 5 – General Discussion
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balance the trade‐off between the risk of being predated and the risk of starvation, and
how these alterations are signalled. Accordingly, 3 key questions were considered:
1) Do animals under perceived risk of predation adjust their body weight?
Results shown that the perceived risk of predation is likely to cause reductions in body
weight in adult mice, reduction in growth rate of pups, and reduction of weight gain when
exposed to enriched fat diets, which strongly supports the proposed hypothesis. Both
animal models were equally affected by the risk of predation, showing 6% to 8% reduction
on the body weight when compared with controls. Moreover, the data on the wood
mouse also points to some sex‐specific responses. In humans, intentional weight loss is
highly sex‐influenced, females are often more concerned about their appearance than
males, and have different eating patterns (Rolls, Fedoroff & Guthrie 1991; Keski‐Rahkonen
et al. 2005). However, in rodents these differences may be associated with social
dominance and reproductive success, larger animals are usually higher in the hierarchy
rank (Huang, Wey & Blumstein 2011) and dominance has been correlated with
reproductive success (Horne & Ylönen 1996; Rolland et al. 2003), which may stimulate
individuals to drift from their upper intervention point.
The dual intervention point model suggests that predation risk is one of the main factors
setting the upper limit boundary for body weight, as the risk of starvation may support
the lower point of intervention (Speakman et al. 2011). The wood mouse response to the
stochastic starvation exposure were not in line with the predictions of the model. The dual
intervention point model suggests that the risk of starvation is responsible for setting the
Chapter 5 – General Discussion
127
lower limit of intervention, therefore an increased risk of starvation should push the fat
storage above the lower boundary, re‐setting the lower limit above the initial limit, to
avoid starvation, during further famine events.
The complexions of these interventions points are unclear, however it is expected that
their genetic encoding comprises multiple genes, with different mutation rates, which
may explain the variability of physiological and behavioural responses encountered to
predation risk treatment.
2) Which physiological and behaviour factors are changed to produce such
adjustments?
The alterations in body weight were mostly explained by adjustments in the energy
intake, modification of the energy expenditure and a combination of both, suggesting that
multiple strategies can be involved in the energy homeostasis.
Energy intake was reduced through reducing the amount of ingested food and not by
alteration of the digestive efficiency, to extract or absorb less energy from the consumed
food, which is unexpected given that the digestive features are highly plastic and often
vary their function according with environmental conditions (McWilliams & Karasov 2001;
Naya, Karasov & Bozinovic 2007). However, these results are consistent with the
compensation mechanisms identified to cope with caloric restriction (Hambly &
Speakman 2005; Mitchell et al. 2015).
Chapter 5 – General Discussion
128
Although animals’ metabolic rate have been linked with the wiliness to take risks (Careau
et al. 2008; Huntingford et al. 2010). A possible explanation for this relation is the fact that
animals with higher metabolic rates have elevated energetic requirements. To fulfil such
demands, animals may require more time foraging and may need to forage more often,
therefore increasing the time expose to predators and the overall risk taking. The energy
expenditure variation pointing to an increase in energy loss is not achieved by modifying
resting metabolic rate.
Physical activity and behaviour were revealed to be highly flexible traits. The adjustments
of physical activity and behaviour resulted into embracing activities less energetically
costly to save energy to cope with starvation, and optimize the regain of the lost weight.
On the other hand, predation risk induced animals to spend more time walking and
moving in the cage, than spending time resting, sleeping or ingesting food, resulting in an
overall increase of energy expenditure and reduction in body weight. These activities are
possibly related with the attempts to escape the risk.
The exposure to predation risk did not cause adjustments in the core body temperature.
Some bird species use facultative hypothermia as a strategy to save energy, and also rats
have altered their body temperature in response to caloric restriction (Severinsen &
Munch 1999). However, it has been suggested that hypothermia involves extra costs, as
being highly vulnerable to predators (Pravosudov & Lucas 2000). Therefore, under severe
risk of predation is likely that hypothermia is avoided, which is supported by the present
results.
Chapter 5 – General Discussion
129
Results provided evidence that the risk of predation has a role modulating the body
weight of small mammals. Thus, is likely to be involved in the environmental components
affecting the upper limits of body weight. The role of starvation is not so clear. Exposure
to stochastic starvation did not provide strong evidence for the role of starvation on the
definition of the lower limits of weight. Animals were able to compensate the body mass
lost during the starvation periods by overfeeding and reducing physical activity, during
the recovery period, but did not overcompensate the food intake to load the fat stores,
as insurance measure against further starvation events.
However, the hypothesis of overstock food stores due to starvation should not be fully
excluded, given that some species, such as the Syrian hamster (Mesocricetus auratus),
respond to starvation by increasing hoarding and supplying their external food storages
(Buckley & Schneider 2003). The wood mouse is a scatter hoarder (Jensen & Nielsen
1986), therefore external energy storage may take part in the energy balance of the
species. Nevertheless, hoarding may have only a partial role, because contrary to the
Syrian hamster whose overfeeding capacity is nearly absent (Day & Bartness 2003), the
wood mice has been demonstrated to be capable of over feeding in energetic demanding
periods. Hoarding is overall considered advantageous (Mcnamara, Houston & Krebs 1990;
Wauters, Suhonen & Dhondt 1995), particularly if the metabolic costs of carrying reserves
are high, or if the intake rate is reduced. However, external storages are extremely
vulnerable to pilferage (Vander Wall & Jenkins 2003) and perishing (Hadj‐Chikh, Steele &
Smallwood 1996), therefore carrying internal storages can be a safer strategy.
Chapter 5 – General Discussion
130
3) What signal the energy balance and imbalance to produce such
adjustments?
Transmission of information across the body is an essential factor to understand the
interaction between nutritional status of the animal and its environment, to respond the
need of acquiring energy through foraging or fat storages, and assess the risks of being
predated. The brain is the main controller that receives, transmits and integrates signals
from multiple sources, hormonal, neural, and sensory, modulating the foraging and
metabolism.
Corticosterone (the primary glucocorticoid in rodents) has been used to provide
information about the perceived risk of predation, and recently was related with obesity
increase and food intake (La Fleur et al. 2004; Sominsky & Spencer 2014). Accordingly, the
exposure to sounds simulating the presence of an avian predator, increased the levels of
faecal corticosterone. As response to the sound treatment animals generally increased
their levels of corticosterone, suggesting an elevated stress level due to the treatment.
However, corticosterone levels were also elevated in the animals under neutral sound
treatment, which may conceal a generalised response towards the sound might be
occurring. Moreover, even though the corticosterone levels were elevated, the effects
over body mass and food intake were different between the wood mouse and the
C57BL/6 mouse. The predation pressure has been eliminated from the C57BL/6 mice
many generations ago, therefore the successful response to predation cues no longer
compromises survival and no longer acts as a selective factor. On the contrary, on the
wood mice the response to the risk of being predated is still an important trait for their
survival.
Chapter 5 – General Discussion
131
The primarily role of leptin is to inform the brain about the nutritional status of the body
triggering the need to eat in case of energy deficiency or to stop eating if energy levels are
sated, working in a feedback loop to maintain constant energy stores. Results shown that
circulating levels of leptin are consistent with the levels of body fatness. However, animals
exposed to starvation revealed unexpected low levels of leptin explained by the absence
of post restriction hyperphagia (Schwartz et al. 2000; Hambly et al. 2012). The mechanism
underpinning the avoidance of hyperphagia after weight loss may be essential to help
humans coping with weight reduction diets, given that clinical trials have suggested that
the absence of leptin signalling after weight loss is responsible for the lack of satiation
(Kissileff et al. 2012).
Implications and future directions
To experimentally test the predation release hypothesis, the predator‐prey complex
dynamic was reduced to a simple one prey species‐one predator species interaction
system. However, multi‐predator environments have also received attention (Lima 1992;
Sih, Englund & Wooster 1998). Some of the multi‐predator models suggest that when
more than one predator is involved, the optimal strategy for survival, may actually be
investing in foraging rather than evade predation (Sih et al. 1998). Few animals in the wild
exist as part of single predator prey relationships and expanding the predation release
hypothesis in the light of a multi predator model may represent a future challenge.
The existence of a lower limit intervention point explains why generally losing weight is
not an easy process, and commonly the resistance to lose weight is higher than to gain
Chapter 5 – General Discussion
132
weight (Levin & Keesey 1998; Levin & Dunn‐Meynell 2002). Current data shows that mice
are able to lose weight either due to exposure to predation or to starvation, and to gain
weight due to diet alteration. These observations suggest that when having free access to
food, and a non‐enriched diet, their body weight is placed comfortably above the lower
limit intervention point.
Assuming that the upper and lower limits of body weight are genetically encoded by
multiple genes, the genetic basis of such regulation system is crucial for understanding
the molecules involved, as well as for the development of animal models for the study of
obesity.
Currently, several animal models are available for the study of obesity and obesity‐
associated human diseases (reviews by (Kanasaki & Koya 2011; Lutz & Woods 2012).
Rodents (mice and rats) are among the most popular models, however given the close
phylogenetic relationship to humans, Old World monkeys, such as macaques, rhesus
monkey, and baboons are also relevant models for the study of obesity (Comuzzie et al.
2003; Wagner et al. 2006).
The use of two animal models, the wood mouse Apodemus sylvaticus and the C57BL/6
strain of laboratory mice, provided a broader view when testing the initial hypothesis, and
helped identify that a very dynamic system can involve multiples strategies of response.
In humans, the framework supporting the predation release hypothesis, and the
expansion of the obese phenotypes is no longer viable at the light of the present
environmental and socio‐economic context. One of the assumptions of the predation
release hypothesis in that the increase of body weight is limited by an upper boundary,
Chapter 5 – General Discussion
133
such limit is in first hand dependent on the risk of predation. Taking this principle to the
edge, the predation risk will restrain the growth of an obesity epidemic. However, if the
population has been released from predation pressure, the body weight will theoretically
grow until the drifted upper point is attained. Even though, the results presented supports
the down regulation of body weight due to predation effect, the information provided
regarding the extent of a possible epidemic is very limited. The predation pressure has
been eliminated from human societies many years ago, however given that recent data
shows some apparent stability on the obesity prevalence (Ogden et al. 2014), the role of
predation may have been replaced by another environmental pressure likely to affect
modern humans.
The World Health Organization has recognised obesity as disease in 1948, and later, as an
epidemic (James 2008). Around the world, many efforts have been devoted to the
prevention of obesity, such as encouraging physical activity (Pratt et al. 2015), and healthy
diets (James, Thomas & Kerr 2007), however the success of these programmes is low, and
medical treatment is often pursued. Obesity can rarely be cured, and the current drugs
available either affect food intake and fat absorption, or affect thermogenesis increasing
energy expenditure (Bray & Tartaglia 2000). Thus one of the most important implications
of uncover the mechanisms underpinning energy homeostasis and body weight
modulation is the development of ways to successfully control it. In other words, once the
mechanisms involved in the body weight regulation are fully established, new
pharmaceuticals and therapies can be produced for the treatment and control of obesity.
Therefore, uncovering the genetic and molecular basis of the body weight regulation is a
fundamental step for the development new therapeutics, the genes related with obesity
Chapter 5 – General Discussion
134
control multiples aspects, such appetite, metabolism and energy homeostasis (Yang, Kelly
& He 2007). The way these phenotypes are expressed under different environmental
conditions, is an interesting approach to disclose the basis the regulation model.
Recent developments on the study of metabolomics (Rauschert et al. 2014) may also
provide good instruments to understand the genetic‐environment interaction of body
weight regulation, offering new analysis tools, which by the discovery of suitable
biomarkers can help identifying precursors of obesity phenotype, being an important
resource for the prevention of associated disorders.
Along with the molecular basis of the system, the way the molecular signals are received
and interpreted by the central nervous system also require further investigation. This will
provide the necessary framework, given that ultimately the brain is the main coordinator
of energy homeostasis, integrating peripheral signals and responding accordingly to fix
the energetic imbalance.
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