Cover
Title Page
List of Contributors
Series Preface
Preface
About the Companion Website
CHAPTER 1: Neuroanatomy of Feeding Pathways
1. Introduction
2. 1.1 Historical background and perspectives
3. 1.2 The arcuate nucleus, an important hub but not the last word in hypothalamic feeding-related pathways
4. 1.3 Other hypothalamic feeding centers – downstream or not?
5. 1.4 Reward-based feeding pathways – interactions between homeostatic and hedonistic neural pathways
6. 1.5 Nodal integration of homeostatic and hedonic feeding pathways
7. 1.6 The importance of distributed neural networks extending to the brainstem
8. 1.7 Perspectives
9. Glossary
10. Cited references
11. Further recommended reading
CHAPTER 2: Afferent Endocrine Control of Eating
1. Introduction
2. 2.1 Background
3. 2.2 Gastrointestinal hormones that affect eating
4. 2.3 Adiposity signals
5. 2.4 Interactions among hormones – from single meals to energy homeostasis
6. 2.5 Perspectives
7. Glossary
8. Cited references
9. Further recommended reading
CHAPTER 3: Ontogeny of Neuroendocrine Feeding Circuits
1. Introduction
2. 3.1 Major stages of hypothalamic development
3. 3.2 Developmental aspects of the hypothalamic response to metabolic hormones
4. 3.3 Hormonal control of hypothalamic development
5. 3.4 Development of appetite-related circuits in obesity-related conditions
6. 3.5 Perspectives
7. Glossary
8. Cited references
CHAPTER 4: Hypothalamic Peptides and Meal Patterns
1. Introduction
2. 4.1 Analysis of how animals feed
3. 4.2 The hypothalamus and feeding-related behavior
4. 4.3 Neuropeptides involved in feeding-related behavior
5. 4.4 Peripheral peptides with central sites of action that affect food intake
6. 4.5 Future perspectives
7. Glossary
8. Cited References
CHAPTER 5: Food Hedonics
1. Introduction
2. 5.1 Food hedonics
3. 5.2 Animal models of food hedonics
4. 5.3 Excessive feeding: binge eating and food addiction
5. 5.4 Hormonal regulation of food hedonics
6. 5.5 Conclusions
7. 5.6 Perspectives
8. Acknowledgements
9. Glossary
10. Cited references
CHAPTER 6: Functional and Anatomical Dissection of Feeding Circuits
1. Introduction
2. 6.1 AgRP neuron circuits that regulate appetite
3. 6.2 Second-order circuit nodes downstream of AgRP neurons
4. 6.3 Third-order nodes downstream of AgRP neurons that elevate food intake
5. 6.4 Circuits presynaptic to AgRP neurons
6. 6.5 Perspectives
7. Glossary
8. Cited references
CHAPTER 7: Exploring Appetite and Hypothalamic Circuitry through Manipulating Gene Expression
1. Introduction
2. 7.1 Genetics and obesity
3. 7.2 Central control of energy balance
4. 7.3 Transgenic technology basics
5. 7.4 Temporal and spatial control of transgenic genes
6. 7.5 CRISPR/Cas mediated genome engineering
7. 7.6 Perspectives
8. Glossary
9. Cited references
10. Further recommended reading
CHAPTER 8: Electrophysiology of the Appetite-Regulating Circuits of the Hypothalamus
1. Introduction
2. 8.1 Background
3. 8.2 Gut–brain signaling
4. 8.3 Central signaling
5. 8.4 Electrophysiological studies of appetite signaling
6. 8.5 Perspectives
7. Glossary
8. Cited references
9. Further recommended reading
CHAPTER 9: Functional Neuroimaging of Appetite and Gut–Brain Interactions
1. Introduction
2. 9.1 Appetite and the brain
3. 9.2 Functional neuroimaging techniques
4. 9.3 Gut–brain interactions: appetite hormones versus brain responses
5. 9.4 Perspectives
6. Glossary
7. Cited references
8. Further recommended reading
CHAPTER 10: Appetite Disorders
1. Introduction
2. 10.1 Etiology of eating disorders
3. 10.2 Eating disorders: subtypes and diagnosis
4. 10.3 Animal models of eating disorders
5. 10.4 Underlying mechanisms and targets for treatment
6. Glossary
7. Cited references
CHAPTER 11: Future Prospects of the Management of Appetite Disorders
1. Introduction
2. 11.1 Historical perspective on bariatric surgery
3. 11.2 Bariatric procedures
4. 11.3 Neurobiological effects and mechanisms
5. 11.4 Food preferences
6. 11.5 New knowledge emerging
7. 11.6 Conclusions
8. 11.7 Perspectives
9. Glossary
10. Cited references
11. Further recommended reading
CHAPTER 12: Discovery of New Drugs for Weight Loss and Prevention of Weight Regain
1. Introduction
2. 12.1 Background
3. 12.2 The drug discovery process
4. 12.3 Drug combinations in obesity
5. 12.4 Multiselective therapeutics (MT)
6. 12.5 The postobese state
7. 12.6 Perspectives
8. Glossary
9. Cited references
Index
End User License Agreement

List of Tables

Chapter 06
1. Table 6.1 Summary of cell type specific neuronal perturbations.
Chapter 10
1. Table 10.1 Main characteristics of the eating disorders: AN (anorexia nervosa restrictive type), BN (bulimia nervosa), and BED (binge eating disorders).
2. Table 10.2 Pros and cons of the activity-based anorexia (ABA) models regarding the symptomatology of the restrictive anorexia nervosa in human. Comparison with the modified ABA model developed by Méquinion et al. (2015b).
Chapter 11
1. Table 11.1 Summary of some of the major mechanisms underlying changes in hunger, food preference, and weight loss after the most commonly performed bariatric surgery procedures. Evidence was obtained from both human and animal studies: RYGB, Roux-en-Y gastric bypass; AGB, adjustable gastric banding; VSG, vertical sleeve gastrectomy; GLP-1, glucagon-like peptide-1; PYY, peptide tyrosine tyrosine; ↑, increase; ↓, decrease; ↔, no change; N/A, no available evidence.
Chapter 12
1. Table 12.1 Regulatory guidelines for efficacy of candidate antiobesity drugs.
2. Table 12.2 Methods to discover new chemical hits (active compounds). In many projects, more than one of the methods are used in parallel.
3. Table 12.3 Special considerations for in vivo evaluation of obesity drugs.
4. Table 12.4 Clinical antiobesity drug trials.
5. Table 12.5 Efficacy of obesity drugs (on the market or withdrawn).
6. Table 12.6 Examples of compounds discontinued in clinical phase.
7. Table 12.7 Technical considerations relevant to the design of studies of energy homeostasis in humans and rodents. The phenotypic and genetic heterogeneity of humans creates unique issues relevant to compliance, control of energy intake, regulation of exercise, and statistical analyses with any research study involving body weight perturbations.
8. Table 12.8 Differences in metabolic and behavioral responses to dynamic weight loss versus maintenance of reduced weight (Rosenbaum et al., 2010; Rosenbaum and Leibel, 2010; Rosenbaum and Leibel, 2014).
9. Table 12.9 Effects of 10% weight loss and leptin repletion on energy output, thyroid, ANS, and skeletal muscle. Changes are relative to pre-weight loss phenotypes, adjusted for body composition (Rosenbaum et al., 2010; Rosenbaum and Leibel, 2010; Rosenbaum and Leibel, 2014); SERCA, sarcoplasmic endoplasmic reticulum Ca²⁺-dependent ATPase.
10. Table 12.10 Phenotypes significantly correlated with weight loss and weight regain. R-values are unadjusted unless otherwise noted.

List of Illustrations

Chapter 01
1. Figure 1.1 Diagrammatic representation of lesions centered in the ventromedial and lateral hypothalamus causing overeating and overweight or starvation and emaciation, respectively. These lesions provided the basis for what became known as the ‘dual center hypothesis’ of body weight control.
2. Figure 1.2 Schematic diagram of the mediobasal hypothalamus shown in a coronal plane, depicting the basic elements and interactions of neurons in key nuclei involved in feeding-related pathways. Factors including leptin and insulin circulating in proportion to nutrient availability as well as ghrelin, which is elevated in the blood just prior to meals, access the hypothalamus preferentially via an incomplete blood–brain barrier in the arcuate nucleus.
3. Figure 1.3 Characterization of tracing techniques that have been used to define feeding related pathways. (a) The possible pathways delineated by a series of injections of monosynaptic tracers into regions that are suspected of projecting to each other. While it is theoretically possible for synaptically connected pathways (red neurons) to be elucidated using such approaches, it is much more likely that neurons (red striped) are labeled that project to the same areas but not the same neurons, which project to the original target site, and therefore cannot be relied upon to define multisynaptic connections to the original injected site. (b) This restriction of multisynaptic mapping does not apply to the use of retrograde transsynaptic tracers, such as the pseudorabies virus, which are transported specifically through chains of synaptically connected neurons. (c) The combination of a monosynaptic retrograde tracer with the detection of the expression of Fos protein as a marker of neuronal activity allows the identification of activated neurons with known efferent projections.
4. Figure 1.4 Schematic diagram showing a sagittal section through the rat brain highlighting multisynaptic projections to sites in the frontal cortex and the shell of the nucleus accumbens, as defined by injections and retrograde multisynaptic transport of the pseudorabies virus. The schema highlights common nodes in the projections from the arcuate nucleus to cortical (insular and anterior cingulate) and limbic (nucleus accumbens) endpoints in the lateral hypothalamus and paraventricular nucleus of the thalamus (surrounded by dotted lines). First, second, and third order neurons are colored in magenta, green, and blue, respectively.
Chapter 02
1. Figure 2.1 Schematic overview of some afferent endocrine controls of eating: AP, area postrema; CCK, cholecystokinin; FB, forebrain; GLP-1, glucagon-like peptide-1; Hypo, hypothalamus; NTS, nucleus tractus solitarii; PYY, peptide tyrosine tyrosine. See text for further details.
2. Figure 2.2 Schematic of ghrelin’s stimulatory effect on eating, including its proposed routes and mechanisms of action: AP, area postrema; CCK, cholecystokinin; FB, forebrain; GLP-1, glucagon-like peptide-1; Hypo, hypothalamus; NTS, nucleus tractus solitarii; PYY, peptide tyrosine tyrosine. See text for further details.
3. Figure 2.3 Schematic of cholecystokinin’s inhibitory effect on eating: AP, area postrema; CCK, cholecystokinin; FB, forebrain; Hypo, hypothalamus; NTS, nucleus tractus solitarii. See text for further details.
4. Figure 2.4 Schematic of glucagon-like peptide-1’s inhibitory effect on eating. AP, area postrema; FB, forebrain; GLP-1, glucagon-like peptide-1; Hypo, hypothalamus; NTS, nucleus tractus solitarii. See text for further details.
Chapter 03
1. Figure 3.1 Development of hypothalamic feeding pathways. Illustration of rodent hypothalamic development, showing periods of hypothalamic neuroepithelial cell proliferation (neurogenesis), cell differentiation, axonal growth, and synapse formation. Each of these developmental processes represents an important period of vulnerability, during which alterations in the pre- and post-natal environments may have long-term and potentially irreversible consequences on hypothalamic cell number and connectivity.
2. Figure 3.2 Ontogeny of the pro-opiomelanocortin system. Confocal images showing POMC cell bodies (upper panels) and POMC-derived axons (lower panels) in the arcuate nucleus (ARC) and paraventricular nucleus (PVH) of mice on embryonic day 12 and postnatal days 4, 10, and 60 (adult).
3. Figure 3.3 Neurodevelopmental action of the adipocyte-derived hormone leptin. In addition to its regulatory role in adults, leptin is an important signal for the development of hypothalamic circuits that control energy homeostasis. Neural projections from the arcuate nucleus (ARC) to the paraventricular nucleus of the hypothalamus (PVH) are disrupted in leptin-deficient (ob/ob) mice. In addition, the application of leptin to isolated explants of the ARC induces neurite extension, which suggests that leptin acts directly on ARC neurons to promote axon growth. A likely molecular mechanism underlying the developmental effects of leptin on hypothalamic circuits is the expression of the leptin receptors by ARC neurons.
4. Figure 3.4 Developmental programming of hypothalamic feeding pathways. The developmental programming of hypothalamic neural systems by the perinatal hormonal environment represents a possible mechanism by which alterations in maternal and/or postnatal nutrition predispose the offspring to obesity. The development axonal projections from the arcuate nucleus (ARC) to the paraventricular nucleus of the hypothalamus (PVH) appears to be highly sensitive to changes in the nutritional environment. Suboptimal and substandard nutrition during pre- and/or post-natal life are associated with structural defects in the hypothalamus. These effects appear to be mediated, to some extent, by abnormal leptin and insulin secretion and/or signaling during critical periods of fetal and/or postnatal development.
Chapter 04
1. Figure 4.1 Hypothetical depiction of how an animal eats (vertical lines) can be considered as meals (horizontal lines) and intermeal intervals (gaps), during which the rate of eating, satiation, and satiety signals strengthen and weaken.
2. Figure 4.2 Representation of meals taken by a rat across a 12 h–12 h light–dark cycle in which most meals (vertical lines) are taken during the dark period (thick horizontal line).
3. Figure 4.3 Hypothetical depiction of how an animal licks (vertical lines) can be categorized into bursts and clusters (horizontal lines) and interburst and intercluster intervals (gaps). The variability in periods of time between individual licks within a burst (interlick interval, ILI) is small. Counts of ILI fall below a normal distribution (inset).
Chapter 05
1. Figure 5.1 Operant chamber. Food pellet delivery into the magazine is contingent upon pressing the active lever (right lever in the figure). Under a progressive ratio (PR) reinforcement schedule, the number of lever presses required for the delivery of a food pellet progressively increases exponentially with each successive reinforcer earned.
2. Figure 5.2 Two-chamber conditioned place preference (CPP) apparatus. During training the rats are isolated in one chamber and are allowed to consume food. In the other chamber, either no food or a less palatable food is given. During CPP testing (depicted in the figure), no food is available and the rat can freely explore in either chamber.
Chapter 06
1. Figure 6.1 Optogenetic and chemogenetic manipulation of neuron activity. (a) rAAV construct design for Cre-dependent expression based on a flip-excision (FLEX) switch recombination (triangle colors, different orthogonal Cre recombinase recognition sequences; WPRE, woodchuck hepatitis virus posttranscriptional regulatory element; ITR, inverted terminal repeat). (b) Optogenetic tools for manipulating neuronal activity. Channelrhodopsin-2 (ChR2) is a blue-light gated cation channel that can depolarize neurons and trigger action potential with millisecond precision. Halorhodopsin (NpHR) and archaerhodopsin (Arch) are yellow-light gated chloride and proton pumps that induce hyperpolarizing currents and can be used for neuronal silencing with high temporal precision. (c) Chemogenetic GPCR (G protein-coupled receptor) tools for manipulating neuronal activity. Human muscarinic acetylcholine (ACh) receptors hM3 and hM4 are modified by mutations to reduce acetylcholine sensitivity and render responsiveness to the otherwise inert synthetic ligand clozapine-N-oxide (CNO). CNO activates hM3Dq and hM4Di, which are coupled to G_iq-protein or G_qi-protein intracellular signaling pathways. This results in neuronal inhibition (in part through increased inward rectifier K⁺ channel conductance) or neuronal activation, respectively. (d) Chemogenetic neuron activity manipulation based on alterations in ionic conductances using chimeric PSAM (pharmacologically selective actuator modules) based ligand gated ion channels: PSAM-5HT3 or PSAM-GlyR for activating or silencing neurons, respectively, when engaged by their cognate synthetic ligands (pharmacologically selective effector molecules, PSEMs).
2. Figure 6.2 Schematic diagram for ChR2-assisted circuit mapping. Long distance synaptic connectivity between molecularly defined neurons can be evaluated through photoactivation of ChR2-expressing presynaptic axons while simultaneously recording postsynaptic currents from candidate target neurons. This method is effective in brain slices even with presynaptic neurons from other areas cut away, because severed ChR2-expressing axons retain photoexcitability. ARC, arcuate nucleus; PVN, hypothalamic paraventricular nucleus.
3. Figure 6.3 Scheme for testing ARC^AgRP → ARC^POMC circuit contribution to acute food intake by simultaneous optical co-activation of AgRP and POMC neurons, which overcomes AgRP neuron-mediated inhibition in POMC neurons. Photostimulation of only AgRP neurons or co-activation of AgRP and POMC neurons elicits similar food consumption, indicating that this circuit interaction is not necessary for AgRP neuron-evoked feeding. The red outline represents ChR2 expression.
4. Figure 6.4 Summary diagram showing axonal wiring configuration from AgRP neurons, with red shaded areas showing regions where AgRP neuron axon photostimulation resulted in an evoked feeding response. Colored circles represent individual AgRP neuron subpopulations that project separately to a distinct area. ARC, arcuate nucleus; aBNST, anterior portion of the bed nucleus of stria terminalis; CEA, central nucleus of the amygdala; LatH, lateral hypothalamus; PAG, periaqueductal grey; PBN, parabrachial nucleus; PVN, hypothalamic paraventricular nucleus; PVT, paraventricular thalamus.
5. Figure 6.5 Examination of AgRP–neuron axon collateralization. Schematic of strategy (upper) and possible outcome configurations (lower) for cell type specific retrograde transduction of individual axon projection fields using EnvA-pseudotyped rabies. Cell type specific axonal transduction is achieved by transgenic TVA receptor expression, which distributes to axons and allows transduction by rabies virus that is microinjected to the axon projection field.
6. Figure 6.6 BNST^GABA → LatH^glutamate neural circuit that elicits feeding. ARC, hypothalamic arcuate nucleus; BNST, bed nucleus of the stria terminalis; LatH, lateral hypothalamus.
7. Figure 6.7 Scheme for testing the necessity of the ARC^AgRP → PVN circuit to AgRP neuron-evoked food intake. ARC^AgRP → PVN^SIM1 and ARC^AgRP → PVN^OXT inhibition is overcome by simultaneous optical co-activation of AgRP axons over the PVN along with photoexcitable SIM1 or oxytocin (OXT) neurons. Red circles represent ChR2 expression.
8. Figure 6.8 Anorexia circuits. (a) Summary diagram illustrating an anorexia pathway downstream of AgRP neurons. (b) Retrograde labeling of PBN → CEA neurons. Canine adenovirus expressing Cre recombinase (CAV2-Cre) is used to transduce axons and afferent neurons that innervate CEA. Stereotactic transduction of the PBN with a Cre-dependent rAAV that expresses hM4Di (AAV-DIO-hM4Di-mCherry) allowed selective chemogenetic inhibition of CEA-projecting PBN neurons with CNO. ARC, arcuate nucleus; CEA, central nucleus of the amygdala; PBN, parabrachial nucleus; AgRP, agouti related peptide; CGRP, calcitonin gene related peptide.
9. Figure 6.9 Summary diagram illustrating the appetite pathways of AgRP neurons and SIM1 neurons. ARC, arcuate nucleus; DR, dorsal raphe nucleus; PAG, periaqueductal grey; PVN: hypothalamic paraventricular nucleus.
10. Figure 6.10 (a) Schematic illustration of cell type specific rabies virus transsynaptic retrograde labeling of neuron populations that make synaptic connections to AgRP neurons. AgRP neurons are tranduced with rAAV vectors expressing TVA (required for uptake of EnvA pseudotyped rabies virus) and rabies glycoprotein G (RG, required for transsynaptic transport of viral particles). (b) Scheme for testing the behavioral role of the PVN^TRH → ARC^AgRP connection. Chemogenetic stimulation of PVN^TRH neurons alone evokes feeding, and this is blocked with simultaneous ARC^AgRP neuron inhibition. ARC, arcuate nucleus; PVN, hypothalamic paraventricular nucleus; TRH, thyrotropin releasing hormone.
Chapter 08
1. Figure 8.1 Hormones and peptides released from the gut, pancreas, and adipose tissue act on the brainstem and hypothalamus central appetite circuit. They signal through the general blood circulation to reach areas of the hypothalamus where the blood–brain barrier is loose and they also reach the brainstem via the stimulation of vagal nerve afferents. In vivo recordings of the electrical activity of hypothalamic neurons can be performed while mimicking the release of appetite-related signals by administrating them peripherally (intravenous injection) and centrally (injection into the third ventricle) in a urethane-anesthetized rat.
2. Figure 8.2 Examples of two very distinct patterns of activity recorded in VMN neurons in vivo. (a) (left) Sample recording from an oscillatory cell, note the rhythmic oscillations of the background field potential, occurring at ~3 Hz. (middle) Interspike-interval distribution of a single oscillatory cell, with modes at ~300, 600, 900, and 1200 ms. (right) Consensus hazard function for oscillatory cells (mean ± S.E. n = 28). (b) (left) Sample recording of a doublet cell showing clusters of two or three spikes. (middle) Interspike-interval distribution of a single ‘doublet’ cell, with a single, early mode at100 ms. (right) Consensus hazard function for ‘doublet’ cells (mean ± S.E., n = 46). After a short post-spike refractoriness, the cells become briefly hyper-excitable, leading to spikes clustered in doublets or triplets. Following this, the hazard is low and constant, consistent with the slow random firing. See Sabatier and Leng (2008) for full details.
3. Figure 8.3 (top) Example of the effect of IV injection of CCK on the mean firing rate of a VMN neuron recorded in vivo. (bottom) Example of the effect of ICV injection of oxytocin (1 ng) on the mean firing rate of a VMN neuron recorded in vivo. See Sabatier and Leng (2010) and Sabatier et al. (2013) for full details.
4. Figure 8.4 Juxtacellular labelling of a single VMN neuron in vivo. (a) Raw trace showing the activity of a neuron during a successful entrainment process. Anodal pulses in the range of 1–10 nA are used to eject neurobiotin from the micropipette. These current pulses cause a vigorous response from the cell called ‘entrainment.’ An entrained cell fires action potentials at a higher frequency than normally observed during the depolarizing phase of the current pulses. (b) Photograph of a single VMN neuron filled with intracellular dye neurobiotin detected by immunocytochemistry using fluorescent streptavidin. Insert shows the labeled neuron at higher magnification (see Pinault, 1996).
Chapter 09
1. Figure 9.1 Major neural systems and pathways involved in the control of ingestive behavior and energy balance regulation with emphasis on interactions between the classical homoeostatic energy regulatory system in the hypothalamus and brainstem (blue boxes and arrows in lower half) and cognitive/emotional brain systems (red boxes and arrows in upper half).
2. Figure 9.2 Overview of the spatial and temporal resolution of different neuroimaging modalities.
3. Figure 9.3 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545646/figure/F3/ Classical simple schematic of the blood oxygen level dependent (BOLD) response to a boxcar stimulus. The current view of the physiological basis of the BOLD response is more sophisticated: hemodynamic changes associated with stimulus presentation include changes in local cerebral blood volume (CBV), blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO₂). This is visualized here:
4. Figure 9.4 Schematic diagram of imaging and labeling regions for (pulsed) continuous ASL (PCASL) and pulsed ASL (PASL). In PCASL, labeling occurs as blood flow through a single labeling plane, while in PASL, a slab of tissue, including arterial blood, is labeled.
5. Figure 9.5 Effect of gastric infusion of water or chocolate milk on brain activity compared with baseline. Left panel: T-map of the increased response to chocolate milk or water infusion versus baseline overlaid onto the mean anatomical scan, thresholded at P = 0.05, family-wise error (FWE) corrected for multiple comparison. Right panel: Mean parameter estimates (a.u. ± SEM) within significant clusters for water or chocolate milk infusion. Figure doi:10.1371/journal.pone.0090872.g002 from Spetter et al. 2014 DOI: 10.1371/journal.pone.0090872, used under the Creative Commons license http://creativecommons.org/licenses/by/3.0/
6. Figure 9.6 Correlation between insulin changes (from baseline) and changes in brain activity in corresponding time bins after gastric infusion of chocolate milk (n = 14, 5 time bins per subject, T-map (left) thresholded at P = 0.001, uncorrected for multiple comparisons). The scatter plot (right) shows parameter estimates for the putamen peak voxel at MNI (34, 0, 6) plotted against insulin changes. Figure extracted from doi:10.1371/journal.pone.0090872.g004 from Spetter et al. 2014 DOI: 10.1371/journal.pone.0090872, used under the Creative Commons license http://creativecommons.org/licenses/by/3.0/
7. Figure 9.7 Ingestion of fat-free (<0.1%) or high-fat yoghurt results in specific changes in rCBF. The upper row shows that high-fat yoghurt leads to a decrease in rCBF in the hypothalamus, which is absent in the low-fat yoghurt. The lower row shows that rCBF in the insula is increased 120 min after ingestion of the fat-free yoghurt only.
Chapter 10
1. Figure 10.1 Modified mouse ABA model . Our recent data indicate that the modified ABA protocol induces a rapid and stable loss of weight, a change in circadian locomotor activity, alterations in energy metabolism, hypoglycaemia, hypoleptinemia, hyperghrelinemia, and central alterations in the hypothalamic feeding centers (Méquinion et al., 2015b). However, even if this mouse model does not present the ‘physiological’ self-starvation observed in the classic ABA, it permits study of the mechanisms underlying negative energy balance in the long term.
2. Figure 10.2 Mouse separation-based anorexia model.
3. Figure 10.3 Possible evolution of the different eating disorders throughout the life and the duration of the disease .
Chapter 11
1. Figure 11.1 (a) Roux-en-Y gastric bypass. The length of the alimentary limb and biliopancreatic limbs can vary, but the basic design of this procedure results in nutrients moving directly from the gut to the post-duodenum small intestine. The relative importance of the effects of duodenal exclusion and increased nutrient exposure to the ileum remains a matter of debate and the focus of much research. (b) Roux-en-Y gastric bypass. The volume of the gastric pouch in this procedure can vary.
2. Figure 11.2 Adjustable gastric band. The pressure applied through the band can be adjusted by injection or withdrawal of solution through the access port.
3. Figure 11.3 Sleeve gastrectomy. This procedure does not alter the direction of nutrient flow through the gut.
4. Figure 11.4 Biliopancreatic diversion. The combination of sleeve gastrectomy and intestinal re-arrangement results in significant changes to the metabolism, but can result in malabsorption.
Chapter 12
1. Figure 12.1 Goals of weight loss and reduced weight maintenance therapy. (a) Weight loss requires changing energy homeostatic systems away from the usual state of energy balance by increasing energy expenditure relative to energy intake. Weight maintenance requires taking an imbalanced system (decreased energy expenditure and increased desire to eat) and attempting to restore it to a usual state of energy balance. (b) The adipocyte-derived hormone leptin is an example of pharmacotherapy that ‘reverses’ changes induced by weight loss to help sustain reduced body weight. The fMRI scan shows brain areas that were more active in response to visual food cues at usual weight and following weight loss in ten subjects who were repleted with leptin. Neuronal signaling in the hypothalamus and middle frontal gyri were both lower as a result of weight loss but these changes were ‘reversed’ by the administration of leptin to weight-reduced subjects. The decreased activity in the amygdala following weight loss does not appear to be affected by leptin repletion.
2. Figure 12.2 The drug discovery and development process. Some of the terminology as well as components of each phase differ between pharmaceutical companies.
3. Figure 12.3 The target validation pyramid. The different levels of validation should be regarded as examples, and criteria as well as the order in which they are applied may vary between pharmaceutical companies.
4. Figure 12.4 The lead generation phase. ‘Hit finding’ refers to discovery of compounds with activity above a predefined level; hit evaluation is the phase where the hits are further characterized, and lead identification the phase when a limited set of analogs of verified hits are synthesized and evaluated in biological models.
5. Figure 12.5 Typical screening cascade in early lead finding. The number of tested compounds falls dramatically after the initial screen, which may include hundreds of thousands to millions of compounds.
6. Figure 12.6 Illustration of a phybrid (one of the two forms of MSBs) and an LSMC. The amylin-Fc-metreleptin molecule would mimic the effects of the metreleptin/amylin combination proven to provide significant weight loss in obese humans (Ravussin et al., 2009) with the advantage of a much longer duration of action. The LMSC is a schematic example with the small molecule moiety boxed.
7. Figure 12.7 Framework combination approach for DMLs. The three examples represent merged DMLs: MCH-1R, melanin concentrating hormone receptor 1; DPP-IV, dipeptidyl peptidase IV. Both linked and fused DMLs may suffer from too high a molecular weight, which may reduce transport across the gastrointestinal epithelium.

List of Contributors

Deniz Atasoy
Istanbul Medipol University
Istanbul, Turkey

Udo Bauer
AstraZeneca R&D Mölndal
Mölndal, Sweden

Sebastien G. Bouret
The Saban Research Institute
Developmental Neuroscience Program, Children’s Hospital Los Angeles
University of Southern California
Los Angeles, California, USA;
INSERM, Jean-Pierre Aubert Research Center
University of Lille
Lille, France

Sophie Croizier
The Saban Research Institute
Developmental Neuroscience Program, Children’s Hospital Los Angeles
University of Southern California
Los Angeles, California, USA;
INSERM, Jean-Pierre Aubert Research Center
University of Lille
Lille, France

Stephan Hjorth
Department of Molecular and Clinical Medicine
Institute of Medicine
The Sahlgrenska Academy at the University of Gothenburg
Gothenburg, Sweden

Jens Juul Holst
Department of Biomedical Sciences
Faculty of Health Sciences
University of Copenhagen
Copenhagen, Denmark

Tara Jois
Department of Physiology
Biomedicine Discovery Institute
Monash University
Clayton, Victoria, Australia

Scott E. Kanoski
Department of Biological Sciences
University of Southern California
Los Angeles, California, USA

Susanne La Fleur
Department of Endocrinology and Metabolism
Laboratory of Endocrinology
University of Amsterdam
Amsterdam, The Netherlands

Wolfgang Langhans
Physiology and Behaviour Laboratory
Institute of Food, Nutrition and Health
ETH Zurich
Schwerzenbach, Switzerland

Carel le Roux
Diabetes Complications Research Centre
UCD Conway Institute
School of Medicine and Medical Science
University College Dublin
Dublin, Ireland

Anders Lehmann
Division of Endocrinology
Department of Physiology
Institute of Neuroscience and Physiology
The Sahlgrenska Academy at the University of Gothenburg
Gothenburg, Sweden

Rudolph L. Leibel
Division of Molecular Genetics, Department of Pediatrics and Naomi Berrie Diabetes Center
Columbia University
College of Physicians and Surgeons
New York, NY, USA

Gareth Leng
Centre for Integrative Physiology
University of Edinburgh
Edinburgh, UK

John Menzies
Centre for Integrative Physiology
University of Edinburgh
Edinburgh, UK

Mathieu Méquinion
Univ. Lille, INSERM, CHU Lille,
UMR-S 1172 - JPArc - Centre de Recherche
Jean-Pierre AUBERT Neurosciences et Cancer
Lille, France;
Department of Physiology Faculty of Medicine, Monash University Melbourne, Victoria, Australia

Paul N. Mirabella
Department of Physiology
Monash University
Melbourne, Victoria, Australia

Timothy H. Moran
Department of Psychiatry and Behavioral Sciences
Johns Hopkins University School of Medicine
Baltimore, Maryland, USA;
Johns Hopkins Global Obesity Prevention Center
Johns Hopkins University School of Medicine Baltimore, Maryland, USA

Karl Neff
Diabetes Complications Research Centre
UCD Conway Institute
School of Medicine and Medical Science
University College Dublin
Dublin, Ireland

Brian J. Oldfield
Department of Physiology
Monash University
Melbourne, Victoria, Australia

Hubert Preissl
Institute for Diabetes Research and Metabolic
Diseases of the Helmholtz Center Munich at the University of Tübingen
Tübingen, Germany

Michael Rosenbaum
Division of Molecular Genetics, Department of Pediatrics and Naomi Berrie Diabetes Center
Columbia University
College of Physicians and Surgeons
New York, NY, USA

Nancy Sabatier
Centre for Integrative Physiology
University of Edinburgh
Edinburgh, UK

Karolina P. Skibicka
Department of Physiology/Metabolic Physiology
Institute of Neuroscience and Physiology
The Sahlgrenska Academy at the University of Gothenburg
Gothenburg, Sweden

Mark W. Sleeman
Departments of Physiology and Biochemistry and Molecular Biology
Biomedicine Discovery Institute
Monash University
Clayton, Victoria, Australia

Paul A.M. Smeets
Image Sciences Institute
University Medical Center Utrecht
Utrecht, The Netherlands and
Division of Human Nutrition
Wageningen University and Research Centre
Wageningen, The Netherlands

Aneta Stefanidis
Department of Physiology
Monash University
Melbourne, Victoria, Australia

Scott M. Sternson
Janelia Research Campus
Howard Hughes Medical Institute
Ashburn, Virginia, USA

Yada Treesukosol
Department of Psychiatry and Behavioral Sciences
Johns Hopkins University School of Medicine
Baltimore, Maryland, USA

Odile Viltart
Univ. Lille, INSERM, CHU Lille,
UMR-S 1172 - JPArc - Centre de Recherche
Jean-Pierre AUBERT Neurosciences et Cancer
Lille, France