Since 1975, the prevalence of worldwide obesity has nearly tripled and has reached epidemic proportions (World Health Organization, 2020). An incomplete understanding surrounding the etiology of obesity has hindered the development of therapeutic interventions. Regardless, it is well known that both genetic and environmental factors contribute to its development (Goodarzi, 2018). Therefore, it is important to identify how specific genetic and environmental factors interact to contribute to the mechanisms underlying the etiology of obesity.

The endocannabinoid system (ECS), comprised of two receptors (CB1 and CB2), endogenous cannabinoids (N-arachidonoylethanolamide, [anandamide], AEA and 2-arachidonoylglycerol), and their synthetic and degradative enzymes, is recognized as a powerful regulator of energy homeostasis and body weight (Mazier et al., 2015). In general, activation of the ECS promotes energy storage, food intake, and increased weight gain. Therefore, components of the ECS are perfectly positioned to moderate individual susceptibility to obesity. In humans, there is a common genetic variant in the gene for fatty acid amide hydrolase (FAAH) (Sipe et al., 2002), the primary enzyme responsible for the inactivation of the endocannabinoid AEA. This missense mutation in FAAH (C385A; rs324420) destabilizes the FAAH protein, reduces AEA metabolism, and increases AEA signaling (Chiang et al., 2004; Sipe et al., 2002). Importantly, increased body mass index (BMI) has been associated with the low-expressing FAAH variant (A-allele carriers) (Durand et al., 2008; Monteleone et al., 2008; Sipe et al., 2005; Zhang et al., 2009). This agrees with the well-known role of AEA to stimulate feeding and enhance energy storage through the activation of CB1 receptor signaling (Mazier et al., 2015). However, the human data surrounding the C385A variant of FAAH and body weight regulation are oftentimes conflicting, with some studies showing no effect of FAAH C385A on metabolic outcomes (Jensen et al., 2007; Lieb et al., 2009). The conflicting findings may be attributed to differences in an individual’s environmental context and resulting endocrine milieu. In this regard, gene × environment interactions could govern whether the FAAH C385A genotype affects obesity susceptibility under particular environmental contexts.

Environmental conditions dictate an individual’s physiological endocrine state. For example, chronic stress and high fat diet feeding are associated with shifts in levels of circulating hormones that regulate appetite and energy homeostasis, including, but not limited to, glucocorticoids (GCs), ghrelin, and leptin (Bouassida et al., 2010; Schwarz et al., 2011; Ulrich-Lai and Ryan, 2014). Interestingly, the metabolic actions of orexigenic signals, such as GCs and ghrelin, increase endocannabinoid signaling (Balsevich et al., 2017; Edwards and Abizaid, 2016). By contrast, the anorectic peptide leptin suppresses endocannabinoid signaling to mediate its effects (Balsevich et al., 2018; Di Marzo et al., 2001). Therefore, differences in FAAH expression, due to distinct genetic FAAH variants, may influence the metabolic outcomes resulting from shifts in endocrine states. Specifically, we have previously shown that chronic exposure to GCs leads to obesity through an endocannabinoid-mediated mechanism (Bowles et al., 2015). It was likewise shown that GCs increase the activity of AMP-activated protein kinase (AMPK), through a CB1 receptor-dependent mechanism in the hypothalamus to promote weight gain (Scerif et al., 2013). Indeed hypothalamic AMPK plays an integral role in the regulation of feeding and body weight, where its activation leads to increased feeding (Andersson et al., 2004; Kim et al., 2004; Minokoshi et al., 2004). Similar to GCs, the orexigenic gastric hormone ghrelin increases the activity of hypothalamic AMPK and consequently feeding through an CB1 receptor-dependent mechanism (Kola et al., 2008). Taken together, we hypothesized that the discrepancies in the human literature for FAAH C385A and body weight outcomes are attributed to differences in their environmental context and ultimately their endocrine milieu. Therefore, we sought to characterize the behavioral, cellular, and molecular changes associated with the FAAH C385A variant in response to GC (corticosterone [CORT]) exposure using the FAAH C385A knock-in mouse model, recapitulating the common human mutation in the FAAH gene. We furthermore assessed a role for FAAH in ghrelin- and leptin-dependent feeding outcomes to determine the generalizability of these effects.

The ECS has an important role in the regulation of body weight. In terms of anandamide, activation of anandamide signaling is known to promote a positive energy balance and weight gain (Mazier et al., 2015). Moreover, endocannabinoid signaling lies downstream of several metabolic signals. For example, it is known that GCs and ghrelin, both orexigenic signals, mobilize endocannabinoid signaling, whereas anorexigenic leptin is known to suppress endocannabinoid signaling (Balsevich et al., 2018; Bowles et al., 2015; Di Marzo et al., 2001; Kola et al., 2008; Tucci et al., 2004). Therefore, genetic factors that modify endocannabinoid signaling may affect individual responses to orexigenic and anorexigenic signals. FAAH, the primary catabolic enzyme for anandamide, regulates anandamide signaling by controlling its levels. In humans, the common missense mutation (C385A) in FAAH affects FAAH protein stability, with the A/A genotype associated with approximately half the FAAH protein expression and enzymatic activity compared with the C/C genotype (Chiang et al., 2004). More specifically, T-lymphocytes from patients homozygous for the FAAH A/A variant express less than half of the FAAH protein and activity compared to WT lymphocytes. Here, we fully characterized the effects of the functional FAAH C385A mutation on GC-dependent metabolic outcomes in a novel C385A knock-in mouse model. Importantly, this FAAH C385A knock-in mouse model recapitulates the reduction in FAAH expression and activity observed in humans expressing the FAAH A/A variant (Dincheva et al., 2015). Using this model, we established the underlying molecular and cellular mechanisms that couple FAAH-AEA signaling to GC-induced feeding responses. We furthermore demonstrated that the FAAH C385A variant is not selective for GC-mediated metabolic responses, but rather governs an individual’s sensitivity to additional orexigenic signals, namely ghrelin.

As a first step, we monitored the metabolic phenotype of FAAH C/C and A/A mice under basal conditions. In this condition, FAAH C/C and A/A mice did not present differences in body weight or body composition. Interestingly, the underlying metabolic parameters contributing to body weight regulation differed between FAAH C/C and A/A mice at baseline. Compared to FAAH C/C mice, FAAH A/A mice showed lower basal food intake and matching lower energy expenditures. This phenotype could be driven by basal elevations in AEA causing reduced basal metabolism, which in turn results in lower food intake to balance the alterations in energy expenditure. Alternately, in addition to AEA, FAAH also regulates the metabolism of fatty acid amides such as oleoylethanolamide, which is known to be a potent anorectic signal (Gaetani et al., 2003). Regardless of the exact mechanism of this subtle basal phenotype, this balance between food intake and energy expenditure offsets any differences in body weight or body composition at baseline. However, it may also predispose subjects with the FAAH A/A genotype to weight gain from overconsumption. Indeed, a low basal metabolic rate is a risk factor for weight gain in both humans and rodents (Astrup et al., 1996; Astrup et al., 1999; Buscemi et al., 2005; Kunz et al., 2000; Lazzer et al., 2010; Sadowska et al., 2017). Lower basal energy expenditure relates to lower basal maintenance costs, and consequently an energy imbalance could easily arise from overconsumption in response to specific environmental contexts (abundant access to palatable food) or endocrine states (elevated levels of orexigenic signals). In fact, this is in line with our findings that FAAH A/A mice show an amplified feeding response to GC and ghrelin orexigenic signals.

In terms of GC-endocannabinoid signaling interactions, it is recognized that the duration of GC exposure affects the interaction between them (reviewed by Balsevich et al., 2017). For example, GCs mobilize endocannabinoids through both rapid, non-genomic mechanisms and delayed, genomic mechanisms. Therefore, we examined three treatment durations to assess whether the FAAH C385A polymorphism is relevant to the chronic (4 weeks), immediate (48 hr), and/or acute (single exposure) metabolic outcomes of GCs. For chronic and immediate GC treatments, we delivered a low dose of CORT (25 μg/ml) in the drinking water, which maintains the diurnal rhythmicity of circulating GCs (Karatsoreos et al., 2010). We found that regardless of treatment duration, GC exposure increased food intake exclusively in FAAH A/A mice. Furthermore, continuous GC exposure delivered for 48 hr, increased weight gain within the first 24 hr and 4 weeks of continuous GC exposure exaggerated GC-dependent weight gain. By contrast, there was no body weight phenotype 24 hr following acute GC injections, suggesting that continuous GC treatment is required to elicit weight gain. This agrees with the body of literature demonstrating that in the short term, GC actions are adaptive, redirecting energy stores and increasing food intake without affecting body weight maintenance (Dallman et al., 2004). However, prolonged GC exposure becomes maladaptive, as seen with chronic stress, which is associated with hypercortisolism, fat accumulation, and central obesity. In this context, our findings indicate that individuals with the FAAH A/A genotype are more susceptible to weight gain when confronted with elevations in GC signaling, and therefore may be particularly vulnerable to the influence of stress (Pecoraro et al., 2004; Ulrich-Lai, 2016).

The effects of the FAAH C385A variant on GC-mediated metabolic outcomes were specific to feeding responses. In particular, whereas the FAAH A/A genotype amplified the hyperphagic effects of GCs, there was no genotype effect on GC-dependent changes to energy expenditure or substrate utilization. Specifically, GC exposure decreased energy expenditure and increased carbohydrate utilization in both FAAH C/C and A/A mice. This agrees with the known role of GCs to suppress energy expenditure and promote carbohydrate utilization (Dulloo et al., 1990; Flatt, 1993; Poggioli et al., 2013). Therefore, our data suggest that while GCs mobilize AEA signaling to mediate feeding responses, it is unclear whether AEA signaling lies downstream of GC actions to regulate energy expenditure or substrate utilization. Regardless, the data herein delineate how the FAAH C385A variant affects energy balance by demonstrating that the FAAH C385A polymorphism modifies GC-induced weight gain through its effect on feeding rather than energy expenditure.

The exaggerated hyperphagic response to GC exposure was associated with increased hypothalamic anandamide levels exclusively in FAAH A/A mice. The hypothalamus is certainly recognized as one of the major brain regions responsible for endocannabinoid-mediated effects on feeding. Therefore, it is likely that elevated hypothalamic anandamide in the FAAH A/A genotype drives the heightened feeding response to orexigenic signals. Interestingly, one human study found that the C385A SNP did not associate with increased BMI per se, but rather higher levels of anandamide were positively associated with both the FAAH A/A genotype and a higher BMI (Martins et al., 2015). In agreement with this, we show that under basal conditions there is neither an effect of the A/A genotype on body weight nor anandamide content in brain regions central to endocannabinoid-mediated feeding. However, under the influence of specific endocrine drivers, such as GCs, anandamide levels increased exclusively in FAAH A/A mice within the hypothalamus, which likewise associated with a hyperphagic response selectively in the FAAH A/A mice. Taken together, GCs elevate hypothalamic anandamide levels selectively in the FAAH A/A genotype to sensitize the FAAH A/A genotype to hyperphagic feeding outcomes.

We next sought to characterize the cellular population within the hypothalamus driving the AEA-dependent exaggerated feeding response to GC exposure. Within the arcuate nucleus of the hypothalamus, POMC- and AgRP-expressing neurons are considered the prototypic regulators of homeostatic food intake (Gautron et al., 2015). Whereas POMC neuronal activity promotes satiety, AgRP neuronal activity promotes feeding. Interestingly, GCs increase AgRP neuronal firing rates to induce hyperphagia (Perry et al., 2019). Therefore, we examined whether FAAH knockdown exclusively in AgRP neurons is sufficient to recapitulate the exaggerated feeding response to GC exposure seen in FAAH A/A mice. For this purpose, we employed a recently described conditional single vector CRISPR/SaCas9 virus system (Hunker et al., 2020) for efficient cre-dependent mutagenesis of Faah (or control) into AgRP-Ires-cre mice. Considering there is low FAAH activity in the sparsely distributed hypothalamic AgRP neurons, it was unfeasible to functionally measure changes in AgRP FAAH activity, driven by the saCas9-FAAH construct, using an enzymatic FAAH activity assay. Therefore, to confirm cre-dependent reductions of FAAH activity on account of our SaCas9-FAAH construct, we assessed FAAH enzymatic activity in the hippocampus where FAAH expression is high (Egertová et al., 1998; Gulyas et al., 2004; Tsou et al., 1998). Following targeted intra-hippocampal delivery of the SaCas9-FAAH construct in CaMKIIα-cre mice to drive cre expression in hippocampal CA1 pyramidal cells (Tsien et al., 1996), we measured a dramatic, significant reduction in FAAH activity compared to the SaCas9-Control vector, validating the construct. Remarkably decreasing FAAH expression selectively in AgRP neurons paralleled the feeding phenotype of FAAH A/A mice in response to acute CORT injections. Specifically, under vehicle conditions AgRP^FAAH mice displayed reduced cumulative food intake despite no difference in basal body weights compared to AgRP^Control mice. However, following a single injection of CORT, AgRP^FAAH mice increased their light phase feeding, a period when feeding is typically low. By contrast, this hyperphagic response to acute CORT was absent in Agrp^Control mice.

Since there are no reliable FAAH antibodies to detect FAAH expression nor is there a fluorophore in the AgRP-Ires-cre mice, we could not quantify the percent of AgRP neurons targeted by our viral delivery system. Therefore, it is possible that only a subset of AgRP neurons were targeted and lacked FAAH. In fact, our HA-tagged SaCas9 protein expression profile, used to confirm cre-mediated DNA recombination, supports the notion that a discrete subset of AgRP neurons was targeted. Specifically, the HA-tagged SaCas9 protein expression spread into the dorsal aspects of the arcuate nuclei whereas AgRP neurons are largely concentrated in the ventral aspects of the arcuate nuclei. This suggests that indeed only a subset of AgRP neurons were targeted. It is possible that we targeted more dorsally positioned AgRP neurons based on our injection strategy wherein we positioned the tip of the cannula toward the dorsal aspects of the arcuate nuclei to avoid penetrating the base of the brain and cerebrospinal fluid. Nevertheless, this study demonstrates that lowering FAAH expression exclusively in AgRP neurons (targeted or widespread) is sufficient to recapitulate the hyperphagic feeding response of FAAH A/A mice to acute GC treatment. Of course, both GCs and AEA act broadly across several cell types and brain regions to regulate feeding. Our findings that reducing FAAH expression in AgRP neurons heightens GC-mediated feeding does not preclude the involvement of other brain regions. Whether other neuronal populations are likewise involved will require further work.

Given the effects of FAAH knockdown in AgRP neurons on GC-dependent feeding responses, we investigated whether AgRP neurons are likewise important cellular mediators of FAAH’s effects on leptin-dependent feeding outcomes. It is known that leptin suppresses hypothalamic endocannabinoid signaling to decrease feeding (Balsevich et al., 2018; Cardinal et al., 2012; Di Marzo et al., 2001; Jo et al., 2005). Furthermore, we have specifically shown that elevated AEA signaling in FAAH A/A mice prevents the anorectic effects of leptin following 16 hr food deprivation (Balsevich et al., 2018). Therefore, we hypothesized that FAAH deletion specifically in AgRP neurons would dampen the hypophagic responses to leptin, similar to what was seen in FAAH A/A mice. In agreement with this, AgRP^Control mice exhibited weight loss and reduced feeding in response to acute leptin exposure. However, AgRP^FAAH mice were unresponsive to acute leptin. Our results indicate that AgRP neurons are an important cellular population mediating the effects of FAAH-AEA signaling events of multiple upstream endocrine signals regulating feeding behavior.

We next explored whether the FAAH C385A variant modulates the metabolic outcomes of additional orexigenic signals. Specifically, we examined the metabolic phenotypes of FAAH C/C and A/A mice in response to ghrelin exposure based on the known interactions between ghrelin and endocannabinoid signaling pathways (Edwards and Abizaid, 2016). A single, subthreshold dose of ghrelin paralleled the outcomes arising from acute GC exposure, wherein ghrelin increased light phase food intake exclusively in FAAH A/A mice. Again, the effects were specific to feeding as ghrelin had no effect on energy expenditure across genotypes. Considering chronic stress elevates circulating GC and ghrelin levels (Abizaid, 2019), it is reasonable to suspect that individuals presenting the FAAH A/A genotype are more susceptible to stress-associated weight gain on account of amplified responses to these orexigenic signals. Follow-up studies should address the feeding response of FAAH C/C and A/A mice to acute and chronic stress exposures.

An important question raised by this study is how does the FAAH C385A variant coordinate the feeding responses to multiple orexigenic signals? For instance, is there a common pathway downstream of FAAH-AEA signaling driving the orexigenic feeding responses? Along these lines, it has previously been shown that GCs and ghrelin activate hypothalamic AMPK through a CB1R-dependent mechanism (Kola et al., 2008; Scerif et al., 2013). Indeed, hypothalamic AMPK activity elicits powerful feeding responses (Minokoshi et al., 2004). In agreement with this, we found that AMPK signaling is heightened in the hypothalamus following acute exposure to either GCs or ghrelin exclusively in FAAH A/A mice. Moreover, pretreatment with an AMPK inhibitor blocked the orexigenic response to acute GC treatment in FAAH A/A mice. The data herein provide unequivocal evidence that AMPK lies downstream of FAAH-AEA signaling to coordinate ghrelin and GC signaling.

The current study clearly demonstrates that the FAAH A/A genotype predisposes individuals to the feeding responses of orexigenic signals, CORT and ghrelin. Specifically, our working model is that orexigenic signals mobilize AEA in AgRP neurons of the hypothalamus. This effect is amplified in the A/A genotype (with low FAAH expression) because there is reduced enzymatic degradation of AEA. The exaggerated AEA levels lead to heightened CB1R activation and downstream AMPK activation to ultimately sensitize A/A individuals to orexigenic signaling outcomes. By contrast, our previous work determined that FAAH A/A mice are less responsive to the anorexigenic signal leptin (Balsevich et al., 2018). Furthermore, the current study established that FAAH knockdown exclusively in AgRP neurons is sufficient to blunt feeding and body weight responses to leptin. Thus, based on the current study and our previous findings, we demonstrate that the FAAH C385A variant shifts an individual’s sensitivity to metabolic feeding signals, where individuals with the FAAH A/A genotype are more responsive to orexigenic signals at the expense of being less responsive to anorexigenic signals (Figure 7). Given orexigenic signals favor weight gain and anorexigenic signals favor weight loss, individuals with the minor A/A genotype may show exaggerated weight gain in response to environmental conditions that increase orexigenic signals (such as GCs and ghrelin) and decrease anorexigenic signals (such as leptin). Chronic stress, for example, is known to increase levels of both GCs and ghrelin while also producing decreased levels of leptin (Bouillon-Minois et al., 2021; Lu et al., 2006). Collectively, our data indicate that human studies be conducted for the FAAH C385A polymorphism in a manner that considers external environmental contexts.

Figure 7

Download asset Open asset

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all figures and supplemental figures.

1. 1. Abizaid A

   (2019) Stress and obesity: the ghrelin connection

   Journal of Neuroendocrinology 31:1–8.

   https://doi.org/10.1111/jne.12693

   • PubMed
   • Google Scholar
2. 1. Andersson U
   2. Filipsson K
   3. Abbott CR
   4. Woods A
   5. Smith K
   6. Bloom SR
   7. Carling D
   8. Small CJ

   (2004) Amp-Activated protein kinase plays a role in the control of food intake

   The Journal of Biological Chemistry 279:12005–12008.

   https://doi.org/10.1074/jbc.C300557200

   • PubMed
   • Google Scholar
3. 1. Astrup A
   2. Buemann B
   3. Toubro S
   4. Ranneries C
   5. Raben A

   (1996) Low resting metabolic rate in subjects predisposed to obesity: a role for thyroid status

   The American Journal of Clinical Nutrition 63:879–883.

   https://doi.org/10.1093/ajcn/63.6.879

   • PubMed
   • Google Scholar
4. 1. Astrup A
   2. Gøtzsche PC
   3. van de Werken K
   4. Ranneries C
   5. Toubro S
   6. Raben A
   7. Buemann B

   (1999) Meta-Analysis of resting metabolic rate in formerly obese subjects

   The American Journal of Clinical Nutrition 69:1117–1122.

   https://doi.org/10.1093/ajcn/69.6.1117

   • PubMed
   • Google Scholar
5. 1. Balsevich G
   2. Petrie GN
   3. Hill MN

   (2017) Endocannabinoids: Effectors of glucocorticoid signaling

   Frontiers in Neuroendocrinology 47:86–108.

   https://doi.org/10.1016/j.yfrne.2017.07.005

   • PubMed
   • Google Scholar
6. 1. Balsevich G
   2. Sticht M
   3. Bowles NP
   4. Singh A
   5. Lee TTY
   6. Li Z
   7. Chelikani PK
   8. Lee FS
   9. Borgland SL
   10. Hillard CJ
   11. McEwen BS
   12. Hill MN

   (2018) Role for fatty acid amide hydrolase (faah) in the leptin-mediated effects on feeding and energy balance

   PNAS 115:7605–7610.

   https://doi.org/10.1073/pnas.1802251115

   • PubMed
   • Google Scholar
7. 1. Bouassida A
   2. Chamari K
   3. Zaouali M
   4. Feki Y
   5. Zbidi A
   6. Tabka Z

   (2010) Review on leptin and adiponectin responses and adaptations to acute and chronic exercise

   British Journal of Sports Medicine 44:620–630.

   https://doi.org/10.1136/bjsm.2008.046151

   • PubMed
   • Google Scholar
8. 1. Bouillon-Minois JB
   2. Trousselard M
   3. Thivel D
   4. Benson AC
   5. Schmidt J
   6. Moustafa F
   7. Bouvier D
   8. Dutheil F

   (2021) Leptin as a biomarker of stress: a systematic review and meta-analysis

   Nutrients 13:3350.

   https://doi.org/10.3390/nu13103350

   • PubMed
   • Google Scholar
9. 1. Bowles NP
   2. Karatsoreos IN
   3. Li X
   4. Vemuri VK
   5. Wood JA
   6. Li Z
   7. Tamashiro KLK
   8. Schwartz GJ
   9. Makriyannis AM
   10. Kunos G
   11. Hillard CJ
   12. McEwen BS
   13. Hill MN

   (2015) A peripheral endocannabinoid mechanism contributes to glucocorticoid-mediated metabolic syndrome

   PNAS 112:285–290.

   https://doi.org/10.1073/pnas.1421420112

   • PubMed
   • Google Scholar
10. 1. Buscemi S
    2. Verga S
    3. Caimi G
    4. Cerasola G

    (2005) Low relative resting metabolic rate and body weight gain in adult caucasian italians

    International Journal of Obesity 29:287–291.

    https://doi.org/10.1038/sj.ijo.0802888

    • PubMed
    • Google Scholar
11. 1. Cardinal P
    2. Bellocchio L
    3. Clark S
    4. Cannich A
    5. Klugmann M
    6. Lutz B
    7. Marsicano G
    8. Cota D

    (2012) Hypothalamic CB1 cannabinoid receptors regulate energy balance in mice

    Endocrinology 153:4136–4143.

    https://doi.org/10.1210/en.2012-1405

    • PubMed
    • Google Scholar
12. 1. Chiang KP
    2. Gerber AL
    3. Sipe JC
    4. Cravatt BF

    (2004) Reduced cellular expression and activity of the P129T mutant of human fatty acid amide hydrolase: evidence for a link between defects in the endocannabinoid system and problem drug use

    Human Molecular Genetics 13:2113–2119.

    https://doi.org/10.1093/hmg/ddh216

    • PubMed
    • Google Scholar
13. 1. Dallman MF
    2. la Fleur SE
    3. Pecoraro NC
    4. Gomez F
    5. Houshyar H
    6. Akana SF

    (2004) Minireview: glucocorticoids -- food intake, abdominal obesity, and wealthy nations in 2004

    Endocrinology 145:2633–2638.

    https://doi.org/10.1210/en.2004-0037

    • PubMed
    • Google Scholar
14. 1. Di Marzo V
    2. Goparaju SK
    3. Wang L
    4. Liu J
    5. Bátkai S
    6. Járai Z
    7. Fezza F
    8. Miura GI
    9. Palmiter RD
    10. Sugiura T
    11. Kunos G

    (2001) Leptin-regulated endocannabinoids are involved in maintaining food intake

    Nature 410:822–825.

    https://doi.org/10.1038/35071088

    • PubMed
    • Google Scholar
15. 1. Dincheva I
    2. Drysdale AT
    3. Hartley CA
    4. Johnson DC
    5. Jing D
    6. King EC
    7. Ra S
    8. Gray JM
    9. Yang R
    10. DeGruccio AM
    11. Huang C
    12. Cravatt BF
    13. Glatt CE
    14. Hill MN
    15. Casey BJ
    16. Lee FS

    (2015) FAAH genetic variation enhances fronto-amygdala function in mouse and human

    Nature Communications 6:6395.

    https://doi.org/10.1038/ncomms7395

    • PubMed
    • Google Scholar
16. 1. Dulloo AG
    2. Seydoux J
    3. Girardier L

    (1990) Role of corticosterone in adaptive changes in energy expenditure during refeeding after low calorie intake

    American Journal of Physiology-Endocrinology and Metabolism 259:E658–E664.

    https://doi.org/10.1152/ajpendo.1990.259.5.E658

    • Google Scholar
17. 1. Durand E
    2. Lecoeur C
    3. Delplanque J
    4. Benzinou M
    5. Degraeve F
    6. Boutin P
    7. Marre M
    8. Balkau B
    9. Charpentier G
    10. Froguel P
    11. Meyre D

    (2008) Evaluating the association of FAAH common gene variation with childhood, adult severe obesity and type 2 diabetes in the french population

    Obesity Facts 1:305–309.

    https://doi.org/10.1159/000178157

    • PubMed
    • Google Scholar
18. 1. Edwards A
    2. Abizaid A

    (2016) Driving the need to feed: Insight into the Collaborative interaction between ghrelin and endocannabinoid systems in modulating brain reward systems

    Neuroscience and Biobehavioral Reviews 66:33–53.

    https://doi.org/10.1016/j.neubiorev.2016.03.032

    • PubMed
    • Google Scholar
19. 1. Egertová M
    2. Giang DK
    3. Cravatt BF
    4. Elphick MR

    (1998) A new perspective on cannabinoid signalling: complementary localization of fatty acid amide hydrolase and the CB1 receptor in rat brain

    Proceedings. Biological Sciences 265:2081–2085.

    https://doi.org/10.1098/rspb.1998.0543

    • PubMed
    • Google Scholar
20. 1. Flatt JP

    (1993) Dietary fat, carbohydrate balance, and weight maintenance

    Annals of the New York Academy of Sciences 683:122–140.

    https://doi.org/10.1111/j.1749-6632.1993.tb35699.x

    • PubMed
    • Google Scholar
21. 1. Gaetani S
    2. Oveisi F
    3. Piomelli D

    (2003) Modulation of meal pattern in the rat by the anorexic lipid mediator oleoylethanolamide

    Neuropsychopharmacology 28:1311–1316.

    https://doi.org/10.1038/sj.npp.1300166

    • PubMed
    • Google Scholar
22. 1. Gautron L
    2. Elmquist JK
    3. Williams KW

    (2015) Neural control of energy balance: Translating circuits to therapies

    Cell 161:133–145.

    https://doi.org/10.1016/j.cell.2015.02.023

    • PubMed
    • Google Scholar
23. 1. Goodarzi MO

    (2018) Genetics of obesity: What genetic association studies have taught us about the biology of obesity and its complications

    The Lancet Diabetes & Endocrinology 6:223–236.

    https://doi.org/10.1016/S2213-8587(17)30200-0

    • Google Scholar
24. 1. Gray JM
    2. Vecchiarelli HA
    3. Morena M
    4. Lee TTY
    5. Hermanson DJ
    6. Kim AB
    7. McLaughlin RJ
    8. Hassan KI
    9. Kühne C
    10. Wotjak CT
    11. Deussing JM
    12. Patel S
    13. Hill MN

    (2015) Corticotropin-releasing hormone drives anandamide hydrolysis in the amygdala to promote anxiety

    The Journal of Neuroscience 35:3879–3892.

    https://doi.org/10.1523/JNEUROSCI.2737-14.2015

    • PubMed
    • Google Scholar
25. 1. Gulyas AI
    2. Cravatt BF
    3. Bracey MH
    4. Dinh TP
    5. Piomelli D
    6. Boscia F
    7. Freund TF

    (2004) Segregation of two endocannabinoid-hydrolyzing enzymes into pre- and postsynaptic compartments in the rat hippocampus, cerebellum and amygdala

    European Journal of Neuroscience 20:441–458.

    https://doi.org/10.1111/j.1460-9568.2004.03428.x

    • Google Scholar
26. 1. Hardwick AJ
    2. Linton EA
    3. Rothwell NJ

    (1989) Thermogenic effects of the antiglucocorticoid RU-486 in the rat: involvement of corticotropin-releasing factor and sympathetic activation of brown adipose tissue

    Endocrinology 124:1684–1688.

    https://doi.org/10.1210/endo-124-4-1684

    • PubMed
    • Google Scholar
27. 1. Hill MN
    2. McLaughlin RJ
    3. Morrish AC
    4. Viau V
    5. Floresco SB
    6. Hillard CJ
    7. Gorzalka BB

    (2009) Suppression of amygdalar endocannabinoid signaling by stress contributes to activation of the hypothalamic-pituitary-adrenal axis

    Neuropsychopharmacology 34:2733–2745.

    https://doi.org/10.1038/npp.2009.114

    • PubMed
    • Google Scholar
28. 1. Hunker AC
    2. Soden ME
    3. Krayushkina D
    4. Heymann G
    5. Awatramani R
    6. Zweifel LS

    (2020) Conditional single vector CRISPR/sacas9 viruses for efficient mutagenesis in the adult mouse nervous system

    Cell Reports 30:4303–4316.

    https://doi.org/10.1016/j.celrep.2020.02.092

    • PubMed
    • Google Scholar
29. 1. Jensen DP
    2. Andreasen CH
    3. Andersen MK
    4. Hansen L
    5. Eiberg H
    6. Borch-Johnsen K
    7. Jørgensen T
    8. Hansen T
    9. Pedersen O

    (2007) The functional pro129thr variant of the FAAH gene is not associated with various fat accumulation phenotypes in a population-based cohort of 5,801 whites

    Journal of Molecular Medicine 85:445–449.

    https://doi.org/10.1007/s00109-006-0139-0

    • PubMed
    • Google Scholar
30. 1. Jo YH
    2. Chen YJJ
    3. Chua SC
    4. Talmage DA
    5. Role LW

    (2005) Integration of endocannabinoid and leptin signaling in an appetite-related neural circuit

    Neuron 48:1055–1066.

    https://doi.org/10.1016/j.neuron.2005.10.021

    • PubMed
    • Google Scholar
31. 1. Karatsoreos IN
    2. Bhagat SM
    3. Bowles NP
    4. Weil ZM
    5. Pfaff DW
    6. McEwen BS

    (2010) Endocrine and physiological changes in response to chronic corticosterone: a potential model of the metabolic syndrome in mouse

    Endocrinology 151:2117–2127.

    https://doi.org/10.1210/en.2009-1436

    • PubMed
    • Google Scholar
32. 1. Kim EK
    2. Miller I
    3. Aja S
    4. Landree LE
    5. Pinn M
    6. McFadden J
    7. Kuhajda FP
    8. Moran TH
    9. Ronnett GV

    (2004) C75, a fatty acid synthase inhibitor, reduces food intake via hypothalamic AMP-activated protein kinase

    The Journal of Biological Chemistry 279:19970–19976.

    https://doi.org/10.1074/jbc.M402165200

    • PubMed
    • Google Scholar
33. 1. Kola B
    2. Farkas I
    3. Christ-Crain M
    4. Wittmann G
    5. Lolli F
    6. Amin F
    7. Harvey-White J
    8. Liposits Z
    9. Kunos G
    10. Grossman AB
    11. Fekete C
    12. Korbonits M

    (2008) The orexigenic effect of ghrelin is mediated through central activation of the endogenous cannabinoid system

    PLOS ONE 3:e1797.

    https://doi.org/10.1371/journal.pone.0001797

    • PubMed
    • Google Scholar
34. 1. Krashes MJ
    2. Koda S
    3. Ye C
    4. Rogan SC
    5. Adams AC
    6. Cusher DS
    7. Maratos-Flier E
    8. Roth BL
    9. Lowell BB

    (2011) Rapid, reversible activation of AgRP neurons drives feeding behavior in mice

    The Journal of Clinical Investigation 121:1424–1428.

    https://doi.org/10.1172/JCI46229

    • PubMed
    • Google Scholar
35. 1. Krashes MJ
    2. Shah BP
    3. Madara JC
    4. Olson DP
    5. Strochlic DE
    6. Garfield AS
    7. Vong L
    8. Pei H
    9. Watabe-Uchida M
    10. Uchida N
    11. Liberles SD
    12. Lowell BB

    (2014) An excitatory paraventricular nucleus to AGRP neuron circuit that drives hunger

    Nature 507:238–242.

    https://doi.org/10.1038/nature12956

    • PubMed
    • Google Scholar
36. 1. Kunz I
    2. Schorr U
    3. Klaus S
    4. Sharma AM

    (2000) Resting metabolic rate and substrate use in obesity hypertension

    Hypertension 36:26–32.

    https://doi.org/10.1161/01.hyp.36.1.26

    • PubMed
    • Google Scholar
37. 1. Lazzer S
    2. Bedogni G
    3. Lafortuna CL
    4. Marazzi N
    5. Busti C
    6. Galli R
    7. De Col A
    8. Agosti F
    9. Sartorio A

    (2010) Relationship between basal metabolic rate, gender, age, and body composition in 8,780 white obese subjects

    Obesity 18:71–78.

    https://doi.org/10.1038/oby.2009.162

    • PubMed
    • Google Scholar
38. 1. Lieb W
    2. Manning AK
    3. Florez JC
    4. Dupuis J
    5. Cupples LA
    6. McAteer JB
    7. Vasan RS
    8. Hoffmann U
    9. O’Donnell CJ
    10. Meigs JB
    11. Fox CS

    (2009) Variants in the CNR1 and the FAAH genes and adiposity traits in the community

    Obesity 17:755–760.

    https://doi.org/10.1038/oby.2008.608

    • PubMed
    • Google Scholar
39. 1. Lu XY
    2. Kim CS
    3. Frazer A
    4. Zhang W

    (2006) Leptin: a potential novel antidepressant

    PNAS 103:1593–1598.

    https://doi.org/10.1073/pnas.0508901103

    • Google Scholar
40. 1. Maccarrone M
    2. Bari M
    3. Agrò AF

    (1999) A sensitive and specific radiochromatographic assay of fatty acid amide hydrolase activity

    Analytical Biochemistry 267:314–318.

    https://doi.org/10.1006/abio.1998.2964

    • PubMed
    • Google Scholar
41. 1. Martins CdeM
    2. Genelhu V
    3. Pimentel MMG
    4. Celoria BMJ
    5. Mangia RF
    6. Aveta T
    7. Silvestri C
    8. Di Marzo V
    9. Francischetti EA
    10. Murillo-Rodriguez E

    (2015) Circulating endocannabinoids and the polymorphism 385C > A in fatty acid amide hydrolase (FAAH) gene may identify the obesity phenotype related to cardiometabolic risk: A study conducted in A brazilian population of complex interethnic admixture

    PLOS ONE 10:e0142728.

    https://doi.org/10.1371/journal.pone.0142728

    • Google Scholar
42. 1. Mazier W
    2. Saucisse N
    3. Gatta-Cherifi B
    4. Cota D

    (2015) The endocannabinoid system: pivotal orchestrator of obesity and metabolic disease

    Trends in Endocrinology and Metabolism 26:524–537.

    https://doi.org/10.1016/j.tem.2015.07.007

    • PubMed
    • Google Scholar
43. 1. Minokoshi Y
    2. Alquier T
    3. Furukawa N
    4. Kim Y-B
    5. Lee A
    6. Xue B
    7. Mu J
    8. Foufelle F
    9. Ferré P
    10. Birnbaum MJ
    11. Stuck BJ
    12. Kahn BB

    (2004) Amp-Kinase regulates food intake by responding to hormonal and nutrient signals in the hypothalamus

    Nature 428:569–574.

    https://doi.org/10.1038/nature02440

    • PubMed
    • Google Scholar
44. 1. Monteleone P
    2. Tortorella A
    3. Martiadis V
    4. Di Filippo C
    5. Canestrelli B
    6. Maj M

    (2008) The cdna 385C to A missense polymorphism of the endocannabinoid degrading enzyme fatty acid amide hydrolase (FAAH) is associated with overweight/obesity but not with binge eating disorder in overweight/obese women

    Psychoneuroendocrinology 33:546–550.

    https://doi.org/10.1016/j.psyneuen.2008.01.004

    • PubMed
    • Google Scholar
45. 1. Osei-Hyiaman D
    2. Liu J
    3. Zhou L
    4. Godlewski G
    5. Harvey-White J
    6. Jeong W
    7. Bátkai S
    8. Marsicano G
    9. Lutz B
    10. Buettner C
    11. Kunos G

    (2008) Hepatic CB1 receptor is required for development of diet-induced steatosis, dyslipidemia, and insulin and leptin resistance in mice

    The Journal of Clinical Investigation 118:3160–3169.

    https://doi.org/10.1172/JCI34827

    • PubMed
    • Google Scholar
46. 1. Pagotto U
    2. Cervino C
    3. Vicennati V
    4. Marsicano G
    5. Lutz B
    6. Pasquali R

    (2006) How many sites of action for endocannabinoids to control energy metabolism?

    International Journal of Obesity 30:S39–S43.

    https://doi.org/10.1038/sj.ijo.0803277

    • Google Scholar
47. 1. Patel S
    2. Carrier EJ
    3. Ho WSV
    4. Rademacher DJ
    5. Cunningham S
    6. Reddy DS
    7. Falck JR
    8. Cravatt BF
    9. Hillard CJ

    (2005) The postmortal accumulation of brain N-arachidonylethanolamine (anandamide) is dependent upon fatty acid amide hydrolase activity

    Journal of Lipid Research 46:342–349.

    https://doi.org/10.1194/jlr.M400377-JLR200

    • PubMed
    • Google Scholar
48. 1. Pecoraro N
    2. Reyes F
    3. Gomez F
    4. Bhargava A
    5. Dallman MF

    (2004) Chronic stress promotes palatable feeding, which reduces signs of stress: feedforward and feedback effects of chronic stress

    Endocrinology 145:3754–3762.

    https://doi.org/10.1210/en.2004-0305

    • Google Scholar
49. 1. Perry RJ
    2. Resch JM
    3. Douglass AM
    4. Madara JC
    5. Rabin-Court A
    6. Kucukdereli H
    7. Wu C
    8. Song JD
    9. Lowell BB
    10. Shulman GI

    (2019) Leptin’s hunger-suppressing effects are mediated by the hypothalamic-pituitary-adrenocortical axis in rodents

    PNAS 116:13670–13679.

    https://doi.org/10.1073/pnas.1901795116

    • PubMed
    • Google Scholar
50. 1. Pezeshki A
    2. Fahim A
    3. Chelikani PK

    (2015) Dietary whey and casein differentially affect energy balance, gut hormones, glucose metabolism, and taste preference in diet-induced obese rats

    The Journal of Nutrition 145:2236–2244.

    https://doi.org/10.3945/jn.115.213843

    • PubMed
    • Google Scholar
51. 1. Poggioli R
    2. Ueta CB
    3. Drigo RAE
    4. Castillo M
    5. Fonseca TL
    6. Bianco AC

    (2013) Dexamethasone reduces energy expenditure and increases susceptibility to diet-induced obesity in mice

    Obesity 21:E415–E420.

    https://doi.org/10.1002/oby.20338

    • PubMed
    • Google Scholar
52. 1. Ramage LE
    2. Akyol M
    3. Fletcher AM
    4. Forsythe J
    5. Nixon M
    6. Carter RN
    7. van Beek EJR
    8. Morton NM
    9. Walker BR
    10. Stimson RH

    (2016) Glucocorticoids acutely increase brown adipose tissue activity in humans, revealing species-specific differences in UCP-1 regulation

    Cell Metabolism 24:130–141.

    https://doi.org/10.1016/j.cmet.2016.06.011

    • PubMed
    • Google Scholar
53. 1. Ruiz de Azua I
    2. Mancini G
    3. Srivastava RK
    4. Rey AA
    5. Cardinal P
    6. Tedesco L
    7. Zingaretti CM
    8. Sassmann A
    9. Quarta C
    10. Schwitter C
    11. Conrad A
    12. Wettschureck N
    13. Vemuri VK
    14. Makriyannis A
    15. Hartwig J
    16. Mendez-Lago M
    17. Bindila L
    18. Monory K
    19. Giordano A
    20. Cinti S
    21. Marsicano G
    22. Offermanns S
    23. Nisoli E
    24. Pagotto U
    25. Cota D
    26. Lutz B

    (2017) Adipocyte cannabinoid receptor CB1 regulates energy homeostasis and alternatively activated macrophages

    Journal of Clinical Investigation 127:4148–4162.

    https://doi.org/10.1172/JCI83626

    • Google Scholar
54. 1. Ruiz de Azua I
    2. Lutz B

    (2019) Multiple endocannabinoid-mediated mechanisms in the regulation of energy homeostasis in brain and peripheral tissues

    Cellular and Molecular Life Sciences 76:1341–1363.

    https://doi.org/10.1007/s00018-018-2994-6

    • PubMed
    • Google Scholar
55. 1. Sadowska J
    2. Gębczyński AK
    3. Konarzewski M
    4. Chowen JA

    (2017) Metabolic risk factors in mice divergently selected for Bmr fed high fat and high CarB diets

    PLOS ONE 12:e0172892.

    https://doi.org/10.1371/journal.pone.0172892

    • Google Scholar
56. 1. Scerif M
    2. Füzesi T
    3. Thomas JD
    4. Kola B
    5. Grossman AB
    6. Fekete C
    7. Korbonits M

    (2013) CB1 receptor mediates the effects of glucocorticoids on AMPK activity in the hypothalamus

    The Journal of Endocrinology 219:79–88.

    https://doi.org/10.1530/JOE-13-0192

    • PubMed
    • Google Scholar
57. 1. Schwarz NA
    2. Rigby BR
    3. La Bounty P
    4. Shelmadine B
    5. Bowden RG

    (2011) A review of weight control strategies and their effects on the regulation of hormonal balance

    Journal of Nutrition and Metabolism 2011:237932.

    https://doi.org/10.1155/2011/237932

    • PubMed
    • Google Scholar
58. 1. Sipe JC
    2. Chiang K
    3. Gerber AL
    4. Beutler E
    5. Cravatt BF

    (2002) A missense mutation in human fatty acid amide hydrolase associated with problem drug use

    PNAS 99:8394–8399.

    https://doi.org/10.1073/pnas.082235799

    • PubMed
    • Google Scholar
59. 1. Sipe JC
    2. Waalen J
    3. Gerber A
    4. Beutler E

    (2005) Overweight and obesity associated with a missense polymorphism in fatty acid amide hydrolase (FAAH)

    International Journal of Obesity 29:755–759.

    https://doi.org/10.1038/sj.ijo.0802954

    • Google Scholar
60. 1. Strack AM
    2. Bradbury MJ
    3. Dallman MF

    (1995) Corticosterone decreases nonshivering thermogenesis and increases lipid storage in brown adipose tissue

    The American Journal of Physiology 268:R183–R191.

    https://doi.org/10.1152/ajpregu.1995.268.1.R183

    • PubMed
    • Google Scholar
61. 1. Tschöp MH
    2. Speakman JR
    3. Arch JRS
    4. Auwerx J
    5. Brüning JC
    6. Chan L
    7. Eckel RH
    8. Farese RV
    9. Galgani JE
    10. Hambly C
    11. Herman MA
    12. Horvath TL
    13. Kahn BB
    14. Kozma SC
    15. Maratos-Flier E
    16. Müller TD
    17. Münzberg H
    18. Pfluger PT
    19. Plum L
    20. Reitman ML
    21. Rahmouni K
    22. Shulman GI
    23. Thomas G
    24. Kahn CR
    25. Ravussin E

    (2011) A guide to analysis of mouse energy metabolism

    Nature Methods 9:57–63.

    https://doi.org/10.1038/nmeth.1806

    • PubMed
    • Google Scholar
62. 1. Tsien JZ
    2. Chen DF
    3. Gerber D
    4. Tom C
    5. Mercer EH
    6. Anderson DJ
    7. Mayford M
    8. Kandel ER
    9. Tonegawa S

    (1996) Subregion- and cell type-restricted gene knockout in mouse brain

    Cell 87:1317–1326.

    https://doi.org/10.1016/s0092-8674(00)81826-7

    • PubMed
    • Google Scholar
63. 1. Tsou K
    2. Nogueron MI
    3. Muthian S
    4. Sañudo-Peña MC
    5. Hillard CJ
    6. Deutsch DG
    7. Walker JM

    (1998) Fatty acid amide hydrolase is located preferentially in large neurons in the rat central nervous system as revealed by immunohistochemistry

    Neuroscience Letters 254:137–140.

    https://doi.org/10.1016/S0304-3940(98)00700-9

    • Google Scholar
64. 1. Tucci SA
    2. Rogers EK
    3. Korbonits M
    4. Kirkham TC

    (2004) The cannabinoid CB1 receptor antagonist SR141716 blocks the orexigenic effects of intrahypothalamic ghrelin

    British Journal of Pharmacology 143:520–523.

    https://doi.org/10.1038/sj.bjp.0705968

    • PubMed
    • Google Scholar
65. 1. Ulrich-Lai YM
    2. Ryan KK

    (2014) Neuroendocrine circuits governing energy balance and stress regulation: Functional overlap and therapeutic implications

    Cell Metabolism 19:910–925.

    https://doi.org/10.1016/j.cmet.2014.01.020

    • PubMed
    • Google Scholar
66. 1. Ulrich-Lai YM

    (2016) Self-Medication with sucrose

    Current Opinion in Behavioral Sciences 9:78–83.

    https://doi.org/10.1016/j.cobeha.2016.02.015

    • PubMed
    • Google Scholar
67. 1. van den Beukel JC
    2. Grefhorst A
    3. Quarta C
    4. Steenbergen J
    5. Mastroberardino PG
    6. Lombès M
    7. Delhanty PJ
    8. Mazza R
    9. Pagotto U
    10. van der Lely AJ
    11. Themmen APN

    (2014) Direct activating effects of adrenocorticotropic hormone (ACTH) on brown adipose tissue are attenuated by corticosterone

    FASEB Journal 28:4857–4867.

    https://doi.org/10.1096/fj.14-254839

    • PubMed
    • Google Scholar
68. 1. Westerterp KR

    (2017) Control of energy expenditure in humans

    European Journal of Clinical Nutrition 71:340–344.

    https://doi.org/10.1038/ejcn.2016.237

    • PubMed
    • Google Scholar
69. Website

    1. World Health Organization

    (2020) Obesity and Overweight

    Accessed April 26, 2023.

    https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
70. 1. Zhang Y
    2. Sonnenberg GE
    3. Baye TM
    4. Littrell J
    5. Gunnell J
    6. DeLaForest A
    7. MacKinney E
    8. Hillard CJ
    9. Kissebah AH
    10. Olivier M
    11. Wilke RA

    (2009) Obesity-related dyslipidemia associated with FAAH, independent of insulin response, in multigenerational families of northern european descent

    Pharmacogenomics 10:1929–1939.

    https://doi.org/10.2217/pgs.09.122

    • PubMed
    • Google Scholar

Author details

1. Georgia Balsevich

   Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   georgia.balsevich@ucalgary.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7839-018X

2. Gavin N Petrie

   Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

3. Daniel E Heinz

   Neurohomeostasis Research Group, Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7103-9621

4. Arashdeep Singh

   Monell Chemical Senses Center and Department of Neuroscience, University of Pennsylvania, Philadelphia, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2477-1438

5. Robert J Aukema

   Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3490-3390

6. Avery C Hunker

   Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

7. Haley A Vecchiarelli

   Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6331-6107

8. Hiulan Yau

   Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Martin Sticht

   Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Roger J Thompson

    Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7019-7246

11. Francis S Lee

    Weill Cornell Medical College, Cornell University, New York, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7108-9650

12. Larry S Zweifel

    Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States

    Contribution

    Resources, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3465-5331

13. Prasanth K Chelikani

    Texas Tech University, School of Veterinary Medicine, Amarillo, United States

    Contribution

    Resources, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Nils C Gassen

    Neurohomeostasis Research Group, Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. Matthew N Hill

    Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

    For correspondence

    mnhill@ucalgary.ca

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7144-9209

• 1,052

  views

• 132

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.