Diet-induced obesity (DIO) drastically increases susceptibility to the development of metabolic, cardiovascular, and neurological diseases, as well as the recent coronavirus disease (Kahn et al., 2006; Ren et al., 2021; Clemmensen et al., 2020; Xia et al., 2021; Benito-León et al., 2013). Leptin, a hormone secreted by white adipocytes, interacts with receptors in the brain to communicate peripheral fuel status, suppress appetite following a meal, promote energy expenditure, and maintain blood glucose stability (Friedman, 2009; Friedman, 2014; Flak and Myers, 2016; Friedman, 2019; Knight et al., 2010; Berglund et al., 2012; Caron et al., 2018). One of the key pathological characteristics of DIO is impaired metabolic homeostasis (Considine et al., 1995; Maffei et al., 1995). There is much evidence that central leptin signaling plays a vital role in the regulation of body weight, primarily via action within the arcuate nucleus (ARC)(Friedman, 2014; Flak and Myers, 2016; Minokoshi et al., 2002; Huynh et al., 2010; Pereira et al., 2019; Xu et al., 2018; Varela and Horvath, 2012; Vong et al., 2011). The long-form leptin receptors are abundantly expressed in the agouti-related protein (AgRP) neurons of the ARC (Xu et al., 2018; Baskin et al., 1999; Xu and Xie, 2016; Scott et al., 2009; Scott et al., 2011). Previous studies demonstrate that, under the physiological condition, leptin directly inhibits leptin receptor (LepR)-expressing AgRP neurons (Cowley et al., 2001; van den Top et al., 2004). However, clinical studies suggest that treatment of leptin in common obese patients has limited efficacy (Heymsfield et al., 1999; Farr et al., 2015; Hinney et al., 2022). The neural-circuit and transmitter-signaling mechanisms underlying leptin-mediated control of body weight and energy balance are not established.

AgRP neurons play fundamental roles in regulating feeding behavior and body weight by releasing inhibitory neuropeptide Y (NPY), AgRP and GABA transmitters into many downstream brain areas (Kishi and Elmquist, 2005; Han et al., 2021b; Xia et al., 2021). The afferents to these AgRP neurons and their postsynaptic targets have been identified as key players in the regulation of energy balance and systemic insulin sensitivity (Myers et al., 2009; Gropp et al., 2005; Luquet et al., 2005; Aponte et al., 2011; Könner et al., 2007; Steculorum et al., 2016). Leptin acts to decrease food intake and promote energy expenditure by suppressing the activity of AgRP neurons (Cowley et al., 2001; van den Top et al., 2004; Halaas et al., 1997; Friedman and Halaas, 1998). Selective ablation of LepR in AgRP neurons gives rise to an obese phenotype and diabetes (Xu et al., 2018; van de Wall et al., 2008). Although the LepR signaling within AgRP neurons has been implicated as the dominant component for the regulation of metabolic homeostasis, the underlying neural circuit mechanism is still poorly understood. We hypothesize that identification of the critical transmitter signaling components underlying the leptin-responsive neural circuit is crucial for the development of more efficient therapeutics for obesity.

Emerging data suggest a critical role of GABA signaling on the control of feeding behavior and energy homeostasis (Vong et al., 2011; Cone, 2005; Meister, 2007; Morton et al., 2006; Saper et al., 2002; Wu and Palmiter, 2011; Liu et al., 2012; Wu et al., 2013; Wu et al., 2009; Wu et al., 2012; Rossi and Stuber, 2018; Richard, 2015; Meng et al., 2016; Garfield et al., 2016; Pool et al., 2014; Cai et al., 2014; Krashes et al., 2013). Numerous studies suggest that central GABA_A- and GABA_B-receptor signaling exert prominent influences on feeding behavior under various metabolic states (Berridge, 2009; Cooper, 2005; Duke et al., 2006; Martire et al., 2010; Peciña and Berridge, 1996; Strader et al., 1997; Berner et al., 2009). For example, pharmacological activation of GABA_A-receptor signaling in the hindbrain parabrachial nucleus (PBN) enhances the positive hedonic perception of tastes and foods, thereby promoting food intake and motivational response to food reward (Berridge, 2009; Cooper, 2005; Peciña and Berridge, 1996; Berridge and Peciña, 1995; Higgs and Cooper, 1996; Söderpalm and Berridge, 2000; Berridge and Treit, 1986). Utilizing advanced genetic techniques, we have shown that GABAergic signaling from AgRP neurons plays a bidirectional, post-developmental role in the regulation of food intake and body weight (Meng et al., 2016). Our recent work shows that the α5-containing GABA_A receptor signaling within the bed nucleus of the stria terminalis neurons reciprocally regulates mental disorders and obesity, implying that GABA_A-receptor signaling exerts a role in controlling obesity and comorbid diseases (Xia et al., 2021).

In this report, we examined the neural-circuit mechanism underlying leptin action upon the AgRP neurons using a newly established and robust method of rapid inactivation of LepR signaling within AgRP neurons (Meng et al., 2016). We found that the LepR in AgRP neurons plays a pivotal role in the control of obesity and glucose homeostasis. Moreover, a subset of leptin-responsive AgRP neurons send GABAergic projections to a group of α3-containing GABA_A-expressing neurons in the DMH for regulation of leptin-mediated metabolic homeostasis. These findings suggest that the identification of key GABA_A signaling pathways within the post-synaptic target of a novel leptin-responsive GABAergic neural circuit forebode more efficient treatments for obesity.

To test the role of LepR in AgRP neurons, we applied our newly developed conditional knockout approach by generating two different lines of mice: Agrp^nsCre/+::Lepr^lox/lox::Neo^R::Rosa26^fs-tdTomato mice, termed the knockout group (Agrp-Lepr KO), and Agrp^+/+::Lepr^lox/lox::Neo^R::Rosa26^fs-tdTomato mice, termed the control group (Meng et al., 2016). Immunohistochemistry and qPCR results showed the expression of tdTomato and deletion of Lepr in AgRP neurons 4 days after treatment of NB124, a synthetic nonsense-suppressor that can rapidly restore the function of Cre recombinase within the AgRP neurons (Figure 1A–F and Figure 1—figure supplement 1). Agrp-Lepr KO mice showed a significant increase in feeding and body weight one week after deletion of LepR signaling from AgRP neurons (Figure 1G and H). As a recapitulation of the severe hyperphagia caused by CRISPR-mediated LepR deletion (Xu et al., 2018), our Agrp-Lepr KO mice exhibited 44.5% increase in feeding as compared to the control mice. Furthermore, along with the increase of body weight and food intake, Agrp-Lepr KO mice exhibited impaired glucose tolerance (Figure 1I). Leptin induces phosphorylation of signal transducer and activator of transcription 3 (pSTAT3). pSTAT3 staining in the ARC showed enhanced extent in control mice but not in Agrp-Lepr KO mice 1  hr after leptin treatment in overnight fasted mice (Figure 1—figure supplement 2), indicating the role of leptin in the ARC was ablated. No significant changes of pSTAT3 level were observed in the DMH and VMH of control and Agrp-Lepr KO mice (Figure 1—figure supplement 3). Intriguingly, chronic infusion of bicuculline (Bic, 1 ng, a GABA_A receptor antagonist) into the 3rd ventricle abolished the hyperphagia responses in Agrp-Lepr KO mice (Figure 1J), suggesting that facilitating the post-synaptic GABA_A signaling to the AgRP neural circuit is important in regulating leptin-mediated feeding. The feeding efficiency (body weight gain [mg]/kilocalorie consumed) was significantly higher in Agrp-Lepr KO mice but was rescued by infusion of Bic (Figure 1K). The expansion of white adipose tissue (WAT) was observed in Agrp-Lepr KO mice (Figure 1L). We observed a significant decrease in respiratory quotient (RQ) from the Agrp-Lepr KO mice that can be further normalized by infusion of GABA_A antagonist (Figure 1M). In line with other studies, these results suggest that GABA is a crucial signaling molecule by which AgRP neurons control adiposity and nutrient utilization (Wu et al., 2009; Wu et al., 2008a; Tong et al., 2008). The control and Agrp-Lepr KO mice were also analyzed for their glucose tolerance. Ablation of LepR in the AgRP neurons impaired glucose tolerance, which was fully restored by infusion of GABA_A antagonist (Figure 1N). These results indicate that leptin regulates feeding and energy metabolism via post-synaptic GABA_A signaling from AgRP neurons.

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To identify the key downstream targets of AgRP neurons that contribute to leptin-mediated responses, we examined the Npas4 expression in potential AgRP target neurons. Traditional immediate early genes are regulated by neuromodulators via cAMP, neurotrophins, and other paracrine factors, and their kinetics are relatively slow. In contrast, Npas4 has a more dynamic response to activity-dependent signaling via Ca²⁺, and it can be bidirectionally regulated by either excitatory or inhibitory input (Shepard et al., 2019). The qPCR results showed that rapid deletion of LepR signaling from AgRP neurons resulted in a significant decrease of Npas4 abundance within the DMH (Figure 2A). To identify the functional relevance of each downstream target to leptin signaling, we administered DT into neonatal Agrp^DTR mice to ablate all AgRP neurons and examined the neural activity of their downstream targets in response to leptin (Figure 2B; Luquet et al., 2007; Zimmer et al., 2019). Without ablation of AgRP neurons, leptin induced robust Fos expression in almost all major targets of AgRP neurons (Wu et al., 2009; Wu et al., 2012; Wu et al., 2008b). However, neonatal ablation of AgRP neurons significantly diminished the leptin-mediated Fos induction within post-synaptic neurons residing in the DMH (Figure 2C–F and Figure 2—figure supplement 1). These results suggest that the DMH could be the key downstream target that critically contributes to the regulation of leptin-mediated appetitive and metabolic responses. To further visualize the neural circuit from AgRP neurons to DMH, we injected a retrograde herpes simplex virus (HSV) encoding a red fluorescent protein (HSV-hEf1α-mCherry) into the DMH of Npy^GFP mice (Figure 2G). Both HSV-based retrograde tracing and cholera toxin subunit B (CTB)-based retrograde neuroanatomical tracing from the DMH showed that ~17% (CTB tracing) or 39% (HSV tracing) of AgRP neurons project to the DMH (Figure 2H–L and Figure 2—figure supplement 2).

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To establish a role of the DMH within the leptin-responsive AgRP neural circuit, we combined the transsynaptic tracer (WGA-ZsGreen) and whole-cell, patch-clamp recordings to facilitate electrophysiological analysis of neurons in the DMH that are innervated by AgRP neurons (Figure 3A and B; Han et al., 2021b; Han et al., 2021a; Xu et al., 2017; Xia et al., 2021). RNA in situ hybridization confirmed that ~78% of AgRP-targeted neurons in the DMH express melanocortin receptor 4 (MC4R) (Figure 3C and Figure 3—figure supplement 1). To test whether this connectivity was monosynaptic, we perfused tetrodotoxin (TTX) and 4-aminopyridine (4-AP) into the bath to remove any network activity. We observed that inhibitory postsynaptic currents (IPSCs) in the DMH neurons triggered by photostimulation of ChR2-expressing axonal terminals of AgRP neurons were fully blocked by Bic (Figure 3D and E), confirming that these terminals were releasing GABA. In the presence of DNQX (a competitive AMPA/kainate receptor antagonist), AP5 (a selective NMDA receptor antagonist) and Bic, photostimulation of the ChR2-expressing AgRP terminals resulted in inhibition of action potentials in postsynaptic ZsGreen-positive neurons of the DMH in a reversible manner with significant reduction of firing rate and resting membrane potential (Figure 3F–I). We further examined the effect of leptin on the firing of postsynaptic ZsGreen-positive neurons in the DMH. We found that systemic treatment of leptin significantly enhanced neural activities of DMH neurons (Figure 3J and K).

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The original data of firing rate and rest membrane potential from in vitro electrophysiological recording in Figure 3.

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To examine the role of the AgRP→DMH circuit in the regulation of feeding and glucose, we performed optogenetic manipulation in Agrp^Cre::Roas26^fs-ChR2 (Ai32) mice where ChR2-EYFP was selectively expressed in AgRP neurons and axonal terminals (Han et al., 2021b; Xia et al., 2021). Photostimulation of AgRP fibers in the DMH promoted feeding and impaired glucose tolerance (Figure 4A and B). We found that about 13% of DMH neurons could be sensitive to the activation of AgRP neurons (Figure 4—figure supplement 1). We did not observe significant changes of Fos expression in the ARC after activation of AgRP fiber in the DMH, which excludes the potential effects of activating AgRP neurons (Figure 4—figure supplement 1D, H and J). In contrast, photostimulation of post-synaptic MC4R^DMH neurons decreased feeding and improved glucose tolerance (Figure 4C and D). Bilateral infusion of Bic (4 ng) into the DMH of Agrp-Lepr KO mice with micro-osmotic pumps resulted in reduced feeding and a significant reduction of body weight, coupled with improved glucose tolerance (Figure 4E–G). Blockage of GABA_A receptor in the DMH significantly rescued the feeding efficiency, expansion of WAT, and the RQ profiles in Agrp-Lepr KO mice (Figure 4H–K). The effect of drug diffusion was examined by Fos expression in the DMH and the neighboring brain regions, showing no significant difference of Fos expression in these regions. (Figure 4—figure supplement 2). These results suggest that GABA_A receptor signaling in the DMH is critical for the feeding, body weight, and glucose tolerance regulated by LepR in AgRP neurons.

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To better understand the physiological responses of these GABAergic neurons in the context of feeding and glucose regulation, we employed an in vivo opto-tetrode system to reveal the dynamic activities of MC4R^DMH neurons (Han et al., 2021a). With the injection of AAV2-DIO-ChR2-GFP into the DMH of Mc4r^Cre mice, we could record the activity of DMH neurons and identify those that express MC4R by their short-latency response to photostimulation as well as by the identical waveforms of evoked and spontaneous spikes (Han et al., 2021a). We investigated the activities of MC4R^DMH neurons and non-MC4R^DMH neurons under chow diet and hyperglycemia conditions. A total of 16 MC4R^DMH neurons and 12 non-MC4R^DMH neurons were identified through optogenetic-invoked spikes. The results showed that 10 out of 16 identified MC4R neurons showed decreased firing rate during foraging (prior to meal initiation), with an average reduction of firing rate from 9.8 Hz to 5.1 Hz (Figure 4L, M and P). We also observed 10 out of 16 MC4R neurons that responded to the enhancement of blood glucose with a reduction of firing rate from 10.0 Hz to 7.1 Hz (Figure 4N, O and Q). These results are consistent with MC4R^DMH neurons’ mediation of foraging and glucose tolerance.

Our results showed that GABA_A receptors in the DMH are involved in the regulation of feeding and glucose tolerance; thus, we attempted to identify the key GABA_A receptor subunits that are functionally relevant to glucose and feeding regulation. Results of qPCR analysis showed that, among all major regulatory α subunits, the transcript level of Gabra3 (encoding GABA_A receptor α3 subunits) in the DMH neurons of Agrp-Lepr KO mice was the highest (Figure 5A). Immunostaining confirmed that α3-GABA_A was abundantly expressed within the DMH (Figure 5B). Our transsynaptic tracing study further confirmed that the α3-GABA_A signaling was highly co-localized within the post-synaptic targets of the AgRP→DMH neural circuit (Figure 5C–E). To understand the functional roles of α3-GABA_A signaling within DMH neurons for leptin-mediated feeding and glucose, Mc4r^Cre::Rosa26^fs-Cas9 mice were injected with AAV9-Gabra3^sgRNA-tdTomato into the DMH. We found that knockout of Gabra3 signaling in the MC4R^DMH neurons reduced feeding and body weight while glucose tolerance was significantly improved (Figure 5F–H and Figure 5—figure supplement 1). Deficiency in α3-GABA_A signaling also significantly decreased feeding efficiency and WAT (Figure 5I and J). A gain-of-function study showed that overexpression of α3-GABA_A within the same MC4R^DMH neurons manifested opposite phenotypes, including moderately increased feeding and body weight and significantly increased feeding efficiency and WAT adiposity (Figure 5F–J). Importantly, overexpression of α3-GABA_A in the DMH neurons blunted the actions of leptin on feeding, body weight, glucose tolerance, feeding efficiency, and WAT adiposity (Figure 5K–O). To determine the role of α3-GABA_A receptor signaling from the MC4R^DMH neurons in the regulation of feeding and glucose tolerance by activation of AgRP-DMH circuit, Npy^Flp::Mc4^Cre mice with were injected with AAV9-fDIO-ChR2-EYFP into the ARC, AAV9-DIO-Cas9-mCitrine and AAV9-Gabra3^sgRNA-tdTomato into the DMH of followed with implantation of optic fiber in the DMH. We found that knockout of Gabra3 signaling in the MC4R^DMH neurons abolished the enhancement of feeding and impairment of glucose tolerance during activation of AgRP-DMH circuit (Figure 5—figure supplement 2). These results suggest the AgRP-DMH circuit regulates feeding and glucose tolerance depending on the α3-GABA_A receptor signaling in the MC4R^DMH neurons. We further examined the effects of ablating α3-GABA_A in the DMH on feeding and body weight in diet-induced obese mice. We found that genetic deletion of Gabra3 signaling in the MC4R^DMH neurons led to a decrease in daily food consumption associated with a moderate weight loss and that these knockout mice showed reduced refeeding responses (Figure 5P–R). These results suggest that the MC4R^DMH neurons play a significant role in control of glucose tolerance and feeding through the α3-GABA_A signaling.

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Leptin exerts its behavioral and metabolic effects by modulating signaling of AgRP neurons that are susceptible to obesity-induced leptin resistance. In this report, we explored the neural circuit and transmitter-signaling mechanisms underlying leptin-mediated feeding and energy metabolism (Figure 5S). We identified and characterized a unique GABAergic neural circuit with functional significance: the neural circuit from LepR-expressing AgRP neurons to α3-GABA_A receptor expressing DMH neurons plays a critical role in the control of feeding, body weight and glucose tolerance through α3-GABA_A receptor signaling. This leptin-responsive neural circuit plays a fundamental role in the regulation of hyperphagia and metabolism in obesity, which suggests manipulation of the neural circuit pharmacologically could lead to novel obesity therapeutics.

This study utilized a newly established inducible-knockout strategy to achieve rapid, post-developmental, targeted inactivation of LepR signaling, which precisely illuminates the pathophysiological roles of central leptin signaling on the control of nutrient partitioning and feeding efficiency. Compared with the mild effects of the non-inducible manipulation of brain LepR signaling, our Agrp-Lepr KO model showed a robust disruption in various feeding and metabolism parameters, culminating in severe obesity, which was previously achieved by global genetic deletion or the CRISPR–Cas9 technique (Xu et al., 2018; van de Wall et al., 2008; Gonçalves et al., 2014; Egan et al., 2017). Our technique has many attractive features, such as the large repertoire of conditional mouse lines, transient and non-BBB-crossing inducer, and easy combination with numerous viral tools, that can be applied to many neurological and endocrine questions.

We applied HSV to map the AgRP-DMH circuit. HSV-hEF1α has been extensively used to retrogradely transport from the peripheral nerve terminals to the central nervous system through axonal transport (Fang et al., 2018; Tan et al., 2016; Eagle et al., 2020; Yamaguchi et al., 2020; Garcia et al., 2021; Gremel et al., 2016; Valentinova et al., 2019; Marcinkiewcz et al., 2016), while some applications take advantage of the anterograde capacity of other different strains for example HSV-H129 (Azevedo et al., 2019; Lo and Anderson, 2011). To further verify the AgRP-DMH circuit, we applied CTB which is capable of being taken up by neurons and transported in a retrograde direction towards the cell body. Consistent with HSV tracing results, CTB tracing strategy also showed the connection between AgRP neurons and DMH.

We applied pharmacological approaches to block GABA_A signaling within the AgRP-DMH circuit, which can blunt the hyperphagia responses and improve glucose intolerance in Agrp-Lepr KO mice. These results suggest that the GABA_A signaling system within the AgRP-DMH circuit exerts rapid actions on feeding behaviors and glucose metabolism. This shows the consistency that GABA in the AgRP neurons is required for the stimulation of feeding (Krashes et al., 2013). Furthermore, we observed decreased food intake and weight loss as a result of chronic blockage of GABA_A signaling in the DMH, indicating that GABA_A signaling within the AgRP-DMH circuit may also participate in the chronic regulation of feeding and body weight. On the other hand, some studies showed that AgRP neurons can chronically regulate feeding through the mechanism of long-term suppression over the downstream brain targets independent of GABA_A signaling (Krashes et al., 2013; Wang et al., 2021). Together, we suggest that the GABA signaling in the AgRP neurons may exert chronic effects on appetite and weight control via a distinct neural pathway from the co-released AgRP and NPY.

The profiling assay using neonatal ablation of AgRP neurons revealed key neural populations responding to leptin. Among various downstream targets of the AgRP circuit, the DMH neurons are the most sensitive to AgRP-dependent leptin signaling. Acute stimulation of post-synaptic MC4R neurons within the DMH blunted glucose tolerance and feeding.

The GABA_A subunits in the hypothalamus are involved in the regulation of feeding. We employed the CRISPR-Cas9, gene-editing method to comprehensively evaluate the function of GABA_A subunits α3 within a leptin-responsive neural circuit. Genetic deletion of α3 in the MC4R neurons in the DMH suppressed feeding and glucose tolerance. These studies demonstrate that the GABA_A subunits α3 within the secondary structure of the GABAergic neural circuit play an important role in the control of leptin-mediated hypometabolism and obesity.

Utilizing the in vivo optrode recording system, we performed stable intracellular recordings from MC4R neurons in the DMH in intact and awake mice, allowing us to study complex behaviors and physiological responses. Here, we focused on the effects of MC4R neurons in the DMH on sensing glucose levels. There are several brain regions such as ARC, LH, PVH, VMH, and DMH that could sense peripheral glucose levels including glucose-excited and glucose-inhibited neurons (Routh et al., 2014; Claret et al., 2007; He et al., 2020; Huang et al., 2022; Burdakov et al., 2005). Our results showed that 62.5% of MC4R neurons in the DMH neurons are relevant to hyperglycemia due to their suppressed neural activity associated with high glucose levels. Others showed either unresponsive or opposite phenotypes. Overall, the effects of glucose on MC4R neurons in the DMH suggest that glucose is a complex and dynamic metabolic signal that interacts with multiple neural circuits to regulate glucose homeostasis.

In conclusion, LepR in the AgRP neurons and the associated neural circuit and receptor are the primary mediators of leptin action in feeding and metabolic homeostasis. We suggest that new therapeutics selectively targeting α3-GABA_A associated signaling components within this GABAergic neural circuit would prevent obesity.

All data generated or analyzed during this study are included in the manuscript and supporting file.

1. 1. Anikeeva P
   2. Andalman AS
   3. Witten I
   4. Warden M
   5. Goshen I
   6. Grosenick L
   7. Gunaydin LA
   8. Frank LM
   9. Deisseroth K

   (2011) Optetrode: A multichannel readout for optogenetic control in freely moving mice

   Nature Neuroscience 15:163–170.

   https://doi.org/10.1038/nn.2992

   • PubMed
   • Google Scholar
2. 1. Aponte Y
   2. Atasoy D
   3. Sternson SM

   (2011) Agrp neurons are sufficient to orchestrate feeding behavior rapidly and without training

   Nature Neuroscience 14:351–355.

   https://doi.org/10.1038/nn.2739

   • PubMed
   • Google Scholar
3. 1. Aubrecht J
   2. Goad MEP
   3. Czopik AK
   4. Lerner CP
   5. Johnson KA
   6. Simpson EM
   7. Schiestl RH

   (2011) A high G418-resistant Neo (R) transgenic mouse and mouse embryonic fibroblast (mef) feeder layers for cytotoxicity and gene targeting in vivo and in vitro

   Drug and Chemical Toxicology 34:433–439.

   https://doi.org/10.3109/01480545.2010.544316

   • PubMed
   • Google Scholar
4. 1. Azevedo EP
   2. Pomeranz L
   3. Cheng J
   4. Schneeberger M
   5. Vaughan R
   6. Stern SA
   7. Tan B
   8. Doerig K
   9. Greengard P
   10. Friedman JM

   (2019) A role of DRD2 hippocampal neurons in context-dependent food intake

   Neuron 102:873–886.

   https://doi.org/10.1016/j.neuron.2019.03.011

   • PubMed
   • Google Scholar
5. 1. Baskin DG
   2. Breininger JF
   3. Schwartz MW

   (1999) Leptin receptor mRNA identifies a subpopulation of neuropeptide Y neurons activated by fasting in rat hypothalamus

   Diabetes 48:828–833.

   https://doi.org/10.2337/diabetes.48.4.828

   • PubMed
   • Google Scholar
6. 1. Benito-León J
   2. Mitchell AJ
   3. Hernández-Gallego J
   4. Bermejo-Pareja F

   (2013) Obesity and impaired cognitive functioning in the elderly: A population-based cross-sectional study (NEDICES)

   European Journal of Neurology 20:899–906.

   https://doi.org/10.1111/ene.12083

   • PubMed
   • Google Scholar
7. 1. Berglund ED
   2. Vianna CR
   3. Donato J
   4. Kim MH
   5. Chuang JC
   6. Lee CE
   7. Lauzon DA
   8. Lin P
   9. Brule LJ
   10. Scott MM
   11. Coppari R
   12. Elmquist JK

   (2012) Direct leptin action on POMC neurons regulates glucose homeostasis and hepatic insulin sensitivity in mice

   The Journal of Clinical Investigation 122:1000–1009.

   https://doi.org/10.1172/JCI59816

   • PubMed
   • Google Scholar
8. 1. Berner LA
   2. Bocarsly ME
   3. Hoebel BG
   4. Avena NM

   (2009) Baclofen suppresses binge eating of pure fat but not a sugar-rich or sweet-fat diet

   Behavioural Pharmacology 20:631–634.

   https://doi.org/10.1097/FBP.0b013e328331ba47

   • PubMed
   • Google Scholar
9. 1. Berridge KC
   2. Treit D

   (1986) Chlordiazepoxide directly enhances positive ingestive reactions in rats

   Pharmacology, Biochemistry, and Behavior 24:217–221.

   https://doi.org/10.1016/0091-3057(86)90341-2

   • PubMed
   • Google Scholar
10. 1. Berridge KC
    2. Peciña S

    (1995) Benzodiazepines, appetite, and taste palatability

    Neuroscience and Biobehavioral Reviews 19:121–131.

    https://doi.org/10.1016/0149-7634(94)00026-w

    • PubMed
    • Google Scholar
11. 1. Berridge KC

    (2009) “Liking” and “wanting” food rewards: brain substrates and roles in eating disorders

    Physiology & Behavior 97:537–550.

    https://doi.org/10.1016/j.physbeh.2009.02.044

    • PubMed
    • Google Scholar
12. 1. Burdakov D
    2. Luckman SM
    3. Verkhratsky A

    (2005) Glucose-Sensing neurons of the hypothalamus

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 360:2227–2235.

    https://doi.org/10.1098/rstb.2005.1763

    • PubMed
    • Google Scholar
13. 1. Cai H
    2. Haubensak W
    3. Anthony TE
    4. Anderson DJ

    (2014) Central amygdala PKC-δ (+) neurons mediate the influence of multiple anorexigenic signals

    Nature Neuroscience 17:1240–1248.

    https://doi.org/10.1038/nn.3767

    • PubMed
    • Google Scholar
14. 1. Caron A
    2. Dungan Lemko HM
    3. Castorena CM
    4. Fujikawa T
    5. Lee S
    6. Lord CC
    7. Ahmed N
    8. Lee CE
    9. Holland WL
    10. Liu C
    11. Elmquist JK

    (2018) POMC neurons expressing leptin receptors coordinate metabolic responses to fasting via suppression of leptin levels

    eLife 7:e33710.

    https://doi.org/10.7554/eLife.33710

    • PubMed
    • Google Scholar
15. 1. Claret M
    2. Smith MA
    3. Batterham RL
    4. Selman C
    5. Choudhury AI
    6. Fryer LGD
    7. Clements M
    8. Al-Qassab H
    9. Heffron H
    10. Xu AW
    11. Speakman JR
    12. Barsh GS
    13. Viollet B
    14. Vaulont S
    15. Ashford MLJ
    16. Carling D
    17. Withers DJ

    (2007) Ampk is essential for energy homeostasis regulation and glucose sensing by POMC and AGRP neurons

    The Journal of Clinical Investigation 117:2325–2336.

    https://doi.org/10.1172/JCI31516

    • PubMed
    • Google Scholar
16. 1. Clemmensen C
    2. Petersen MB
    3. Sørensen TIA

    (2020) Will the COVID-19 pandemic worsen the obesity epidemic?

    Nature Reviews. Endocrinology 16:469–470.

    https://doi.org/10.1038/s41574-020-0387-z

    • PubMed
    • Google Scholar
17. 1. Cohen P
    2. Zhao C
    3. Cai X
    4. Montez JM
    5. Rohani SC
    6. Feinstein P
    7. Mombaerts P
    8. Friedman JM

    (2001) Selective deletion of leptin receptor in neurons leads to obesity

    The Journal of Clinical Investigation 108:1113–1121.

    https://doi.org/10.1172/JCI13914

    • PubMed
    • Google Scholar
18. 1. Cone RD

    (2005) Anatomy and regulation of the central melanocortin system

    Nature Neuroscience 8:571–578.

    https://doi.org/10.1038/nn1455

    • PubMed
    • Google Scholar
19. 1. Considine RV
    2. Considine EL
    3. Williams CJ
    4. Nyce MR
    5. Magosin SA
    6. Bauer TL
    7. Rosato EL
    8. Colberg J
    9. Caro JF

    (1995) Evidence against either a premature stop codon or the absence of obese gene mRNA in human obesity

    Journal of Clinical Investigation 95:2986–2988.

    https://doi.org/10.1172/JCI118007

    • PubMed
    • Google Scholar
20. 1. Cooper SJ

    (2005) Palatability-Dependent appetite and benzodiazepines: new directions from the pharmacology of GABA (a) receptor subtypes

    Appetite 44:133–150.

    https://doi.org/10.1016/j.appet.2005.01.003

    • PubMed
    • Google Scholar
21. 1. Cowley MA
    2. Smart JL
    3. Rubinstein M
    4. Cerdán MG
    5. Diano S
    6. Horvath TL
    7. Cone RD
    8. Low MJ

    (2001) Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus

    Nature 411:480–484.

    https://doi.org/10.1038/35078085

    • PubMed
    • Google Scholar
22. 1. Daigle TL
    2. Madisen L
    3. Hage TA
    4. Valley MT
    5. Knoblich U
    6. Larsen RS
    7. Takeno MM
    8. Huang L
    9. Gu H
    10. Larsen R
    11. Mills M
    12. Bosma-Moody A
    13. Siverts LA
    14. Walker M
    15. Graybuck LT
    16. Yao Z
    17. Fong O
    18. Nguyen TN
    19. Garren E
    20. Lenz GH
    21. Chavarha M
    22. Pendergraft J
    23. Harrington J
    24. Hirokawa KE
    25. Harris JA
    26. Nicovich PR
    27. McGraw MJ
    28. Ollerenshaw DR
    29. Smith KA
    30. Baker CA
    31. Ting JT
    32. Sunkin SM
    33. Lecoq J
    34. Lin MZ
    35. Boyden ES
    36. Murphy GJ
    37. da Costa NM
    38. Waters J
    39. Li L
    40. Tasic B
    41. Zeng H

    (2018) A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality

    Cell 174:465–480.

    https://doi.org/10.1016/j.cell.2018.06.035

    • PubMed
    • Google Scholar
23. 1. Duke AN
    2. Platt DM
    3. Cook JM
    4. Huang S
    5. Yin W
    6. Mattingly BA
    7. Rowlett JK

    (2006) Enhanced sucrose pellet consumption induced by benzodiazepine-type drugs in squirrel monkeys: role of GABAA receptor subtypes

    Psychopharmacology 187:321–330.

    https://doi.org/10.1007/s00213-006-0431-2

    • PubMed
    • Google Scholar
24. 1. Eagle AL
    2. Manning CE
    3. Williams ES
    4. Bastle RM
    5. Gajewski PA
    6. Garrison A
    7. Wirtz AJ
    8. Akguen S
    9. Brandel-Ankrapp K
    10. Endege W
    11. Boyce FM
    12. Ohnishi YN
    13. Mazei-Robison M
    14. Maze I
    15. Neve RL
    16. Robison AJ

    (2020) Circuit-specific hippocampal δfosb underlies resilience to stress-induced social avoidance

    Nature Communications 11:4484.

    https://doi.org/10.1038/s41467-020-17825-x

    • PubMed
    • Google Scholar
25. 1. Egan OK
    2. Inglis MA
    3. Anderson GM

    (2017) Leptin signaling in AGRP neurons modulates puberty onset and adult fertility in mice

    The Journal of Neuroscience 37:3875–3886.

    https://doi.org/10.1523/JNEUROSCI.3138-16.2017

    • PubMed
    • Google Scholar
26. 1. Fang YY
    2. Yamaguchi T
    3. Song SC
    4. Tritsch NX
    5. Lin D

    (2018) A hypothalamic midbrain pathway essential for driving maternal behaviors

    Neuron 98:192–207.

    https://doi.org/10.1016/j.neuron.2018.02.019

    • PubMed
    • Google Scholar
27. 1. Farr OM
    2. Gavrieli A
    3. Mantzoros CS

    (2015) Leptin applications in 2015: what have we learned about leptin and obesity?

    Current Opinion in Endocrinology, Diabetes, and Obesity 22:353–359.

    https://doi.org/10.1097/MED.0000000000000184

    • PubMed
    • Google Scholar
28. 1. Flak JN
    2. Myers MG

    (2016) Minireview: CNS mechanisms of leptin action

    Molecular Endocrinology 30:3–12.

    https://doi.org/10.1210/me.2015-1232

    • PubMed
    • Google Scholar
29. 1. Friedman JM
    2. Halaas JL

    (1998) Leptin and the regulation of body weight in mammals

    Nature 395:763–770.

    https://doi.org/10.1038/27376

    • PubMed
    • Google Scholar
30. 1. Friedman JM

    (2009) Leptin at 14 Y of age: an ongoing story

    The American Journal of Clinical Nutrition 89:973S–979S.

    https://doi.org/10.3945/ajcn.2008.26788B

    • PubMed
    • Google Scholar
31. 1. Friedman J

    (2014) 20 years of leptin: Leptin at 20: An overview

    The Journal of Endocrinology 223:T1–T8.

    https://doi.org/10.1530/JOE-14-0405

    • PubMed
    • Google Scholar
32. 1. Friedman JM

    (2019) Leptin and the endocrine control of energy balance

    Nature Metabolism 1:754–764.

    https://doi.org/10.1038/s42255-019-0095-y

    • PubMed
    • Google Scholar
33. 1. Garcia AF
    2. Crummy EA
    3. Webb IG
    4. Nooney MN
    5. Ferguson SM

    (2021) Distinct populations of cortical pyramidal neurons mediate drug reward and aversion

    Nature Communications 12:182.

    https://doi.org/10.1038/s41467-020-20526-0

    • PubMed
    • Google Scholar
34. 1. Garfield AS
    2. Shah BP
    3. Burgess CR
    4. Li MM
    5. Li C
    6. Steger JS
    7. Madara JC
    8. Campbell JN
    9. Kroeger D
    10. Scammell TE
    11. Tannous BA
    12. Myers MG Jr
    13. Andermann ML
    14. Krashes MJ
    15. Lowell BB

    (2016) Dynamic GABAergic afferent modulation of AgRP neurons

    Nature Neuroscience 19:1628–1635.

    https://doi.org/10.1038/nn.4392

    • PubMed
    • Google Scholar
35. 1. Gonçalves GHM
    2. Li W
    3. Garcia AVCG
    4. Figueiredo MS
    5. Bjørbæk C

    (2014) Hypothalamic agouti-related peptide neurons and the central melanocortin system are crucial mediators of leptin’s antidiabetic actions

    Cell Reports 7:1093–1103.

    https://doi.org/10.1016/j.celrep.2014.04.010

    • PubMed
    • Google Scholar
36. 1. Gremel CM
    2. Chancey JH
    3. Atwood BK
    4. Luo G
    5. Neve R
    6. Ramakrishnan C
    7. Deisseroth K
    8. Lovinger DM
    9. Costa RM

    (2016) Endocannabinoid modulation of orbitostriatal circuits gates habit formation

    Neuron 90:1312–1324.

    https://doi.org/10.1016/j.neuron.2016.04.043

    • PubMed
    • Google Scholar
37. 1. Gropp E
    2. Shanabrough M
    3. Borok E
    4. Xu AW
    5. Janoschek R
    6. Buch T
    7. Plum L
    8. Balthasar N
    9. Hampel B
    10. Waisman A
    11. Barsh GS
    12. Horvath TL
    13. Brüning JC

    (2005) Agouti-Related peptide-expressing neurons are mandatory for feeding

    Nature Neuroscience 8:1289–1291.

    https://doi.org/10.1038/nn1548

    • PubMed
    • Google Scholar
38. 1. Halaas JL
    2. Boozer C
    3. Blair-West J
    4. Fidahusein N
    5. Denton DA
    6. Friedman JM

    (1997) Physiological response to long-term peripheral and central leptin infusion in lean and obese mice

    PNAS 94:8878–8883.

    https://doi.org/10.1073/pnas.94.16.8878

    • PubMed
    • Google Scholar
39. 1. Han Y
    2. Xia G
    3. He Y
    4. He Y
    5. Farias M
    6. Xu Y
    7. Wu Q

    (2021a) A hindbrain dopaminergic neural circuit prevents weight gain by reinforcing food satiation

    Science Advances 7:eabf8719.

    https://doi.org/10.1126/sciadv.abf8719

    • PubMed
    • Google Scholar
40. 1. Han Y
    2. Xia G
    3. Srisai D
    4. Meng F
    5. He Y
    6. Ran Y
    7. He Y
    8. Farias M
    9. Hoang G
    10. Tóth I
    11. Dietrich MO
    12. Chen MH
    13. Xu Y
    14. Wu Q

    (2021b) Deciphering an agrp-serotoninergic neural circuit in distinct control of energy metabolism from feeding

    Nature Communications 12:3525.

    https://doi.org/10.1038/s41467-021-23846-x

    • PubMed
    • Google Scholar
41. 1. He Y
    2. Shu G
    3. Yang Y
    4. Xu P
    5. Xia Y
    6. Wang C
    7. Saito K
    8. Hinton A
    9. Yan X
    10. Liu C
    11. Wu Q
    12. Tong Q
    13. Xu Y

    (2016) A small potassium current in agrp/NPY neurons regulates feeding behavior and energy metabolism

    Cell Reports 17:1807–1818.

    https://doi.org/10.1016/j.celrep.2016.10.044

    • PubMed
    • Google Scholar
42. 1. He Y
    2. Xu P
    3. Wang C
    4. Xia Y
    5. Yu M
    6. Yang Y
    7. Yu K
    8. Cai X
    9. Qu N
    10. Saito K
    11. Wang J
    12. Hyseni I
    13. Robertson M
    14. Piyarathna B
    15. Gao M
    16. Khan SA
    17. Liu F
    18. Chen R
    19. Coarfa C
    20. Zhao Z
    21. Tong Q
    22. Sun Z
    23. Xu Y

    (2020) Estrogen receptor-α expressing neurons in the ventrolateral VMH regulate glucose balance

    Nature Communications 11:2165.

    https://doi.org/10.1038/s41467-020-15982-7

    • PubMed
    • Google Scholar
43. 1. Heymsfield SB
    2. Greenberg AS
    3. Fujioka K
    4. Dixon RM
    5. Kushner R
    6. Hunt T
    7. Lubina JA
    8. Patane J
    9. Self B
    10. Hunt P
    11. McCamish M

    (1999) Recombinant leptin for weight loss in obese and lean adults: a randomized, controlled, dose-escalation trial

    JAMA 282:1568–1575.

    https://doi.org/10.1001/jama.282.16.1568

    • PubMed
    • Google Scholar
44. 1. Higgs S
    2. Cooper SJ

    (1996) Hyperphagia induced by direct administration of midazolam into the parabrachial nucleus of the rat

    European Journal of Pharmacology 313:1–9.

    https://doi.org/10.1016/0014-2999(96)00446-3

    • PubMed
    • Google Scholar
45. 1. Hinney A
    2. Körner A
    3. Fischer-Posovszky P

    (2022) The promise of new anti-obesity therapies arising from knowledge of genetic obesity traits

    Nature Reviews Endocrinology 18:623–637.

    https://doi.org/10.1038/s41574-022-00716-0

    • Google Scholar
46. 1. Huang Z
    2. Liu L
    3. Zhang J
    4. Conde K
    5. Phansalkar J
    6. Li Z
    7. Yao L
    8. Xu Z
    9. Wang W
    10. Zhou J
    11. Bi G
    12. Wu F
    13. Seeley RJ
    14. Scott MM
    15. Zhan C
    16. Pang ZP
    17. Liu J

    (2022) Glucose-Sensing glucagon-like peptide-1 receptor neurons in the dorsomedial hypothalamus regulate glucose metabolism

    Science Advances 8:eabn5345.

    https://doi.org/10.1126/sciadv.abn5345

    • PubMed
    • Google Scholar
47. 1. Huynh FK
    2. Levi J
    3. Denroche HC
    4. Gray SL
    5. Voshol PJ
    6. Neumann UH
    7. Speck M
    8. Chua SC
    9. Covey SD
    10. Kieffer TJ

    (2010) Disruption of hepatic leptin signaling protects mice from age- and diet-related glucose intolerance

    Diabetes 59:3032–3040.

    https://doi.org/10.2337/db10-0074

    • PubMed
    • Google Scholar
48. 1. Kahn SE
    2. Hull RL
    3. Utzschneider KM

    (2006) Mechanisms linking obesity to insulin resistance and type 2 diabetes

    Nature 444:840–846.

    https://doi.org/10.1038/nature05482

    • PubMed
    • Google Scholar
49. 1. Kishi T
    2. Elmquist JK

    (2005) Body weight is regulated by the brain: a link between feeding and emotion

    Molecular Psychiatry 10:132–146.

    https://doi.org/10.1038/sj.mp.4001638

    • PubMed
    • Google Scholar
50. 1. Knight ZA
    2. Hannan KS
    3. Greenberg ML
    4. Friedman JM

    (2010) Hyperleptinemia is required for the development of leptin resistance

    PLOS ONE 5:e11376.

    https://doi.org/10.1371/journal.pone.0011376

    • PubMed
    • Google Scholar
51. 1. Könner AC
    2. Janoschek R
    3. Plum L
    4. Jordan SD
    5. Rother E
    6. Ma X
    7. Xu C
    8. Enriori P
    9. Hampel B
    10. Barsh GS
    11. Kahn CR
    12. Cowley MA
    13. Ashcroft FM
    14. Brüning JC

    (2007) Insulin action in agrp-expressing neurons is required for suppression of hepatic glucose production

    Cell Metabolism 5:438–449.

    https://doi.org/10.1016/j.cmet.2007.05.004

    • PubMed
    • Google Scholar
52. 1. Krashes MJ
    2. Shah BP
    3. Koda S
    4. Lowell BB

    (2013) Rapid versus delayed stimulation of feeding by the endogenously released AGRP neuron mediators GABA, NPY, and AGRP

    Cell Metabolism 18:588–595.

    https://doi.org/10.1016/j.cmet.2013.09.009

    • PubMed
    • Google Scholar
53. 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
54. 1. Kvitsiani D
    2. Ranade S
    3. Hangya B
    4. Taniguchi H
    5. Huang JZ
    6. Kepecs A

    (2013) Distinct behavioural and network correlates of two interneuron types in prefrontal cortex

    Nature 498:363–366.

    https://doi.org/10.1038/nature12176

    • PubMed
    • Google Scholar
55. 1. Liu F
    2. Jiang H
    3. Zhong W
    4. Wu X
    5. Luo J

    (2010) Changes in ensemble activity of hippocampus CA1 neurons induced by chronic morphine administration in freely behaving mice

    Neuroscience 171:747–759.

    https://doi.org/10.1016/j.neuroscience.2010.09.052

    • PubMed
    • Google Scholar
56. 1. Liu T
    2. Wang Q
    3. Berglund ED
    4. Tong Q

    (2012) Action of neurotransmitter: a key to unlock the AGRP neuron feeding circuit

    Frontiers in Neuroscience 6:200.

    https://doi.org/10.3389/fnins.2012.00200

    • PubMed
    • Google Scholar
57. 1. Lo L
    2. Anderson DJ

    (2011) A CRE-dependent, anterograde transsynaptic viral tracer for mapping output pathways of genetically marked neurons

    Neuron 72:938–950.

    https://doi.org/10.1016/j.neuron.2011.12.002

    • PubMed
    • Google Scholar
58. 1. Luquet S
    2. Perez FA
    3. Hnasko TS
    4. Palmiter RD

    (2005) NPY/agrp neurons are essential for feeding in adult mice but can be ablated in neonates

    Science 310:683–685.

    https://doi.org/10.1126/science.1115524

    • PubMed
    • Google Scholar
59. 1. Luquet S
    2. Phillips CT
    3. Palmiter RD

    (2007) NPY/agrp neurons are not essential for feeding responses to glucoprivation

    Peptides 28:214–225.

    https://doi.org/10.1016/j.peptides.2006.08.036

    • PubMed
    • Google Scholar
60. 1. Madisen L
    2. Mao T
    3. Koch H
    4. Zhuo J
    5. Berenyi A
    6. Fujisawa S
    7. Hsu Y-WA
    8. Garcia AJ
    9. Gu X
    10. Zanella S
    11. Kidney J
    12. Gu H
    13. Mao Y
    14. Hooks BM
    15. Boyden ES
    16. Buzsáki G
    17. Ramirez JM
    18. Jones AR
    19. Svoboda K
    20. Han X
    21. Turner EE
    22. Zeng H

    (2012) A toolbox of CRE-dependent optogenetic transgenic mice for light-induced activation and silencing

    Nature Neuroscience 15:793–802.

    https://doi.org/10.1038/nn.3078

    • PubMed
    • Google Scholar
61. 1. Maffei M
    2. Halaas J
    3. Ravussin E
    4. Pratley RE
    5. Lee GH
    6. Zhang Y
    7. Fei H
    8. Kim S
    9. Lallone R
    10. Ranganathan S

    (1995) Leptin levels in human and rodent: Measurement of plasma leptin and ob RNA in obese and weight-reduced subjects

    Nature Medicine 1:1155–1161.

    https://doi.org/10.1038/nm1195-1155

    • PubMed
    • Google Scholar
62. 1. Marcinkiewcz CA
    2. Mazzone CM
    3. D’Agostino G
    4. Halladay LR
    5. Hardaway JA
    6. DiBerto JF
    7. Navarro M
    8. Burnham N
    9. Cristiano C
    10. Dorrier CE
    11. Tipton GJ
    12. Ramakrishnan C
    13. Kozicz T
    14. Deisseroth K
    15. Thiele TE
    16. McElligott ZA
    17. Holmes A
    18. Heisler LK
    19. Kash TL

    (2016) Serotonin engages an anxiety and fear-promoting circuit in the extended amygdala

    Nature 537:97–101.

    https://doi.org/10.1038/nature19318

    • PubMed
    • Google Scholar
63. 1. Martire SI
    2. Parkes SL
    3. Westbrook RF

    (2010) The effects of FG 7142 on sensory-specific satiety in rats

    Behavioural Brain Research 209:131–136.

    https://doi.org/10.1016/j.bbr.2010.01.034

    • PubMed
    • Google Scholar
64. 1. Meister B

    (2007) Neurotransmitters in key neurons of the hypothalamus that regulate feeding behavior and body weight

    Physiology & Behavior 92:263–271.

    https://doi.org/10.1016/j.physbeh.2007.05.021

    • PubMed
    • Google Scholar
65. 1. Meng F
    2. Han Y
    3. Srisai D
    4. Belakhov V
    5. Farias M
    6. Xu Y
    7. Palmiter RD
    8. Baasov T
    9. Wu Q

    (2016) New inducible genetic method reveals critical roles of GABA in the control of feeding and metabolism

    PNAS 113:3645–3650.

    https://doi.org/10.1073/pnas.1602049113

    • PubMed
    • Google Scholar
66. 1. Minokoshi Y
    2. Kim Y-B
    3. Peroni OD
    4. Fryer LGD
    5. Müller C
    6. Carling D
    7. Kahn BB

    (2002) Leptin stimulates fatty-acid oxidation by activating AMP-activated protein kinase

    Nature 415:339–343.

    https://doi.org/10.1038/415339a

    • PubMed
    • Google Scholar
67. 1. Morton GJ
    2. Cummings DE
    3. Baskin DG
    4. Barsh GS
    5. Schwartz MW

    (2006) Central nervous system control of food intake and body weight

    Nature 443:289–295.

    https://doi.org/10.1038/nature05026

    • PubMed
    • Google Scholar
68. 1. Myers MG Jr
    2. Münzberg H
    3. Leinninger GM
    4. Leshan RL

    (2009) The geometry of leptin action in the brain: More complicated than a simple Arc

    Cell Metabolism 9:117–123.

    https://doi.org/10.1016/j.cmet.2008.12.001

    • PubMed
    • Google Scholar
69. 1. Peciña S
    2. Berridge KC

    (1996) Brainstem mediates diazepam enhancement of palatability and feeding: Microinjections into fourth ventricle versus lateral ventricle

    Brain Research 727:22–30.

    https://doi.org/10.1016/0006-8993(96)00325-3

    • PubMed
    • Google Scholar
70. 1. Pereira S
    2. O’Dwyer SM
    3. Webber TD
    4. Baker RK
    5. So V
    6. Ellis CE
    7. Yoon JS
    8. Mojibian M
    9. Glavas MM
    10. Karunakaran S
    11. Clee SM
    12. Covey SD
    13. Kieffer TJ

    (2019) Metabolic effects of leptin receptor knockdown or reconstitution in adipose tissues

    Scientific Reports 9:3307.

    https://doi.org/10.1038/s41598-019-39498-3

    • PubMed
    • Google Scholar
71. 1. Platt RJ
    2. Chen S
    3. Zhou Y
    4. Yim MJ
    5. Swiech L
    6. Kempton HR
    7. Dahlman JE
    8. Parnas O
    9. Eisenhaure TM
    10. Jovanovic M
    11. Graham DB
    12. Jhunjhunwala S
    13. Heidenreich M
    14. Xavier RJ
    15. Langer R
    16. Anderson DG
    17. Hacohen N
    18. Regev A
    19. Feng G
    20. Sharp PA
    21. Zhang F

    (2014) Crispr-Cas9 knockin mice for genome editing and cancer modeling

    Cell 159:440–455.

    https://doi.org/10.1016/j.cell.2014.09.014

    • PubMed
    • Google Scholar
72. 1. Pool A-H
    2. Kvello P
    3. Mann K
    4. Cheung SK
    5. Gordon MD
    6. Wang L
    7. Scott K

    (2014) Four GABAergic interneurons impose feeding restraint in Drosophila

    Neuron 83:164–177.

    https://doi.org/10.1016/j.neuron.2014.05.006

    • PubMed
    • Google Scholar
73. 1. Ren J
    2. Wu NN
    3. Wang S
    4. Sowers JR
    5. Zhang Y

    (2021) Obesity cardiomyopathy: Evidence, mechanisms, and therapeutic implications

    Physiological Reviews 101:1745–1807.

    https://doi.org/10.1152/physrev.00030.2020

    • PubMed
    • Google Scholar
74. 1. Richard D

    (2015) Cognitive and autonomic determinants of energy homeostasis in obesity

    Nature Reviews. Endocrinology 11:489–501.

    https://doi.org/10.1038/nrendo.2015.103

    • PubMed
    • Google Scholar
75. 1. Rossi MA
    2. Stuber GD

    (2018) Overlapping brain circuits for homeostatic and hedonic feeding

    Cell Metabolism 27:42–56.

    https://doi.org/10.1016/j.cmet.2017.09.021

    • PubMed
    • Google Scholar
76. 1. Routh VH
    2. Hao L
    3. Santiago AM
    4. Sheng Z
    5. Zhou C

    (2014) Hypothalamic glucose sensing: Making ends meet

    Frontiers in Systems Neuroscience 8:236.

    https://doi.org/10.3389/fnsys.2014.00236

    • PubMed
    • Google Scholar
77. 1. Saper CB
    2. Chou TC
    3. Elmquist JK

    (2002) The need to feed: Homeostatic and hedonic control of eating

    Neuron 36:199–211.

    https://doi.org/10.1016/s0896-6273(02)00969-8

    • PubMed
    • Google Scholar
78. 1. Scott MM
    2. Lachey JL
    3. Sternson SM
    4. Lee CE
    5. Elias CF
    6. Friedman JM
    7. Elmquist JK

    (2009) Leptin targets in the mouse brain

    The Journal of Comparative Neurology 514:518–532.

    https://doi.org/10.1002/cne.22025

    • PubMed
    • Google Scholar
79. 1. Scott MM
    2. Williams KW
    3. Rossi J
    4. Lee CE
    5. Elmquist JK

    (2011) Leptin receptor expression in hindbrain GLP-1 neurons regulates food intake and energy balance in mice

    The Journal of Clinical Investigation 121:2413–2421.

    https://doi.org/10.1172/JCI43703

    • PubMed
    • Google Scholar
80. 1. Shepard R
    2. Heslin K
    3. Hagerdorn P
    4. Coutellier L

    (2019) Downregulation of npas4 in parvalbumin interneurons and cognitive deficits after neonatal NMDA receptor blockade: Relevance for schizophrenia

    Translational Psychiatry 9:99.

    https://doi.org/10.1038/s41398-019-0436-3

    • PubMed
    • Google Scholar
81. 1. Söderpalm AH
    2. Berridge KC

    (2000) The hedonic impact and intake of food are increased by midazolam microinjection in the parabrachial nucleus

    Brain Research 877:288–297.

    https://doi.org/10.1016/s0006-8993(00)02691-3

    • PubMed
    • Google Scholar
82. 1. Steculorum SM
    2. Ruud J
    3. Karakasilioti I
    4. Backes H
    5. Engström Ruud L
    6. Timper K
    7. Hess ME
    8. Tsaousidou E
    9. Mauer J
    10. Vogt MC
    11. Paeger L
    12. Bremser S
    13. Klein AC
    14. Morgan DA
    15. Frommolt P
    16. Brinkkötter PT
    17. Hammerschmidt P
    18. Benzing T
    19. Rahmouni K
    20. Wunderlich FT
    21. Kloppenburg P
    22. Brüning JC

    (2016) Agrp neurons control systemic insulin sensitivity via myostatin expression in brown adipose tissue

    Cell 165:125–138.

    https://doi.org/10.1016/j.cell.2016.02.044

    • PubMed
    • Google Scholar
83. 1. Strader CD
    2. MacIntyre DE
    3. Candelore MR
    4. Parker EM

    (1997) Molecular approaches to the discovery of new treatments for obesity

    Current Opinion in Chemical Biology 1:204–209.

    https://doi.org/10.1016/s1367-5931(97)80011-7

    • PubMed
    • Google Scholar
84. 1. Tan CL
    2. Cooke EK
    3. Leib DE
    4. Lin YC
    5. Daly GE
    6. Zimmerman CA
    7. Knight ZA

    (2016) Warm-sensitive neurons that control body temperature

    Cell 167:47–59.

    https://doi.org/10.1016/j.cell.2016.08.028

    • PubMed
    • Google Scholar
85. 1. Tong Q
    2. Ye CP
    3. Jones JE
    4. Elmquist JK
    5. Lowell BB

    (2008) Synaptic release of GABA by AGRP neurons is required for normal regulation of energy balance

    Nature Neuroscience 11:998–1000.

    https://doi.org/10.1038/nn.2167

    • PubMed
    • Google Scholar
86. 1. Valentinova K
    2. Tchenio A
    3. Trusel M
    4. Clerke JA
    5. Lalive AL
    6. Tzanoulinou S
    7. Matera A
    8. Moutkine I
    9. Maroteaux L
    10. Paolicelli RC
    11. Volterra A
    12. Bellone C
    13. Mameli M

    (2019) Morphine withdrawal recruits lateral habenula cytokine signaling to reduce synaptic excitation and sociability

    Nature Neuroscience 22:1053–1056.

    https://doi.org/10.1038/s41593-019-0421-4

    • PubMed
    • Google Scholar
87. 1. van de Wall E
    2. Leshan R
    3. Xu AW
    4. Balthasar N
    5. Coppari R
    6. Liu SM
    7. Jo YH
    8. MacKenzie RG
    9. Allison DB
    10. Dun NJ
    11. Elmquist J
    12. Lowell BB
    13. Barsh GS
    14. de Luca C
    15. Myers MG
    16. Schwartz GJ
    17. Chua SC

    (2008) Collective and individual functions of leptin receptor modulated neurons controlling metabolism and ingestion

    Endocrinology 149:1773–1785.

    https://doi.org/10.1210/en.2007-1132

    • PubMed
    • Google Scholar
88. 1. van den Pol AN
    2. Yao Y
    3. Fu L-Y
    4. Foo K
    5. Huang H
    6. Coppari R
    7. Lowell BB
    8. Broberger C

    (2009) Neuromedin B and gastrin-releasing peptide excite arcuate nucleus neuropeptide Y neurons in a novel transgenic mouse expressing strong Renilla green fluorescent protein in NPY neurons

    The Journal of Neuroscience 29:4622–4639.

    https://doi.org/10.1523/JNEUROSCI.3249-08.2009

    • PubMed
    • Google Scholar
89. 1. van den Top M
    2. Lee K
    3. Whyment AD
    4. Blanks AM
    5. Spanswick D

    (2004) Orexigen-sensitive NPY/AgRP pacemaker neurons in the hypothalamic arcuate nucleus

    Nature Neuroscience 7:493–494.

    https://doi.org/10.1038/nn1226

    • PubMed
    • Google Scholar
90. 1. Varela L
    2. Horvath TL

    (2012) Leptin and insulin pathways in POMC and agrp neurons that modulate energy balance and glucose homeostasis

    EMBO Reports 13:1079–1086.

    https://doi.org/10.1038/embor.2012.174

    • PubMed
    • Google Scholar
91. 1. Vong L
    2. Ye C
    3. Yang Z
    4. Choi B
    5. Chua S
    6. Lowell BB

    (2011) Leptin action on gabaergic neurons prevents obesity and reduces inhibitory tone to POMC neurons

    Neuron 71:142–154.

    https://doi.org/10.1016/j.neuron.2011.05.028

    • PubMed
    • Google Scholar
92. 1. Wang C
    2. Zhou W
    3. He Y
    4. Yang T
    5. Xu P
    6. Yang Y
    7. Cai X
    8. Wang J
    9. Liu H
    10. Yu M
    11. Liang C
    12. Yang T
    13. Liu H
    14. Fukuda M
    15. Tong Q
    16. Wu Q
    17. Sun Z
    18. He Y
    19. Xu Y

    (2021) Agrp neurons trigger long-term potentiation and facilitate food seeking

    Translational Psychiatry 11:11.

    https://doi.org/10.1038/s41398-020-01161-1

    • PubMed
    • Google Scholar
93. 1. Wu Q
    2. Howell MP
    3. Cowley MA
    4. Palmiter RD

    (2008a) Starvation after AGRP neuron ablation is independent of melanocortin signaling

    PNAS 105:2687–2692.

    https://doi.org/10.1073/pnas.0712062105

    • PubMed
    • Google Scholar
94. 1. Wu Q
    2. Howell MP
    3. Palmiter RD

    (2008b) Ablation of neurons expressing agouti-related protein activates fos and gliosis in postsynaptic target regions

    The Journal of Neuroscience 28:9218–9226.

    https://doi.org/10.1523/JNEUROSCI.2449-08.2008

    • PubMed
    • Google Scholar
95. 1. Wu Q
    2. Boyle MP
    3. Palmiter RD

    (2009) Loss of GABAergic signaling by AGRP neurons to the parabrachial nucleus leads to starvation

    Cell 137:1225–1234.

    https://doi.org/10.1016/j.cell.2009.04.022

    • PubMed
    • Google Scholar
96. 1. Wu Q
    2. Palmiter RD

    (2011) Gabaergic signaling by AGRP neurons prevents anorexia via a melanocortin-independent mechanism

    European Journal of Pharmacology 660:21–27.

    https://doi.org/10.1016/j.ejphar.2010.10.110

    • PubMed
    • Google Scholar
97. 1. Wu Q
    2. Clark MS
    3. Palmiter RD

    (2012) Deciphering a neuronal circuit that mediates appetite

    Nature 483:594–597.

    https://doi.org/10.1038/nature10899

    • PubMed
    • Google Scholar
98. 1. Wu Q
    2. Zheng R
    3. Srisai D
    4. McKnight GS
    5. Palmiter RD

    (2013) NR2B subunit of the NMDA glutamate receptor regulates appetite in the parabrachial nucleus

    PNAS 110:14765–14770.

    https://doi.org/10.1073/pnas.1314137110

    • PubMed
    • Google Scholar
99. 1. Xia G
    2. Han Y
    3. Meng F
    4. He Y
    5. Srisai D
    6. Farias M
    7. Dang M
    8. Palmiter RD
    9. Xu Y
    10. Wu Q

    (2021) Reciprocal control of obesity and anxiety-depressive disorder via a GABA and serotonin neural circuit

    Molecular Psychiatry 26:2837–2853.

    https://doi.org/10.1038/s41380-021-01053-w

    • PubMed
    • Google Scholar
100. 1. Xu B
     2. Xie X

     (2016) Neurotrophic factor control of satiety and body weight

     Nature Reviews. Neuroscience 17:282–292.

     https://doi.org/10.1038/nrn.2016.24

     • PubMed
     • Google Scholar
101. 1. Xu P
     2. He Y
     3. Cao X
     4. Valencia-Torres L
     5. Yan X
     6. Saito K
     7. Wang C
     8. Yang Y
     9. Hinton A Jr
     10. Zhu L
     11. Shu G
     12. Myers MG Jr
     13. Wu Q
     14. Tong Q
     15. Heisler LK
     16. Xu Y

     (2017) Activation of serotonin 2C receptors in dopamine neurons inhibits binge-like eating in mice

     Biological Psychiatry 81:737–747.

     https://doi.org/10.1016/j.biopsych.2016.06.005

     • PubMed
     • Google Scholar
102. 1. Xu J
     2. Bartolome CL
     3. Low CS
     4. Yi X
     5. Chien C-H
     6. Wang P
     7. Kong D

     (2018) Genetic identification of leptin neural circuits in energy and glucose homeostases

     Nature 556:505–509.

     https://doi.org/10.1038/s41586-018-0049-7

     • PubMed
     • Google Scholar
103. 1. Yamaguchi T
     2. Wei D
     3. Song SC
     4. Lim B
     5. Tritsch NX
     6. Lin D

     (2020) Posterior amygdala regulates sexual and aggressive behaviors in male mice

     Nature Neuroscience 23:1111–1124.

     https://doi.org/10.1038/s41593-020-0675-x

     • PubMed
     • Google Scholar
104. 1. Yaylaoglu MB
     2. Titmus A
     3. Visel A
     4. Alvarez-Bolado G
     5. Thaller C
     6. Eichele G

     (2005) Comprehensive expression atlas of fibroblast growth factors and their receptors generated by a novel robotic in situ hybridization platform

     Developmental Dynamics 234:371–386.

     https://doi.org/10.1002/dvdy.20441

     • PubMed
     • Google Scholar
105. 1. Zhang F
     2. Wang L-P
     3. Brauner M
     4. Liewald JF
     5. Kay K
     6. Watzke N
     7. Wood PG
     8. Bamberg E
     9. Nagel G
     10. Gottschalk A
     11. Deisseroth K

     (2007) Multimodal fast optical interrogation of neural circuitry

     Nature 446:633–639.

     https://doi.org/10.1038/nature05744

     • PubMed
     • Google Scholar
106. 1. Zimmer MR
     2. Fonseca AHO
     3. Iyilikci O
     4. Pra RD
     5. Dietrich MO

     (2019) Functional ontogeny of hypothalamic AGRP neurons in neonatal mouse behaviors

     Cell 178:44–59.

     https://doi.org/10.1016/j.cell.2019.04.026

     • PubMed
     • Google Scholar

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the paper "Identification of a GABAergic neural circuit governing leptin-mediated obesity" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.

Comments to the Authors:

We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by eLife at this time.

The reviewers thought the work was of potential interest, but that substantial additional experiments were required to validate claims. It was discussed that if comments can be addressed in full, a re-submission would be encouraged, but that the revisions were substantial enough that they would not fit in the timeline for an invited eLife revision.

Reviewer #1 (Recommendations for the authors):

1. It is confusing why DMH neuron activity is suppressed in response to food, and the authors should explain this counterintuitive observation in the results or discussion. Food intake should decrease AGRP neuron activity, which should decrease inhibitory inputs to DMH neurons and thus increase neuronal firing unless I am missing something.

2. The bicuculline experiments are not so clean; in Figure 1 panels, % changes generally seem similar between the control and test groups. It seems possible/likely that effects are not epistatic, but rather involve two separate pathways whose effects are additive. This is a major challenge to the conclusions of the paper and should be discussed. Also, bicuculline applications in control mice are missing in all relevant panels of Figure 5.

3. In figure 3C, what is the percentage of WGA+ neurons that express MC4R, and what is the percentage of MC4R neurons that express WGA? Mc4r mice are used later in the text, but it is not clear that they provide a suitable tool. On a related note, for Figure 5, what percentage of Mc4R neurons are sensitive to AGRP neuron activation?

4. An inhibitory connection between AGRP neurons and Mc4R neurons is proposed, but there is no discussion of AGRP itself, which should provide a complementary pathway for inhibition over a longer time scale.

5. In several cases, the language is confusing as is and additional information should be provided.

5a. The title should be edited, as leptin does not mediate obesity; do the authors mean '…governing leptin mutation-induced obesity' or '…governing obesity induced by leptin mutation'?

5b. 'GABA signaling' in lines 181-193 should be replaced with 'GABA synthesis' or 'GABA production'. GABA signaling confusingly implies that receptor manipulations were made- it took me a few reads before I realized what the authors' manipulations actually were and that there was a 3rd negative feedback loop at play.

5c. On line 150, the transsynaptic tracer is called 'ZsGreen' but should be called 'WGA-ZsGreen' for precision; WGA is the key component that enables tracing.

5d. It should be clarified whether the control mice in Figure 1 also received NB124.

5e. Text in the discussion about GABAA signaling and feeding regulation is redundant with the introduction. It only needs to be discussed once.

5f. The abstract and text should be edited for grammar. For example, there are several 'the's missing in some places and extra 'the's in others.

5g. In reference to Figure 1L, the expansion of WAT is claimed to account for the 'entire' body weight change, but 1L shows a 1g increase, which is much less than the overall body weight increase in 1H.

5h. The authors should clarify why the baselines are different between 5B and 5D in the legend. I assume animals were in different fed/fasted states but this should be explicitly stated.

5i. The positions of Figure 5 panels should be adjusted so that it is clear that the legends above I apply to K and L as well.

Reviewer #2 (Recommendations for the authors):

The new genetic system for ablation of Lepr from Agrp neurons is poorly explained, and no data are shown to validate its effects.

Figure 2 – HSV has been shown to trace anterogradely, yet it is used here as a retrograde tracer without validation.

Figure 3 – This new tracing system seems interesting, but is not explained or validated. The amount of overlap between zsGreen and MC4R is not quantified – how equivalent are these cell types? The sample size for the ephys experiments (especially in panels G-K) is woefully small.

Are MC4R and AgRP target neurons in the DMH identical?

Figure 4 – This system also seems like it should ablate GABA signaling in Agrp neurons. There is no validation of the system.

Figure 5 – There is no validation of the KO or OE. The manipulation of the GABAergic system here is very general, and there is no analysis of the effects of these manipulations on the action of AgRP neurons.

Reviewer #3 (Recommendations for the authors):

1) The framework of the paper is misleading – they argue that (1) the ARC is the primary site of action for leptin on food intake and (2) that a lack of knowledge of the circuits mediating leptin's effect undermines its utility for treatment. However, this framework does not make sense with the current manuscript. First, many papers have highlighted roles for LepR signaling outside of the ARC and even outside of the hypothalamus for critical roles in feeding. Second, this work does not investigate leptin resistance which is at least in part a major contributor to the failure of leptin for treating obesity. While the manuscript provides insight into leptin receptor circuitry that mediates feeding, the current framework does not fit with the experiments and results.

2) The studies that utilize GABA receptor pharmacology are inconclusive given the doses and major effects that the antagonist has on its own. For example, in figure 1, bicuculline on its own has huge effects on food intake feed efficiency and glucose tolerance. Therefore, the data are hard to interpret. A dose of the antagonist that is ineffective on its own but blocks the effects of the KO is necessary to claim an interaction between the systems, especially since GABA is such an omnipresent neurotransmitter throughout the brain.

3) The experiments manipulating AgRP-DMH neurons lack proper controls.

– The DMH is not very far from the ARC. How did the authors verify that they did not stimulate ARC cell bodies with the laser light?

– Similarly, how did the authors verify that the infusion of bicuculline did not spread beyond DMH?

– In the experiment where GABA signaling in AgRP-DMH neurons was knocked out, how did the authors verify that these effects were specific to only the AgRP neurons that project to the DMH?

It is very important to verify selective manipulation of this pathway in order to make the claims that are made in the manuscript.

4) A few questions about the opto-tetrode recordings:

– 13 MC4R neurons and 9 non-MC4R neurons were recorded – this is a low number. How many mice were used for this study? Is this enough for a representative sample?

– I am confused why glucose seemingly inhibits MC4R neurons. If AgRP neurons inhibit MC4R, and glucose inhibits AgRP neurons, shouldn't they be activated?

5) Some of the histological data lack quantification.

6) Overall, the language throughout the manuscript is very operational, especially when describing the experiments. I would recommend conceptualizing the experiments, in addition to describing the tools (both within the text and in the figure legends) to improve flow and readability.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Identification of a GABAergic neural circuit governing leptin-mediated obesity" for further consideration by eLife. Your revised article has been evaluated by Ma-Li Wong (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below.

Both reviewers felt strongly that there are several claims that are not substantiated in the manuscript and that all remaining comments need to be addressed in full prior to re-evaluation. The full comments are detailed below.

Reviewer #2 (Recommendations for the authors):

The authors have added new data to the manuscript, including some important controls, and the manuscript is much improved.

Some issues remain, however:

Figure 1 (or appropriate supplement)- should show nsCre reporter activity in nsCre mice that have not received NB124.

Supplemental Figure 1- is pSTAT3 normal in other areas (e.g., the DMH, VMH)?

Figure 3- the biccuculine experiment still suffers from the unknown extent of diffusion. Perhaps staining for FOS might show the extent of diffusion (based on the absence of FOS).

Figure 4M-O- as noted previously, expressing Flp-dependent WGA-Cre in Npy neurons may also delete Gad1/2 in these neurons. It should also cause deletion in other regions that are targeted by Agrp neurons. Thus, this is not DMH-specific.

While the finding that deleting Gabra3 in the DMH blocks leptin-induced anorexia is useful, it would be better to show that this maneuver blocks the effects of deleting Lepr from Agrp neurons or some other Agrp neuron-specific manipulation.

Overall, while the manuscript remains interesting, the lack of specificity in some of the experiments does not allow the authors to cleanly conclude the model that they propose.

Reviewer #3 (Recommendations for the authors):

The authors addressed some of my previous concerns and have also provided several new verifications for techniques which improves confidence in the manuscript. Some concerns remain which are described below. Another major issue is that, unless I missed it, there is no methods section included in the revised manuscript, making it difficult to evaluate many of the new experiments.

Specific remaining issues:

– While the authors revised the manuscript using a lower dose of bicuculline, significant effects remain compared to controls for food intake, feed efficiency, and glucose tolerance, and therefore the original concerns remain for Figure 1. In Figure 4, analyses from repeated measures ANOVA were not reported and so it is unclear if there are differences between control and biculculline groups, and given the lack of methods here the experimental paradigm is unclear (e.g., what is a chronic, 5-min injection via osmotic minipump? – Figure 4 legend)?

– The fact that the HSV results yielded 2x the overlap as the CTB experiment raises concern about using it as an anterograde tracer, as previously noted by another reviewer. This should be discussed at the very least, or perhaps the HSV results should be omitted completely.

– The authors' editorial attempts to address concerns about site specificity are not very compelling and this is important as ARC and DMH are relatively near each other (and DMH is close to the ventricle – where infused substances could diffuse). Data demonstrating that optogenetic stimulation only activates AgRP neurons that project to DMH, and that infused substances do not reach 4V or ARC, would be helpful, and if this is not possible a discussion of these limitations is warranted.

– I am still confused by the recordings of MC4R neurons – where both foraging and hyperglycemia inhibit MC4R neurons. The authors cite one study suggesting that glucose activates AgRP neurons but do not discuss the many other studies that show the opposite. At the very least this should be discussed rather than the conclusion being that "these findings are consistent with MC4R-DMH neurons' mediation of food intake and blood glucose"

– I am also still confused with how the DMH GABA KO studies fit into the narrative of the paper. The effect size here is quite large (and it is an interesting finding), but the leptin signaling is systemic and not specific to AgRP neurons.

[Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]

Essential revisions:

Reviewer #1 (Recommendations for the authors):

1. It is confusing why DMH neuron activity is suppressed in response to food, and the authors should explain this counterintuitive observation in the results or discussion. Food intake should decrease AGRP neuron activity, which should decrease inhibitory inputs to DMH neurons and thus increase neuronal firing unless I am missing something.

Thanks for your comments. We agree to the common view that AgRP neurons generally decrease their activities when food ingestion has been initiated or animals are sated. In this manuscript, however, we focus on the neural activities of AgRP neurons during the foraging stage in which AgRP neurons are known to be activated by hunger. Therefore, the neural firing of DMH neurons is subsequently suppressed as shown here (Figure 5E). To rule out any confusion, we updated the main text (lines 191), and legend (line 568-569).

2. The bicuculline experiments are not so clean; in Figure 1 panels, % changes generally seem similar between the control and test groups. It seems possible/likely that effects are not epistatic, but rather involve two separate pathways whose effects are additive. This is a major challenge to the conclusions of the paper and should be discussed. Also, bicuculline applications in control mice are missing in all relevant panels of Figure 5.

We appreciate these valuable comments. We performed a new experiment with lower dose of bicuculline to show the difference between the control and experimental groups (Figure 1J-N). Under a lower dose, bicuculline treatment in the DMH showed little effects on food intake, glucose tolerance, and energy metabolism on control group. But this treatment can abolish the hyperphagia responses in Agrp-Lepr KO mice. These observations strongly argue that bicuculline exerts the rescuing effect via direct action upon this neural circuit.

We also updated new Figure 4 with additional bicuculline applications in control groups.

3. In figure 3C, what is the percentage of WGA+ neurons that express MC4R, and what is the percentage of MC4R neurons that express WGA? Mc4r mice are used later in the text, but it is not clear that they provide a suitable tool. On a related note, for Figure 5, what percentage of Mc4R neurons are sensitive to AGRP neuron activation?

Thanks for your comments. We performed further histological analysis and updated the manuscript. We found that 78% WGA+ neurons within the DMH co-express MC4R, and that 55% MC4R neurons within the DMH co-express WGA (Figure S4G and lines 139). We photostimulated the AgRP fibers in the DMH in Agrp-Cre::Ai32 mice, and performed c-Fos staining to examine the percentage of DMH neurons could be inactivated by photostimulation of AgRP neurons. We found there are ~13% of DMH neurons responded to the activation of AgRP (Figure S5)

4. An inhibitory connection between AGRP neurons and Mc4R neurons is proposed, but there is no discussion of AGRP itself, which should provide a complementary pathway for inhibition over a longer time scale.

We added more discussions (lines 244-257) to the manuscript accordingly.

5. In several cases, the language is confusing as is and additional information should be provided.

5a. The title should be edited, as leptin does not mediate obesity; do the authors mean '…governing leptin mutation-induced obesity' or '…governing obesity induced by leptin mutation'?

Thanks for your comments. We modified the title in our revised manuscript per your suggestion.

5b. 'GABA signaling' in lines 181-193 should be replaced with 'GABA synthesis' or 'GABA production'. GABA signaling confusingly implies that receptor manipulations were made- it took me a few reads before I realized what the authors' manipulations actually were and that there was a 3rd negative feedback loop at play.

Thanks for your comments. We have modified the description (lines 170-180) as reviewer suggested.

5c. On line 150, the transsynaptic tracer is called 'ZsGreen' but should be called 'WGA-ZsGreen' for precision; WGA is the key component that enables tracing.

We corrected the error in our revised manuscript (line 136).

5d. It should be clarified whether the control mice in Figure 1 also received NB124.

We updated our revised manuscript (lines 490-492).

5e. Text in the discussion about GABAA signaling and feeding regulation is redundant with the introduction. It only needs to be discussed once.

Thanks for your comments. We updated it in our revised manuscript.

5f. The abstract and text should be edited for grammar. For example, there are several 'the's missing in some places and extra 'the's in others.

We corrected the error in our revised manuscript.

5g. In reference to Figure 1L, the expansion of WAT is claimed to account for the 'entire' body weight change, but 1L shows a 1g increase, which is much less than the overall body weight increase in 1H.

We updated our revised manuscript (lines 105-106) to describe the results more accurately.

5h. The authors should clarify why the baselines are different between 5B and 5D in the legend. I assume animals were in different fed/fasted states but this should be explicitly stated.

Thanks for your comments. The neural firing of MC4R neurons and non-MC4R neurons in the DMH from Figure 5B was recorded under hunger condition, and the neural firing from Figure 5D was recorded under well-fed and glucose treatment state. We updated the legend accordingly (lines 568-569).

5i. The positions of Figure 5 panels should be adjusted so that it is clear that the legends above I apply to K and L as well.

The current arrangement of Figure 5 has a better flow for the ephys recording assay, and genetic studies regarding the GABAA receptor subunits.

Reviewer #2 (Recommendations for the authors):

The new genetic system for ablation of Lepr from Agrp neurons is poorly explained, and no data are shown to validate its effects.

We appreciate these critiques. We examined the pSTAT3 in the arcuate nucleus of fasted mice with or without leptin treatment. We found that pSTAT3-positive neurons were much more abundant upon leptin treatment in the control group. In contrast, we observed a significant decrease of pSTAT3 signals in Agrp-Lepr KO mice as compared to the control (Figure S1 and lines 96-100). These results suggest that our genetic KO model can efficiently and acutely disrupt the leptin receptor signaling within AgRP neurons.

Figure 2 – HSV has been shown to trace anterogradely, yet it is used here as a retrograde tracer without validation.

Thanks for your comments. Several previous studies established that HSV showed retrograding capacity (1-3). To further validate the AgRP-DMH neural circuit, we performed another retrograde tracing assay using CTB as the retrograde tracer. Consistent with the HSV results, the CTB tracing results indicated that AgRP neurons project to the DMH (Figure S3, lines 131134).

Figure 3 – This new tracing system seems interesting, but is not explained or validated. The amount of overlap between zsGreen and MC4R is not quantified – how equivalent are these cell types? The sample size for the ephys experiments (especially in panels G-K) is woefully small.

Thanks for your comments. We updated the manuscript with statistically quantified data (Figure S4 and lines 131-134) and added new data in the ephys recording.

Are MC4R and AgRP target neurons in the DMH identical?

We performed further histological analysis and updated the manuscript. We found that 78% WGA+ neurons within the DMH co-express MC4R, and that 55% MC4R neurons within the DMH co-express WGA (Figure S4). These results suggest that a large cohort of AgRP postsynaptic neurons are MC4R neurons.

Figure 4 – This system also seems like it should ablate GABA signaling in Agrp neurons. There is no validation of the system.

We performed the ablation of GABA synthesis in DMH neurons (not in AgRP neurons) that receive projection from AgRP neurons in Figure 4. We added validation data showing the KO of Gad1, Gad2 and the decrease of GABA neurons in DMH (Figure S6 and S7).

Figure 5 – There is no validation of the KO or OE. The manipulation of the GABAergic system here is very general, and there is no analysis of the effects of these manipulations on the action of AgRP neurons.

We performed the qPCR of GABAA-α3 in KO and OE mice (Figure S8).

Reviewer #3 (Recommendations for the authors):

1) The framework of the paper is misleading – they argue that (1) the ARC is the primary site of action for leptin on food intake and (2) that a lack of knowledge of the circuits mediating leptin's effect undermines its utility for treatment. However, this framework does not make sense with the current manuscript. First, many papers have highlighted roles for LepR signaling outside of the ARC and even outside of the hypothalamus for critical roles in feeding. Second, this work does not investigate leptin resistance which is at least in part a major contributor to the failure of leptin for treating obesity. While the manuscript provides insight into leptin receptor circuitry that mediates feeding, the current framework does not fit with the experiments and results.

We appreciate these valuable comments and critiques. Although the LepR signaling outside of the ARC and those extra-hypothalamus have been recently documented involved in various aspects of feeding and metabolism regulation, a recent genetic study established a pivotal role of the LepR within the AgRP neurons in appetite and obesity control as the AgRP LepR-KO model can near perfectly phenocopy db/db mice by some key parameters, such as hyperphagia, massive weight gain, and severe metabolic deficiency (4). Our current study provides the first-hand evidence in understanding the complex secondary structure of the leptin responsive neural circuit and signaling mechanism that had never been explored. Further study in deciphering the post-synaptic targets of non-ARC LepR-expressing neurons will provide a more comprehensive knowledge in the neural circuit control of the leptin signaling.

We agree to the reviewer that it is of high significance of understanding the neural mechanisms underlying leptin resistance for the treatment of obesity. However, our current study focuses, for the first time, on a neural circuit that governs the leptin-responsive feeding, weight control, and glucose metabolism under regulation of a specific post-synaptic GABAA signaling pathways with the effect against obesity. The current manuscript has been thoroughly revamped to fully support the proposed neural circuit framework as our initial attempt to understand the complex secondary structure of the leptin-responsive neural circuit in control of appetite and body weight. This study will undoubtedly justify further study of the leptin-mediated brain mechanism.

2) The studies that utilize GABA receptor pharmacology are inconclusive given the doses and major effects that the antagonist has on its own. For example, in figure 1, bicuculline on its own has huge effects on food intake feed efficiency and glucose tolerance. Therefore, the data are hard to interpret. A dose of the antagonist that is ineffective on its own but blocks the effects of the KO is necessary to claim an interaction between the systems, especially since GABA is such an omnipresent neurotransmitter throughout the brain.

We appreciate these valuable comments. We performed a new experiment with lower dose of bicuculline to show little effect on food intake, glucose tolerance, and energy metabolism on the knockout and control groups (Figure 1J-N). Notably, bicuculline at this lower dose, while showing little effect on food intake in the control group, can effectively abolish the hyperphagia responses in Agrp-Lepr KO mice. These observations strongly argue that bicuculline exerts the rescuing effect via direct action upon this neural circuit.

3) The experiments manipulating AgRP-DMH neurons lack proper controls.

– The DMH is not very far from the ARC. How did the authors verify that they did not stimulate ARC cell bodies with the laser light?

Thanks for your comments. To avoid non-specific deep penetration of laser in the brain tissue we controlled the laser intensity by measuring the laser power from the tip of fiber. The power of laser is limited to 0.5mW that will only stimulate neurons within the DMH (~0.3mm below the optic fiber (5), also refer to: https://nicneuro.net/optogenetics-depth-calculator/) so that ensuring no direct impact upon AgRP cell bodies in the ARC.

– Similarly, how did the authors verify that the infusion of bicuculline did not spread beyond DMH?

We verified the placement of cannula within the DMH region upon completion of all behavioral assays and excluded the data from individuals with off-targeted infusion.

– In the experiment where GABA signaling in AgRP-DMH neurons was knocked out, how did the authors verify that these effects were specific to only the AgRP neurons that project to the DMH?

It is very important to verify selective manipulation of this pathway in order to make the claims that are made in the manuscript.

In the experiment of Figure 4M-O, we performed the genetic ablation of GABA synthesis in the DMH GABAergic neurons (not AgRP neurons) that receive projection from AgRP neurons. Our viral knockout strategy permits specific targeting the DMH neurons that are transsynaptically connected with AgRP neurons, and only GABA synthesis in these Cre+ DMH neurons was disrupted. We added validation data showing the KO of Gad1, Gad2 and the decrease of GABA neurons in DMH (Figure S6 and S7).

4) A few questions about the opto-tetrode recordings:

– 13 MC4R neurons and 9 non-MC4R neurons were recorded – this is a low number. How many mice were used for this study? Is this enough for a representative sample?

Thanks for your comments. We updated the manuscript by adding more recorded neurons (Figure 5A-F). The successfully recorded neurons were collected from three mice per group.

– I am confused why glucose seemingly inhibits MC4R neurons. If AgRP neurons inhibit MC4R, and glucose inhibits AgRP neurons, shouldn't they be activated?

According an in vitro ephys study the increase of extracellular glucose depolarized AgRP neurons that may mimic the hyperglycemic state in our experiment (6). Therefore, it is reasonable that the MC4R neurons in the DMH were inhibited after glucose treatment due to the activation of AgRP neurons.

5) Some of the histological data lack quantification.

We added quantification data and updated our manuscript.

6) Overall, the language throughout the manuscript is very operational, especially when describing the experiments. I would recommend conceptualizing the experiments, in addition to describing the tools (both within the text and in the figure legends) to improve flow and readability.

Per the suggestions, we have improved the writing in our manuscript.

References

• Y. Y. Fang, T. Yamaguchi, S. C. Song, N. X. Tritsch, D. Lin, A Hypothalamic Midbrain Pathway Essential for Driving Maternal Behaviors. Neuron 98, 192-207 e110 (2018).

• G. Ugolini, H. G. Kuypers, A. Simmons, Retrograde transneuronal transfer of herpes simplex virus type 1 (HSV 1) from motoneurones. Brain Res 422, 242-256 (1987).

• C. L. Tan et al., Warm-Sensitive Neurons that Control Body Temperature. Cell 167, 47-59.e15 (2016).

• J. Xu et al., Genetic identification of leptin neural circuits in energy and glucose homeostases. Nature 556, 505-509 (2018).

• A. M. Aravanis et al., An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology. J Neural Eng 4, S143-156 (2007).

• M. Claret et al., AMPK is essential for energy homeostasis regulation and glucose sensing by POMC and AgRP neurons. J Clin Invest 117, 2325-2336 (2007).

[Editors’ note: what follows is the authors’ response to the second round of review.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below.

Both reviewers felt strongly that there are several claims that are not substantiated in the manuscript and that all remaining comments need to be addressed in full prior to re-evaluation. The full comments are detailed below.

Reviewer #2 (Recommendations for the authors):

The authors have added new data to the manuscript, including some important controls, and the manuscript is much improved.

Some issues remain, however:

Figure 1 (or appropriate supplement)- should show nsCre reporter activity in nsCre mice that have not received NB124.

Thanks for your positive comments on our studies. We added histological data in Figure 1—figure supplement 1 showing no tdTomato+ neurons observed in nsCre+ mice that have not received NB124.

Supplemental Figure 1- is pSTAT3 normal in other areas (e.g., the DMH, VMH)?

We examined the pSTAT3 level in DMH and VMH (Figure 1—figure supplement 3). We found that there were no significant differences between control and Agrp-Lepr KO mice.

Figure 3- the biccuculine experiment still suffers from the unknown extent of diffusion. Perhaps staining for FOS might show the extent of diffusion (based on the absence of FOS).

Thanks for your suggestion. We performed Fos immunostaining in the DMH and neighboring brain regions such as PLH, VMH, and ARC after bicuculline infusion experiment (Figure 4—figure supplement 2). Even though we observed a mild increase of Fos expression in the PLH, there is no significant difference. Therefore, the bicuculline showed minimal effects on neighboring brain regions in our study.

Figure 4M-O- as noted previously, expressing Flp-dependent WGA-Cre in Npy neurons may also delete Gad1/2 in these neurons. It should also cause deletion in other regions that are targeted by Agrp neurons. Thus, this is not DMH-specific.

We appreciate these valuable comments. Because this experiment about ablating GABA in DMH caused confusion to both reviewers and is not closely relevant to the scope of the manuscript, we removed this experiment from our updated manuscript as to best ensure the coherence and clarity of the main scheme.

While the finding that deleting Gabra3 in the DMH blocks leptin-induced anorexia is useful, it would be better to show that this maneuver blocks the effects of deleting Lepr from Agrp neurons or some other Agrp neuron-specific manipulation.

We appreciate these valuable comments. We performed a new experiment with injection of AAV9-fDIO-ChR2-EYFP into the ARC, AAV9-DIO-Cas9-mCitrine and AAV9-

Gabra3^sgRNA-tdTomato into the DMH in the Npy^Flp::Mc4^Cre mice, which may help examine whether the deletion of α3-GABAA receptor signaling from the DMH blocks the effects induced by activation of AgRP-DMH circuit (Figure 5—figure supplement 2). Our data showed that AgRP-DMH circuit regulates feeding and glucose tolerance depending on the α3-GABAA receptor signaling in the MC4R^DMH neurons.

Reviewer #3 (Recommendations for the authors):

The authors addressed some of my previous concerns and have also provided several new verifications for techniques which improves confidence in the manuscript. Some concerns remain which are described below. Another major issue is that, unless I missed it, there is no methods section included in the revised manuscript, making it difficult to evaluate many of the new experiments.

Thanks for these positive comments on our studies. In the revised manuscript, we updated the Methods section including all necessary information.

Specific remaining issues:

– While the authors revised the manuscript using a lower dose of bicuculline, significant effects remain compared to controls for food intake, feed efficiency, and glucose tolerance, and therefore the original concerns remain for Figure 1.

Thanks for your comments. We applied a lower dose of bicuculline that was ineffective on the control mice but blocked the effects of the Agrp-Lepr KO mice (Figure 1J-N).

In Figure 4, analyses from repeated measures ANOVA were not reported and so it is unclear if there are differences between control and biculculline groups, and given the lack of methods here the experimental paradigm is unclear (e.g., what is a chronic, 5-min injection via osmotic minipump? – Figure 4 legend)?

We updated methods and ANOVA analysis in Figure 4 legend.

– The fact that the HSV results yielded 2x the overlap as the CTB experiment raises concern about using it as an anterograde tracer, as previously noted by another reviewer. This should be discussed at the very least, or perhaps the HSV results should be omitted completely.

Thanks for your comments. We discussed in the updated manuscript that HSV could be used as a reliable retrograde tracing method. (Line 249-256)

– The authors' editorial attempts to address concerns about site specificity are not very compelling and this is important as ARC and DMH are relatively near each other (and DMH is close to the ventricle – where infused substances could diffuse). Data demonstrating that optogenetic stimulation only activates AgRP neurons that project to DMH, and that infused substances do not reach 4V or ARC, would be helpful, and if this is not possible a discussion of these limitations is warranted.

Thanks for your comments. As ARC and DMH are relatively near each other, we examined the Fos expression in the ARC after activation of AgRP fiber in the DMH (Figure 4—figure supplement 1). We did not observe significant changes of Fos expression in the ARC.

To check if infusion of bicuculline into DMH affected brain regions around 4V or ARC, we performed Fos immunostaining in the DMH and neighboring brain regions such as PLH, VMH, and ARC after bicuculline infusion experiment (Figure 4—figure supplement 2). We did not observe significant difference of Fos expression in these regions.

Therefore, the BIC infused into DMH showed efficient GABAA antagonism to block LepR-mediated function, while showing the minimal effects on neighboring brain regions.

– I am still confused by the recordings of MC4R neurons – where both foraging and hyperglycemia inhibit MC4R neurons. The authors cite one study suggesting that glucose activates AgRP neurons but do not discuss the many other studies that show the opposite. At the very least this should be discussed rather than the conclusion being that "these findings are consistent with MC4R-DMH neurons' mediation of food intake and blood glucose"

Thanks for your comments. We added discussion to the manuscript accordingly (Line 283-293).

– I am also still confused with how the DMH GABA KO studies fit into the narrative of the paper. The effect size here is quite large (and it is an interesting finding), but the leptin signaling is systemic and not specific to AgRP neurons.

We appreciate these valuable comments. Because this experiment about ablating GABA in DMH caused confusion to both reviewers and is not closely relevant to the scope of the manuscript, we removed this experiment from our updated manuscript as to best ensure the coherence and clarity of the main scheme.

Author details

1. Yong Han

   USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, United States

   Contribution

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

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0768-046X

2. Yang He

   USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Lauren Harris

   USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

4. Yong Xu

   USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, United States

   Contribution

   Resources, Funding acquisition

   Competing interests

   No competing interests declared

5. Qi Wu

   USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, United States

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   qiw@bcm.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0303-4065

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