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. Author manuscript; available in PMC: 2025 Dec 1.
Published in final edited form as: Neuropeptides. 2024 Aug 6;108:102461. doi: 10.1016/j.npep.2024.102461

Upregulation of Xbp1 in NPY/AgRP neurons reverses diet-induced obesity and ameliorates leptin and insulin resistance

Jason Ajwani 1, Eunsang Hwang 1, Bryan Portillo 1, Linh Lieu 1, Briana Wallace 1, Anita Kabahizi 1, Zhenyan He 1, Yanbin Dong 1, Kyle Grose 1, Kevin W Williams 1,*
PMCID: PMC11568921  NIHMSID: NIHMS2021781  PMID: 39180950

Abstract

The molecular mechanisms underlying neuronal leptin and insulin resistance in obesity and diabetes are not fully understood. In this study, we show that induction of the unfolded protein response transcription factor, spliced X-box binding protein 1 (Xbp1s), in Agouti-Related Peptide (AgRP) neurons alone, is sufficient to not only protect against but also significantly reverse diet-induced obesity (DIO) as well as improve leptin and insulin sensitivity, despite activation of endoplasmic reticulum stress. We also demonstrate that constitutive expression of Xbp1s in AgRP neurons contributes to improved insulin sensitivity and glucose tolerance. Together, our results identify critical molecular mechanisms linking ER stress in arcuate AgRP neurons to acute leptin and insulin resistance as well as liver glucose metabolism in DIO and diabetes.

Keywords: Arcuate, NPY, AgRP, ER stress, UPR, Xbp1

1. Introduction

The prevalence of obesity has been rapidly rising. Since 1990, adult obesity has doubled worldwide, and adolescent obesity has tripled (Wang et al., 2014). Obese or overweight individuals have an increased risk of multiple comorbidities including type 2 diabetes and heart disease (Wang et al., 2014). Given that 2.5 billion adults were categorized as overweight in 2022, this epidemic continues to affect billions of people today (Collaborators, 2020). Many of these issues are closely linked with the disruption or increase in natural resistance to endogenous metabolic hormones and peptides. In particular, obesity is often associated with leptin resistance (Pan and Myers Jr., 2018), where leptin, a key hormone for metabolic and energy regulation synthesized by adipose tissue, increases proportionally with adiposity. In healthy conditions, leptin communicates with the brain, particularly the hypothalamus, prompting a reduction in food intake and an increase in energy expenditure. Conversely, when adiposity decreases and leptin concentrations fall, it causes increased appetite and reduced energy expenditure (Bays et al., 2013; Pan and Myers Jr., 2018; Wang et al., 2014). Type 2 diabetes, on the other hand, is characterized by insulin resistance, a condition where insulin, a critical hormone for glucose metabolism produced by the pancreas, becomes less effective at lowering blood sugar levels (Considine et al., 1996; Frederich et al., 1995; Reaven, 1995). This resistance has primarily been studied at the cellular level in muscle, fat, and liver, but also occurs in the brain, exacerbating glucose dysregulation while increasing the risk of severe health complications (Galicia-Garcia et al., 2020). This interplay between leptin and insulin resistance highlights the complexity of metabolic dysregulation associated with obesity and diabetes.

Within the brain, several key hypothalamic brain regions are involved in this process, including: the arcuate nucleus of the hypothalamus (ARC), ventromedial hypothalamic nucleus (VMH), dorsomedial hypothalamic nucleus (DMH), and lateral hypothalamic area (LHA) (Elmquist et al., 1998; Elmquist et al., 1997; Fei et al., 1997; Schwartz et al., 1996), in addition to extra-hypothalamic brain regions, such as the nucleus tractus solitarius (NTS) (Neyens et al., 2020). In particular, the ARC consists of insulin- and leptin-sensing neuronal populations that co-express neuropeptide Y (NPY) and agouti-related peptide (AgRP) (Jais and Bruning, 2022). ARC NPY/AgRP neurons play an important role in maintaining energy balance by regulating feeding behavior and energy expenditure (Deem et al., 2022; Qi et al., 2022; Rafiei et al., 2024; Zhang and Herzog, 2024; Zhang et al., 2020). These neurons release neuropeptides like NPY and AgRP, which integrate signals from metabolic hormones such as leptin and ghrelin to adjust feeding and energy balance, ensuring stable body weight and energy reserves (Lee et al., 2023; Spanswick et al., 1997; Su et al., 2019; Wu et al., 2019). Dysregulation of these neurons, as occurs during leptin and insulin resistance, contributes to obesity and metabolic disorders (Gropp et al., 2005; Ollmann et al., 1997; Zhang et al., 2020). Notably, the cellular mechanisms underlying leptin and insulin resistance as well as the metabolic dysregulation associated with obesity and diabetes is not fully understood.

Recently, endoplasmic reticulum (ER) stress and dysregulation of the unfolded protein response (UPR) have both been shown to play key roles in cellular leptin and insulin resistance, with implications on hypothalamic cell populations including NPY/AgRP neurons (Henry et al., 2015; Kim et al., 2018; Kim et al., 2023; Park et al., 2020; Williams et al., 2014). In the absence of a functional UPR, cells cannot prevent the accumulation of misfolded proteins, leading to cellular toxicity and triggering pathways that may result in apoptosis (Rao and Bredesen, 2004). Conversely, pathway-specific upregulation of the UPR has been shown to produce multiple favorable metabolic outcomes (Deng et al., 2018; Deng et al., 2013; Grandjean et al., 2020; He et al., 2021; Korennykh et al., 2009; Madhavan et al., 2022; Moncan et al., 2021; Peng et al., 2022; Williams et al., 2014; Yang et al., 2015). In particular, one branch of the UPR involves activation of the inositol-requiring enzyme 1/X-box binding protein 1 (IRE1-Xbp1) pathway, which selectively cleaves unspliced Xbp1, forming a spliced Xbp1 (Xbp1s), a transcription factor responsible for stimulating protein chaperones along with ER-associated protein degradation components (Rao and Bredesen, 2004). Induction of ER stress or deficiency of Xbp1 in neurons can lead to hyperleptinemia, obesity, and reduced metabolic rate, emphasizing the vital connection between neural signaling and metabolic health (Ozcan et al., 2009; Ozcan et al., 2004; Ozcan et al., 2006; Williams et al., 2014). On the other hand, driving Xbp1s expression broadly or in a cell-specific manner improves energy balance and glucose metabolism (Deng et al., 2018; Deng et al., 2013; Grandjean et al., 2020; Korennykh et al., 2009; Madhavan et al., 2022; Ozcan et al., 2009; Ozcan et al., 2004; Ozcan et al., 2006; Peng et al., 2022; Williams et al., 2014; Yang et al., 2015). In the current study, we assessed the requirement of Xbp1 in NPY/AgRP neurons to regulate glucose metabolism and diet-induced obesity (DIO). Additionally, we investigated changes in glucose metabolism associated with Xbp1 expression in NPY/AgRP neurons to provide future groundwork for novel pharmacological pathways in metabolic regulation.

2. Methods

2.1. Animal

Male, pathogen-free mice were used for all experiments. All mice were housed under standard laboratory conditions (12-h light/dark cycle; lights on at 7:00 am) in a temperature-controlled environment. Mice were provided a Harlan Teklad chow diet (Chow), or high-fat diet (HFD) (D12331; Research Diets), or high-fat diet enriched with doxy-cycline (600 mg/kg) (HFD-Dox) and water ad-libitum unless otherwise noted. To yield Xbp1 deletion in NPY/AgRP neurons, AgRP-iCre mice were crossed with Xbp1lox/lox mice to generate AgRP-iCre::Xbp1lox/lox mice. To generate mice with Xbp1s overexpression in NPY/AgRP neurons, we generated AgRP-iCre::Rosa26-rTTA::Xbp1s (AIX) mice. Body composition was measured using nuclear magnetic resonance (NMR; Bruker minispec). All mice used in experiments were age- and bodyweight-matched with their respective controls. All experiments were performed in accordance with the guidelines established by the National Institute of Health Guide for the Care and Use of Laboratory Animals and approved by the University of Texas Institutional Animal Care and Use Committee.

2.2. Metabolic cages

Experiments were performed in a temperature-controlled room containing 36 TSE metabolic cages maintained by University of Texas Southwestern (UTSW) Animal Resources personnel. One week prior to study, mice were single-housed to acclimate to new housing. Three days prior to study, mice were transported to the room containing the metabolic cages to habituate to a new environment. Mice that lost >10% of their total body weight during the acclimation period for metabolic cage studies were excluded from study. High-fat diet (HFD), if applicable, was also introduced at the beginning of this acclimation period. After 3 days of acclimation, cages were connected to the TSE system for a total of 5–10 days. Day 1 was excluded in all data analysis to account for any residual acclimation.

2.3. Glucose Tolerance Tests (GTTs)

After an overnight fast, age- and body weight-matched male mice received intraperitoneal injections of 1.5 g/kg d-glucose. Blood glucose was measured from tail blood using a glucometer at serial time points as indicated in the figures.

2.4. Insulin Tolerance Tests (ITTs)

After a 4-h fast to empty the stomach, age- and body weight-matched male mice received intraperitoneal injections of insulin (1.2 units/kg for chow-fed mice or 1.4 units/kg for HFD fed mice). Blood glucose was measured from tail blood at serial time points as indicated in the figures.

2.5. Electrophysiology

Whole-cell patch-clamp recordings of NPY/AgRP neurons from NPY-hrGFP (humanized, Renilla reniformis green fluorescent protein) or AgRP-iCre mice maintained in hypothalamic slice preparations and data analysis were performed as previously described (Hill et al., 2008). In brief, mice were deeply anesthetized with intraperitoneal injections of 7% chloral hydrate and transcardially perfused with a modified ice-cold artificial cerebrospinal fluid (ACSF; described below), in which an equiosmolar amount of sucrose was substituted for NaCl. The mice were then decapitated, and the entire brain was removed and immediately submerged in ice-cold, carbogen-saturated (95% O2 and 5% CO2) ACSF (126 mmol/L NaCl, 2.8 mmol/L KCl, 1.2 mmol/L MgCl2, 2.5 mmol/L CaCl2, 1.25 mmol/L NaH2PO4, 26 mmol/L NaHCO3, and 5 mmol/L glucose). Coronal sections (250 μm) were cut with a Leica VT1000S vibratome and then incubated in oxygenated ACSF (32 °C–34 °C) for at least 1 h before recording. Slices were transferred to the recording chamber and allowed to equilibrate for 10–20 min before recording. During recording, the slices were bathed in oxygenated ACSF (32 °C–34 °C) at a flow rate of ~2 mL/min.

In electrophysiological experiments involving acute leptin following a pretreatment of tunicamycin (tm), brain slices were treated with an incubation of oxygenated ACSF (32 °C–34 °C) that contained either DMSO for control (2 h or 6 h) or tunicamycin (30 μM; 2 h or 6 h) (Williams et al., 2014; Yao et al., 2017). Slices were then transferred to the recording chamber and allowed to equilibrate for 10–20 min before recording. During recording, the slices were bathed in oxygenated ACSF (32 °C–34 °C) at a flow rate of ~2 mL/min before the bath was switched to a solution of leptin in ACSF (100 nM; ~5 min) (Williams et al., 2014; Yao et al., 2017). After the acute application of leptin, the bath was switched back to control ACSF.

The pipette solution for whole-cell recording was modified to include an intracellular dye (Alexa Fluor 594 or Alexa Fluor 350) for whole-cell recording: 120 mmol/L K-gluconate, 10 mmol/L KCl, 10 mmol/L HEPES, 5 mmol/L EGTA, 1 mmol/L CaCl2, 1 mmol/L MgCl2, and 2 mmol/L MgATP and either 0.03 mmol/L Alexa Fluor 594 or Alexa Fluor 350 hydrazide dye (pH 7.3). Epifluorescence was briefly used to target fluorescent cells, after which time the light source was switched to infrared differential interference contrast imaging to obtain the whole-cell recording (Zeiss Axioskop FS2 Plus equipped with a fixed stage and a QuantEM:512SC electron-multiplying charge-coupled device camera). Electrophysiological signals were recorded using an Axopatch 700B amplifier (Molecular Devices), low-pass filtered at 2–5 kHz, and analyzed offline on a PC with pCLAMP programs (Molecular Devices). Recording electrodes had resistances of 2.5–5 mol/LΩ when filled with the K-gluconate internal solution.

2.6. Fluorescence-activated cell sorting (FACS)

Fluorescence-activated cell sorting collection and analysis of neurons were performed as previously described (Cravo et al., 2011; Williams et al., 2014). RNA extraction was performed using the PicoPure RNA Isolation kit (Arcturis, St Louis, Missouri). Before quantitative PCR (QPCR), cDNA was preamplified using 2× TaqMan PreAmp Master Mix (Applied Biosystems, Foster City, California).

2.7. Analysis of gene expression by quantitative PCR

A coronal slice between bregma −1.22 mm and −2.70 mm was made from age- and body weight-matched mice, and then the ARC was micro-dissected with a scalpel under a microscope. Total RNA was extracted from tissues with TRIzol reagent (Invitrogen), according to the manufacturer’s instructions. Total RNA (1 μg) was converted into first-strand cDNA with oligo(dT) primers as described by the manufacturer (Clontech). PCR was performed in a Mx3000P Q-PCR system (Stratagene) with specific primers and SYBR Green PCR Master Mix (Stratagene). The relative abundance of mRNAs was standardized with 18S mRNA as the invariant control.

2.8. Statistics and figures

Results are reported as the mean ± SEM unless indicated otherwise. Significance was set at p < 0.05 for all statistical measures. All data were evaluated using a 2-tailed Student’s t-test or one- or two-way ANOVA with post hoc analyses where applicable. All graphs were made using Graph Pad Prism 10.0 software or CorelDraw C8 (64 Bit) 10 software. The summary figure was made using Procreate v5.3.10 and CorelDraw C8 (64 Bit) 10 software. Pre-established criteria for excluding data points were data two SDs outside the mean or any data obtained from mice that died or lost >10% of body weight due to metabolic cage acclimation.

3. Results

3.1. Xbp1s deficiency increases susceptibility to DIO

Xbp1s improves energy expenditure and glucose metabolism in the periphery as well as the central nervous system (CNS) (Deng et al., 2013; Ozcan et al., 2009; Williams et al., 2014). We previously demonstrated that specific overexpression of Xbp1s in ARC pro-opiomelanocortin (POMC) neurons was sufficient to protect against DIO while improving glucose and insulin homeostasis (Williams et al., 2014). This protection occurred concomitantly with improvement of leptin and insulin sensitivity that occurred systemically and at the cellular level (Williams et al., 2014). Notably, the role of the IRE1-Xbp1 pathway in NPY/AgRP neurons remains undefined. This is a striking omission, as NPY/AgRP (independent of POMC) neurons have been suggested to be required for leptin to regulate energy and glucose metabolism (Xu et al., 2018). To test the requirement of the Xbp1s in AgRP neurons to regulate body weight, we generated mice that were deficient for Xbp1 specifically in AgRP neurons, AgRP-iCre::Xbp1lox/lox mice.

On a chow diet, AgRP-iCre::Xbp1lox/lox mice exhibited similar body weights when compared to littermate control mice (p > 0.05; Fig. 1A). However, when fed a HFD, AgRP-iCre::Xbp1lox/lox mice had increased body weight compared to control mice (p < 0.05; Fig. 1B), which attributed to an increase in fat mass (t(19) = 2.803, p < 0.05) independent of changes in lean mass (t(19) = 0.9379, p > 0.05) (Fig. 1C). As expected, the mRNA level for Xbp1 in chow-fed AgRP-iCre::Xbp1lox/lox was diminished in the ARC when compared to littermate controls (t(10) = 2.754, p < 0.05; Fig. 1D). This effect appeared specific to the ARC, as the difference in expression of Xbp1 remained statistically insignificant in the forebrain and liver of AgRP-iCre::Xbp1lox/lox mice (Forebrain: t(10) = 0.8021, p > 0.05; Liver: t(10) = 0.4731, p > 0.05; Fig. 1D). Total energy expenditure consists of basal metabolism and energy required for physical activities. Age- and body weight-matched AgRP-iCre::Xbp1lox/lox male mice were hypometabolic when fed a HFD, as demonstrated by decreased energy expenditure through lower heat production (Dark t(6) = 2.881, p < 0.05; Day t(4) = 4.976, p < 0.01; Fig. 1G), independent of alterations in food intake (Dark t(6) = 0.5854, p > 0.05; Day t(4) = 1.147, p > 0.05; Fig. 1H), total activity (Dark t(6) = 1.894, p > 0.05; Day t(4) = 0.5506, p > 0.05; Fig. 1I) and respiratory exchange rate (RER) (Dark t(6) = 0.9435, p > 0.05; Day t(4) = 1.194, p > 0.05; Fig. 1J).

Fig. 1. Body weight and metabolic assessment of male control and AgRP-iCre::Xbp1lox/lox mice.

Fig. 1.

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Body weight curve of male control and AgRP-iCre::Xbp1lox/lox mice on a chow diet (A) or a HFD (B). Bar graph of body fat composition of male control (black) and AgRP-iCre::Xbp1lox/lox mice (red) on a high fat diet (HFD) at 6 months (C). Bar graph showing Xbp1 mRNA expression level in (Left to Right) ARC, forebrain, and liver from control (black) and AgRP-iCre::Xbp1lox/lox (red) mice (D). Bar graph of dark cycle (left) and day cycle (right) of VO2 (E), VCO2 (F), heat production(G), food intake (H), ambulatory activity (I), and respiratory exchange ratio (J) under HFD of control (black) and AgRP-iCre::Xbp1lox/lox mice (red). Data are expressed as mean ± SEM. Statistical analyses were performed using 2-way repeated-measures ANOVA, with Šídák’s post hoc analyses applied for comparisons at relevant time-points (A-B) and Unpaired t-test (C-J). (A-C) n = 8–18 per group; (D–J) n = 5–6 per group. (nsp > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

3.2. Mice lacking Xbp1 in AgRP neurons exhibit a diet-dependent impairment of insulin sensitivity and glycemia

To investigate if Xbp1 deficiency in ARC AgRP neurons impaired glucose metabolism, we conducted glucose and insulin tolerance tests (GTT and ITT) on AgRP-iCre::Xbp1lox/lox mice fed either chow or HFD. Along with the systemic effects on whole-body energy expenditure and body weight, deficiency of Xbp1 in AgRP neurons also leads to diet-dependent glucometabolic changes. For instance, AgRP-iCre::Xbp1lox/lox mice fed a chow diet failed to show differences in glucose (AUC: t(27) = 0.1294, p > 0.05; Fig. 2A) or insulin tolerance (AUC: t(28) = 0.09693, p > 0.05; Fig. 2B) when compared to control mice. However, AgRP-iCre:: Xbp1lox/lox fed HFD presented a significantly higher glucose excursion compared to the littermate HFD controls (AUC: t(29) = 2.810, p < 0.01; Fig. 2C) and were intolerant to bolus injections of insulin (AUC: t(14) = 3.130, p < 0.01; Fig. 2D). Glucose production and/or liver secretion is typically responsible for how mammals maintain a tight euglycemic range. The livers of HFD-fed AgRP-iCre::Xbp1lox/lox mice displayed elevated gluconeogenic markers (Foxo1: t(10) = 1.444, p > 0.05; G6pc: t (10) = 1.196, p > 0.05; HNF4α: t(10) = 2.452, p < 0.05; Pcx: t(10) = 2.462, p < 0.05; Pepck: t(10) = 3.715, p < 0.05; Fig. 2E), supportive of increased glucose production capacity within the liver.

Fig. 2. Impaired glucose regulation in HFD fed mice deficient for Xbp1 in AgRP neurons.

Fig. 2.

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Plots for glucose tolerance tests and insulin tolerance tests (area under the curve inset), respectively from control (black) and AgRP-iCre::Xbp1lox/lox (red) mice on a chow diet (A-B) or a high fat diet (HFD), (C–D). (E) Bar graph of the relative expression of Foxo1, G6pc, Hnf4α, Pcx, and Pepck mRNA level from control (black) and AgRP-iCre::Xbp1lox/lox (red) mice in the liver. Data are from male mice and are expressed as mean ± SEM. Statistical analyses are performed using 2-way repeated-measures ANOVA with Šídák’s post hoc analyses for comparisons at relevant time-points and area under the curve analyzed by Unpaired t-test (A-D) or Unpaired t-test (E). (A-B) n = 14–15 per group; (C–D) n = 8–17 per group; (E) n = 6 per group. Fold change is relative to 18S mRNA. (nsp > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

3.3. Persistent and intermittent overexpression of Xbp1 protects against DIO

Xbp1s protects against cellular and systemic leptin and insulin resistance, both in the periphery and in the CNS, while also mitigating adverse metabolic outcomes (Ozcan et al., 2009; Williams et al., 2014; Zhang et al., 2008). We recently utilized a mouse model that expresses an inducible “dominant active” Xbp1s transgene via a conventional Tet-On system (Williams et al., 2014). The Xbp1s transgene under the control of a tetracycline-responsive element (TRE) supports inducible expression by the tetracycline reverse transcriptional activator (rtTA) in the presence of Dox. The rtTA transgene is driven by the Rosa26 promoter with a transcriptional stop cassette flanked by two loxP sites upstream of rtTA (Belteki et al., 2005; Deng et al., 2013; Williams et al., 2014). Combined with an AgRP promoter-driven Cre transgene (Tong et al., 2008), we obtained a mouse model (AIXs) with AgRP-specific inducible expression of Xbp1s.

When continuously fed HFD-Dox starting at 6 weeks of age, AIX mice experienced a significantly slower rate of bodyweight gain when compared to HFD fed control mice (Week 12: p < 0.001; Week 15: p < 0.0001; Fig. 3A). Interestingly, when AIX mice with DIO were introduced to HFD-Dox starting at 16 weeks of age, induction of Xbp1s expression in AgRP neurons resulted in a decrease of body weight to levels resembling mice fed a chow diet, most notable at week 20 (Week 19 through 24: p > 0.05, Week 20: p > 0.9999; Fig. 3B). This weight loss was accompanied by a reduction in fat mass (t(6) = 2.288, p < 0.01), independent of changes in lean mass (t(6) = 4.712, p > 0.05) (Fig. 3C). Removal of Dox completely reversed this weight loss, resulting in body weight similar to that of control mice on HFD (Week 27: p > 0.9999; Fig. 3B). The slower rate of weight gain with immediate overexpression of Xbp1s and the rapid decrease of body weight in DIO with delayed overexpression of Xbp1s in AgRP neurons of AIX mice, coupled with their subsequent body weight reversal after Dox removal, underscores the role of Xbp1s expression in protecting against and mitigating diet-induced weight gain.

Fig. 3. Body weight and metabolic assessment of male control and AIX mice.

Fig. 3.

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Body weight curve of male AIX (red) and control mice (black) on a HFD-Dox (A). Body weight curve of male control and AIX on a chow (control: purple, AIX: green) or a HFD (control: black, AIX: red) with or without Dox (B). Bar graph of fat mass and lean mass of male control (black) and AIX (red) mice on a HFD-Dox at 20 weeks (C). Bar graphs of dark cycle (left) and day cycle (right); of VO2 (D), VCO2 (E), heat production (F), food intake (G), and respiratory exchange ratio (H) under HFD-DOX of control (black) and AIX mice (red). Data are expressed as mean ± SEM. Statistical analyses were performed using 2-way repeated-measures ANOVA with Šídák’s post hoc analyses applied for comparisons at relevant time-points (A-B) and Unpaired t-test (C–I). (A) n = 10–14 per group; (B) n = 3–18 per group; (C) n = 4 per group, (D–H) n = 5–6 per group. (nsp > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

To better assess the metabolic parameters that contribute to this reduction in body weight from delayed Dox-mediated Xbp1s overexpression, AIX and control mice fed HFD-Dox diet were placed in metabolic chambers. AIX mice displayed increased energy expenditure, demonstrated by significant increases in heat production in the dark (Dark: t(12) = 2.435, p < 0.05; Day: t(10) = 0.1171, p > 0.05 Fig. 3F); decreased caloric intake, demonstrated by significantly lower food intake during the day (Dark: t(12) = 0.08743, p > 0.05; Day: t(10) = 3.231, p < 0.01; Fig. 3G); and decreased RER in the dark (Dark: t(12) = 3.700, p < 0.01; Day: t (10) = 1.363, p > 0.05 Fig. 3H), indicative of a higher utilization of fat as a fuel source.

3.4. Persistent overexpression of Xbp1s in AgRP neurons improves insulin sensitivity and glycemia in DIO

To investigate the effects of overexpression of Xbp1s in ARC AgRP neurons on glucose metabolism, we conducted GTTs and ITTs on AIX mice fed either chow-Dox or HFD-Dox. AIX mice failed to show differences in glucose (AUC: t(16) = 0.5293, p > 0.05; Fig. 4A) and insulin tolerances (AUC: t(16) = 1.273, p > 0.05; Fig. 4A) when mice were fed a chow-Dox diet and compared to controls (Fig. 4A-B). However, when fed HFD-Dox, AIX mice showed a trend towards improved glucose tolerance without reaching statistical significance (AUC: t(22) = 1.908, p = 0.0695; Fig. 4C) when compared to controls. However, the improvement of insulin tolerance in AIX mice was statistically significant (AUC: t(29) = 2.789, p < 0.01; Fig. 4D) when compared to controls.

Fig. 4. Altered glucose metabolism and insulin sensitivity in mice overexpressing Xbp1 in AgRP neurons on a HFD with Dox but not a chow diet with Dox.

Fig. 4.

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Plots showing the GTT (A) and ITT (B) from male control (black) and AIX (red) mice fed a chow-Dox diet. Plots showing GTT (C) and ITT (D) from male control and AIX (red) mice on a HFD-Dox. Data are expressed as mean ± SEM. AUC, area under the curve. Statistical analyses are performed using 2-way repeated-measures ANOVA, with Šídák’s post hoc analyses applied for comparisons at relevant time-points and area under the curve analyzed by Unpaired t-test. (A-B) n = 8–10 per group; (C–D) 8–22 per group. (nsp > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001).

3.5. ER stress-induced cellular leptin and insulin resistance in AgRP neurons

We next investigated the intrinsic activity of ARC NPY/AgRP neurons. Similar to previous reports (Williams et al., 2014; Yao et al., 2017), prolonged chemical activation of ER stress (tm; 30 μM, 6 h but not 1-2 h) in the ARC results in decreased sensitivity to leptin in control ARC NPY/AgRP neurons when compared to control without tm incubation (Change from baseline resting membrane potential. Control + leptin: −15.28 ± 1.911 mV; Control + leptin + tm 2 h: −18.57 ± 2.382 mV, p > 0.05; Control + leptin + tm 6 h: −0.8273 ± 1.082 mV, p < 0.01; Fig. 5A-B). However, AgRP neurons lacking Xbp1 yielded premature leptin resistance from chemical activation of ER stress after only 2 h of tm incubation when compared to control with and without 2 h tm incubation (Change from baseline resting membrane potential. AgRP-iCre::Xbp1lox/lox + leptin + tm 2 h: −1.349 ± 0.6947; p < 0.001; Fig. 5A). In addition to suggesting that Xbp1 deficiency in AgRP neurons accelerates ER Stress-induced leptin resistance, our data demonstrates that constitutive expression of Xbp1s in AgRP neurons protects against ER stress-induced leptin resistance in ARC NPY/AgRP neurons when compared to control with tm 6 h (Change from baseline resting membrane potential. AIX + leptin + tm 6 h: −12.41 ± 1.164 mV, p < 0.01; Fig. 5B).

Fig. 5. ER stress-induced leptin resistance of NPY/AgRP neurons.

Fig. 5.

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Bar graphs (top) and sample traces (bottom) showing the change in baseline resting membrane potential of ARC NPY/AgRP neurons with or without pre-treatment of tunicamycin after a ~ 5 min acute 100 nM leptin bath application in control (black; A-B), control + tunicamycin (tm; 30 μM, 2 h (A), 6 h (B), red), AgRP-iCre:: Xbp1lox/lox + tunicamycin (tm; 30 μM, 2 h, purple; A) and AIX + tunicamycin (tm; 30 μM, 6 h, green; B). Data are expressed as mean ± SEM. Statistical analyses are performed using one-way ANOVA, with Tukey’s post hoc analyses. (A) n = 4–5 per group; (B) n = 3–7 per group. (nsp > 0.05, **p < 0.01, ***p < 0.001).

3.6. Exercise training upregulates Xbp1 induction and overexpressing Xbp1 inhibits ARC NPY/AgRP neurons

Our lab previously demonstrated that exercise inhibits ARC NPY neurons (He et al., 2018). Exercise influences multiple systems, including the Xbp1s pathway of the UPR (Kim et al., 2010; Ogborn et al., 2014; Ropelle et al., 2010; Wu et al., 2011). We hypothesized that exercise might alter Xbp1 levels within the ARC. To test this, NPY-hrGFP reporter mice underwent high-intensity interval exercise (HIIE) or remained sedentary for one hour. Subsequently, the mice were sacrificed, and NPY neurons were isolated using fluorescence-activated cell sorting (FACS) for mRNA measurement via qPCR. HIIE significantly increased expression of Xbp1s and its target genes in FACS-isolated NPY neurons, supporting an upregulation of Xbp1s and decreased AgRP expression in ARC NPY neurons following exercise (AgRP t(9) = 2.430, p < 0.05; xbp1 (t(33) = 2.733, p < 0.01); Fig. 6A) and consistent with previous data (He et al., 2018). Furthermore, we also targeted AgRP neurons from chow-Dox fed AIX mice for whole-cell electrophysiological recordings. AgRP neurons from AIXs mice exhibited a hyperpolarized resting membrane potential similar to that observed in NPY neurons from exercised mice (t(8) = 2.446, p < 0.05; Fig. 6B) and from previous reports (He et al., 2018). These data suggest that high-intensity exercise may physiologically upregulate expression of Xbp1s, leading to the chronic inhibition of ARC NPY/AgRP neurons.

Fig. 6. Inhibition of AgRP neurons and exercise-induced upregulation of Xbp1 in ARC.

Fig. 6.

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Bar graph showing relative expression of NPY, AgRP, Xbp1, and Erdj4 from NPY-hrGFP of sedentary mice (black) and mice that ran one hour of high-intensity interval exercise (red) in the ARC (A). Bar graph showing HIIE (black) and Xbp1s overexpression (red) inhibits ARC NPY/AgRP neurons to a similar degree (B). Data are expressed as mean ± SEM. Statistical analyses are performed using Unpaired t-test. (A-B) n = 4–6 per group. (*p < 0.05 **p < 0.01).

4. Discussion

ER stress increases sensitivity to DIO and impairs glucose and insulin tolerance in mice. Within the hypothalamus, the IRE1-Xbp1 branch of the UPR mitigates ER stress, protects against DIO, and improves blood glucose control (Deng et al., 2018; Deng et al., 2013; Grandjean et al., 2020; Korennykh et al., 2009; Madhavan et al., 2022; Ozcan et al., 2009; Ozcan et al., 2004; Ozcan et al., 2006; Peng et al., 2022; Williams et al., 2014; Yang et al., 2015). While various studies highlight different phenotypes resulting from the induction or deletion of Xbp1 in multiple organ systems, its role in specific neuronal populations like NPY/AgRP neurons, which critically regulate feeding, is not entirely understood. Our findings demonstrate that Xbp1 deficiency in AgRP neurons (AgRP-iCre::Xbp1lox/lox mice) exacerbates weight gain in DIO. This increased body weight is associated with an increase in fat mass along with a reduced metabolic rate, as characterized by reduced energy expenditure and heat production, independent of changes in RER, food intake, or overall activity levels. Notably, the absence of Xbp1 in AgRP neurons does not impair glucose or insulin tolerance in lean mice. However, obese mice lacking Xbp1 in AgRP neurons exhibit reduced glucose and insulin tolerance. Further supporting the glucometabolic impairment in these mice, we observed elevated levels of gluconeogenic markers, indicating an increased capacity for glucose production in the liver. At the cellular level, AgRP-iCre::Xbp1lox/lox mice displayed an accelerated insensitivity to leptin administered into the ARC following acute chemical activation of ER stress and leptin, indicating a heightened sensitivity to ER stress and leptin resistance. These observations support a model where the loss of Xbp1 in NPY/AgRP neurons accelerates HFD-induced obesity and glucose dysregulation.

Specific upregulation of Xbp1 in ARC POMC neurons and throughout the entire brain improves metabolism (He et al., 2021; Jais and Bruning, 2022; Williams et al., 2014). Similarly, our study demonstrates that upregulation of Xbp1s in NPY/AgRP neurons offers protection against DIO and improves glucose metabolism. Interestingly, even after mice undergo DIO, overexpression of Xbp1s in ARC AgRP neurons is sufficient to completely reverse the DIO weight gain. Notably, pharmacological activation of the IRE1-Xbp1 pathway is an active area of study (Shokat, 2010; Wiseman et al., 2010). In particular, several groups have successfully employed pharmacological interventions to upregulate this pathway, leading to improvements and even prevention of various diseases (Deng et al., 2018; Deng et al., 2013; Grandjean et al., 2020; Korennykh et al., 2009; Madhavan et al., 2022; Peng et al., 2022; Yang et al., 2015). These findings position Xbp1s as a promising pharmacological target, potentially replicating the UPR benefits observed in our AIX model to improve metabolism. This therapeutic potential may hinge on inhibiting ARC NPY/AgRP neurons and/or enhancing systemic or neuronal leptin and insulin sensitivity (Hwang et al., 2022; Lieu et al., 2020).

Notably, NPY/AgRP neurons are inhibited by chronic activation of Xbp1s. Typically, during periods of food deprivation, ARC NPY/AgRP neurons are activated, leading to increased food intake and impaired glucose metabolism (Aponte et al., 2011; Kim et al., 2024; Steculorum et al., 2016). Meanwhile, food presentation, refeeding, and exercise inhibit these neurons. Overexpressing Xbp1s in ARC NPY/AgRP neurons mimics conditions that improve energy balance and glucose metabolism (Hwang et al., 2023; Lieu et al., 2020). Specifically, AIX mice exhibited increased energy expenditure and a lower RER compared to controls, indicating a metabolic shift from carbohydrates to fats as the primary energy source. This shift is also observed post-exercise, where metabolism adjusts, and the body utilizes more “long-term” energy stores rather than “short-term” energy stores (Pendergast et al., 2000; Rothschild et al., 2022; Simonson and DeFronzo, 1990). Further supporting this “exercise-like” state, NPY/AgRP neurons in AIX mice remained sensitive to acute pharmacological leptin application after chronic ER stress, while control mice were no longer responsive. Interestingly, this “exercise-like” state observed in the context of Xbp1s expression is consistent with an upregulation of Xbp1 post-exercise. Together, these data support a model in which constitutive activation of Xbp1s in AgRP neurons not only protects against but also reverses DIO while enhancing glucose homeostasis and insulin sensitivity. Importantly, the ability of Xbp1s in AgRP neurons to reverse DIO, coupled with mimicking this “exercise-like” state, seen through mechanisms that drive food intake and increase utilization of internal fat stores, further highlights the necessity for developing ER stress-targeted anti-obesity pharmacotherapy.

Consistent with our data, induction of ER stress in neurons of the whole hypothalamus leads to leptin insensitivity and glucose dysregulation while increased neuronal ER stress capacity in the hypothalamus enhances leptin sensitivity (Ozcan et al., 2009; Won et al., 2009). Similar to effects seen with Xbp1s dysregulation in the whole hypothalamus, it is important to note that the contribution of Xbp1 expression might have distinct effects on the degree of leptin sensitivity and glucose homeostasis that depend on hypothalamic cell types. Specifically, constitutively expressing Xbp1s in POMC neurons in the ARC led to robust improvements in both glucose and insulin regulation, independent of DIO (Williams et al., 2014). Similarly, downregulation of Xbp1 in these neurons led to poor glucose and insulin tolerance, independent of diet type (Yao et al., 2017). On the other hand, glucose and insulin tolerance effects with either constitutive expression of Xbp1s or Xbp1 deletion in ARC NPY/AgRP neurons in the current study are only seen in mice under DIO conditions. However, in both POMC and NPY/AgRP neurons, constitutive expression of Xbp1s protected against DIO and ER stress-induced leptin resistance while deletion of Xbp1 in both hypothalamic cell populations led to accelerated ER stress-induced leptin resistance (He et al., 2021; Williams et al., 2014; Yao et al., 2017). The glucor-egulatory effects of Xbp1 expression depending on metabolic state, whether DIO or lean, in NPY/AgRP, but not POMC, indicate a possible hierarchy in which the ER stress arm is utilizedin regulation of the UPR. Notably, protein kinase R–like ER kinase (PERK), involved in one of the major signaling pathways that work to reduce ER stress, had no effect on glucose homeostasis when deleted in POMC neurons, independent DIO (He et al., 2021). Under lean conditions, POMC may depend on UPR pathways or arms other than the Xbp1 (or PERK) to regulate glucose homeostasis while switching to Xbp1 utilization under DIO conditions. However, it appears that both POMC and NPY/AgRP require Xbp1 to regulate leptin sensitivity in lean conditions. Importantly, these differences highlight the potential for the UPR to alternate prioritization of UPR pathways, specifically Xbp1, based on metabolic state, needs and cell type.

5. Conclusion

ARC NPY/AgRP neurons play a significant role in maintaining metabolic and glucose homeostasis. Our study demonstrates a high and low tolerance for ER stress and the UPR’s capability for cellular regulation and leptin sensitivity. Furthermore, we demonstrate the degree of requirement of Xbp1 in the context of ER stress, the UPR and multiple metabolic phenotypes that arise in response to modulating this pathway. While contributing to the growing body of evidence suggesting the need for pharmacological targets activating the UPR, this study incorporates a unique approach to investigating the metabolic effects of IRE1-Xbp1 pathway activation and inhibition. Overall, these results further highlight the growing importance of the UPR and cellular ER stress tolerance in the hypothalamus and its regulation in diabetes and obesity.

Acknowledgements

This work was supported by grants to: E.H. (National Research Foundation of Korea – NRF 2021R1A6A3A14044733) and K.W.W. (R01 DK100699, DK119169, and DK119130–5830).

Abbreviations:

ARC

arcuate nucleus of the hypothalamus

VMH

ventromedial hypothalamic nucleus

DMH

dorsomedial hypothalamic nucleus

LHA

lateral hypothalamic area

NTS

nucleus tractus solitarius

ACSF

artificial cerebrospinal fluid

NPY

neuropeptide Y

AgRP

agouti-related peptide

POMC

pro-opiomelanocortin

ER

endoplasmic reticulum

UPR

unfolded protein response

IRE1

inositol-requiring enzyme 1

Xbp1

X-box binding protein 1

Xbp1s

spliced X-box binding protein 1

PERK

protein kinase R–like ER kinase

CNS

central nervous system

Foxo1

forkhead Box 01

HNF4α

hepatocyte nuclear factor-4 alpha

Pcx

pyruvate carboxylase

G6pc

glucose 6-phosphatase

Pepck

phosphoenolpyruvate carboxykinase

hrGFP,

humanized, Renilla reniformis green fluorescent protein

DIO

diet-induced obesity

HFD

high-fat diet

Dox

doxycycline

Tm

tunicamycin

(TRE)

tetracycline-responsive element

AIX

AgRP-iCre::Rosa26-rTTA::Xbp1s

GTT

glucose tolerance test

ITT

insulin tolerance test

FACS

fluorescence-activated cell sorting

RER

respiratory exchange rate

(rtTA)

tetracycline reverse transcriptional activator

HIIE

high-intensity interval exercise.

Footnotes

CRediT authorship contribution statement

Jason Ajwani: Writing – review & editing, Writing – original draft, Project administration, Formal analysis, Data curation. Eunsang Hwang: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology. Bryan Portillo: Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration. Linh Lieu: Project administration, Methodology, Investigation. Briana Wallace: Visualization, Methodology, Investigation, Formal analysis. Anita Kabahizi: Methodology, Investigation, Formal analysis. Zhenyan He: Validation, Methodology, Investigation. Yanbin Dong: Methodology, Formal analysis. Kyle Grose: Writing – review & editing, Writing – original draft, Validation. Kevin W. Williams: Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Data curation, Conceptualization.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT for the purpose of editing. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Declaration of competing interest

No potential conflicts of interest relevant to this article were reported.

Data availability

Data will be made available on request.

References

  1. Aponte Y, Atasoy D, Sternson SM, 2011. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci 14, 351–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bays HE, Toth PP, Kris-Etherton PM, Abate N, Aronne LJ, Brown WV, Gonzalez-Campoy JM, Jones SR, Kumar R, La Forge R, Samuel VT, 2013. Obesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association. J. Clin. Lipidol 7, 304–383. [DOI] [PubMed] [Google Scholar]
  3. Belteki G, Haigh J, Kabacs N, Haigh K, Sison K, Costantini F, Whitsett J, Quaggin SE, Nagy A, 2005. Conditional and inducible transgene expression in mice through the combinatorial use of Cre-mediated recombination and tetracycline induction. Nucleic Acids Res. 33, e51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Collaborators, G.B.D.R.F, 2020. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Considine RV, Sinha MK, Heiman ML, Kriauciunas A, Stephens TW, Nyce MR, Ohannesian JP, Marco CC, McKee LJ, Bauer TL, et al. , 1996. Serum immunoreactive-leptin concentrations in normal-weight and obese humans. N. Engl. J. Med 334, 292–295. [DOI] [PubMed] [Google Scholar]
  6. Cravo RM, Margatho LO, Osborne-Lawrence S, Donato J Jr., Atkin S, Bookout AL, Rovinsky S, Frazao R, Lee CE, Gautron L, Zigman JM, Elias CF, 2011. Characterization of Kiss1 neurons using transgenic mouse models. Neuroscience 173, 37–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Deem JD, Faber CL, Morton GJ, 2022. AgRP neurons: regulators of feeding, energy expenditure, and behavior. FEBS J. 289, 2362–2381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Deng Y, Wang ZV, Tao C, Gao N, Holland WL, Ferdous A, Repa JJ, Liang G, Ye J, Lehrman MA, Hill JA, Horton JD, Scherer PE, 2013. The Xbp1s/GalE axis links ER stress to postprandial hepatic metabolism. J. Clin. Invest 123, 455–468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Deng Y, Wang ZV, Gordillo R, Zhu Y, Ali A, Zhang C, Wang X, Shao M, Zhang Z, Iyengar P, Gupta RK, Horton JD, Hill JA, Scherer PE, 2018. Adipocyte Xbp1s overexpression drives uridine production and reduces obesity. Mol Metab 11, 1–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Elmquist JK, Ahima RS, Maratos-Flier E, Flier JS, Saper CB, 1997. Leptin activates neurons in ventrobasal hypothalamus and brainstem. Endocrinology 138, 839–842. [DOI] [PubMed] [Google Scholar]
  11. Elmquist JK, Ahima RS, Elias CF, Flier JS, Saper CB, 1998. Leptin activates distinct projections from the dorsomedial and ventromedial hypothalamic nuclei. Proc. Natl. Acad. Sci. U. S. A 95, 741–746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Fei H, Okano HJ, Li C, Lee GH, Zhao C, Darnell R, Friedman JM, 1997. Anatomic localization of alternatively spliced leptin receptors (Ob-R) in mouse brain and other tissues. Proc. Natl. Acad. Sci. U. S. A 94, 7001–7005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Frederich RC, Hamann A, Anderson S, Lollmann B, Lowell BB, Flier JS, 1995. Leptin levels reflect body lipid content in mice: evidence for diet-induced resistance to leptin action. Nat. Med 1, 1311–1314. [DOI] [PubMed] [Google Scholar]
  14. Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, Ostolaza H, Martin C, 2020. Pathophysiology of type 2 diabetes mellitus. Int. J. Mol. Sci 21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Grandjean JMD, Madhavan A, Cech L, Seguinot BO, Paxman RJ, Smith E, Scampavia L, Powers ET, Cooley CB, Plate L, Spicer TP, Kelly JW, Wiseman RL, 2020. Pharmacologic IRE1/XBP1s activation confers targeted ER proteostasis reprogramming. Nat. Chem. Biol 16, 1052–1061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gropp E, Shanabrough M, Borok E, Xu AW, Janoschek R, Buch T, Plum L, Balthasar N, Hampel B, Waisman A, Barsh GS, Horvath TL, Bruning JC, 2005. Agouti-related peptide-expressing neurons are mandatory for feeding. Nat. Neurosci 8, 1289–1291. [DOI] [PubMed] [Google Scholar]
  17. He Z, Gao Y, Alhadeff AL, Castorena CM, Huang Y, Lieu L, Afrin S, Sun J, Betley JN, Guo H, Williams KW, 2018. Cellular and synaptic reorganization of arcuate NPY/AgRP and POMC neurons after exercise. Mol Metab 18, 107–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. He Z, Lieu L, Dong Y, Afrin S, Chau D, Kabahizi A, Wallace B, Cao J, Hwang ES, Yao T, Huang Y, Okolo J, Cheng B, Gao Y, Hu L, Williams KW, 2021. PERK in POMC neurons connects celastrol with metabolism. JCI. Insight 6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Henry FE, Sugino K, Tozer A, Branco T, Sternson SM, 2015. Cell type-specific transcriptomics of hypothalamic energy-sensing neuron responses to weight-loss. Elife 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hill JW, Williams KW, Ye C, Luo J, Balthasar N, Coppari R, Cowley MA, Cantley LC, Lowell BB, Elmquist JK, 2008. Acute effects of leptin require PI3K signaling in hypothalamic proopiomelanocortin neurons in mice. J. Clin. Invest 118, 1796–1805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hwang E, Scarlett JM, Baquero AF, Bennett CM, Dong Y, Chau D, Brown JM, Mercer AJ, Meek TH, Grove KL, Phan BAN, Morton GJ, Williams KW, Schwartz MW, 2022. Sustained inhibition of NPY/AgRP neuronal activity by FGF1. JCI Insight 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hwang E, Portillo B, Grose K, Fujikawa T, Williams KW, 2023. Exercise-induced hypothalamic neuroplasticity: implications for energy and glucose metabolism. Mol Metab 73, 101745. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jais A, Bruning JC, 2022. Arcuate nucleus-dependent regulation of metabolism-pathways to obesity and diabetes mellitus. Endocr. Rev 43, 314–328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kim Y, Park M, Boghossian S, York DA, 2010. Three weeks voluntary running wheel exercise increases endoplasmic reticulum stress in the brain of mice. Brain Res. 1317, 13–23. [DOI] [PubMed] [Google Scholar]
  25. Kim GH, Shi G, Somlo DR, Haataja L, Song S, Long Q, Nillni EA, Low MJ, Arvan P, Myers MG Jr., Qi L, 2018. Hypothalamic ER-associated degradation regulates POMC maturation, feeding, and age-associated obesity. J. Clin. Invest 128, 1125–1140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kim KK, Lee TH, Park BS, Kang D, Kim DH, Jeong B, Kim JW, Yang HR, Kim HR, Jin S, Back SH, Park JW, Kim JG, Lee BJ, 2023. Bridging energy need and feeding behavior: the impact of eIF2alpha phosphorylation in AgRP neurons. Diabetes 72, 1384–1396. [DOI] [PubMed] [Google Scholar]
  27. Kim KS, Park JS, Hwang E, Park MJ, Shin HY, Lee YH, Kim KM, Gautron L, Godschall E, Portillo B, Grose K, Jung S-H, Baek SL, Yun YH, Lee D, Kim E, Ajwani J, Yoo SH, Güler AD, Williams KW, Choi HJ, 2024. GLP-1 Increases Preingestive Satiation Via Hypothalamic Circuits in Mice and Humans. Science. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Korennykh AV, Egea PF, Korostelev AA, Finer-Moore J, Zhang C, Shokat KM, Stroud RM, Walter P, 2009. The unfolded protein response signals through high-order assembly of Ire1. Nature 457, 687–693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lee NJ, Oraha J, Qi Y, Enriquez RF, Tasan R, Herzog H, 2023. Altered function of arcuate leptin receptor expressing neuropeptide Y neurons depending on energy balance. Mol Metab 76, 101790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lieu L, Chau D, Afrin S, Dong Y, Alhadeff AL, Betley JN, Williams KW, 2020. Effects of metabolic state on the regulation of melanocortin circuits. Physiol. Behav 224, 113039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Madhavan A, Kok BP, Rius B, Grandjean JMD, Alabi A, Albert V, Sukiasyan A, Powers ET, Galmozzi A, Saez E, Wiseman RL, 2022. Pharmacologic IRE1/XBP1s activation promotes systemic adaptive remodeling in obesity. Nat. Commun 13, 608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Moncan M, Mnich K, Blomme A, Almanza A, Samali A, Gorman AM, 2021. Regulation of lipid metabolism by the unfolded protein response. J. Cell. Mol. Med 25, 1359–1370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Neyens D, Zhao H, Huston NJ, Wayman GA, Ritter RC, Appleyard SM, 2020. Leptin sensitizes NTS neurons to vagal input by increasing postsynaptic NMDA receptor currents. J. Neurosci 40, 7054–7064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Ogborn DI, McKay BR, Crane JD, Parise G, Tarnopolsky MA, 2014. The unfolded protein response is triggered following a single, unaccustomed resistance-exercise bout. Am. J. Physiol. Regul. Integr. Comp. Physiol 307, R664–R669. [DOI] [PubMed] [Google Scholar]
  35. Ollmann MM, Wilson BD, Yang YK, Kerns JA, Chen Y, Gantz I, Barsh GS, 1997. Antagonism of central melanocortin receptors in vitro and in vivo by agouti-related protein. Science 278, 135–138. [DOI] [PubMed] [Google Scholar]
  36. Ozcan U, Cao Q, Yilmaz E, Lee AH, Iwakoshi NN, Ozdelen E, Tuncman G, Gorgun C, Glimcher LH, Hotamisligil GS, 2004. Endoplasmic reticulum stress links obesity, insulin action, and type 2 diabetes. Science 306, 457–461. [DOI] [PubMed] [Google Scholar]
  37. Ozcan U, Yilmaz E, Ozcan L, Furuhashi M, Vaillancourt E, Smith RO, Gorgun CZ, Hotamisligil GS, 2006. Chemical chaperones reduce ER stress and restore glucose homeostasis in a mouse model of type 2 diabetes. Science 313, 1137–1140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Ozcan L, Ergin AS, Lu A, Chung J, Sarkar S, Nie D, Myers MG Jr., Ozcan U, 2009. Endoplasmic reticulum stress plays a central role in development of leptin resistance. Cell Metab. 9, 35–51. [DOI] [PubMed] [Google Scholar]
  39. Pan WW, Myers MG Jr., 2018. Leptin and the maintenance of elevated body weight. Nat. Rev. Neurosci 19, 95–105. [DOI] [PubMed] [Google Scholar]
  40. Park S, Jang A, Bouret SG, 2020. Maternal obesity-induced endoplasmic reticulum stress causes metabolic alterations and abnormal hypothalamic development in the offspring. PLoS Biol. 18, e3000296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Pendergast DR, Leddy JJ, Venkatraman JT, 2000. A perspective on fat intake in athletes. J. Am. Coll. Nutr 19, 345–350. [DOI] [PubMed] [Google Scholar]
  42. Peng J, Qin C, Ramatchandirin B, Pearah A, Guo S, Hussain M, Yu L, Wondisford FE, He L, 2022. Activation of the canonical ER stress IRE1-XBP1 pathway by insulin regulates glucose and lipid metabolism. J. Biol. Chem 298, 102283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Qi Y, Lee NJ, Ip CK, Enriquez R, Tasan R, Zhang L, Herzog H, 2022. NPY derived from AGRP neurons controls feeding via Y1 and energy expenditure and food foraging behaviour via Y2 signalling. Mol Metab 59, 101455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Rafiei N, Mitchell CS, Tedesco CR, Chen J, Choi EA, Roughley S, Jean-Richard-Dit-Bressel P, Kumar NN, McNally GP, Herzog H, Begg DP, 2024. Chemogenetic activation ofarcuate nucleus NPY and NPY/AgRP neurons increases feeding behaviour in mice. Neuropeptides 107, 102454. [DOI] [PubMed] [Google Scholar]
  45. Rao RV, Bredesen DE, 2004. Misfolded proteins, endoplasmic reticulum stress and neurodegeneration. Curr. Opin. Cell Biol 16, 653–662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Reaven GM, 1995. Pathophysiology of insulin resistance in human disease. Physiol. Rev 75, 473–486. [DOI] [PubMed] [Google Scholar]
  47. Ropelle ER, Flores MB, Cintra DE, Rocha GZ, Pauli JR, Morari J, de Souza CT, Moraes JC, Prada PO, Guadagnini D, Marin RM, Oliveira AG, Augusto TM, Carvalho HF, Velloso LA, Saad MJ, Carvalheira JB, 2010. IL-6 and IL-10 anti-inflammatory activity links exercise to hypothalamic insulin and leptin sensitivity through IKK beta and ER stress inhibition. PLoS Biol. 8. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  48. Rothschild JA, Kilding AE, Stewart T, Plews DJ, 2022. Factors influencing substrate oxidation during submaximal cycling: a modelling analysis. Sports Med. 52, 2775–2795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Schwartz MW, Seeley RJ, Campfield LA, Burn P, Baskin DG, 1996. Identification of targets of leptin action in rat hypothalamus. J. Clin. Invest 98, 1101–1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Shokat KM, 2010. A drug-drug interaction crystallizes a new entry point into the UPR. Mol. Cell 38, 161–163. [DOI] [PubMed] [Google Scholar]
  51. Simonson DC, DeFronzo RA, 1990. Indirect calorimetry: methodological and interpretative problems. Am. J. Physiol 258, E399–E412. [DOI] [PubMed] [Google Scholar]
  52. Spanswick D, Smith MA, Groppi VE, Logan SD, Ashford ML, 1997. Leptin inhibits hypothalamic neurons by activation of ATP-sensitive potassium channels. Nature 390, 521–525. [DOI] [PubMed] [Google Scholar]
  53. Steculorum SM, Ruud J, Karakasilioti I, Backes H, Engstrom Ruud L, Timper K, Hess ME, Tsaousidou E, Mauer J, Vogt MC, Paeger L, Bremser S, Klein AC, Morgan DA, Frommolt P, Brinkkotter PT, Hammerschmidt P, Benzing T, Rahmouni K, Wunderlich FT, Kloppenburg P, Bruning JC, 2016. AgRP neurons control systemic insulin sensitivity via Myostatin expression in Brown adipose tissue. Cell 165, 125–138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Su M, Yan M, Gong Y, 2019. Ghrelin fiber projections from the hypothalamic arcuate nucleus into the dorsal vagal complex and the regulation of glycolipid metabolism. Neuropeptides 78, 101972. [DOI] [PubMed] [Google Scholar]
  55. Tong Q, Ye CP, Jones JE, Elmquist JK, Lowell BB, 2008. Synaptic release of GABA by AgRP neurons is required for normal regulation of energy balance. Nat. Neurosci 11, 998–1000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Wang B, Chandrasekera PC, Pippin JJ, 2014. Leptin- and leptin receptor-deficient rodent models: relevance for human type 2 diabetes. Curr. Diabetes Rev 10, 131–145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Williams KW, Liu T, Kong X, Fukuda M, Deng Y, Berglund ED, Deng Z, Gao Y, Liu T, Sohn JW, Jia L, Fujikawa T, Kohno D, Scott MM, Lee S, Lee CE, Sun K, Chang Y, Scherer PE, Elmquist JK, 2014. Xbp1s in Pomc neurons connects ER stress with energy balance and glucose homeostasis. Cell Metab. 20, 471–482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Wiseman RL, Zhang Y, Lee KP, Harding HP, Haynes CM, Price J, Sicheri F, Ron D, 2010. Flavonol activation defines an unanticipated ligand-binding site in the kinase-RNase domain of IRE1. Mol. Cell 38, 291–304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Won JC, Jang PG, Namkoong C, Koh EH, Kim SK, Park JY, Lee KU, Kim MS, 2009. Central administration of an endoplasmic reticulum stress inducer inhibits the anorexigenic effects of leptin and insulin. Obesity (Silver Spring) 17, 1861–1865. [DOI] [PubMed] [Google Scholar]
  60. Wu J, Ruas JL, Estall JL, Rasbach KA, Choi JH, Ye L, Bostrom P, Tyra HM, Crawford RW, Campbell KP, Rutkowski DT, Kaufman RJ, Spiegelman BM, 2011. The unfolded protein response mediates adaptation to exercise in skeletal muscle through a PGC-1alpha/ATF6alpha complex. Cell Metab. 13, 160–169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Wu CS, Bongmba OYN, Lee JH, Tuchaai E, Zhou Y, Li DP, Xue B, Chen Z, Sun Y, 2019. Ghrelin receptor in agouti-related peptide neurones regulates metabolic adaptation to calorie restriction. J. Neuroendocrinol 31, e12763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Xu J, Bartolome CL, Low CS, Yi X, Chien CH, Wang P, Kong D, 2018. Genetic identification of leptin neural circuits in energy and glucose homeostases. Nature 556, 505–509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Yang L, Calay ES, Fan J, Arduini A, Kunz RC, Gygi SP, Yalcin A, Fu S, Hotamisligil GS, 2015. METABOLISM. S-Nitrosylation links obesity-associated inflammation to endoplasmic reticulum dysfunction. Science 349, 500–506. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Yao T, Deng Z, Gao Y, Sun J, Kong X, Huang Y, He Z, Xu Y, Chang Y, Yu KJ, Findley BG, Berglund ED, Wang RT, Guo H, Chen H, Li X, Kaufman RJ, Yan J, Liu T, Williams KW, 2017. Ire1alpha in Pomc neurons is required for thermogenesis and Glycemia. Diabetes 66, 663–673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Zhang L, Herzog H, 2024. Important role of NPY-Y4R signalling in the dual control of feeding and physical activity. Neuropeptides 105, 102425. [DOI] [PubMed] [Google Scholar]
  66. Zhang X, Zhang G, Zhang H, Karin M, Bai H, Cai D, 2008. Hypothalamic IKKbeta/NF-kappaB and ER stress link overnutrition to energy imbalance and obesity. Cell 135, 61–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zhang L, Reed F, Herzog H, 2020. Leptin signalling on arcuate NPY neurones controls adiposity independent of energy balance or diet composition. J. Neuroendocrinol 32, e12898. [DOI] [PubMed] [Google Scholar]

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Data Availability Statement

Data will be made available on request.

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