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Leptin receptor neurons in the dorsomedial hypothalamus regulate diurnal patterns of feeding, locomotion, and metabolism

  1. Chelsea L Faber  Is a corresponding author
  2. Jennifer D Deem
  3. Bao Anh Phan
  4. Tammy P Doan
  5. Kayoko Ogimoto
  6. Zaman Mirzadeh
  7. Michael W Schwartz
  8. Gregory J Morton  Is a corresponding author
  1. UW Medicine Diabetes Institute, Department of Medicine, University of Washington, United States
  2. Department of Neurosurgery, Barrow Neurological Institute, United States
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Cite this article as: eLife 2021;10:e63671 doi: 10.7554/eLife.63671

Abstract

The brain plays an essential role in driving daily rhythms of behavior and metabolism in harmony with environmental light–dark cycles. Within the brain, the dorsomedial hypothalamic nucleus (DMH) has been implicated in the integrative circadian control of feeding and energy homeostasis, but the underlying cell types are unknown. Here, we identify a role for DMH leptin receptor-expressing (DMHLepR) neurons in this integrative control. Using a viral approach, we show that silencing neurotransmission in DMHLepR neurons in adult mice not only increases body weight and adiposity but also phase-advances diurnal rhythms of feeding and metabolism into the light cycle and abolishes the normal increase in dark-cycle locomotor activity characteristic of nocturnal rodents. Finally, DMHLepR-silenced mice fail to entrain to a restrictive change in food availability. Together, these findings identify DMHLepR neurons as critical determinants of the daily time of feeding and associated metabolic rhythms.

Introduction

Synchrony between behavior and environmental rhythms enables animals to predict food availability and optimize metabolism in anticipation of daily periods of fasting and feeding (Saper et al., 2005). Conversely, mistimed feeding (i.e., food consumption during the normal resting period) impairs metabolism and increases susceptibility to obesity and associated metabolic impairment (Greco and Sassone-Corsi, 2019; Stephan et al., 1979). While the hypothalamic suprachiasmatic nucleus (SCN) is well known to entrain circadian rhythmicity in accordance with light–dark cycles, food availability can also entrain metabolic rhythms independently from the SCN (Greco and Sassone-Corsi, 2019). Illustrating this point, although rodents with SCN lesions exhibit profound disruptions in circadian rhythms, they retain the ability to retrain metabolic and behavioral rhythms in accordance with a scheduled meal (Stephan et al., 1979). Moreover, scheduled feeding has no effect on rhythmic gene expression in the SCN (Damiola, 2000), suggesting the existence of extra-SCN food-entrainable oscillators that function to align behavior and metabolism with food availability (Saper et al., 2005). Although somewhat controversial (Landry et al., 2006), evidence suggests the dorsomedial hypothalamic nucleus (DMH) may play such a role. First, the DMH receives both direct and indirect input from the SCN (Watts et al., 1987), and DMH neurons, in turn, project to neurons in brain areas regulating metabolism and feeding, including the arcuate nucleus (ARC; Garfield et al., 2016; Gautron et al., 2010). Moreover, DMH lesioning in rats not only disrupts circadian rhythms in feeding, locomotion, and core temperature (Gooley et al., 2006; Chou et al., 2003) but also precludes entrainment to scheduled feeding (Chou et al., 2003). However, the relevant DMH cell types mediating these effects are unknown. Based on recent evidence that DMH neurons expressing leptin receptor (DMHLepR) are both sensitive to food availability and make inhibitory synaptic connections with agouti-related protein (AgRP) neurons to modulate feeding (Garfield et al., 2016), we identified DMHLepR neurons as a candidate population for circadian control of food intake and associated metabolic rhythms.

Results and discussion

Silencing DMHLepR neurons elicits transient hyperphagia and increased adiposity

To determine the role of DMHLepR neurons in feeding and metabolism, we used a viral loss-of-function approach (Figure 1A). Specifically, synaptic neurotransmission by DMHLepR neurons was permanently blocked by bilateral microinjection into the DMH of an adeno-associated virus (AAV) encoding Cre-dependent tetanus toxin light chain fused with a GFP reporter (AAV1-CBA-DIO-GFP:TeTx; Kim et al., 2009; Han et al., 2015; Campos et al., 2017). Viral transduction was confirmed by histochemical detection of GFP in the DMH (Figure 1B–C); as expected, GFP was undetected in Cre-negative controls (not shown). Although GFP+ cell bodies were not detected outside of the DMH, abundant GFP+ terminals were detected in the ARC (Figure 1B–C), consistent with previous evidence of an inhibitory DMHLepR→ ARCAgRP neurocircuit implicated in feeding control (Garfield et al., 2016; Krashes et al., 2014).

Figure 1 with 2 supplements see all
Silencing DMHLepR neurons elicits transient hyperphagia and increased adiposity in adult male mice.

(A) Experimental schematic for chronic inhibition of DMHLepR by microinjection on day 0 of an AAV1 containing a Cre-dependent GFP-fused TeTx delivered bilaterally to the DMH of LepR-Cre+ male mice (TeTx; n=7) and Cre-negative littermate controls (control; n=5). (B) Stereological fluorescent images from a representative animal showing the rostral-caudal extent of GFP:TeTx expression. (C) Left: colorized, higher magnification view of the boxed orange region from (B). Middle: higher magnification view of the boxed orange region showing neuronal cell bodies targeted within the DMH. Right: higher magnification view of the boxed red region showing GFP:TeTx+ terminals of targeted DMHLepR neurons within the arcuate nucleus (ARC). (D) Mean daily food intake following viral microinjection. Two-way ANOVA: F(1,10)=4.658; p=0.0563 (main effect of TeTx); F(14,140)=4.886; p<0.0001 (time x TeTx interaction). (E) Mean daily food intake from week 1 relative to week 4. Two-way ANOVA: F(1,10)=5.575; p=0.0399 (main effect of TeTx); F(1,10)=39; p<0.001 (time x TeTx interaction). (F) Body weight expressed as %day 0 value. Two-way ANOVA: F(1,10)=20.18; p=0.0012 (main effect of TeTx). F(19,190)=14.67; p<0.0001 (time x TeTx interaction). (G) Fat, lean, and total mass 26 days after viral microinjection. Multiple t-tests; tfat=4.847; p=0.0014; ttotal=2.884; p=0.016. (H) Plasma leptin 21 days after viral microinjection. Unpaired t-test, t=5.17, p=0.0017. Data are mean ± SEM. For repeated measures, post hoc Sidak’s test for each time point is indicated on the graph. *p<0.05,**p<0.01, ***p<0.001, ****p<0.0001.

Whereas previous evidence showed no effect of acute inhibition of DMHLepR neurons on feeding (Garfield et al., 2016), chronic silencing of DMHLepR neurons resulted in hyperphagia that was sustained for several days (Figure 1D–E). This transient hyperphagic response was associated with sustained weight gain (Figure 1F) and a modest increase in adipose mass (Figure 1G) that persisted despite daily food intake eventually falling below that of controls (Figure 1E). Consistent with the increased adiposity, we also detected modestly increased plasma leptin levels (Figure 1H) and elevated fasting levels of both blood glucose (control vs. TeTx: 72.0 ± 5.6 vs. 107.1 ± 7.3, t9.969=3.816; p=0.003) and plasma insulin (control vs. TeTx: 0.49 ± 0.04 vs. 1.24 ± 0.12, t6.092=5.807; p=0.001), suggestive of insulin resistance. These findings extend and refine previous work implicating a physiological role for DMHLepR neurons in energy homeostasis (Garfield et al., 2016; Rezai-Zadeh et al., 2014).

Neurotransmission by DMHLepR neurons is required for suppression of feeding by leptin

Previous work has shown both that leptin treatment depolarizes DMH neurons expressing LepR (Simonds et al., 2014) and that leptin receptor signal transduction in GABAergic DMH neurons is required for the acute anorectic effect of leptin (Xu et al., 2018). To investigate the possibility that leptin’s anorectic effects involve activation of DMHLepR neurons, we next tested whether DMHLepR inactivation blunts leptin-mediated anorexia. First, the specificity of GFP:TeTx expression in DMHLepR neurons was confirmed by establishing that leptin-induced pSTAT3, a marker of LepR signal transduction, colocalizes with virally transduced cells following systemic leptin injection (Figure 2A). Next, control and DMHLepR-silenced mice were fasted for 24 hr, followed by i.p. injection of either leptin or saline vehicle, after which food was returned. Interestingly, control animals lost more weight during the fast (Figure 2B) and consequently exhibited a greater refeeding response following saline treatment than TeTx mice (Figure 2C; dashed bars). Nonetheless, the effect of leptin to further suppress food intake was readily detected in controls but absent in DMHLepR-silenced mice (Figure 2C; solid bars). These findings extend previous evidence (Xu et al., 2018) suggesting a key role for DMHLepR neurons in leptin-mediated suppression of fasting-induced refeeding.

Validation of DMHLepR neuronal targeting and evidence that activation of these neurons is required for leptin-induced anorexia.

(A) Left: Representative image showing extensive overlap of pSTAT3 expression in GFP:TeTx-expressing DMHLepR in mice sacrificed 90 min after leptin administration (i.p. 5 mg/kg). Right: Higher magnification view of the boxed region from the left. (B) Change in body weight (unpaired t-test, t=8.483, p=0.0001) following a 24 hr (ZT2–ZT2’) fast 5 weeks following viral microinjection and before food was returned in (C). (C) Post-fast (24 hr) refeeding following i.p. injection of saline or leptin (3 mg/kg). Two-way ANOVA: F(1,4)=47.33; p=0.0023 (controls, main effect of leptin). F(1,6)=0.1203; p=0.7405 (TeTx, main effect of leptin). v-, c-, and dDMH = ventral, central, and dorsal compartments of the DMH, respectively. Data are mean ± SEM. For repeated measures, post hoc, Sidak’s test at each time point is indicated on the graph. *p<0.05, ***p<0.001, ****p<0.0001.

We note that since DMHLepR neurons directly synapse onto and inhibit AgRP neurons (Garfield et al., 2016), TeTx-mediated DMHLepR silencing is predicted to increase AgRP neuron activity. As AgRP activation not only increases feeding (Aponte et al., 2011) but also promotes de novo lipogenesis and suppresses lipolysis during fasting (Cavalcanti-de-Albuquerque et al., 2019), AgRP disinhibition offers a feasible explanation for the observed effects of DMHLepR silencing not only to promote hyperphagia and weight gain, but also to preserve body weight during fasting. Future studies are warranted to test this possibility.

DMHLepR inactivation disrupts diurnal feeding, locomotion, and metabolic rhythms

To determine whether the observed impairments in energy homeostasis were associated with changes in diurnal rhythmicity in normal (14:10) light–dark cycles, we obtained continuous measures of energy intake, energy expenditure, and locomotor activity (LMA) over several days using indirect calorimetry. We found that, unlike control mice which exhibited typical nocturnal feeding behavior, DMHLepR-silenced mice exhibited a rapid (i.e., within 1 week; Figure 3—figure supplement 1C–D) and permanent phase advance in daily food intake (Figure 3A), such that dark-cycle food intake was less than light-cycle intake (Figure 3B). Similarly, while control mice displayed the expected increase in dark-cycle LMA, this pattern became undetectable within the first week after viral microinjection in DMHLepR-silenced mice (Figure 3C–D; Figure 3—figure supplement 1G–H). Rhythms in other metabolic parameters were similarly shifted and blunted by DMHLepR inactivation. Specifically, we found that heat production in DMHLepR-silenced mice was reduced selectively in the dark cycle (Figure 3E–F) and respiratory-exchange ratio (RER) was elevated in the light cycle (Figure 3G–H). These metabolic responses likely reflect the shift of a substantial fraction of daily caloric intake from the dark to the light cycle in DMHLepR-silenced mice (Figure 3A–B). Our finding that the rapid increase of RER following viral microinjection (Figure 3—figure supplement 1E–F) coincides with the timing of excess fat accumulation (Figure 1F; Figure 3—figure supplement 1B) suggests that DMHLepR inactivation may also increase de novo lipogenesis, as is reported, following AgRP neuron activation (Garfield et al., 2016; Cavalcanti-de-Albuquerque et al., 2019).

Figure 3 with 2 supplements see all
DMHLepR neuron inactivation disrupts diurnal patterns of food intake, LMA, heat production, and substrate utilization.

Two-hour binned continuous measures (left panels) and mean values across the light (L) and dark (D) periods (right panels) 30 days following microinjection of GFP:TeTx (TeTx; n=7) or GFP control (control; n=7) to the dorsomedial hypothalamic nucleus (DMH) of LepR-Cre+ male mice. Shaded areas indicate dark cycle (ZT14 – ZT24). (A) Food intake. Two-way ANOVA: F(1,12)=12; p=0.0047 (main effect of TeTx). F(87,1044)=2.354; p<0.0001 (time x TeTx interaction). (B) Mean food intake from (A) during L, D, and 24-hr periods. Two-way ANOVA: F(1,12)=9.567; p=0.0093 (main effect of TeTx). (C) Locomotor activity (LMA). Two-way ANOVA: F(1,12)=93.22; p<0.0001 (main effect of TeTx). (D) Mean LMA from (C) during L, D, and 24-hr periods. Two-way ANOVA: F(1,12)=110.4; p<0.0001 (main effect of TeTx). (E) Heat production. Two-way ANOVA: F(1,12)=1.006; p=0.3357 (main effect of TeTx). (F) Mean heat production from (E) during L and D periods. Two-way ANOVA: F(1,12)=1.209; p=0.2930 (main effect of TeTx). (G) Respiratory exchange ratio (RER). Two-way ANOVA: F(1,12)=2.789; p=0.1208 (main effect of TeTx). (H) Mean RER from (G) during L and D periods. Two-way ANOVA: F(1,12)=2.04; p=0.1788 (main effect of TeTx). Data are mean ± SEM. For repeated measures, post hoc, Sidak’s test at each time point is indicated on the graph. *p<0.05,**p<0.01, ***p<0.001, ****p<0.0001.

Together, these findings suggest that DMHLepR neuron activity is required for the normal coupling of daily rhythms in feeding, LMA, and associated metabolic parameters to the light–dark cycle. Whether and how DMHLepR neuron activity influences these parameters in the absence of a normal light–dark cycle (i.e., in constant darkness) or in alternate light–dark schedules (e.g., 12:12 vs. 14:10 employed here) are important unanswered questions.

Female DMHLepR-silenced mice recapitulate weight gain and disrupted diurnal rhythmicity seen in males

Although female DMHLepR-silenced mice did not exhibit the transient hyperphagia observed in males (Figure 1—figure supplement 2A–B), they nonetheless developed a similar degree of obesity (Figure 1—figure supplement 2C–D) that was associated with disrupted diurnal rhythms in food intake (Figure 3—figure supplement 2A–B), LMA (Figure 3—figure supplement 2C–D), heat production (Figure 3—figure supplement 2E–F), and RER (Figure 3—figure supplement 2G–H), similar to responses observed in male DMHLepR-silenced mice. The key role for DMHLepR neurons in diurnal behavioral and metabolic control identified in males, therefore, extends to females as well. Given that, compared to male mice (Chao et al., 2011), female mice are protected from both hyperphagia and disrupted circadian rhythms during high-fat diet (HFD) feeding (Palmisano et al., 2017), future studies are warranted to determine both whether sensitivity to HFD requires DMHLepR activity and, if so, whether these neurons lie downstream of circuits mediating sexually dimorphic responses to HFD.

Silencing DMHLepR neurons prevents behavioral adaptation to restricted feeding

To investigate both the extent to which diurnal metabolic disruption in DMHLepR-silenced mice is secondary to the shift in daily patterns of food intake and whether DMHLepR neurons are required to entrain feeding behavior, we implemented a time-restricted feeding (TRF) paradigm. To minimize baseline differences in satiety status that could influence TRF adaptation, control and DMHLepR-silenced mice were fasted for 24 hr, from ZT14 (dark-cycle onset) until ZT14’ of the following day. We then restricted food availability to the dark-cycle, active period (ZT14–ZT24) in both groups. After a 5-day TRF acclimation period, both groups were subjected to four additional days of TRF during which measurements were made using indirect calorimetry (for a total of 9 days of TRF), followed by 3 days of ad lib feeding (Figure 4A).

Figure 4 with 1 supplement see all
DMHLepR neurons are required for adaptation to a dark-cycle restricted feeding schedule.

(A) Experimental timeline. Six weeks following bilateral microinjection of Cre-dependent GFP:TeTx (TeTx; n=7) or GFP control (control; n=7) to the dorsomedial hypothalamic nucleus (DMH) of LepR-Cre+ male mice, mice were acclimated to time-restricted feeding (TRF) in their home cages for a 5-day lead-in before transfer into direct calorimetry. TRF was maintained in calorimetry for an additional 4 days, followed by ad lib feeding. (B) Two-hour binned continuous measures of food intake during TRF and transition back to ad lib feeding. Shaded areas indicate dark cycle (ZT14–ZT24). (C) Mean L:D food intake from (B) under TRF and ad lib feeding. Two-way ANOVA: F(1,12)=5.084; p=0.0436 (main effect of TeTx); F(3,36)=27.91; p<0.0001 (time x TeTx interaction). (D) Mean 24-hr food intake from (C) during TRF and ad lib feeding. Two-way ANOVA: F(1,12)=5.097; p=0.0434 (main effect of TeTx); F(1,12)=47.8; p<0.0001 (main effect of TRF); F(1,12)=19.58; p=0.0008 (TRF x TeTx interaction). Within treatment comparison (TRF vs. ad lib): control t(12)=1.759; p=0.1971; TeTx t(12)=8.018; p<0.0001. Data are mean ± SEM.  For repeated measures, post hoc, Sidak’s test at each time point is indicated on the graph. *p<0.05,**p<0.01, ***p<0.001, ****p<0.0001.

During TRF acclimation, body weight oscillated daily as expected in both groups, being higher after food was available during the dark cycle, and lower after light-cycle fasting. However, whereas control mice were able to maintain their weight during TRF by increasing dark-cycle food intake, DMHLepR-silenced mice failed to compensate for the imposed light-cycle fast and consequently exhibited a small reduction in body weight (Figure 4—figure supplement 1B). Upon restoration of ad lib feeding, DMHLepR-silenced mice exhibited rebound hyperphagia sufficient to recover lost weight (Figure 4B–D), but this hyperphagic response was limited to the light cycle, as DMHLepR-silenced mice rapidly reverted to their mistimed feeding rhythms (Figure 4B–C). Based on these findings, we conclude that DMHLepR neuron activity is required to entrain feeding behavior during dark-cycle TRF. In addition, the capacity to increase dark-cycle intake in response to weight loss appears to depend upon DMHLepR neuron activity, since DMHLepR-silenced mice exhibit rebound hyperphagia following TRF only during the light cycle, a time when normal mice consume little food. Although mechanisms underlying this adaptive response await further study, the capacity to increase intake when food is available for a restricted window each day requires the ability to anticipate when food will be available, in association with a variety of metabolic and neuroendocrine adaptations (Drazen et al., 2006). Future studies are warranted to evaluate the extent to which DMHLepR neuronal activity is required for adaptation to an equivalent feeding window during the light cycle, and/or narrower periods of food availability.

Conclusion

Our work identifies a crucial physiological role for DMHLepR neurons in diurnal patterning of feeding behavior, locomotion, and associated metabolic parameters with normal light–dark cycles, as well as the ability to adapt food intake to a restricted feeding paradigm. Given evidence from both humans and rodents that mistimed feeding can predispose to obesity and T2D (Challet, 2019; Huang et al., 2011), these findings have relevance to the pathogenesis of both disorders. An improved understanding of the neural circuits underlying endogenous rhythms of behavior, feeding, and metabolism may facilitate the development of new therapeutic and dietary strategies for the treatment of obesity and related metabolic disorders in humans.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Genetic reagent (Mus musculus)B6; LeprIRES-Cre/+Jackson LabsRRID:IMSR_JAX:008320
AntibodyAnti-GFP (chicken polyclonal)AbcamCat# ab13970; RRID:AB_300798IF (1:10,000)
AntibodyAnti-pSTAT3 (rabbit monoclonal)Cell Signaling TechnologyCat# 9145; RRID:AB_2491009IF (1:300)
Recombinant DNA reagentAAV1-CBA-DIO-GFP:TeTxA gift from Richard Palmiter and Larry Zweifel, Han et al., 2015NA
Recombinant DNA reagentAAV5-hSyn-DIO-EGFPAddGeneAddgene viral prep #50457-AAV5; RRID:Addgene_50457pAAV-hSyn-DIO-EGFP was a gift from Bryan Roth
Sequence-based reagentLepR_WT_forward primerJackson LabsPCR primerFor: 5'- TGCACATTCCCAGCCCAGTGT
Sequence-based reagentLepr_forward primerJackson LabsPCR primerFor: 5' - CACGACCAAGTGACAGCAAT
Sequence-based reagentLepr_common_reverse primerJackson LabsPCR primerRev: 5' - GACAGGCTCTACTGGAATGGA
Peptide, recombinant proteinRecombinant mouse leptinA F Parlow; National Hormone and Peptide ProgramLeptin
Commercial assay or kitMouse leptin ELISACrystal Chem Cat #90030RRID:AB_2722664
Commercial assay or kitMouse insulin ELISACrystal Chem Cat #90080RRID:AB_2783626
Software, algorithmPrism 9GraphPadRRID:SCR_002798
Software, algorithmImageJFijiRRID:SCR_002285
Software, algorithmIllustratorAdobeRRID:SCR_010279

Mice

All procedures were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Animal Care Committee at the University of Washington. Following stereotaxic surgery, all studied animals were individually housed with ad lib access to standard chow diet (LabDiet 5053) in a temperature- and humidity-controlled facility with 14:10 light–dark cycles. Adult LeprIRES-Cre/+ (LepR-Cre) mice (Jackson Laboratory no. 008320) or Cre-littermate controls were used for all experiments, as described below.

Order of experiments

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In one cohort of male LepR-Cre (TeTx; n=7) and Cre-littermate (control; n=5) mice, longitudinal measurements of body weight, food intake, and body composition were assessed first (Figure 1B–H and Figure 1—figure supplement 1), followed by indirect calorimetry (data available upon request), and then fast–refeeding studies with leptin administration (Figure 2B–C). A second cohort of male LepR-Cre animals receiving TeTx (n=6) was used to validate the ability of leptin to induce pSTAT3 signaling in DMHLepR neurons (Figure 2A). In a third cohort of male LepR-Cre mice (TeTx; n = 7) using GFP as a control (control; n=7), the effect of DMHLepR silencing to induce hyperphagia and weight gain was confirmed (Figure 3—figure supplement 1, panels A and B) and the mice were subjected to indirect calorimetry 48 hr after surgery (Figure 3—figure supplement 1, panels C–H) and again after 4 weeks (Figure 3), which was followed by TRF studies (Figure 4 and Figure 4—figure supplement 1). Finally, a fourth cohort of female LepR-Cre (TeTx; n=8) and Cre-littermate (control; n=8) were used to generate the data shown in Figure 1—figure supplement 2, and Figure 3—figure supplement 2.

Stereotaxic surgeries

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The viral vector AAV1-CBA-DIO-GFP:TeTx (TeTx) was generated and validated as described (Han et al., 2015; Chen et al., 2018) and generously provided by Dr. Richard Palmiter and Dr. Larry Zweifel (University of Washington, Seattle, WA). As a control in some studies (see ‘Order of Experiments’), the viral vector AAV5-hSyn-DIO-EGFP (AddGene, Watertown, MA; cat: 50457-AAV5) was used. For viral microinjection, animals were placed in a stereotaxic frame (Kopf 1900; Cartesian Research Inc, Tujunga, CA) under isoflurane anesthesia. The skull was exposed with a small incision, and two small holes were drilled for bilateral 200 nL injection volume of TeTx into the DMH of LepR-Cre or Cre-negative littermate mice based on coordinates from the Mouse Brain Atlas: anterior-posterior (AP) −1.6, dorsal-ventral (DV) −5.6 mm, and lateral 0.40 mm from bregma, identified as the approximate point at which coronal and saggital sutures intersect, and where the bregma–lambda distance approximates 4.21 mm, as previously described (Faber et al., 2020; Franklin and Paxinos, 2008). AAV) was delivered using a Hamilton syringe with a 33-gauge needle at a rate of 50 nL/min (Micro4 controller), followed by a 5 min wait at the injection site and a 1 min wait 0.05 mm dorsal to the injection site before needle withdrawal. Animals received a perioperative subcutaneous injection of buprenorphine hydrochloride (0.05 mg/kg; Reckitt Benckiser, Richmond, VA). Viral expression was verified post hoc in all animals, and any data from animals in which the virus expressed outside the targeted area were excluded from the analysis.

Body composition analysis

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Measurements of body lean and fat mass were determined in live, conscious mice by use of quantitative magnetic resonance spectroscopy (QMR; EchoMRI 3-in-1; Echo MRI, Houston, TX) by the University of Washington Nutrition Obesity Research Center Energy Balance Core.

Leptin effects on food intake and pSTAT3 induction

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To validate the ability of leptin to elicit pSTAT3 signaling in DMHLepR neurons, ad lib fed mice were injected intraperitoneally with leptin (5 mg/kg; Dr. Parlow; National Hormone Peptide Program) and perfused 90 min later, as described below.

To assess the ability of leptin to suppress the compensatory hyperphagia that normally follows a prolonged fast, mice were fasted for 24 hr from ZT2 to ZT2’. On the second day, leptin (3 mg/kg) or vehicle control (PBS, pH 7.9) was injected intraperitoneally in mice 15 min before preweighed food was placed back in the cage, and intake was monitored for the following 24 hr.

Indirect calorimetry, food intake, and activity

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Prior to surgery, mice were acclimated to calorimetry cages for at least 7 days, during which time baseline pre-intervention calorimetry values were obtained for each subject. To account for potential confounding effects of stress resulting from rehousing into calorimetry during study, the first 12–16 hr of data (including the first dark cycle) were omitted from all analyses, except for the TRF experiment where continuous intake measurements were required. Energy expenditure measurements were obtained by a computer-controlled indirect calorimeter system (Promethion, Sable Systems, Las Vegas NV) with support from the Energy Balance Core of the NORC at the University of Washington, as previously described (Kaiyala et al., 2015). Oxygen consumption (VO2) and carbon dioxide production (VCO2) were sequentially measured for each mouse for 30 s at 5 min intervals, and food and water intakes were measured continuously while mice were housed in a temperature- and humidity-controlled cabinet (Caron Products and Services, Marietta, OH). Ambulatory activity was determined simultaneously and beam breaks in the y-axis were scored as an activity count, and a tally was recorded every 3 min. Data acquisition and instrument control were coordinated by MetaScreen v2.3.15.11, and raw data were processed and binned into 2-hr increments using ExpeData v1.9.14 (Sable Systems, Las Vegas, NV) using an analysis script documenting all aspects of data transformation.

Time-restricted feeding

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To eliminate the initial effects of varying fed status of animals, 1 day before TRF, animals were placed into clean bedding and fasted for 24 hr from ZT14 (on day 1) to ZT14’ (on day 0) before TRF began. Food was removed each morning at the start of the light cycle (ZT0) and the bedding inspected to ensure no residual food debris remained accessible. Food was returned at the start of the dark cycle (ZT14) each day. Body weight was also measured at both ZT0 and ZT14 daily. Animals were maintained on TRF for a total of 5 days in home cages, then subjected to indirect calorimetry for four additional days of TRF before returning to ad lib feeding for the remaining 3 days of study (Figure 4A).

Immunohistochemistry

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For brain immunohistochemical analyses, animals were terminally anesthetized with ketamine:xylazine and transcardially perfused with PBS followed by 4% paraformaldehyde in 0.1 mol/L PBS. Brains were removed and postfixed overnight, then transferred into 30% sucrose overnight or until brains sunk in solution. Brains were subsequently sectioned on a freezing-stage microtome (Leica) to obtain 30 μm coronal sections in four series. A single series of sections per animal was used in histological studies, and the remainder stored in −20°C in cryoprotectant. Brain sections were washed in PBS with Tween-20, pH 7.4 (PBST) overnight at 4°C. Sections were then washed at room temperature in PBST (3 x 8 min), followed by a blocking buffer (5% normal donkey serum [NDS], 1% bovine serum albumin [BSA] in PBST with azide) for 60 min with rocking. Sections were then incubated overnight at 4°C in blocking buffer containing primary antiserum (goat anti-GFP, Fitzgerald, 1:1000; rabbit anti-pSTAT3, Sigma-Aldrich, St Louis, MO, 1:1000). Next, sections were washed (3 x 8 min) in PBST before incubating in secondary donkey anti-goat IgG Alexa 488 (Jackson ImmunoResearch Laboratories, West Grove, PA) diluted 1:1000 in blocking buffer. Sections were washed (3 x 8 min) in PBST before incubating with DAPI for 8 min, followed by a final wash (3 x 10 min) in PBS. Sections were mounted to slides and imaged using a Leica SP8X confocal.

Tissue processing and blood collection

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Tail blood for plasma hormonal measurement was collected at indicated times. Blood was collected via EDTA-coated capillary tubes and centrifuged at 4°C (7000 rpm, 4 min), and plasma was subsequently removed and stored at −80°C for subsequent assay. Plasma leptin (Crystal Chem, Elk Grove Village, IL; #90030) and plasma insulin (Crystal Chem; #90080) were determined by ELISA.

Statistical analyses

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Prior to analysis, all data were tested for normality by Shapiro–Wilk normality test. All results are presented as means ± SEM. P values for unpaired comparisons were calculated by two-tailed Student’s t test. Time course comparisons between groups were analyzed using a two-way repeated-measures ANOVA with main effects of treatment (control vs. TeTx) and time. All post hoc comparisons were determined using Sidak’s correction for multiple comparisons. All statistical tests indicated were performed using Prism (version 7.4; GraphPad, San Diego, CA) software.

References

    1. Franklin K
    2. Paxinos G
    (2008)
    The Mouse Brain in Stereotaxic Coordinates
    Academic Press.

Decision letter

  1. Joel K Elmquist
    Reviewing Editor; University of Texas Southwestern Medical Center, United States
  2. Kate M Wassum
    Senior Editor; University of California, Los Angeles, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The interactions of brain pathways regulating circadian patterns and metabolism is an important area of study and will be of wide interest to the readers of eLife. Your work also provides a novel role for leptin signaling in the dorsal medial nucleus of the hypothalamus.

Decision letter after peer review:

Thank you for submitting your article "LepR neurons in the dorsomedial hypothalamus regulate the timing of circadian rhythms in feeding and metabolism" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Kate Wassum as the Senior Editor. The reviewers have opted to remain anonymous.

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Summary:

Faber and colleagues describe results from a series of studies investigating the effects of knocking down leptin receptor expression in the dorsal medial nucleus (DMH) of the hypothalamus. They report that silencing of leptin receptor expression induces increased weight gain and adiposity, a shift in timing of daily food intake and associated metabolic rhythms, and decreased activity and heat production. Specifically, they show that chronic inactivation of DMH-LepR+ neurons results in mistimed feeding under light:dark conditions and bouts of hyperphagia leading to increased body weight. Conversely, loss of the DMH-LepR+ results in less acute weight loss upon 24-hr fasting and elimination of leptin responsive rebound feeding with this fast. Lastly, the animals are unable to entrain to food provision under a time-restricted feeding schedule with access limited to the "dark" period. Their work suggests a role for LepR-expressing DMH cell types in the relationship between feeding rhythm and homeostatic energy signaling. The findings implicate these neurons in mediating food anticipatory activity. Overall, the studies are interesting and are potentially of wide interest. However, several technical issues need to be addressed.

Essential revisions:

1) It has been reported that AAV1 has the capability for anterograde transport (https://www.sciencedirect.com/science/article/pii/S0896627316309138). This may limit/temper the interpretations as AAV1-CBA-DIO-GFP:TeTX will likely silence other LepR-Cre expressing neuronal groups which are downstream of DMHLepR neurons. If these downstream are LeprCre-expressing POMC or other critical hypothalamic neurons, the body weight phenotype or leptin-induced anorexic effects may come from the silencing of downstream LeprCre-expressing neurons, not DMHLepR neurons themselves. Whether this occurred and its impact on the results needs clarification.

2) Given that different serotypes and are more or less able to spread and (also based on different promoters) infect anatomical regions and their neurons, maintaining serotype and the promoter type would have reduced the risk that the results are due to differences in viral characteristics and thus neuronal targeting efficiency. Typically, the AAV5 serotype that was used for controls produces much more sparse targeting in the hypothalamus than serotypes such as 1, 8, 9 or DJ. The authors only show data from the AAV1 group (AAV1-CBA-DIO-GFP:TeTx) but not from the AAV5 GFP control vector. It would be good to show this. Furthermore, it may be good to show weight trajectories for Cre positive and Cre negative animals, given that these animals are used for the data presented in Figure 1 (there, both groups are injected with the same vector, but the effect of Cre recombinase cannot be excluded). Finally, while stereotaxic coordinates are provided for the viral injections, Bregma is not defined (the definition of this can differ and defining this will facilitate replication of the current data).

3) The virus tracing and discussion raises some question as to specificity as cell body GFP patterns appear to be present within the arcuate following injections-is it possible that some virus uptake has occurred within LepR-Cre positive cells outside of DMH (as seen in the figure inset and Figure 1—figure supplement 1)? It would be good to provide a quantification of GFP-positive cell bodies within the DMH vs. ARC for the included animals. Furthermore, with the leptin treatment, was activity of pSTAT3 observed (or different) in other sites?

4) The authors don't directly demonstrate whether TeTx approaches silence of DMHLepR neurons, e.g., by electrophysiology or c-Fos induction after leptin administration. This should be addressed.

5) The most profound circadian disruption in feeding occurs when mice are subject to high-fat diet. In humans, energy-dense diet is also "believed" to drive night time eating. Some people also suffer from night-eating syndrome in which they tend to eat mostly at night. One of the forms of night eating syndrome is observed in Smith-Magenis syndrome. The manuscript uses mice fed a normal chow. But it leaves open the question of whether the DMH-LepR neurons are involved in the HFD induced circadian feeding rhythm disruption or the results are just epiphenomenon of acute disorder of a neuronal circuit implicated in feeding. Without addressing this question, the manuscript leaves open the question of whether the DMH-LepR neurons are, in fact, the actual mediators of circadian feeding rhythm as they relate to disrupted physiology found in ad lib fed mice on HFD or in free-living human. At a minimum, this needs to be discussed.

6) The TRF and ad lib experiment in Figure 4 is difficult to interpret. The experiment protocol of adapting mice to just 5 days of TRF and releasing them to ad lib feeding may not be sufficient for mice to adapt to a TRF condition. Typically, high-fat diet fed mice who consume ~30% food at night can take from 1- 2 weeks to adapt to TRF and consume an isocaloric diet as ad lib feeding "prior to TRF". In other words, do the DMH-LepR neuron function of ad lib fed mice on HFD resemble DMH-LepR silenced mice?

7) On a separate note, mice may also need more than 2 days to adapt to indirect calorimetry before their RER, and other metabolic parameters reach equilibrium. Since this is a crucial experiment that related to the conclusion and title of the manuscript, the experiment protocol and interpretation should be conducted to highest possible standard leaving no chance for alternate interpretation.

8) Circadian rhythm analysis formally requires examination under constant conditions (darkness) in order to observe "free running" endogenous rhythms vs. entrainment to light. Here, there appears to be 14/10 light cycle imposed throughout-so the analyses are that of "diurnal" behaviors-this is a key consideration in the interpretation and discussion throughout (i.e., to replace the term "circadian" with "diurnal"). Was LD 14:10 used throughout?-if so, this may change results especially with respect to entrainment which is usually studied under 12:12 conditions. This needs clarification.

9) The data for 24-hr fasting (Figure 2) indicate less weight loss with fasting upon DMH-LepR+ silencing-in addition to abrogation of rebound feeding. This would seem to indicate that the silenced-cells exert a net activating input into orexigenic (AgRP) cells as their loss reduces feeding drive yet (paradoxically) results in conservation of energy with the 24-hr. fast (i.e., less weight loss). Is DMH-inhibiting an inhibitor such as PVH or POMC vs. directly modulating AgRP (if the latter were the case, one would conclude that DMH is activating)? This needs to be clarified and discussed.

10) With regard to food entrainment and anticipation under the experiments in Figure 4, the results provide intriguing evidence that DMH-LepR neurons play a role in adaptation to food availability within limited time windows under light:dark (diurnal) conditions. Whether this reflects changes in a circadian behavioral process and/or a circadian mechanism is uncertain-the adaptive response is sustained as the authors note even in SCN-ablated animals so the link to clock mechanisms has remained mysterious (this aspect can be included through revision of the text and conclusions rather than new experimentation).

11) When the mice with LepR deletion in the DMH undergo fasting-refeeding, they first exhibit significantly less weight loss (Figure 2B), and then compensate with less rebound feeding, which is not further blunted by leptin (as opposed to the anorexic response to leptin in fasted controls). This initial observation makes it hard to interpret whether the lack of a leptin-mediated effect in TeTx animals is simply due to the attenuated weight loss during fasting in these animals.

https://doi.org/10.7554/eLife.63671.sa1

Author response

Essential revisions:

1) It has been reported that AAV1 has the capability for anterograde transport (https://www.sciencedirect.com/science/article/pii/S0896627316309138). This may limit/temper the interpretations as AAV1-CBA-DIO-GFP:TeTX will likely silence other LepR-Cre expressing neuronal groups which are downstream of DMHLepR neurons. If these downstream are LeprCre-expressing POMC or other critical hypothalamic neurons, the body weight phenotype or leptin-induced anorexic effects may come from the silencing of downstream LeprCre-expressing neurons, not DMHLepR neurons themselves. Whether this occurred and its impact on the results needs clarification.

Although transsynaptic spread of AAV1 has been reported, to our knowledge, this outcome has been largely limited to applications in the cortex; in our hands, we have attempted and failed to achieve transsynaptic spread in hypothalamic brain regions even with very high titers (as determined by AAV1-Cre injection into hypothalamus of Ai14 reporter mice; not shown). To this we add recent evidence that the mechanism for AAV1 transsynaptic spread requires synaptic vesicle release from the starter population (Zingg et al., J Neuro 2020), which TeTx itself precludes, as they show nicely in the paper. Thus, while it remains possible that in our studies a small number of viral particles were trafficked and packaged into synaptic vesicles before TeTx-mediated VAMP2 cleavage, the absence of detectable GFP:TeTx+ cell bodies in the ARC or other known downstream projections of DMHLepR neurons speaks against this possibility. To add clarity to this issue, we now include higher resolution images of the DMH and ARC in which the viral reporter is colocalized with DAPI, in the revised Figure 1C. The images clearly show no overlap between GFP:TeTx+ terminals and ARC nuclei, unlike the pronounced overlap observed in the DMH. Thus, we do not believe that our data are confounded by viral spread to LepR+ neurons located outside of the DMH.

2) Given that different serotypes and are more or less able to spread and (also based on different promoters) infect anatomical regions and their neurons, maintaining serotype and the promoter type would have reduced the risk that the results are due to differences in viral characteristics and thus neuronal targeting efficiency. Typically, the AAV5 serotype that was used for controls produces much more sparse targeting in the hypothalamus than serotypes such as 1, 8, 9 or DJ. The authors only show data from the AAV1 group (AAV1-CBA-DIO-GFP:TeTx) but not from the AAV5 GFP control vector. It would be good to show this. Furthermore, it may be good to show weight trajectories for Cre positive and Cre negative animals, given that these animals are used for the data presented in Figure 1 (there, both groups are injected with the same vector, but the effect of Cre recombinase cannot be excluded).

We thank the reviewer for the suggestions and are happy to provide additional data, as requested. To clarify the experimental design, the original Figure 1 included Cre+ and Cre- animals, each of which were injected with the same AAV1-CMV-DIO-GFP:TeTx vector, and body weight trajectories presented in original Figure 1F. In the revised manuscript, we have included additional data from the second cohort of animals, wherein only Cre+ animals were studied and AAV5-DIO-GFP was delivered as a control – in the revised manuscript, these can be found in a new supplement to Figure 3, entitled: Figure 3—figure supplement 1. We further clarify the animal numbers and controls within the relevant figure legends of the revised manuscript, as well as within the revised Materials and methods (see subsection “Order of Experiments”).

In this figure, in addition to showing the requested targeting validation and body weight trajectories, we also show data from when these animals were placed into calorimetry 48-hr after microinjection surgery to ascertain the timing of feeding and metabolic disruptions relative to changes in body weight and/or adiposity. Although the magnitude of the early hyperphagia is somewhat diminished in this cohort relative to the previous cohort, and failed to achieve statistical significance (perhaps owing to the smaller n and/or the effects of housing mice in calorimetry-relative to home cages), we nonetheless observed the rapid emergence of the phase-advance in food intake following inactivation of DMHLepR neurons using this approach, along with a rapid and robust elevation in respiratory exchange ratio (RER), and diminished dark-cycle locomotor activity (LMA). An expanded discussion of these effects and their interpretation can be found in the Results and Discussion of the revised manuscript.

Finally, while stereotaxic coordinates are provided for the viral injections, Bregma is not defined (the definition of this can differ and defining this will facilitate replication of the current data).

We fully acknowledge the difficulty in surgical targeting where definitions of coordinates may differ, and thank the reviewer for their attention to this important detail. In response, we have clarified our bregma assignment within the Materials and methods (see subsection “Stereotaxic Surgeries”).

3) The virus tracing and discussion raises some question as to specificity as cell body GFP patterns appear to be present within the arcuate following injections – is it possible that some virus uptake has occurred within LepR-Cre positive cells outside of DMH (as seen in the figure inset and Figure 1—figure supplement 1)? It would be good to provide a quantification of GFP-positive cell bodies within the DMH vs. ARC for the included animals. Furthermore, with the leptin treatment, was activity of pSTAT3 observed (or different) in other sites?

As discussed in more detail in response to point 1 above, we do not detect any GFP:TeTx+ cell bodies within the ARC, and we now include higher resolution images with DAPI to add clarity to this issue (see revised Figure 1C). Regarding the level of pSTAT3 activity, we emphasize that TeTx expression does not interfere with intracellular signaling cascades – it simply prevents release of synaptic vesicles. Consequently, we did not expect to see any impairment in leptin-induced activation of pSTAT3 in GFP:TeTx relative to GFP control mice. To expand on this point, we take this opportunity to clarify that we did not delete leptin receptors from DMH neurons in our studies, as is asserted in the Editor’s summary. Had we done so, we would have expected to block leptin-induced pSTAT3 induction, as we have reported previously in studies using a Cre-lox approach to delete leptin receptors from VMNSF1 neurons. In that study, we showed that VMN-specific deletion of LepRb eliminated leptin-induced activation of pSTAT3 in the VMN, while having no effect in the ARC (Meek, Endo 2013), and others have reported similar findings (Dhillon, Neuron 2006). The key point of the study, which we emphasize here, was to use leptin-induced pSTAT3 to validate that GFP:TeTx expression was limited to leptin-responsive neurons within the DMH, rather than to show whether TeTx inactivation interferes with leptin-induced pSTAT3 induction, which was not expected and not observed. We have now clarified this point in the Results and Discussion of the revised manuscript.

4) The authors don't directly demonstrate whether TeTx approaches silence of DMHLepR neurons, e.g., by electrophysiology or c-Fos induction after leptin administration. This should be addressed.

While we acknowledge the importance of this concern, we emphasize that this work has already been done by our collaborators Dr. Palmiter and Dr. Zweifel, who generated, characterized, and provided the TeTx viral construct employed in these studies. Please see Han et al., 2015 describing the validation, as well as subsequent use of this virus by others (Campos et al., 2017). These references are now included in the revised manuscript in support of this important issue. To this we add that because TeTx expression blocks neither action potential firing nor Fos induction, validation by electrophysiology requires assessment of the ability of TeTx-expressing neurons to influence post-synaptic currents, and acquiring such data would delay publication by months, as our lab is not equipped to do this type of analysis. We hope the reviewers will agree that prior validation of the construct performed by our collaborators, together with a phenotype that is highly reproducible across multiple cohorts and our use of both Cre+ and Cre- controls, collectively provide strong evidence that the expected blockade of neurotransmission was achieved following DMHLepR TeTx expression.

5) The most profound circadian disruption in feeding occurs when mice are subject to high-fat diet. In humans, energy-dense diet is also "believed" to drive night time eating. Some people also suffer from night-eating syndrome in which they tend to eat mostly at night. One of the forms of night eating syndrome is observed in Smith-Magenis syndrome. The manuscript uses mice fed a normal chow. But it leaves open the question of whether the DMH-LepR neurons are involved in the HFD induced circadian feeding rhythm disruption or the results are just epiphenomenon of acute disorder of a neuronal circuit implicated in feeding. Without addressing this question, the manuscript leaves open the question of whether the DMH-LepR neurons are, in fact, the actual mediators of circadian feeding rhythm as they relate to disrupted physiology found in ad lib fed mice on HFD or in free-living human. At a minimum, this needs to be discussed.

We agree that the possibility that DMHLepR neurons mediate disrupted feeding rhythms associated with the switch to HFD is quite interesting. Indeed, we made this point in the original manuscript, and as this question is a key element of a grant application in preparation, we are glad to know that the reviewer shares our interest in this topic. While we are happy to expand discussion of this issue beyond its identification as a priority for future studies, we are hard-pressed to do so if we are to adhere to mandatory text limits. Should the reviewers and editors feel this to be of sufficient import to waive the text limit requirements, however, we are happy to expand on this point.

6) The TRF and ad lib experiment in Figure 4 is difficult to interpret. The experiment protocol of adapting mice to just 5 days of TRF and releasing them to ad lib feeding may not be sufficient for mice to adapt to a TRF condition.

To clarify the experimental design, as depicted in Figure 4A of the original manuscript, animals were maintained on TRF for a total of 9 days before release to ad lib feeding. We acknowledge this timeline may be confusing to the reader, given that the first 5 days of TRF occurred in home cages, while the latter 4 days occurred in calorimetry housing, and we have endeavored to clarify this point in the relevant description of the methods; further, the missing axis label for Figure 4A has been added (“Days”) in the revised manuscript.

We also agree with the reviewer’s point that the length of TRF acclimation is an important consideration for the interpretation of the findings. Despite the relatively short duration TRF we employed in comparison to some other studies, we emphasize that whereas control mice were able to adapt effectively so as to increase food intake during the dark cycle and thereby avert weight loss, this adaptation did not occur in DMHLepR silenced mice, resulting in failure to maintain body weight. Thus, the TRF protocol was of sufficient duration to reveal a phenotype that we believe is of interest. While it is possible that experimental animals take longer to adapt to dark-cycle TRF, maintaining TRF for longer periods may have introduced its own set of confounding variables (greater weight loss leading to a confounding increase in homeostatic drive to regain lost body weight, for example). It is out of this concern that we opted to examine the body weight and feeding responses using a shorter, rather than prolonged, TRF paradigm to assess behavioral adaptation to this challenge. However, we agree that future studies are warranted to examine adaptation to different TRF paradigms, including equivalent periods throughout the dark-light cycle, a point we highlight in the Results and Discussion.

Typically, high-fat diet fed mice who consume ~30% food at night can take from 1- 2 weeks to adapt to TRF and consume an isocaloric diet as ad lib feeding "prior to TRF". In other words, do the DMH-LepR neuron function of ad lib fed mice on HFD resemble DMH-LepR silenced mice?

We are similarly interested in the potential role played by DMHLepR neurons in the circadian disruption and “mistimed” feeding that occurs following HFD introduction. We hope that the publication of studies included herein will provide a useful foundation for future work (and associated funding) that investigate whether HFD feeding disrupts feeding cycle through these same neuronal subsets. To this end, we hope that the “Short Report” manuscript format will allow timely dissemination of this informative work to the research community and provide a foundation upon which future studies may be built.

7) On a separate note, mice may also need more than 2 days to adapt to indirect calorimetry before their RER, and other metabolic parameters reach equilibrium. Since this is a crucial experiment that related to the conclusion and title of the manuscript, the experiment protocol and interpretation should be conducted to highest possible standard leaving no chance for alternate interpretation.

We acknowledge and are fully aware of the importance of allowing mice to adapt to indirect calorimetry cages prior to study. The studies were performed in the NIDDK-funded Nutrition Obesity Research Center (NORC) Energy Balance Core (Director: Dr. Morton), which has nearly 20 years of experience and has conducted hundreds of these types of studies. In so doing, this facility has developed and implemented steps designed specifically to reduce the metabolic and behavioral impact of re-housing, and to both minimize confounding influences on indirect calorimetry data and recognize them when they occur. To add clarity to the steps taken for studies in the current manuscript, we have added detail to the Materials and methods section (see subsection “Indirect Calorimetry, Food Intake, and Activity”). Specifically, before any surgical intervention (e.g., viral microinjection), all study animals were subjected to calorimetry for at least 7 days both to collect baseline data, and to acclimate animals to experimental conditions. In addition, we routinely omit the first 16-hr block of data (including the entire first dark-cycle) from all analyses (except for the TRF study where continuous measures were necessary), based on prior validation of this step as a way to eliminate confounding effects arising from a change in housing. In support of the efficacy of these quality control measures, we note that relevant data from control mice (food intake, locomotor activity, heat production and RER) are quite reproducible over many successive light and dark cycles; had the animals not yet equilibrated when the study began, the data would have evolved over time, but this was not observed.

8) Circadian rhythm analysis formally requires examination under constant conditions (darkness) in order to observe "free running" endogenous rhythms vs. entrainment to light. Here, there appears to be 14/10 light cycle imposed throughout-so the analyses are that of "diurnal" behaviors-this is a key consideration in the interpretation and discussion throughout (i.e., to replace the term "circadian" with "diurnal"). Was LD 14:10 used throughout?-if so, this may change results especially with respect to entrainment which is usually studied under 12:12 conditions. This needs clarification.

We acknowledge the limitation inherent in studying animals in 14:10 light:dark cycles, and intend in future studies to explore the effects of DMHLepR inactivation on free-running circadian rhythms in mice housed in constant darkness. (This is another project included in a pending grant proposal.) With this objective in mind, we have revised the text to better reflect the primary outcome of disrupted diurnal rhythms in association with normal light:dark cycles, and we reserve the term circadian to refer to outcomes in environmentally constant conditions. We further clarify the 14:10 light:dark schedule utilized in the Results and Discussion, and suggest that future studies may be needed to clarify whether alternate lighting schedules may influence the phenotype.

9) The data for 24-hr fasting (Figure 2) indicate less weight loss with fasting upon DMH-LepR+ silencing-in addition to abrogation of rebound feeding. This would seem to indicate that the silenced-cells exert a net activating input into orexigenic (AgRP) cells as their loss reduces feeding drive yet (paradoxically) results in conservation of energy with the 24-hr. fast (i.e., less weight loss). Is DMH-inhibiting an inhibitor such as PVH or POMC vs. directly modulating AgRP (if the latter were the case, one would conclude that DMH is activating)? This needs to be clarified and discussed.

We thank the reviewer for raising this issue, which we agree is of interest. Based upon similar comments in point 11 below, we have expanded the Discussion to address the potential contribution made by post-synaptic AgRP neurons in the phenotype resulting from DMHLepR inactivation.

Previous retrograde tracing and electrophysiology data (see Garfield et al., 2016), indicates that a subset of DMHLepR neurons are GABAergic and directly inhibit post-synaptic AgRP neurons. Consequently, silencing DMHLepR neurons is predicted to relieve this inhibition and thereby increase AgRP neuron activity. In support of this possibility, we observed transient hyperphagia and positive energy balance following TeTx-mediated inhibition of DMHLepR neurons, but we are not yet able to confirm that this results from disinhibition of AgRP neurons. Nonetheless, AgRP disinhibition seems likely to explain both this early outcome and the preservation of body weight during fasting, given that AgRP activation promotes lipogenesis while impairing lipolysis (see Cavalcante-de-Albuquerque et al., 2019). Taken together, our findings are consistent with AgRP neuron disinhibition as a predicted consequence of TeTx-mediated silencing of DMHLepR neurons, but ascertaining the precise contribution of AgRP disinhibition to the phenotype we observed is an important goal for future studies.

10) With regard to food entrainment and anticipation under the experiments in Figure 4, the results provide intriguing evidence that DMH-LepR neurons play a role in adaptation to food availability within limited time windows under light:dark (diurnal) conditions. Whether this reflects changes in a circadian behavioral process and/or a circadian mechanism is uncertain-the adaptive response is sustained as the authors note even in SCN-ablated animals so the link to clock mechanisms has remained mysterious (this aspect can be included through revision of the text and conclusions rather than new experimentation).

In addition to the need for alternate feeding windows and/or TRF duration to more fully characterize the ability of DMHLepR-silenced mice to adapt to TRF (see response to point 6 above), we agree with the need for future studies to better clarify the precise contribution of DMHLepR neurons to food-entrainment. For this reason, and due to the spatial constraints of the “Short Report” format, we elected to be conservative in the interpretation of our findings, but have added text to the revised manuscript highlighting the need for additional work (see the Results and Discussion).

11) When the mice with LepR deletion in the DMH undergo fasting-refeeding, they first exhibit significantly less weight loss (Figure 2B), and then compensate with less rebound feeding, which is not further blunted by leptin (as opposed to the anorexic response to leptin in fasted controls). This initial observation makes it hard to interpret whether the lack of a leptin-mediated effect in TeTx animals is simply due to the attenuated weight loss during fasting in these animals.

We acknowledge that the initial refeeding difference may obscure the ability of leptin to further suppress refeeding in DMHLepR-silenced animals. In the revised manuscript, we include additional discussion of this issue and potential mechanism to reflect the nuances alluded to by the reviewers (see the response to point 9 above).

https://doi.org/10.7554/eLife.63671.sa2

Article and author information

Author details

  1. Chelsea L Faber

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    kasperc@uw.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4812-8164
  2. Jennifer D Deem

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8865-5145
  3. Bao Anh Phan

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Tammy P Doan

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  5. Kayoko Ogimoto

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Software, Investigation
    Competing interests
    No competing interests declared
  6. Zaman Mirzadeh

    Department of Neurosurgery, Barrow Neurological Institute, Phoenix, United States
    Contribution
    Conceptualization, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Michael W Schwartz

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1619-0331
  8. Gregory J Morton

    UW Medicine Diabetes Institute, Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    For correspondence
    gjmorton@uw.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8106-8386

Funding

National Institutes of Health (F31-DK113673)

  • Chelsea L Faber

National Institutes of Health (T32-GM095421)

  • Chelsea L Faber

National Institutes of Health (DK089056)

  • Gregory J Morton

National Institutes of Health (DK124238)

  • Gregory J Morton

National Institutes of Health (DK083042)

  • Michael W Schwartz

National Institutes of Health (DK101997)

  • Michael W Schwartz

National Institutes of Health (T32 DK007247)

  • Chelsea L Faber

National Institutes of Health (T32 HL007028)

  • Jennifer D Deem

American Diabetes Association (ADA 1-19-IBS-192)

  • Gregory J Morton

American Diabetes Association (ADA 1-19-PDF-103)

  • Jennifer D Deem

U.S. Department of Defense (W81XWH2010250)

  • Zaman Mirzadeh

National Institutes of Health (DK128802)

  • Zaman Mirzadeh

NIDDK (DK035816)

  • Michael W Schwartz

University of Washington (Dick and Julia McAbeeEndowed Fellowship)

  • Jennifer D Deem

NIDDK (DK017047)

  • Jennifer D Deem

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank R Palmiter, C Campos, L Zweifel, and M Baird for producing the TeTx virus, JT Nelson for assistance with metabolic experiments, and V Damian for maintaining the mouse colony. We also thank R Palmiter for editing the manuscript. We are grateful to N Peters at the University of Washington Keck Imaging Center for technical assistance and the National Institutes of Health (S10-OD-016240) for support to the W.M. Keck Foundation Center for Advanced Studies in Neural Signaling. This work was supported by NIH grants F31-DK113673 (CLF), T32-GM095421 (CLF), DK128802 (ZM), DK089056 and DK124238 (GJM), DK083042 and DK101997 (MWS); the NIDDK-funded Nutrition Obesity Research Center (DK035816) and Diabetes Research Center (DK017047) and the Diabetes, Obesity, and Metabolism (T32 DK007247; CLF) and Nutrition, Obesity, and Atherosclerosis (T32 HL007028; JDD) training grants at the University of Washington; a Department of Defense CDMRP/PRMRP grant W81XWH2010250 (ZM); a Dick and Julia McAbee Endowed Fellowship (JDD); an American Diabetes Association Innovative Basic Science Award (ADA 1–19-IBS-192; GJM); and an American Diabetes Association fellowship grant (ADA 1–19-PDF-103; JDD).

Ethics

Animal experimentation: All procedures were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Animal Care Committee at the University of Washington. (Jackson Laboratory no. 008320).

Senior Editor

  1. Kate M Wassum, University of California, Los Angeles, United States

Reviewing Editor

  1. Joel K Elmquist, University of Texas Southwestern Medical Center, United States

Publication history

  1. Received: October 2, 2020
  2. Accepted: February 1, 2021
  3. Accepted Manuscript published: February 2, 2021 (version 1)
  4. Version of Record published: February 12, 2021 (version 2)

Copyright

© 2021, Faber et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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    Ma Feilong et al.
    Research Article

    Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically-organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function, because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies, and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods couldn't resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.

    1. Neuroscience
    Wenbo Tang et al.
    Research Article

    The prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (~seconds) and fast theta sequences (~100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity in rats during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current choices during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation, and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision-making.