Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A neural circuit for excessive feeding driven by environmental context in mice

Nature Neuroscience volume 24, pages 1132–1141 (2021)Cite this article

Subjects

Abstract

Despite notable genetic influences, obesity mainly results from the overconsumption of food, which arises from the interplay of physiological, cognitive and environmental factors. In patients with obesity, eating is determined more by external cues than by internal physiological needs. However, how environmental context drives non-homeostatic feeding is elusive. Here, we identify a population of somatostatin (TNSST) neurons in the mouse hypothalamic tuberal nucleus that are preferentially activated by palatable food. Activation of TNSST neurons enabled a context to drive non-homeostatic feeding in sated mice and required inputs from the subiculum. Pairing a context with palatable food greatly potentiated synaptic transmission between the subiculum and TNSST neurons and drove non-homeostatic feeding that could be selectively suppressed by inhibiting TNSST neurons or the subiculum but not other major orexigenic neurons. These results reveal how palatable food, through a specific hypothalamic circuit, empowers environmental context to drive non-homeostatic feeding.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: TNSST neurons respond to palatable food.
Fig. 2: TNSST and LHVGAT neurons can establish CFC.
Fig. 3: The connection between vSub and TNSST neurons is potentiated by CFC.
Fig. 4: The vSub to TNSST circuitry is necessary for the expression and formation of CFC.
Fig. 5: pCFC potentiates the vSub to TNSST circuitry.
Fig. 6: The vSub to TNSST circuitry is critical for pCFC.

Similar content being viewed by others

Data availability

All data are contained in the main text or extended data. Source data can be downloaded at https://doi.org/10.6084/m9.figshare.14532090.v1.

Code availability

Source code can be downloaded at https://doi.org/10.6084/m9.figshare.14532096.v1.

References

  1. Ng, M. et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766–781 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Swinburn, B., Sacks, G. & Ravussin, E. Increased food energy supply is more than sufficient to explain the US epidemic of obesity. Am. J. Clin. Nutr. 90, 1453–1456 (2009).

    Article  CAS  PubMed  Google Scholar 

  3. Schachter, S. Obesity and eating. Internal and external cues differentially affect the eating behavior of obese and normal subjects. Science 161, 751–756 (1968).

    Article  CAS  PubMed  Google Scholar 

  4. Woods, S. C. Signals that influence food intake and body weight. Physiol. Behav. 86, 709–716 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Holland, P. C. & Petrovich, G. D. A neural systems analysis of the potentiation of feeding by conditioned stimuli. Physiol. Behav. 86, 747–761 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hill, J. O. & Peters, J. C. Environmental contributions to the obesity epidemic. Science 280, 1371–1374 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. Weingarten, H. P. Conditioned cues elicit feeding in sated rats: a role for learning in meal initiation. Science 220, 431–433 (1983).

    Article  CAS  PubMed  Google Scholar 

  8. Stern, S. A., Doerig, K. R., Azevedo, E. P., Stoffel, E. & Friedman, J. M. Control of non-homeostatic feeding in sated mice using associative learning of contextual food cues. Mol. Psychiatry https://doi.org/10.1038/s41380-018-0072-y (2018).

  9. Petrovich, G. D., Ross, C. A., Gallagher, M. & Holland, P. C. Learned contextual cue potentiates eating in rats. Physiol. Behav. 90, 362–367 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. de Araujo, I. E., Schatzker, M. & Small, D. M. Rethinking food reward. Annu. Rev. Psychol. 71, 139–164 (2020).

    Article  PubMed  Google Scholar 

  11. Wise, R. A. Role of brain dopamine in food reward and reinforcement. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 1149–1158 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lutter, M. & Nestler, E. J. Homeostatic and hedonic signals interact in the regulation of food intake. J. Nutr. 139, 629–632 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Berthoud, H. R., Munzberg, H. & Morrison, C. D. Blaming the brain for obesity: integration of hedonic and homeostatic mechanisms. Gastroenterology 152, 1728–1738 (2017).

    Article  PubMed  Google Scholar 

  14. Petrovich, G. D. Forebrain networks and the control of feeding by environmental learned cues. Physiol. Behav. 121, 10–18 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sternson, S. M. & Eiselt, A. K. Three pillars for the neural control of appetite. Annu. Rev. Physiol. 79, 401–423 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Aponte, Y., Atasoy, D. & Sternson, S. M. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351–355 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Krashes, M. J. et al. Rapid, reversible activation of AgRP neurons drives feeding behavior in mice. J. Clin. Invest. 121, 1424–1428 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jennings, J. H. et al. Visualizing hypothalamic network dynamics for appetitive and consummatory behaviors. Cell 160, 516–527 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang, X. & van den Pol, A. N. Rapid binge-like eating and body weight gain driven by zona incerta GABA neuron activation. Science 356, 853–859 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Luo, S. X. et al. Regulation of feeding by somatostatin neurons in the tuberal nucleus. Science 361, 76–81 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Chen, Y., Lin, Y. C., Kuo, T. W. & Knight, Z. A. Sensory detection of food rapidly modulates arcuate feeding circuits. Cell 160, 829–841 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Betley, J. N. et al. Neurons for hunger and thirst transmit a negative-valence teaching signal. Nature 521, 180–185 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. DeNardo, L. A. et al. Temporal evolution of cortical ensembles promoting remote memory retrieval. Nat. Neurosci. 22, 460–469 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sternson, S. M. & Roth, B. L. Chemogenetic tools to interrogate brain functions. Annu. Rev. Neurosci. 37, 387–407 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Mickelsen, L. E. et al. Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons. Nat. Neurosci. 22, 642–656 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Callaway, E. M. & Luo, L. Monosynaptic circuit tracing with glycoprotein-deleted rabies viruses. J. Neurosci. 35, 8979–8985 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Torromino, G. et al. Offline ventral subiculum–ventral striatum serial communication is required for spatial memory consolidation. Nat. Commun. 10, 5721 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bienkowski, M. S. et al. Integration of gene expression and brain-wide connectivity reveals the multiscale organization of mouse hippocampal networks. Nat. Neurosci. 21, 1628–1643 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Vardy, E. et al. A new DREADD facilitates the multiplexed chemogenetic interrogation of behavior. Neuron 86, 936–946 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mu, M. D. et al. A limbic circuitry involved in emotional stress-induced grooming. Nat. Commun. 11, 2261 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Velazquez-Sanchez, C. et al. Seeking behavior, place conditioning, and resistance to conditioned suppression of feeding in rats intermittently exposed to palatable food. Behav. Neurosci. 129, 219–224 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Spyraki, C., Fibiger, H. C. & Phillips, A. G. Attenuation by haloperidol of place preference conditioning using food reinforcement. Psychopharmacology (Berl.) 77, 379–382 (1982).

    Article  CAS  Google Scholar 

  33. Han, W. et al. A neural circuit for gut-induced reward. Cell 175, 665–678.e23 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Luquet, S., Perez, F. A., Hnasko, T. S. & Palmiter, R. D. NPY/AgRP neurons are essential for feeding in adult mice but can be ablated in neonates. Science 310, 683–685 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Zhu, C. et al. Profound and redundant functions of arcuate neurons in obesity development. Nat. Metab. 2, 763–774 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhao, Z. D. et al. Zona incerta GABAergic neurons integrate prey-related sensory signals and induce an appetitive drive to promote hunting. Nat. Neurosci. 22, 921–932 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Li, Y. et al. Hypothalamic circuits for predation and evasion. Neuron 97, 911–924.e5 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Shang, C. et al. A subcortical excitatory circuit for sensory-triggered predatory hunting in mice. Nat. Neurosci. 22, 909–920 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Jais, A. et al. PNOCARC neurons promote hyperphagia and obesity upon high-fat-diet feeding. Neuron 106, 1009–1025.e10 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cole, S., Hobin, M. P. & Petrovich, G. D. Appetitive associative learning recruits a distinct network with cortical, striatal, and hypothalamic regions. Neuroscience 286, 187–202 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Walker, A. K., Ibia, I. E. & Zigman, J. M. Disruption of cue-potentiated feeding in mice with blocked ghrelin signaling. Physiol. Behav. 108, 34–43 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Petrovich, G. D., Ross, C. A., Holland, P. C. & Gallagher, M. Medial prefrontal cortex is necessary for an appetitive contextual conditioned stimulus to promote eating in sated rats. J. Neurosci. 27, 6436–6441 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Euston, D. R., Gruber, A. J. & McNaughton, B. L. The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057–1070 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gogolla, N. The insular cortex. Curr. Biol. 27, R580–R586 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Wardle, J. Conditioning processes and cue exposure in the modification of excessive eating. Addict. Behav. 15, 387–393 (1990).

    Article  CAS  PubMed  Google Scholar 

  46. Jansen, A., Schyns, G., Bongers, P. & van den Akker, K. From lab to clinic: extinction of cued cravings to reduce overeating. Physiol. Behav. 162, 174–180 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Garfield, A. S. et al. Dynamic GABAergic afferent modulation of AgRP neurons. Nat. Neurosci. 19, 1628–1635 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Seeley, R. J., Payne, C. J. & Woods, S. C. Neuropeptide Y fails to increase intraoral intake in rats. Am. J. Physiol. 268, R423–R427 (1995).

    CAS  PubMed  Google Scholar 

  49. Taniguchi, H. et al. A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex. Neuron 71, 995–1013 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Vong, L. et al. Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons. Neuron 71, 142–154 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tong, Q., Ye, C. P., Jones, J. E., Elmquist, J. K. & Lowell, B. B. Synaptic release of GABA by AgRP neurons is required for normal regulation of energy balance. Nat. Neurosci. 11, 998–1000 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Harris, J. A. et al. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front. Neural Circuits 8, 76 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Daigle, T. L. et al. A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174, 465–480.e22 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. He, M. et al. Strategies and tools for combinatorial targeting of GABAergic neurons in mouse cerebral cortex. Neuron 91, 1228–1243 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Resendez, S. L. et al. Visualization of cortical, subcortical and deep brain neural circuit dynamics during naturalistic mammalian behavior with head-mounted microscopes and chronically implanted lenses. Nat. Protoc. 11, 566–597 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Azevedo, E. P. et al. A role of Drd2 hippocampal neurons in context-dependent food intake. Neuron 102, 873–886.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. Tellez, L. A. et al. Separate circuitries encode the hedonic and nutritional values of sugar. Nat. Neurosci. 19, 465–470 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: an online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Zhou, P. et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife https://doi.org/10.7554/eLife.28728 (2018).

  61. Zhang, Z. et al. Activation of the dopaminergic pathway from VTA to the medial olfactory tubercle generates odor-preference and reward. eLife https://doi.org/10.7554/eLife.25423 (2017).

Download references

Acknowledgements

We thank X. Xu, X. K. Chen and the Neuroscience Pioneer Club for critical reading and discussion of the manuscript. We thank J. Huang for providing the SST-Flp mice and B. Li for providing the AAV-fDIO-Cre plasmid. This work was supported by an A*STAR Investigatorship provided by the Biomedical Research Council (BMRC) of A*STAR (1530700142 to Y.F.), the Singapore Ministry of Education (MOE2017-T3-1-002 to G.J.A.) and the National Natural Science Foundation of China (31900708 to Q. Li). E.S. is supported by a SINGA graduate scholarship from the A*STAR Graduate Academy.

Author information

Author notes

  1. These authors contributed equally: Hasan Mohammad, Esra Senol.

Authors and Affiliations

  1. Singapore Bioimaging Consortium, Agency for Science Technology and Research (A*STAR), Singapore, Singapore

    Hasan Mohammad, Esra Senol, Chun-Yao Lee, Xin Yi Yeo, Menghan Wang, Achilleas Laskaratos, Sarah Xinwei Luo, Sangyong Jung & Yu Fu

  2. Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Esra Senol, Xin Yi Yeo, Sangyong Jung & Yu Fu

  3. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Martin Graf, George J. Augustine & Yu Fu

  4. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China

    Qin Li, Qing Liu & Fuqiang Xu

  5. CAS Key Lab of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Qing Liu & Fuqiang Xu

  6. Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore

    Sarah Xinwei Luo & Yu Fu

Authors
  1. Hasan Mohammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esra Senol
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Graf
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chun-Yao Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xin Yi Yeo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Menghan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Achilleas Laskaratos
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Fuqiang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sarah Xinwei Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sangyong Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. George J. Augustine
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yu Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.F. conceived the project and wrote the manuscript. H.M., E.S. and Y.F. designed and implemented the study. M.G., C.-Y.L., X.Y.Y., S.J. and G.J.A. contributed the electrophysiological recordings. Q. Li, Q. Liu and F.X. contributed the fiberphotometry recordings. M.W. assisted with optical systems. A.L. contributed anatomical tracing. S.X.L. helped establish the initial CFC paradigm. All authors discussed the results, commented on and revised the manuscript.

Corresponding authors

Correspondence to Hasan Mohammad or Yu Fu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Ivan de Araujo, Michael Krashes, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 TNSST neuronal activity is increased with palatable diets.

a, Representative images of FISH for VGAT (red) and cFos (green) mRNA and quantification of c-Fos-positive LHVGAT neurons in mice after fasting (7 sections from n = 3), or consuming chow (8 sections from n = 4) or HFD (8 sections from n = 4), or chocolate pellets (14 sections from n = 3) for 1 hour post-fasting (one-way ANOVA with Tukey’s multiple comparison test F(3, 33) = 0.7216; p(fasted vs chow)= 0.9183; p(fasted vs high fat)= 0.6731; p(fasted vs choc)= 0.9787). b, Representative images of FISH of VGAT (red) and cFos (green) mRNA and quantification of c-Fos-positive ZIVGAT neurons after fasting (7 sections from n = 3), or consuming chow (8 sections from n = 4 mice) or HFD (7 sections from n = 4) or chocolate pellets (14 sections from n = 3) for 1 hour post-fasting (F(3, 32) = 1.140; p(fasted vs chow)= 0.2928; p(fasted vs high fat)= 0.9994; p(fasted vs choc)= 0.8855). Scale bars (a and b), 200 m. c, Quantification of cFos-positive TNSST neurons rostro-caudally in mice after fasting (18 sections from n = 4), or consuming normal chow (20 sections from n = 5) or HFD (19 sections from n = 6) for 1 hour post-fasting (Anterior: F(2, 12) = 11.86, p(fasted vs chow)= 0.2949, p(fasted vs high fat)= 0.018, p(chow vs high fat)= 0.0011; Medial: F(2,25)= 26.33, p(fasted vs chow)= 0.1555, p(fasted vs high fat)= 0.0001, p(chow vs high fat)<0.0001; Posterior: F(2,11)=29.49, p(fasted vs chow)= 0.1089, p(fasted vs high fat)= 0.0013, p(chow vs high fat)<0.0001, **p < 0.01). d, Quantification of Ghsr+ and Ghsr- c-Fos+ TNSST neurons along rostro-caudal axis after consuming HFD for 1 hour post- fasting (Ghsr + : F(2,10)=2.101; p = 0.1730; Ghsr-: F(2,10)=2.101; p = 0.1730). e, Quantification of cFos+ TNSST neurons using immunohistochemical staining, with representative coronal sections after fasting (17 sections from n = 3), or consuming normal chow (17 sections from n = 3) or HFD (18 sections from n = 3) for 2 hour post-fasting (F(2,49)=37.57; p(fasted vs chow)= 0.9273; p(fasted vs high fat)<0.0001; p(chow vs high fat)<0.0001, ***p < 0.001). One-way ANOVA with Tukey’s multiple comparison test was used for all panels in this figure. All data in figure are presented as mean values±SEM.

Extended Data Fig. 2 Further characterisation of TNSST neuronal response to palatable diets.

a, Experimental design for fibre photometry experiment. b and c, Population activity of TNSST neurons (mean ± SEM) while approaching chocolate pellets (b) (two-sided paired t-test p = 0.0231), and while consuming chocolate pellets (c) (two-sided paired t-test p = 0.0417). d, Experimental design showing microendoscopic imaging in freely behaving mice in an arena with high fat corner (HFC) and chocolate corner (CC). e, Venn diagram showing overlap between CP-responsive and HFD-responsive TNSST neurons. f, Traces of TNSST activity (mean ± SEM) when entering the HFC or CC corner in the arena. g, Experimental strategy to identify proportion of TNSST neurons responding to both HFD and CP. The schematic shows that TNSST neurons were TRAPed in response to HFD (similarly, the CP-responsive TNSST neurons were TRAPed by changing HFD with CP). h, Images showing TNSST neurons responding to HFD (#), CP(*) or to both (white arrows) when HFD was paired with 4-OHT administration for TRAP. i, Images showing TNSST neurons responding to HFD (*), CP(#) or to both (white arrows) when CP was paired with 4-OHT administration for TRAP. # = YFP + Tdtomato + , * = YFP + c-Fos + , white arrow = YFP + Tdtomato+c-Fos + . Scale bar, 200 µm. j, Illustration showing how above strategy label putative neurons that respond to both HFD and CP. k, Pie graphs showing percentage of TNSST neurons responding to HFD, CP or to both quantified from (h) and (i) n = 3 mice for both pie graphs. All data in figure are presented as mean values±SEM.

Extended Data Fig. 3 TNSST neurons were not activated by post-ingestive calories but their activity is critical for HFD consumption.

a, Experimental design. SST-Cre::Ai14 mice were administered water, sucralose (non-caloric sweet), sucrose, saturated fat, chocolate shake or ensure by feeding needle into the stomach. b, Representative images of c-Fos expression in TNSST neurons after administration of water, sucralose (non-caloric sweet) sucrose, saturated fat, chocolate shake, or ensure into the stomach, from 3 independent experiments. Scale bar, 200 µm. c, Scatter plot showing quantification of c-Fos positive TNSST neurons in mice after administration of water (12 sections from n = 4), sucralose (12 sections from n = 4), sucrose(12 sections from n = 4), saturated fat(20 sections from n = 3), chocolate shake(22 sections from n = 3) or ensure (15 sections from n = 2). (one-way ANOVA F(5,14)= 1.422; p = 0.2761). d, Experimental design with virus injection strategy and arena with high fat diet corner (HFC, yellow), normal chow diet corner (NCC, red), plastic food mimic corner (FMC, violet) and empty corner (EC, green). e and f, Food consumption after TNSST neurons were chemogenetically activated in sated (e) (n = 9; two-sided paired t-test; high fat: p < 0.0001, chow: p = 0.7802, ***p < 0.001) or chemogenetically inhibited in fasted state (f) (n = 9; two-sided paired t-test; high fat: p < 0.0001, chow: p = 0.7458, ***p < 0.001). All data in figure are presented as mean values±SEM.

Extended Data Fig. 4 Control experiments for TNSST or LHVGAT mediated CFC.

a, Virus injection strategy for chemogenetically activating orexigenic neurons in hypothalamus. b, Representative images showing bilateral injections targeting TNSST, LHVGAT and AgRP neurons from at least 3 independent experiments. Scale bar: 500 µm. c, Schematic of CFC paradigm without food (for d and e, food was not available during the conditioning phase of CFC, and food was available on test day), or with wood stick (for f-h, only wood stick was available during the conditioning phase of CFC, and both food and wood stick were available on test day). d and e, Data showing mice did not get conditioned to eat more in the test phase than baseline after activating TNSST (n = 8) (d) (two-sided paired t-test p = 0.781) or LHVGAT (n = 7) (e) (two-sided paired t-test p = 0.3253) neurons during CFC without food. f and g, Data showing mice did not get conditioned to eat more in the test phase than baseline after activating TNSST (n = 6) (f) (paired t-test p = 0.2228), or LHVGAT (n = 6) (g) (paired t-test p = 0.1342) neurons during CFC with only wood stick. h, Data showing activating TNSST (n = 6) neurons did not induce chewing of wood stick during conditioning and mice did not chew wood stick in test phase of CFC, whereas activating LHVGAT (n = 6) neurons induced chewing of wood stick in conditioning phase but not in test phase (LHVGAT: one-way ANOVA with Tukey’s multiple comparison F(2,18)=13.83; p(baseline vs conditioning)= 0.0005, p(baseline vs test)=0.969, p(conditioning vs test)=0.0009). i, Schematic of protocol for evaluating whether the effect on eating specifically depends on the context after TNSST mediated CFC (blue boxes = Phenotyper, orange boxes = Phenotyper with contextual cue, blue circle with red spikes =chemogenetically activating neurons). j, Data showing significant decrease in food intake in the absence of contextual cue on day 5. Food intake was increased on the following day 6 when contextual cue was presented (n = 5; two-sided paired t-test; p(day4 vs day5)= 0.0063, p(day4 vs day6)=0.86, p(day5 vs day6)=0.01). k, Data showing no effect on food intake after activating LHSST neurons (n = 4 mice; Wilcoxon matched-pairs test p = 0.125). l, Representative image showing expression of hM3D in LHSST neurons in SST-cre mice from 3 independent experiments. m, Representative image showing expression of hM3D in DMHSST neurons in SST-cre (left panel) mice and an image of FISH of Vglut 2 and SST mRNA in coronal section of DMH (right panel). n, Data showing no effect on food intake after activation of DMHSST neurons in fed mice during day (n = 8; two-sided paired t-test p = 0.0794, left panel) and after overnight fasting (n = 5; paired t-test p = 0.137, right panel). o, Schematic of protocol for assessing food intake 6 days after TNSST (n = 6) or LHVGAT (n = 4) mediated CFC. p, Data showing food intake in mice conditioned either by activating TNSST neurons (one-way ANOVA with Tukey’s multiple comparison F(6,30)=6.396; p(baseline vs test1)=0.0002; p(baseline vs test2)=0.0018; p(baseline vs test3)=0.0003; p(baseline vs test4)=0.0137; p(baseline vs test5)=0.0064; p(baseline vs test6)=0.0663) or LHVGAT neurons (one-way ANOVA with Tukey’s multiple comparison F(6,18)=3.19; p(baseline vs test1)=0.0213; p(baseline vs test2)=0.0935; p(baseline vs test3)=0.351; p(baseline vs test4)=0.0424; p(baseline vs test5)=0.1061; p(baseline vs test6)=0.0486). All data in figure are presented as mean values±SEM with individual data plots.

Extended Data Fig. 5 Control experiments for characterizing vSub to hypothalamus projections and their response in CFC.

a, Rabies tracing strategy to identify the inputs from ventral subiculum (vSub) to LHVGAT neurons (n = 3). b, Representative coronal section images showing starter LHVGAT neurons (white arrows) and inputs in the vSub from 3 independent experiments. c, Injection strategy for identifying vSub neurons projecting to lateral hypothalamus and TNSST. Rabies viral tracing was combined with the retrograde tracer cholera toxin B subunit 647 (CTB 647) to identify vSub projection patterns in these two hypothalamic regions. d, Coronal section showing TNSST starter cells in TN (white arrows) and CTB 647 injection (blue) in LH from 3 independent experiments. e, Coronal section showing vSub neurons either labelled by RV-mCherry alone or co-labelled (white arrows) by rabies mCherry and CTB from 3 independent experiments. f, Quantification showing around 50% of TNSST-projecting vSub neurons also project to LH. g, Injection strategy for determining if glutamatergic or GABAergic vSub neurons project to the TN. Vglut-1 cre and VGAT-cre mice were injected with AAV1-CAG-FLEX-GFP and retrograde CTB647 was targeted to TN in the same mice. h, Representative coronal sections showing targeting of CTB647 in TN in Vglut1-cre and VGAT-cre mice from 3 independent experiments. i, Representative coronal sections showing very dense projections in the TN from vSub in Vglut1-cre mice in comparison to VGAT-cre mice from 3 independent experiments. j, Representative coronal sections of vSub showing GFP labelled neurons overlap with the retrograde tracer CTB647 injected in TN of Vglut1-cre mice, however no overlap was seen in VGAT-cre mice. k, Injection strategy for testing whether the connection between vSub and TNSST is glutamatergic or GABAergic. AAV5-hSyn-ChR2-EYFP was injected to vSub of SST-Cre:Ai14 mice. l, Representative trace showing glutamatergic response was completely blocked upon application of glutamate receptor antagonist CNQX + APV from 3 independent experiments. m, Quantification showing complete abolition of amplitude response in all cells after CNQX + APV treatment (n = 8 cells from 3 mice; Mann-Whitney U test; p = 0.0002, *** p < 0.001). n, Schematic for Chr2 intensities quantification around patched cells after electrophysiological recordings. o, Data showing correlation between AMPA magnitude at -70mV and mean intensity of YFP expression. p, Scatter plot showing no difference in Chr2 expression level between control and conditioned mice (control=21 cells from 4 mice, conditioned=25 cells from 5 mice; two-sided unpaired t-test with Welch’s correction p = 0.1068). q, Data showing significant difference in the EPSCs of the above 21 control cells and 25 conditioned cells whose Chr2 intensity was measured (two sided unpaired t-test with Welch’s correction p = 0.02). r, Table showing number of connected cells between vSub to TNSST in our recordings in control and conditioned mice. Chi-square test showed no significant difference. All data in figure are presented as mean values±SEM.

Extended Data Fig. 6 Inhibition of vSub inputs abolished LHVGAT mediated CFC and activating vSub inputs were not sufficient to induce food intake.

a, Viral injection strategy for inhibiting vSub inputs during test phase. b, Schematic of inhibiting vSub inputs during test phase of CFC paradigm. c, Chemogenetic inhibition of vSub inputs by Salvinorin B on test day 2 reduced the food intake after CFC. Data shown as mean values±SEM (n = 6; one-way ANOVA with Tukey’s multiple comparison, F(1.187, 5.933) =17.63; p(test day1 vs salb)=0.0015; p(test day1 vs dmso)=0.1348; p(salb vs dmso)=0.0103. d, Representative images showing expression of KORD-mCitrine in vSub and hM3D-mcherry in LHVGAT neurons from 3 independent experiments. Scale bar, 500 µm (vSub) and 200 µm (LH). e, Viral injection strategy for activating vSub to TN circuitry. AAVrg-cre virus was injected in TN and cre-dependent AAV-DIO-hM3D-mCherry was injected in vSub. f, Representative coronal section showing expression of hM3D-mcherry in the vSub from 3 independent experiments. Scale bar, 500 µm. g-j, TN-projecting vSub neurons were chemogenetically activated and 3 hrs post injection eating behavior was analyzed. Food intake (g) (two-sided paired t-test p = 0.569), mean eating bout duration (h) (two-sided paired t-test p = 0.969), eating frequency (i) (two-sided paired t-test p = 0.31) per hour, and cumulative eating time (j) (two-sided paired t-test p = 0.59) were measured. No significant difference was observed between saline (black dots) and CNO (red dots) treatments (n = 6) Data shown as mean values±SEM. k and l, Data showing no effect of CNO on food intake in wildtype mice after fast-refeeding (k) (two-sided paired t-test p = 0.1) and during day in fed mice (l) (two-sided paired t-test p = 0.13) (n = 8 mice for both).

Extended Data Fig. 7 Physiological contextual feeding conditioning (pCFC) with different food.

a, pCFC paradigm using normal chow diet or chocolate pellet (CP) (gray boxes = home-cage, orange boxes = context). During the conditioning phase only chocolate pellets (CP) or chow were provided in the conditioning context. b, Data showing increased food intake during test phase only in mice conditioned with CP (n = 5 control, n = 4 chow, n = 7 chocolate pellet) (one-way ANOVA with Tukey’s multiple comparison F(2,13)=43.78; p(control vs conditioned with CP) < 0.0001; p(control vs conditioned with chow)=10.92). c, Heat map of mice location during pCFC showing CP-conditioned mice spent more time in CP corner after pCFC. d, pCFC paradigm using normal chow diet or High fat diet (HFD) (gray boxes = home-cage, orange boxes = context). e, Data showing increased food intake during test phase also in mice conditioned with HFD (n = 7 control, n = 8 HFD) (unpaired t-test; ptest day(control vs conditioned)=0.0043). f, Representative images FISH for VGAT, c-Fos and SST in control and CP-conditioned mice from 3 independent experiments. g, Quantification showing percentage of cFos positive LHVGAT or ZIVGAT neurons in control (n = 4) and conditioned mice (n = 4). No significant difference was observed between control and conditioned mice in c-Fos positive percentage. (Mann-Whitney U test, LHVGAT: p = 0.281; ZIVGAT: p = 0.1188). All data in figure are presented as mean values±SEM.

Extended Data Fig. 8 vSub-LH connection is not potentiated after pCFC and inhibition of vSub inputs to LH has no effect on feeding.

a, Representative images showing expression of AAV5-CamkII-ChR2-EYFP in vSub of control and conditioned VGAT-Cre::Ai14 mice from 3 independent experiments. Scale bar, 500 µm. b, Data showing similar fluorescence intensity of ChR2-EYFP expression in vSub of control and conditioned mice. (Mann-Whitney U test, p = 0.743; 8 slices from control and 9 slices from conditioned from n = 3 mice/group) c, Viral injection strategy for ex-vivo whole cell voltage-clamp recording of light evoked synaptic transmission between vSub and LHVGAT neurons in VGAT-Cre::Ai14 mice after pCFC paradigm. d, Quantification of light evoked EPSC amplitudes after pCFC in control (9 cells from 3 mice) and conditioned mice (9 cells from 4 mice), each dot represents one neuron (two sided unpaired t-test with Welch’s correction p = 0.6572). e, Data showing similar fluorescence intensity of ChR2-EYFP expression around patched neurons in control (n = 5 neurons) and conditioned (n = 7 neurons) mice (two-sided unpaired t-test with Welch’s correction, p = 0.3692). f, Representative images to show AAV-DIO-hM4D-mCherry expression in ArcAgRP, TNSST and LHVGAT in AgRP-Cre, SST-Cre and VGAT-Cre mice respectively from 3 independent experiments. g, Viral injection strategy for inhibiting vSub inputs to LH. AAVrg-cre virus was injected in LH and cre-dependent AAV-DIO-hM4D-mCherry was injected in vSub. vSub neurons were chemogenetically inhibited before providing food. h, Food intake in saline (black) and CNO (red) injected mice in refeeding after overnight fasting (n = 8, two-sided paired t-test, p = 0.7837). i, Representative image showing expression of AAV-DIO-hM4D-mCherry in vSub from 3 independent experiments. All data in figure are presented as mean values±SEM.

Extended Data Fig. 9 Inhibition of vSub inputs to TN has no effect on homeostatic feeding, exploratory and grooming behaviours.

a, Viral injection strategy to inhibit vSub inputs to TN. AAVrg-cre virus was injected in TN and cre-dependent AAV-DIO-hM4D-mCherry was injected in vSub. b-d, Data showing no overall change in food intake during refeeding after overnight fasting (b) (two-sided paired t-test p = 0.9056), and in overnight food intake (c) (two-sided paired t-test p = 0.297) as well when measured 3 hrs after CNO injection at the onset of dark phase (d) (two-sided paired t-test p = 0.6428). e-g, Continuously measured mean eating bout duration (e), eating frequency (f) per hour, and cumulative eating time (g) pattern remained relatively similar as previous days. Thick lines represent mean value, thin gray lines represent individual mouse. Gray arrowheads show saline injection, purple arrowheads show CNO injection in panel. h, Representative heat map of exploration after saline or CNO injection in the open field arena from 3 independent experiments. i-k, Data showing effects on distance travelled (i) (two-sided paired t-test p = 0.107), grooming (j) (two-sided paired t-test p = 0.1083) and rearing (k) (two-sided paired t-test p = 0.031) in the open field. l-o, Elevated plus maze (EPM) analysis also showed no significant changes after inhibiting vSub input to TN. Representative heat map showing exploration in open and closed arms after saline or CNO injection in the EPM from 3 independent experiments (l). Mice showed no difference in time spent in open and closed arms (m) (two-sided unpaired t-test with Welch’s correction, open arm: p = 0.3672; closed arm: p = 0.6741), frequency to enter open and closed arms (n) (two-sided unpaired t-test with Welch’s correction, open arm: p = 0.4582; closed arm: p = 0.6815) and latency to enter open and closed arms (o) (two-sided unpaired t-test with Welch’s correction, open arm: p = 0.8782; closed arm: p = 0.9906) (n = 8 mice for both saline and CNO groups). Box plots show median (centre line) quartiles (boxes) and range (whiskers) in panels m-o. p, Representative images showing CTB 555 and CTB 488 injection sites in LS and TN respectively along with input cells in anterior and posterior vSub from 3 independent experiments.

Extended Data Fig. 10 Food consumption during conditioning phase of CPP.

Averaged food intake after inhibiting hypothalamic orexigenic neurons during conditioning phase of CPP (SST n = 9, AgRP n = 9, VGAT n = 7) (one-way ANOVA with Tukey’s multiple comparison; HFD: F(2,22)=0.0063, p(sst vs agrp)=0.9996, p(sst vs vgat)=0.9935, p(agrp vs vgat)=0.9962; chow: F(2,22)=5.442, p(sst vs agrp)=0.831, p(sst vs vgat)=0.418, p(agrp vs vgat)=0.0126). All data in figure are presented as mean values±SEM.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammad, H., Senol, E., Graf, M. et al. A neural circuit for excessive feeding driven by environmental context in mice. Nat Neurosci 24, 1132–1141 (2021). https://doi.org/10.1038/s41593-021-00875-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00875-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing