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Neurotensin neurons in the extended amygdala control dietary choice and energy homeostasis

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Abstract

Obesity is a global pandemic that is causally linked to many life-threatening diseases. Apart from some rare genetic conditions, the biological drivers of overeating and reduced activity are unclear. Here, we show that neurotensin-expressing neurons in the mouse interstitial nucleus of the posterior limb of the anterior commissure (IPAC), a nucleus of the central extended amygdala, encode dietary preference for unhealthy energy-dense foods. Optogenetic activation of IPACNts neurons promotes obesogenic behaviors, such as hedonic eating, and modulates food preference. Conversely, acute inhibition of IPACNts neurons reduces feeding and decreases hedonic eating. Chronic inactivation of IPACNts neurons recapitulates these effects, reduces preference for sweet, non-caloric tastants and, furthermore, enhances locomotion and energy expenditure; as a result, mice display long-term weight loss and improved metabolic health and are protected from obesity. Thus, the activity of a single neuronal population bidirectionally regulates energy homeostasis. Our findings could lead to new therapeutic strategies to prevent and treat obesity.

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Fig. 1: IPACNts neurons are activated by palatable food in vivo.
Fig. 2: IPACNts neurons encode the hedonic value of a tastant.
Fig. 3: IPACNts neurons encode the hedonic value of an odor.
Fig. 4: Activation of IPACNts neurons regulates dietary choices.
Fig. 5: Inhibition and inactivation of IPACNts neurons both disrupt feeding.
Fig. 6: Inactivation of IPACNts neurons protects from obesity and ameliorates metabolic syndrome.
Fig. 7: IPACNts neurons send output and receive input to and from brain regions involved in energy homeostasis.

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All data are contained in the main text, Extended Data or Supplementary Information. Source data are provided with this paper.

Code availability

Custom code is available on GitHub at https://github.com/Alefurlan/IPACpaper.

References

  1. Bluher, M. Obesity: global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 15, 288–298 (2019).

    Article  PubMed  Google Scholar 

  2. Fenselau, H. et al. A rapidly acting glutamatergic ARC→PVH satiety circuit postsynaptically regulated by α-MSH. Nat. Neurosci. 20, 42–51 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Li, M. M. et al. The paraventricular hypothalamus regulates satiety and prevents obesity via two genetically distinct circuits. Neuron 102, 653–667 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. Speakman, J. R. et al. Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity. Dis. Model Mech. 4, 733–745 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Trexler, E. T., Smith-Ryan, A. E. & Norton, L. E. Metabolic adaptation to weight loss: implications for the athlete. J. Int Soc. Sports Nutr. 11, 7 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hill, J. O., Wyatt, H. R. & Peters, J. C. Energy balance and obesity. Circulation 126, 126–132 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rossi, M. A. & Stuber, G. D. Overlapping brain circuits for homeostatic and hedonic feeding. Cell Metab. 27, 42–56 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Alheid, G. F. Extended amygdala and basal forebrain. Ann. N. Y. Acad. Sci. 985, 185–205 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Tanaka, D. H., Li, S., Mukae, S. & Tanabe, T. Genetic access to gustatory disgust-associated neurons in the interstitial nucleus of the posterior limb of the anterior commissure in male mice. Neuroscience 413, 45–63 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Tanaka, D. H., Li, S., Mukae, S. & Tanabe, T. Genetic recombination in disgust-associated bitter taste-responsive neurons of the central nucleus of amygdala in male mice. Neurosci. Lett. 742, 135456 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Gehrlach, D. A. et al. A whole-brain connectivity map of mouse insular cortex. eLife 9, e55585 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

  14. Shammah-Lagnado, S. J., Alheid, G. F. & Heimer, L. Striatal and central extended amygdala parts of the interstitial nucleus of the posterior limb of the anterior commissure: evidence from tract-tracing techniques in the rat. J. Comp. Neurol. 439, 104–126 (2001).

    Article  CAS  PubMed  Google Scholar 

  15. Steculorum, S. M. et al. AgRP neurons control systemic insulin sensitivity via myostatin expression in brown adipose tissue. Cell 165, 125–138 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Atasoy, D., Betley, J. N., Su, H. H. & Sternson, S. M. Deconstruction of a neural circuit for hunger. Nature 488, 172–177 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. O’Connor, E. C. et al. Accumbal D1R neurons projecting to lateral hypothalamus authorize feeding. Neuron 88, 553–564 (2015).

    Article  PubMed  Google Scholar 

  19. Tan, H. E. et al. The gut–brain axis mediates sugar preference. Nature 580, 511–516 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Yeomans, M. R. Taste, palatability and the control of appetite. Proc. Nutr. Soc. 57, 609–615 (1998).

    Article  CAS  PubMed  Google Scholar 

  21. Patel, J. M. et al. Sensory perception drives food avoidance through excitatory basal forebrain circuits. eLife 8, e44548 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Riera, C. E. et al. The sense of smell impacts metabolic health and obesity. Cell Metab. 26, 198–211 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Cabanac, M. Physiological role of pleasure. Science 173, 1103–1107 (1971).

    Article  CAS  PubMed  Google Scholar 

  24. Jennings, J. H., Rizzi, G., Stamatakis, A. M., Ung, R. L. & Stuber, G. D. The inhibitory circuit architecture of the lateral hypothalamus orchestrates feeding. Science 341, 1517–1521 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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 

  26. Murray, A. J. et al. Parvalbumin-positive CA1 interneurons are required for spatial working but not for reference memory. Nat. Neurosci. 14, 297–299 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Strekalova, T., Spanagel, R., Dolgov, O. & Bartsch, D. Stress-induced hyperlocomotion as a confounding factor in anxiety and depression models in mice. Behav. Pharmacol. 16, 171–180 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Trajcevski, K. E. et al. Enhanced lipid oxidation and maintenance of muscle insulin sensitivity despite glucose intolerance in a diet-induced obesity mouse model. PLoS ONE 8, e71747 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. O’Neal, T. J., Friend, D. M., Guo, J., Hall, K. D. & Kravitz, A. V. Increases in physical activity result in diminishing increments in daily energy expenditure in mice. Curr. Biol. 27, 423–430 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Berthoud, H. R. & Munzberg, H. The lateral hypothalamus as integrator of metabolic and environmental needs: from electrical self-stimulation to opto-genetics. Physiol. Behav. 104, 29–39 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 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  Google Scholar 

  32. Lowell, B. B. New neuroscience of homeostasis and drives for food, water, and salt. N. Engl. J. Med. 380, 459–471 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Terral, G. et al. CB1 receptors in the anterior piriform cortex control odor preference memory. Curr. Biol. 29, 2455–2464 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Xu, W. & Wilson, D. A. Odor-evoked activity in the mouse lateral entorhinal cortex. Neuroscience 223, 12–20 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Bitzenhofer, S. H., Westeinde, E. A., Zhang, H. B. & Isaacson, J. S. Rapid odor processing by layer 2 subcircuits in lateral entorhinal cortex. eLife 11, e75065 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. de Araujo, I. E. et al. Food reward in the absence of taste receptor signaling. Neuron 57, 930–941 (2008).

    Article  PubMed  Google Scholar 

  37. Beeler, J. A. et al. Taste uncoupled from nutrition fails to sustain the reinforcing properties of food. Eur. J. Neurosci. 36, 2533–2546 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Urban, D. J. et al. Elucidation of the behavioral program and neuronal network encoded by dorsal raphe serotonergic neurons. Neuropsychopharmacology 41, 1404–1415 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Blaha, C. D. & Phillips, A. G. Pharmacological evidence for common mechanisms underlying the effects of neurotensin and neuroleptics on in vivo dopamine efflux in the rat nucleus accumbens. Neuroscience 49, 867–877 (1992).

    Article  CAS  PubMed  Google Scholar 

  40. Woodworth, H. L. et al. Neurotensin receptor-1 identifies a subset of ventral tegmental dopamine neurons that coordinates energy balance. Cell Rep. 20, 1881–1892 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Woodworth, H. L., Brown, J. A., Batchelor, H. M., Bugescu, R. & Leinninger, G. M. Determination of neurotensin projections to the ventral tegmental area in mice. Neuropeptides 68, 57–74 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Aldiss, P. et al. Exercise-induced ‘browning’ of adipose tissues. Metabolism 81, 63–70 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shimizu, I. et al. Vascular rarefaction mediates whitening of brown fat in obesity. J. Clin. Invest. 124, 2099–2112 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Recena Aydos, L. et al. Nonalcoholic fatty liver disease induced by high-fat diet in C57BL/6 models. Nutrients 11, 3067 (2019).

    Article  PubMed Central  Google Scholar 

  45. Stephenson-Jones, M. et al. A basal ganglia circuit for evaluating action outcomes. Nature 539, 289–293 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zhang, X. & Li, B. Population coding of valence in the basolateral amygdala. Nat. Commun. 9, 5195 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Gamba, O. F. M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).

    Article  Google Scholar 

  48. Xiao, X. et al. A genetically defined compartmentalized striatal direct pathway for negative reinforcement. Cell 183, 211–227 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mehlem, A., Hagberg, C. E., Muhl, L., Eriksson, U. & Falkevall, A. Imaging of neutral lipids by oil red O for analyzing the metabolic status in health and disease. Nat. Protoc. 8, 1149–1154 (2013).

    Article  PubMed  Google Scholar 

  50. Stephenson-Jones, M. et al. Opposing contributions of GABAergic and glutamatergic ventral pallidal neurons to motivational behaviors. Neuron 105, 921–933 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank T. Russo for technical assistance and members of the Li laboratory for helpful discussions. This work was supported by grants from EMBO (ALTF 458–2017, A.F.), the Swedish Research Council (2017-00333, A.F.), the Charles H. Revson Senior Fellowship in Biomedical Science (19–23, A.F.), the National Institutes of Health (R01MH101214, R01MH108924, R01DA050374 and R01NS104944, B.L.), the Cold Spring Harbor Laboratory and Northwell Health Affiliation (B.L.), the Feil Family Neuroscience Endowment (B.L.) and the German Academic Scholarship Foundation (E.C.G.).

Author information

Authors and Affiliations

Authors

Contributions

A.F. and B.L. conceived and designed the study. A.F. conducted the experiments and analyzed data. A.C. assisted with the photometry experiments with food odors and the data analysis. S. Boyle set up behavioral rigs and generated MATLAB code for controlling behavioral devices and analyzing photometry data. R.S. assisted with the smFISH experiments. R.R. and J.H. assisted with operating the metabolic cages. E.C.G. assisted with the GTT and ITT experiments. R.S. and E.C.G. collected tissue samples and performed quantitative PCR experiments. J.G. assisted with the EPM and OF experiments. S. Beyaz provided critical reagents. T.J. supervised the experiments by E.C.G. and assisted with interpreting metabolic data. S.D.S. supervised the experiments by A.C. and assisted with analyzing and interpreting the data. A.F. and B.L. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Alessandro Furlan or Bo Li.

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The authors declare no competing interests.

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Nature Neuroscience thanks Roger Adan, Alexander Nectow, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 IPACNts neurons encode the hedonic value of a stimulus.

(a) Schematics showing the locations of optic fiber placement in the mice used in Figs. 2 and 3. (b) Feeding behavior of mice when presented with HFD (green) or chow (orange), for 20 minutes, in food restricted (FR, left) or sated condition (right). N = 8. Group effect: F(1,7)=95.19, p < 0.0001, **p < 0.01; ****p < 0.0001; Two-way RM ANOVA, Sidak’s test. (c, d) Schematics of the experimental setup (c) and task structure (d) used in Fig. 2. Bottom panel: representative raster plot showing licking behavior following liquid delivery. (e) Drinking behavior of wild-type mice when presented with Intralipid 0.5% (Fat 0.5%, green) or water (orange), in a 2-bottle preference test, for 72-h, in sated condition. N = 6 mice. ***P = 0.0003, paired t-test. (f-h) Food-restricted (FR) mice (f) and water-restricted (WR) mice (g, h) were presented with equal volumes of liquids in the same session. Left: Area under the curve (AUC) of GCAMP6f signals. Right: licking behavior (behavior) of mice. AUC and licking behavior were measured in a 3-s window following the first lick. Paired t-tests, n = 5 mice/group in all panels. (f) Sucrose (green) or sucralose (orange); AUC: p = 0.1071 (n.s.); Behavior: *p = 0.0462. (g) Monosodium glutamate (MSG, green) or water (orange); AUC: p = 0.3008 (n.s.); Behavior: p = 0.7061 (n.s.). (h) Citric acid (green) or water (orange); AUC: p = 0.1997 (n.s.); Behavior: p = 0.8677 (n.s.).

Source data

Extended Data Fig. 2 Response of IPACNts neurons to odors from several diets.

(a, b) Heatmaps of the response of IPACNts neurons in individual mice to odors derived from different food sources, under sated or food-restricted condition, as indicated. Dashed lines indicate the onset of odor presentation. (c) Average GCaMP6f signals from IPACNts neurons in food-restricted mice aligned to the presentation of different odors (dashed line). (d) Area under the curve (AUC) of the responses in individual mice in (c) in a 3-s window following odor presentation. N = 4 mice. F(3,9)=10.36, p = 0.0028; *p < 0.05, **p < 0.01, n.s., p > 0.05; one-way RM ANOVA, Holm-Sidak’s test. (e) Average GCaMP6f signals from IPACNts neurons in sated mice aligned to odor presentation (dashed line). (f) Area under the curve (AUC) of the responses in individual mice in (e) in a 3-s window following odor presentation. N = 5 mice. F(3,12)=5.169, p = 0.0160; *p < 0.05, p > 0.05 (n.s); one-way RM ANOVA, Holm-Sidak’s test.

Source data

Extended Data Fig. 3 Characterization of behavioral effects following activation of IPACNts neurons.

(a) Optic fiber placement for mice in Fig. 4. (b) Effect of photostimulation of IPACNts neurons in mice fed dark chocolate (DCh), chow, sucrose, HFD, white chocolate (WCh). ChR2 (n = 9) or GFP (n = 6) for DCh, ChR2 (n = 9) or GFP (n = 8) for chow, sucrose, HFD, WCh. Two-way RM ANOVA, Sidak’s test. DCh, group effect: F(1,13)=7.374, p = 0.0177; chow, group effect: F(1,15)=8.999, p = 0.0090; sucrose, group effect: F(1,15)=7.829, p = 0.0135; HFD, group effect: F(1,15)=21.22, p = 0.0003; WCh, group effect: F(1,15)=22.56, p = 0.0003. (c) Effect of photostimulation of IPACNts neurons in mice presented with an inedible pencil eraser. ChR2 (n = 9) or GFP (n = 6). two-way RM ANOVA, Sidak’s test. Gnawing, interaction effect: F(2,26)=4.939, p = 0.0152; intake: F(2,26)=1.066, p = 3591 (n.s.). ****p < 0.0001; p > 0.05(n.s.). (d, e) Photostimulation of IPACNts neurons increased the number (d) and the duration (e) of feeding bouts in ChR2 (n = 9) but not GFP mice (n = 7). (d) GFP: *p = 0.0465, ChR2: **p = 0.0028; (e) GFP: p = 0.0982(n.s.), ChR2: ***p = 0.0007, paired t-test. (f) Effect of photostimulation of IPACNts neurons in ChR2 mice fed quinine-flavored or plain chow pellets (n = 5). Interaction effect: F(2,8)=9.476, p = 0.0078, **p < 0.01, two-way RM ANOVA, Sidak’s comparisons test. (g) Effect of photostimulation of IPACNts neurons on liquid consumption (control for Fig. 4e). GFP mice (n = 5), interaction effect: F (4, 16)=1.119, p = 0.3820 (n.s.). Two-way RM ANOVA. (h) Self-stimulation paradigm (left) and quantification of the poking responses of ChR2 (n = 9) and GFP mice (n = 8). Group effect: F(1,15)=37.63, p < 0.0001; ****p < 0.0001; p > 0.05 (n.s.), two-way RM ANOVA, Sidak’s test. (i) Distance traveled in the RTPP/A task. ChR2 (n = 11) and GFP (n = 8) mice. Group effect: F(1,17)=34.11, p < 0.0001; ****p < 0.0001; p > 0.05 (n.s.), two-way RM ANOVA, Sidak’s test. (j) Distance traveled in the open field test. ChR2 (n = 8) and GFP (n = 6) mice. Group effect: F(1,12)=17.30, p = 0.0013; ****p < 0.0001; p > 0.05 (n.s.) two-way RM ANOVA, Sidak’s test.

Source data

Extended Data Fig. 4 Inactivation of IPACNts neurons impairs hedonic perception.

(a) HFD intake over a 2-h period in sated NtsCre mice expressing mCherry (gray, control) or KORD (red) injected with DMSO or SalB. mCherry mice, n = 7; KORD mice, n = 5. paired t-test. Cherry DMSO-SaLB: p = 0.7748, (n.s.); KORD DMSO-SaLB: p = 0.1066 (n.s.). Paired t-test.(b) Percentage change of HFD intake in food-restricted mice expressing mCherry (gray, control) or KORD (red) when injected with SaLB, normalized to their intake when injected with DMSO, within 30 minutes from food presentation. mCherry mice, n = 7; KORD mice, n = 5. *p = 0.0326. Unpaired t-test. (c) Daily water intake of the GFP mice (n = 10) and TeLC mice (n = 8) fed chow. p = 0.8023 (n.s.), unpaired t-test. (d) Daily water intake of the GFP mice (n = 10) and TeLC mice (n = 8) fed HFD. *p = 0.0305, unpaired t-test. (e) Schematic of the 2-bottle preference test (left) for sucralose (center) and sucrose (right). Sucralose: GFP mice (n = 5), TeLC mice (n = 5): **p = 0.0055, unpaired t-test. Sucrose: GFP mice (n = 6); TeLC mice (n = 5); p = 0.6488 (n.s.), unpaired t-test. Legend: L, left bottle, R, right bottle. (f) Comparison of energy intake from chow and HFD diets (derived from Fig. 5i and j). GFP (n = 10): ****p < 0.0001; TeLC mice (n = 8): p = 0.3562 (n.s.); paired t-test. (g) Change in energy intake after the switch from chow to HFD. ***P = 0.0002, unpaired t-test.

Source data

Extended Data Fig. 5 Inactivation of IPACNts neurons has positive metabolic effects.

(a) Changes in body weight (BW) following injection (d0). GFP mice (n = 11): F(3, 30)=6.588, p = 0.0015; **p < 0.01; ***p < 0.001; TeLC mice (n = 10): F(3, 27)=28.11, p < 0.0001; ****p < 0.0001, p > 0.05 (n.s.); one-way RM ANOVA, Sidak’s test. (b) Volume of carbon dioxide produced (VCO2) by GFP (n = 10) and TeLC mice (n = 8). Group effect: F(1, 16)=5.745, p = 0.0291, two-way RM ANOVA. (c) Average carbon dioxide production (VCO2) of the mice in (b). GFP (n = 10); TeLC (n = 8). Group effect: F(1, 16)=5.603, p = 0.0309; *p < 0.05, p > 0.05 (n.s.); two-way RM ANOVA, Sidak’s test. (d) Respiratory exchange ratio (RER) of GFP (n = 10) and TeLC mice (n = 8). Interaction effect: F(70,1120) = 5.042, p < 0.0001, two-way RM ANOVA. (e) Average RER of GFP (n = 10) and TeLC mice (n = 8) fed chow. Interaction effect: F(1, 16)=7.546, *p = 0.0143; two-way RM ANOVA, Sidak’s test. (f) Average energy expenditure of GFP (n = 10) and TeLC mice (n = 8) fed HFD. Group effect: F(1, 16)=6.526, p = 0.0212; *p < 0.05, n.s., p > 0.05; two-way RM ANOVA, Sidak’s test. (g) Average oxygen consumption (VO2) of GFP (n = 10) and TeLC mice (n = 8) fed HFD. Group effect: F(1, 16)=6.066, *p = 0.0255, p > 0.05 (n.s.); two-way RM ANOVA, Sidak’s test. (h) Average carbon dioxide production (VCO2) of GFP (n = 10) and TeLC mice (n = 8) fed HFD. Group effect: F(1, 16)=5.276, *p = 0.0355, p > 0.05 (n.s.); two-way RM ANOVA, Sidak’s test. (i) Average locomotor activity of GFP (n = 10) and TeLC mice (n = 8) fed HFD. Group effect: F(1,16)=25.21, p < 0.0001; ****p < 0.0001, p > 0.05 (n.s.), two-way RM ANOVA, Sidak’s test.

Source data

Extended Data Fig. 6 Network of IPACNts neurons.

(a) Representative images of brain areas innervated by IPACNts (green) and mBSTNts (red) neurons. Scale bar: 100 μm. STLV: ventral lateral division of the BNST; STMA: anterior medial division; VP: ventral pallidum; CPu: caudate putamen; IPAC: interstitial nucleus of the posterior limb of the anterior commissure (aca); LC: locus coeruleus; PBN: parabrachial nu.; CeA: central amygdala; BLA: basolateral amygdala; PAG: periacqueductal gray; DR: dorsal raphe. (b) Representative image of smFISH for Nts on retrograde labelled CT-B+ neurons in the IPAC. The square in the image show the high-magnification area showed in Fig. 7h (right). Scale bar: 100 μm. (c) Schematics showing the locations of optic fiber placement in the mice used in Fig. 7. (d) Effect of light delivery into the IPAC of the ChR2 (n = 9) or GFP (n = 5) mice on gnawing (left) and consumption (right) of inedible items (that is, pencil eraser). Gnawing (group effect): F(1,12)=11.51, p = 0.0053, two-way RM ANOVA, Sidak’s test. ****p < 0.0001. Intake: (group effect): F(1,12)=0.5327, p = 4783, two-way RM ANOVA. (e) Preference of ChR2 (n = 8) and GFP mice (n = 6) for the left chamber side. Interaction effect: F(2,24)=125.1; p < 0.0001; ****p < 0.0001; p > 0.05 (n.s.). Two-way RM ANOVA, Sidak’s test.

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Furlan, A., Corona, A., Boyle, S. et al. Neurotensin neurons in the extended amygdala control dietary choice and energy homeostasis. Nat Neurosci 25, 1470–1480 (2022). https://doi.org/10.1038/s41593-022-01178-3

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