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Dysregulation of amyloid precursor protein impairs adipose tissue mitochondrial function and promotes obesity

Nature Metabolism volume 1, pages 1243–1257 (2019)Cite this article

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Abstract

Mitochondrial function in white adipose tissue (WAT) is an important yet understudied aspect of adipocyte biology. Here, we report a role for amyloid precursor protein (APP) in compromising WAT mitochondrial function through a high-fat diet (HFD)-induced, unconventional mis-localization to mitochondria that further promotes obesity. In humans and mice, obese conditions induce substantial APP production in WAT and APP enrichment in mitochondria. Mechanistically, HFD-induced dysregulation of signal recognition particle subunit 54c is responsible for the mis-targeting of APP to adipocyte mitochondria. Mis-localized APP blocks the protein import machinery, leading to mitochondrial dysfunction in WAT. Mice overexpressing adipocyte-specific and mitochondria-targeted APP display increased body mass and reduced insulin sensitivity, along with dysfunctional WAT, owing to a dramatic hypertrophic program in adipocytes. Elimination of adipocyte APP rescues HFD-impaired mitochondrial function with considerable protection from weight gain and systemic metabolic deficiency. Our data highlight an important role for APP in modulating WAT mitochondrial function and obesity-associated metabolic dysfunction.

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Fig. 1: APP is increased in WAT in humans and mice with obesity and accumulates in adipocyte mitochondria.
Fig. 2: Adipocyte-specific APP-overexpressing mice are more sensitive to diet-induced obesity.
Fig. 3: APP overexpression causes dysfunctional WAT and liver steatosis.
Fig. 4: APP overexpression leads to adipocyte hypertrophy by impairing stimulated lipolysis.
Fig. 5: APP impairs adipocyte mitochondrial function, owing to defective mitochondrial protein import.
Fig. 6: App AKO protects mice from obesity.
Fig. 7: App AKO enhances adipocyte mitochondrial function.
Fig. 8: A modulating role of APP in adipocyte mitochondrial function, lipolysis and hypertrophy in the context of obesity.

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Data availability

All data that support the findings of this study are included in the figures, extended data figures and Supplementary Information. Proteomics raw data has been deposited to the Mass Spectrometry Interactive Virtual Environment (MassIVE) databank (MSV000084498).

References

  1. Hruby, A. & Hu, F. B. The epidemiology of obesity: a big picture. Pharmacoeconomics 33, 673–689 (2015).

    PubMed  PubMed Central  Google Scholar 

  2. Kusminski, C. M., Bickel, P. E. & Scherer, P. E. Targeting adipose tissue in the treatment of obesity-associated diabetes. Nat. Rev. Drug Discov. 15, 639–660 (2016).

    CAS  PubMed  Google Scholar 

  3. Choe, S. S., Huh, J. Y., Hwang, I. J., Kim, J. I. & Kim, J. B. Adipose tissue remodeling: its role in energy metabolism and metabolic disorders. Front. Endocrinol. (Lausanne) 7, 30 (2016).

    Google Scholar 

  4. Wang, Q. A., Tao, C., Gupta, R. K. & Scherer, P. E. Tracking adipogenesis during white adipose tissue development, expansion and regeneration. Nat. Med. 19, 1338–1344 (2013).

    PubMed  PubMed Central  Google Scholar 

  5. Tandon, P., Wafer, R. & Minchin, J. E. N. Adipose morphology and metabolic disease. J. Exp. Biol. 221, jeb164970 (2018).

    PubMed  Google Scholar 

  6. Crewe, C., An, Y. A. & Scherer, P. E. The ominous triad of adipose tissue dysfunction: inflammation, fibrosis, and impaired angiogenesis. J. Clin. Invest. 127, 74–82 (2017).

    PubMed  PubMed Central  Google Scholar 

  7. Rosen, E. D. & Spiegelman, B. M. What we talk about when we talk about fat. Cell 156, 20–44 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Stenkula, K. G. & Erlanson-Albertsson, C. Adipose cell size: importance in health and disease. Am. J. Physiol. Regul. Integr. Comp. Physiol. 315, R284–R295 (2018).

    CAS  PubMed  Google Scholar 

  9. Lonn, M., Mehlig, K., Bengtsson, C. & Lissner, L. Adipocyte size predicts incidence of type 2 diabetes in women. FASEB J. 24, 326–331 (2010).

    PubMed  Google Scholar 

  10. de Mello, A. H., Costa, A. B., Engel, J. D. G. & Rezin, G. T. Mitochondrial dysfunction in obesity. Life Sci. 192, 26–32 (2018).

    PubMed  Google Scholar 

  11. Kusminski, C. M. & Scherer, P. E. Mitochondrial dysfunction in white adipose tissue. Trends Endocrinol. Metab. 23, 435–443 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Boudina, S. & Graham, T. E. Mitochondrial function/dysfunction in white adipose tissue. Exp. Physiol. 99, 1168–1178 (2014).

    PubMed  Google Scholar 

  13. Fassina, G., Dorigo, P. & Gaion, R. M. Equilibrium between metabolic pathways producing energy: a key factor in regulating lipolysis. Pharmacol. Res. Commun. 6, 1–21 (1974).

    CAS  PubMed  Google Scholar 

  14. Cedikova, M. et al. Mitochondria in white, brown, and beige adipocytes. Stem Cells Int 2016, 6067349 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. Poher, A. L. et al. Ectopic UCP1 overexpression in white adipose tissue improves insulin sensitivity in Lou/C Rats, a model of obesity resistance. Diabetes 64, 3700–3712 (2015).

    CAS  PubMed  Google Scholar 

  16. Kleiner, S. et al. Development of insulin resistance in mice lacking PGC-1alpha in adipose tissues. Proc. Natl Acad. Sci. USA 109, 9635–9640 (2012).

    CAS  PubMed  Google Scholar 

  17. Kusminski, C. M. et al. MitoNEET-driven alterations in adipocyte mitochondrial activity reveal a crucial adaptive process that preserves insulin sensitivity in obesity. Nat. Med. 18, 1539–1549 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Murphy, M. P. & LeVine, H. 3rd Alzheimer’s disease and the amyloid-beta peptide. J. Alzheimers Dis. 19, 311–323 (2010).

    PubMed  PubMed Central  Google Scholar 

  19. Lee, Y. H. et al. Amyloid precursor protein expression is upregulated in adipocytes in obesity. Obesity (Silver Spring) 16, 1493–1500 (2008).

    CAS  Google Scholar 

  20. Puig, K. L., Floden, A. M., Adhikari, R., Golovko, M. Y. & Combs, C. K. Amyloid precursor protein and proinflammatory changes are regulated in brain and adipose tissue in a murine model of high fat diet-induced obesity. PLoS One 7, e30378 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Zheng, H. et al. β-Amyloid precursor protein-deficient mice show reactive gliosis and decreased locomotor activity. Cell 81, 525–531 (1995).

    CAS  PubMed  Google Scholar 

  22. Pietilainen, K. H. et al. Global transcript profiles of fat in monozygotic twins discordant for BMI: pathways behind acquired obesity. PLoS Med. 5, e51 (2008).

    PubMed  PubMed Central  Google Scholar 

  23. Lee, Y. H. et al. Microarray profiling of isolated abdominal subcutaneous adipocytes from obese vs non-obese Pima Indians: increased expression of inflammation-related genes. Diabetologia 48, 1776–1783 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Keller, P. et al. Gene-chip studies of adipogenesis-regulated microRNAs in mouse primary adipocytes and human obesity. BMC Endocr. Disord. 11, 7 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang, X. et al. Modifications and trafficking of APP in the pathogenesis of Alzheimer’s disease. Front Mol. Neurosci. 10, 294 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. Haczeyni, F., Bell-Anderson, K. S. & Farrell, G. C. Causes and mechanisms of adipocyte enlargement and adipose expansion. Obes. Rev. 19, 406–420 (2018).

    CAS  PubMed  Google Scholar 

  27. Mossmann, D. et al. Amyloid-beta peptide induces mitochondrial dysfunction by inhibition of preprotein maturation. Cell Metab. 20, 662–669 (2014).

    CAS  PubMed  Google Scholar 

  28. Anandatheerthavarada, H. K., Biswas, G., Robin, M. A. & Avadhani, N. G. Mitochondrial targeting and a novel transmembrane arrest of Alzheimer’s amyloid precursor protein impairs mitochondrial function in neuronal cells. J. Cell Biol. 161, 41–54 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Devi, L., Prabhu, B. M., Galati, D. F., Avadhani, N. G. & Anandatheerthavarada, H. K. Accumulation of amyloid precursor protein in the mitochondrial import channels of human Alzheimer’s disease brain is associated with mitochondrial dysfunction. J. Neurosci. 26, 9057–9068 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Manning-Krieg, U. C., Scherer, P. E. & Schatz, G. Sequential action of mitochondrial chaperones in protein import into the matrix. EMBO J. 10, 3273–3280 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Yano, H. et al. Inhibition of mitochondrial protein import by mutant huntingtin. Nat. Neurosci. 17, 822–831 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Martell, J. D., Deerinck, T. J., Lam, S. S., Ellisman, M. H. & Ting, A. Y. Electron microscopy using the genetically encoded APEX2 tag in cultured mammalian cells. Nat. Protoc. 12, 1792–1816 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Costa, E. A., Subramanian, K., Nunnari, J. & Weissman, J. S. Defining the physiological role of SRP in protein-targeting efficiency and specificity. Science 359, 689–692 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Puig, K. L. et al. Amyloid precursor protein modulates macrophage phenotype and diet-dependent weight gain. Sci. Rep. 7, 43725 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. Freeman, L. R. et al. Mutant amyloid precursor protein differentially alters adipose biology under obesogenic and non-obesogenic conditions. PLoS One 7, e43193 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Czeczor, J. K. et al. APP deficiency results in resistance to obesity but impairs glucose tolerance upon high fat feeding. J. Endocrinol. 237, 311–322 (2018).

    CAS  PubMed  Google Scholar 

  37. Pera, M. et al. Increased localization of APP-C99 in mitochondria-associated ER membranes causes mitochondrial dysfunction in Alzheimer disease. EMBO J. 36, 3356–3371 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Bjune, J. I. et al. IRX5 regulates adipocyte amyloid precursor protein and mitochondrial respiration in obesity. Int. J. Obes. (Lond) 43, 2151–2162 (2018).

    Google Scholar 

  39. Kanaji, S., Iwahashi, J., Kida, Y., Sakaguchi, M. & Mihara, K. Characterization of the signal that directs Tom20 to the mitochondrial outer membrane. J. Cell Biol. 151, 277–288 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Das, A. T., Tenenbaum, L. & Berkhout, B. Tet-on systems for doxycycline-inducible gene expression. Curr. Gene Ther. 16, 156–167 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Shao, M. et al. Zfp423 maintains white adipocyte identity through suppression of the beige cell thermogenic gene program. Cell Metab. 23, 1167–1184 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Shao, M. et al. Fetal development of subcutaneous white adipose tissue is dependent on Zfp423. Mol. Metab. 6, 111–124 (2017).

    CAS  PubMed  Google Scholar 

  43. Fabbrini, E. et al. Metabolically normal obese people are protected from adverse effects following weight gain. J. Clin. Invest. 125, 787–795 (2015).

    PubMed  PubMed Central  Google Scholar 

  44. Seo, J. B. et al. Knockdown of ANT2 reduces adipocyte hypoxia and improves insulin resistance in obesity. Nat. Metab. 1, 86–97 (2019).

    PubMed  Google Scholar 

  45. An, Y. A. et al. Angiopoietin-2 in white adipose tissue improves metabolic homeostasis through enhanced angiogenesis. eLife 6, e24071 (2017).

    PubMed  PubMed Central  Google Scholar 

  46. Kotzbeck, P. et al. Brown adipose tissue whitening leads to brown adipocyte death and adipose tissue inflammation. J. Lipid Res. 59, 784–794 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Andreux, P. A. et al. The mitophagy activator urolithin A is safe and induces a molecular signature of improved mitochondrial and cellular health in humans. Nat. Metab. 1, 595–603 (2019).

    Google Scholar 

  48. Crewe, C. et al. An endothelial-to-adipocyte extracellular vesicle axis governed by metabolic state. Cell 175, 695–708.e613 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Pfanner, N., Warscheid, B. & Wiedemann, N. Mitochondrial proteins: from biogenesis to functional networks. Nat. Rev. Mol. Cell Biol. 20, 267–284 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Pallotti, F. & Lenaz, G. Isolation and subfractionation of mitochondria from animal cells and tissue culture lines. Methods Cell. Biol. 80, 3–44 (2007).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to H. Zheng at Baylor College of Medicine for generously sharing Appflox/flox mice for our study. We also sincerely thank the Transgenic Core Facility at University of Texas Southwestern for the generation of the transgenic mouse lines and the Metabolic Phenotyping Core, Proteomics Core (Lemoff A), Pathology Core, Live Cell Imaging Core Facility and Electron Microscopy Core Facility (Luby-Phelps K) at University of Texas Southwestern for their excellent experimental assistance. This study was supported by US National Institutes of Health grants P01-DK088761, R01-DK55758, R01-DK56341, RC2-118620 and R01-DK099110, a Novo Nordisk Foundation Excellence project grant (P.E.S.) and RR024992 as well as a grant from the Pershing Square Foundation (S.K.). I.W.A. was supported by grants from the Novo Nordisk Foundation (NNF19OC0056601), the Swedish Research Council (2017-00792 and 2013-7107) and the Swedish Diabetes Foundation. J.-B.F. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; Grant 414232833).

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Authors and Affiliations

  1. Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Yu A. An, Clair Crewe, Ingrid Wernstedt Asterholm, Kai Sun, Shiuhwei Chen, Fang Zhang, Mengle Shao, Jan-Bernd Funcke, Zhuzhen Zhang, Leon Straub, Christine M. Kusminski & Philipp E. Scherer

  2. Institute of Neuroscience and Physiology (Metabolic Physiology), Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden

    Ingrid Wernstedt Asterholm

  3. Center for Metabolic and Degenerative Diseases, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA

    Kai Sun

  4. Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Fang Zhang

  5. Shanghai Key Laboratory of Fundus Disease, Shanghai, China

    Fang Zhang

  6. Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, USA

    Jun Yoshino & Samuel Klein

  7. Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Philipp E. Scherer

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Contributions

Conceptualization, Y.A.A., I.W.A. and P.E.S.; methodology, Y.A.A. and K.S.; formal analysis, Y.A.A; investigation, Y.A.A., C.C., S.C., F.Z., M.S., J.-B.F., Z.Z., L.S. and C.M.K.; resources, J.Y. and S.K.; original draft writing, Y.A.A.; manuscript review and editing, C.C., I.W.A., S.K., C.M.K. and P.E.S.; supervision, P.E.S.; funding acquisition, P.E.S.

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Extended data

Extended Data Fig. 1 APP is increased in WAT in obesity and adipocyte-specific APP overexpressing mice are more sensitive to diet induced obesity (related to Fig. 1a,h).

a–f, The correlation between APP mRNA levels in sWAT from people with obesity with body mass (a), BMI (b), subcutaneous AT volume (c), triglycerides (d), fasting insulin (e) and HDL-cholesterol (f) levels. n = 23 (a–d) or 24 (e,f) subjects. g, App transcription in the brain in acute HFD-challenged wild-type mice for 0, 1, 2, 5, 7 and 14 d. n = 7 for Day 0 group; n = 5 for Day 1, 2, 5, 7 and 14 groups. Data are shown as mean ± s.e.m. of biologically independent samples. Pearson correlation analysis for correlation coefficient (r) and two-tailed P value (a–f); one-way ANOVA followed by a Tukey post-test, and non-significance was found (g).

Extended Data Fig. 2 Adipocyte-specific APP-overexpressing mice are more sensitive to diet-induced obesity (related to Fig. 2c,d,g).

a, Representative western blotting image for APP protein levels in brain samples from control and APP transgenic mice under HFD/Dox feeding. n = 3 mice per group. b, Western blotting for APP in mitochondrial and post-mitochondrial fractions from sWAT in control and APP-overexpressing mice fed with Dox for 1 week. COXIV, mitochondrial marker; β-tubulin, cytoplasmic marker. n = 2 mice per group. Representative image chosen from three independent experiments in a and b. c, Immunofluorescence staining for APP (red), TIM23 (green) and DAPI (blue) for nuclear labelling in HEK293T cells transfected with empty vector (left) or the mito-APP construct (right). Orange colours indicate the merge between APP and TIM23. Scale bars, 20 μm. The staining experiments have been replicated three times. d, Insulin levels measured in serum samples obtained from OGTT experiments. n = 8 mice per group. Data are shown as mean ± s.e.m. of biologically independent samples. Two-way ANOVA followed by a Tukey post-test (d).

Source data

Extended Data Fig. 3 APP overexpression leads to adipocyte hypertrophy by impairing stimulated lipolysis (related to Fig. 4a,e–j).

a, Representative images for H&E staining in sWAT sections from control mice (APP-) at different time points following induction with Dox (600 mg per kg (diet weight)), chosen from two independent experiments; scale bars, 161 μm. b, Representative H&E staining images in eWAT sections from APP transgenic mice (APP+) at different time points following induction with Dox (600 mg per kg (diet weight)), chosen from two independent experiments; scale bars, 100 μm. c–e, Lipogenesis-related gene transcriptions in sWAT of APP transgenic mice at different time points of Dox 600 feeding (600 mg per kg (diet weight)): Dgat2 (c), Fasn (d), Scd1 (e) and Srebp1 (f); n = 3 mice per time point. Data are shown as mean ± s.e.m. of biologically independent samples. One-way ANOVA followed by a Tukey post-test (c–f) and no statistical significance was found.

Extended Data Fig. 4 APP impairs adipocyte mitochondrial function owing to defective mitochondrial protein import (related to Fig. 5a,b,f).

a, In vitro mitochondrial respiration (OCR) in sWAT SVF differentiated adipocytes from control (APP–) and APP transgenic (APP+) mice. Cells are pre-incubated for 5 μg ml–1 Dox for 48 h to induce APP overexpression. n = 5 per group. b, Relative in vitro ATP production changes from SVF-differentiated adipocytes of APP transgenic mice compared with control mice. Different dosages of Dox were applied to cells. n = 8 per dosage. c, Combined light and fluorescence microscopy images displaying mitochondrial membrane potential (MMP) through TMRE staining (Red) from SVF-differentiated adipocytes of control and APP transgenic mice. DMSO serves as the negative control, and FCCP is a positive control to collapse MMP. Images were chosen from three independent experiments and are representative of at least 12 fields for each group. Scale bars, 50 μm. d–g, Quantification for indirect calorimetry measurements in control and APP transgenic mice in light and dark cycles: oxygen consumption (VO2) (d); CO2 production (VCO2) (e); RER (f) and calculated energy expenditure (g). n = 6 mice per group. h, Western blotting for Aβ-40/42 in mitochondrial and cytoplasmic fractions from sWAT in control and APP-overexpressing mice. Aβ 1-42 protein has been loaded separately as a positive control. n = 2 mice per group. Representative image is chosen from three independent experiments. i, Western blotting for mitochondrial complex components using oxidative phosphorylation antibody cocktail in mitochondrial and cytoplasmic fractions from sWAT in control and APP-overexpressing mice. n = 2 mice per group. A representative image was chosen from three independent experiments. j, Gene expressions for mitochondrial complex components measured in i. n = 5 (APP–) or 6 (APP+) mice per group. k, Western blotting for COX5A in sWAT in control and APP-overexpressing mice. n = 3 mice per group. A representative image was chosen from three independent experiments. Data are shown as mean ± s.e.m. of biologically independent samples. Two-way ANOVA (a); one-way ANOVA followed by a Tukey post-test (b); Two-tailed Student’s t-test (d–g,j).

Source data

Extended Data Fig. 5 Dysregulation of SRP54c is responsible for mistargeting of APP into adipocyte mitochondria (related to Fig. 5).

a,b, SRP subunit gene mRNA levels in floating adipocytes in 30-week-HFD- and chow-fed mice (a; left, sWAT; right, eWAT; n = 3 mice per group) and Srp54c mRNA levels from sWAT in acute-HFD-challenged wild-type mice for 0, 1, 2, 5, 7 and 14 d (b); n = 7 mice in Day 0 group and n = 5 mice in Day 1, 2, 5, 7 and 14 groups. c, The correlation between Srp54c mRNA levels in sWAT from HFD-challenged wild-type mice with App mRNA expression; n = 32. d, Schematic illustration of the adipocyte-specific, Dox-inducible SRP54c transgenic mouse model. e, Validation of SRP54c overexpression in WAT of transgenic mice fed with 1-week Dox (600 mg per kg (diet weight)) diet by detecting Srp54c mRNA levels in different tissues from control (Control) and SRP54c overexpressing (Srp54c Tg) mice (n = 4 mice per group). f, Representative western blotting image (left) for APP in purified mitochondrial, post-mito and whole tissue lysate from sWAT in 1-week Dox-fed mice, and its quantification (right panel). n = 3 mice per group. Images are chosen from three independent experiments. g, Representative autoradiography image (left) and statistics (right) of pOTC import assessed in isolated mitochondria (incubation for 30 min) from sWAT of 1-week Dox-fed control or Srp54c Tg mice; 25% of [35S]pOTC was added to each reaction is loaded as input. n = 4 mice per group. h–i, Upon 3-week Dox feeding, both control and Srp54c Tg mice are subject to metabolic analysis, including body weight monitoring (h) and OGTT assays (i). n = 4 mice per group. j, Proteomics analysis performed in purified mitochondria from WAT of control and Srp54c Tg mice. Left, percentage of secretome proteins among uniquely detected proteins in Srp54c Tg mitochondria; right, heat map depicting enrichment of identified proteins belonging to the secretome (* indicates a polypeptide containing a well-defined ER signalling sequence). For all statistical graphs, numeric data are presented as mean ± s.e.m. of biologically independent samples. Two-tailed Student’s t-test (a,e–g); one-way ANOVA followed by a Tukey post-test (b); Pearson correlation analysis for correlation coefficient (r) and two-tailed P value (c); two-way ANOVA followed by a Tukey post-test (h,i).

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Extended Data Fig. 6 Halting APP overexpression reverses the obese phenotypes (related to Fig. 6).

a–d, APP-overexpressing mice (APP+) were fed with HFD/Dox diets for 8 weeks and were divided into two groups, one group continuously on HFD/Dox feeding (Keep Dox) and the other group fed with HFD without Dox (Withdraw Dox). Two groups of mice are subject to the following metabolic analyses (n = 6 mice per group). a, Body weight for 12 weeks. b, Ex vivo mitochondrial respiration (OCR) in sWAT fat pads from both groups, n = 10 tissues per group. c,d, Glucose levels at different time points from OGTT (c) and ITT experiments (d). e,f,h, Control mice (APP–) were fed with HFD/Dox diets for 8 weeks, and the cohorts were divided into two groups, one group continuously on HFD/Dox feeding and the other group fed with HFD without Dox. Two groups of mice were subjected to the following metabolic analyses. e, Body weights for the 12-week exposure (n = 6 mice per group). f, Ex vivo mitochondrial respiration (OCR) in sWAT fat pads from both groups (n = 10 tissues per group). h, Glucose levels at different time points from (n = 6 mice per group) OGTT experiments. g, Combined high-resolution respirometry measured in isolated mitochondria from four groups including control or APP transgenic mice kept with or without Dox feeding, n = 5 mice per group. i,j, AT inflammatory and liver steatosis phenotypes in control or APP transgenic mice kept with or without Dox feeding. i, Representative H&E staining images for sWAT. j, Representative H&E staining images of liver tissues from four groups. Images are chosen from three independent experiments. For all the statistics: data are presented as mean ± s.e.m. of biologically independent samples. Two-way ANOVA followed by a Tukey post-test (a–h), and no statistical significance was found in e, f and h.

Extended Data Fig. 7 App AKO protects mice from obesity with enhanced adipocyte mitochondrial function (related to Fig. 6e–h).

a,b, Experiments were in 12-week HFD/Dox feeding control or App AKO mice. a, Insulin levels measured in serum samples obtained from OGTT experiments. b, Glucose levels at difference time points during ITT assays. n = 8 mice per group. c–g, For insulin-sensitivity measurement, three groups of mice, including control, APP adipocyte-specific transgenic (App Tg) and App adipocyte-specific knockout (App AKO) mice were subjected to hyperinsulinemic–euglycemic clamp studies. c, Body weight. d, Clamped glucose levels. e, GIR. f, Basal hepatic glucose production. g, 2-DG uptake in different metabolic tissues. For c–f, n = 7 mice in control and App Tg groups, n = 6 mice in App AKO group. For g, n = 6 mice per group. h,i, Representative immunoblot image of p-Akt (Ser 473) and total Akt expression in different metabolic tissues from both control and APP-overexpressing mice (h) or both control and App AKO mice (i) after saline or insulin injection (i.v.) for 5 min. For the western blot image, n = 3 mice per group, and the representative images are chosen from three independent experiments. Dare shown as mean ± s.e.m. of biologically independent samples. Two-way ANOVA followed by a Tukey post-test (a,b); one-way ANOVA followed by a Tukey post-test (c–g).

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An, Y.A., Crewe, C., Asterholm, I.W. et al. Dysregulation of amyloid precursor protein impairs adipose tissue mitochondrial function and promotes obesity. Nat Metab 1, 1243–1257 (2019). https://doi.org/10.1038/s42255-019-0149-1

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