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.

  • Letter
  • Published:

Hepatocyte-secreted DPP4 in obesity promotes adipose inflammation and insulin resistance

Nature volume 555, pages 673–677 (2018)Cite this article

Subjects

Abstract

Obesity-induced metabolic disease involves functional integration among several organs via circulating factors, but little is known about crosstalk between liver and visceral adipose tissue (VAT)1. In obesity, VAT becomes populated with inflammatory adipose tissue macrophages (ATMs)2,3. In obese humans, there is a close correlation between adipose tissue inflammation and insulin resistance4,5, and in obese mice, blocking systemic or ATM inflammation improves insulin sensitivity6,7,8. However, processes that promote pathological adipose tissue inflammation in obesity are incompletely understood. Here we show that obesity in mice stimulates hepatocytes to synthesize and secrete dipeptidyl peptidase 4 (DPP4), which acts with plasma factor Xa to inflame ATMs. Silencing expression of DPP4 in hepatocytes suppresses inflammation of VAT and insulin resistance; however, a similar effect is not seen with the orally administered DPP4 inhibitor sitagliptin. Inflammation and insulin resistance are also suppressed by silencing expression of caveolin-1 or PAR2 in ATMs; these proteins mediate the actions of DPP4 and factor Xa, respectively. Thus, hepatocyte DPP4 promotes VAT inflammation and insulin resistance in obesity, and targeting this pathway may have metabolic benefits that are distinct from those observed with oral DPP4 inhibitors.

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

Figure 1: Hepatocyte-specific DPP4 silencing suppresses VAT inflammation and improves insulin sensitivity in DIO mice.
Figure 2: Silencing of hepatocyte DPP4, but not treatment with the oral DPP4 inhibitor sitagliptin, lowers VAT inflammation and improves metabolism in DIO mice.
Figure 3: DPP4 and FXa synergistically activate inflammatory signalling in macrophages.
Figure 4: Silencing PAR2 or CAV1 in ATMs in obese mice lowers VAT inflammation and improves response to insulin.

Similar content being viewed by others

References

  1. Dasgupta, S. et al. NF-κB mediates lipid-induced fetuin-A expression in hepatocytes that impairs adipocyte function effecting insulin resistance. Biochem. J. 429, 451–462 (2010)

    Article  CAS  Google Scholar 

  2. Weisberg, S. P. et al. Obesity is associated with macrophage accumulation in adipose tissue. J. Clin. Invest. 112, 1796–1808 (2003)

    Article  CAS  Google Scholar 

  3. Xu, H. et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J. Clin. Invest. 112, 1821–1830 (2003)

    Article  CAS  Google Scholar 

  4. Hardy, O. T. et al. Body mass index-independent inflammation in omental adipose tissue associated with insulin resistance in morbid obesity. Surg. Obes. Relat. Dis. 7, 60–67 (2011)

    Article  Google Scholar 

  5. Blüher, M. Adipose tissue inflammation: a cause or consequence of obesity-related insulin resistance? Clin. Sci. (Lond.) 130, 1603–1614 (2016)

    Article  Google Scholar 

  6. Hotamisligil, G. S., Shargill, N. S. & Spiegelman, B. M. Adipose expression of tumor necrosis factor-α: direct role in obesity-linked insulin resistance. Science 259, 87–91 (1993)

    Article  ADS  CAS  Google Scholar 

  7. Lumeng, C. N. & Saltiel, A. R. Inflammatory links between obesity and metabolic disease. J. Clin. Invest. 121, 2111–2117 (2011)

    Article  CAS  Google Scholar 

  8. Aouadi, M. et al. Gene silencing in adipose tissue macrophages regulates whole-body metabolism in obese mice. Proc. Natl Acad. Sci. USA 110, 8278–8283 (2013)

    Article  ADS  CAS  Google Scholar 

  9. Ozcan, L. et al. Calcium signaling through CaMKII regulates hepatic glucose production in fasting and obesity. Cell Metab. 15, 739–751 (2012)

    Article  CAS  Google Scholar 

  10. Ozcan, L. et al. Activation of calcium/calmodulin-dependent protein kinase II in obesity mediates suppression of hepatic insulin signaling. Cell Metab. 18, 803–815 (2013)

    Article  CAS  Google Scholar 

  11. Ozcan, L. et al. Hepatocyte DACH1 is increased in obesity via nuclear exclusion of HDAC4 and promotes hepatic insulin resistance. Cell Reports 15, 2214–2225 (2016)

    Article  CAS  Google Scholar 

  12. Boonacker, E. & Van Noorden, C. J. The multifunctional or moonlighting protein CD26/DPPIV. Eur. J. Cell Biol. 82, 53–73 (2003)

    Article  CAS  Google Scholar 

  13. Kirino, Y., Sei, M., Kawazoe, K., Minakuchi, K. & Sato, Y. Plasma dipeptidyl peptidase 4 activity correlates with body mass index and the plasma adiponectin concentration in healthy young people. Endocr. J. 59, 949–953 (2012)

    Article  CAS  Google Scholar 

  14. Lamers, D. et al. Dipeptidyl peptidase 4 is a novel adipokine potentially linking obesity to the metabolic syndrome. Diabetes 60, 1917–1925 (2011)

    Article  CAS  Google Scholar 

  15. Drucker, D. J. & Nauck, M. A. The incretin system: glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes. Lancet 368, 1696–1705 (2006)

    Article  CAS  Google Scholar 

  16. Guilherme, A., Virbasius, J. V., Puri, V. & Czech, M. P. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat. Rev. Mol. Cell Biol. 9, 367–377 (2008)

    Article  CAS  Google Scholar 

  17. Ristic, S., Byiers, S., Foley, J. & Holmes, D. Improved glycaemic control with dipeptidyl peptidase-4 inhibition in patients with type 2 diabetes: vildagliptin (LAF237) dose response. Diabetes Obes. Metab. 7, 692–698 (2005)

    Article  CAS  Google Scholar 

  18. Aschner, P. et al. Effect of the dipeptidyl peptidase-4 inhibitor sitagliptin as monotherapy on glycemic control in patients with type 2 diabetes. Diabetes Care 29, 2632–2637 (2006)

    Article  CAS  Google Scholar 

  19. Raz, I. et al. Efficacy and safety of the dipeptidyl peptidase-4 inhibitor sitagliptin as monotherapy in patients with type 2 diabetes mellitus. Diabetologia 49, 2564–2571 (2006)

    Article  CAS  Google Scholar 

  20. Kutoh, E. & Ukai, Y. Alogliptin as an initial therapy in patients with newly diagnosed, drug naïve type 2 diabetes: a randomized, control trial. Endocrine 41, 435–441 (2012)

    Article  CAS  Google Scholar 

  21. Kadowaki, T. & Kondo, K. Efficacy, safety and dose-response relationship of teneligliptin, a dipeptidyl peptidase-4 inhibitor, in Japanese patients with type 2 diabetes mellitus. Diabetes Obes. Metab. 15, 810–818 (2013)

    Article  CAS  Google Scholar 

  22. Jung, C. H. et al. A randomized, double-blind, placebo-controlled, phase II clinical trial to investigate the efficacy and safety of oral DA-1229 in patients with type 2 diabetes mellitus who have inadequate glycaemic control with diet and exercise. Diabetes Metab. Res. Rev. 31, 295–306 (2015)

    Article  CAS  Google Scholar 

  23. Mulvihill, E. E. et al. Cellular sites and mechanisms linking reduction of dipeptidyl peptidase-4 activity to control of incretin hormone action and glucose homeostasis. Cell Metab. 25, 152–165 (2017)

    Article  CAS  Google Scholar 

  24. Senden, N. H. et al. Factor Xa induces cytokine production and expression of adhesion molecules by human umbilical vein endothelial cells. J. Immunol. 161, 4318–4324 (1998)

    CAS  PubMed  Google Scholar 

  25. Busch, G. et al. Coagulation factor Xa stimulates interleukin-8 release in endothelial cells and mononuclear leukocytes: implications in acute myocardial infarction. Arterioscler. Thromb. Vasc. Biol. 25, 461–466 (2005)

    Article  CAS  Google Scholar 

  26. McGee, M. P., Wallin, R., Wheeler, F. B. & Rothberger, H. Initiation of the extrinsic pathway of coagulation by human and rabbit alveolar macrophages: a kinetic study. Blood 74, 1583–1590 (1989)

    Article  CAS  Google Scholar 

  27. Ohnuma, K. et al. CD26 mediates dissociation of Tollip and IRAK-1 from caveolin-1 and induces upregulation of CD86 on antigen-presenting cells. Mol. Cell. Biol. 25, 7743–7757 (2005)

    Article  CAS  Google Scholar 

  28. Zuo, P. et al. Factor Xa induces pro-inflammatory cytokine expression in RAW 264.7 macrophages via protease-activated receptor-2 activation. Am. J. Transl. Res. 7, 2326–2334 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Dobrian, A. D. et al. Dipeptidyl peptidase IV inhibitor sitagliptin reduces local inflammation in adipose tissue and in pancreatic islets of obese mice. Am. J. Physiol. Endocrinol. Metab. 300, E410–E421 (2011)

    Article  CAS  Google Scholar 

  30. Pedersen, D. J. et al. A major role of insulin in promoting obesity-associated adipose tissue inflammation. Mol. Metab. 4, 507–518 (2015)

    Article  CAS  Google Scholar 

  31. Fitzgerald, K. et al. A highly durable RNAi therapeutic inhibitor of PCSK9. N. Engl. J. Med. 376, 41–51 (2017)

    Article  CAS  Google Scholar 

  32. Lisowski, L. et al. Selection and evaluation of clinically relevant AAV variants in a xenograft liver model. Nature 506, 382–386 (2014)

    Article  ADS  CAS  Google Scholar 

  33. Ebert, S. M. et al. Stress-induced skeletal muscle Gadd45a expression reprograms myonuclei and causes muscle atrophy. J. Biol. Chem. 287, 27290–27301 (2012)

    Article  CAS  Google Scholar 

  34. Mu, X. et al. Hepatocellular carcinoma originates from hepatocytes and not from the progenitor/biliary compartment. J. Clin. Invest. 125, 3891–3903 (2015)

    Article  Google Scholar 

  35. Shimano, H. et al. Overproduction of cholesterol and fatty acids causes massive liver enlargement in transgenic mice expressing truncated SREBP-1a. J. Clin. Invest. 98, 1575–1584 (1996)

    Article  CAS  Google Scholar 

  36. Orr, J. S., Kennedy, A. J. & Hasty, A. H. Isolation of adipose tissue immune cells. J. Vis. Exp. 22, e50707 (2013)

    Google Scholar 

  37. Nazarian, A. et al. Inhibition of circulating dipeptidyl peptidase 4 activity in patients with metastatic prostate cancer. Mol. Cell. Proteomics 13, 3082–3096 (2014)

    Article  CAS  Google Scholar 

  38. Aouadi, M. et al. Orally delivered siRNA targeting macrophage Map4k4 suppresses systemic inflammation. Nature 458, 1180–1184 (2009)

    Article  ADS  CAS  Google Scholar 

  39. Cohen, J. L. et al. Peptide- and amine-modified glucan particles for the delivery of therapeutic siRNA. Mol. Pharm. 13, 964–978 (2016)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank F. S. Katz for assistance with FPLC; R. Kaufman for adeno-ATF4; C. Adams and S. Bullard for Atf4fl/fl mice; and A. Ferrante, S. Ramakrishnan, J. Weitz and T. McGraw for discussions. E.C. was supported by NIH grant 5P30CA013696-42. I.T. was funded by grants from the NIH (HL087123 and HL075662) and by a grant from the Merck Investigator Studies Program. L.O. was funded by the NIH grant DK106045 and a grant from the Columbia University Diabetes Research Center (P30 DK063608). Y.S., S.M.N. and M.P.C. were funded by NIH grant DK103047. M.B. was funded by the Deutsche Forschungsgemeinschaft grant SFB1052.

Author information

Authors and Affiliations

  1. Department of Medicine, Columbia University Medical Center, New York, 10032, New York, USA

    Devram S. Ghorpade, Lale Ozcan, Ze Zheng & Ira Tabas

  2. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, 01605, Massachusetts, USA

    Sarah M. Nicoloro, Yuefei Shen & Michael P. Czech

  3. Proteomics Shared Resource in the Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, 10032, New York, USA

    Emily Chen

  4. Herbert Irving Comprehensive Cancer Center Proteomics Shared Resource, Columbia University Medical Center, New York, 10032, New York, USA

    Emily Chen

  5. Department of Medicine, University of Leipzig, Leipzig, 04103, Germany

    Matthias Blüher

  6. Department of Pathology & Cell Biology and Department of Physiology, Columbia University Medical Center, New York, 10032, New York, USA

    Ira Tabas

Authors
  1. Devram S. Ghorpade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lale Ozcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ze Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarah M. Nicoloro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuefei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Emily Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matthias Blüher
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael P. Czech
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ira Tabas
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.S.G., L.O. and I.T. designed the study, analysed data and wrote the manuscript. D.S.G., L.O. and Z.Z. conducted the experiments. S.M.N., Y.S. and M.P.C. made the glucan-encapsulated siRNA particles (GERPs) and helped design these experiments and analyse the data. E.C. conducted the LC–MS/MS studies and assisted with data analysis. M.B. helped with interpretation of data.

Corresponding authors

Correspondence to Lale Ozcan or Ira Tabas.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks P. Scherer 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 figures and tables

Extended Data Figure 1 Hepatocyte-specific deletion of CaMKII or ATF4 in DIO mice lowers VAT inflammation.

a–c, Sixteen-week-old Camk2gfl/fl mice that had been previously fed the DIO diet for 13 weeks were injected intravenously with AAV8-TBG-cre (H-CaMKII(KO)) or AAV8-TBG-lacZ (wild-type, WT). Mice were analysed after three additional weeks on the DIO diet. a, Representative images of VAT immunostained for F4/80, with quantification of crown-like-structure (CLS) macrophages, and expression of Adgre1 mRNA, which encodes F4/80. b, As in a, except that the mice were injected with fluorescent beads using a procedure that labels circulating Ly6chi monocytes, and then bead-labelled cells were assayed in VAT sections. c, mRNAs for Il6, Mcp1, Tnf and Il1b in VAT. d, Blood monocyte count. e, Plasma IL6 and TNFα measured by ELISA, and quantification of Adgre1, Mcp1 and Tnfa mRNA in liver. In a–e, n = 4 mice per group; mean ± s.e.m.; *P < 0.05; n.s., not significant by two-tailed Student’s t-test. f, g, Mice similar to those in a–c, and from a third group in which Camk2gfl/fl mice were injected intravenously with adeno-Atf4 and AAV8-TBG-cre (H-CaMKII(KO) + adeno-Atf4). f, CLS macrophages and Adgre1 mRNA in VAT were quantified, with representative images of F4/80-stained VAT. g, Il6, Mcp1, Tnfa and Il1b mRNA in VAT and Adgre1, Mcp1 and Tnfa mRNA in liver were quantified. Note that the first two groups of mice received adeno-lacZ instead of adeno-Atf4. n = 4 mice per group; mean ± s.e.m.; *P < 0.05 by one-way ANOVA; n.s., not significant. h, AAV8-TBG-cre (H-ATF4(KO)) or AAV8-TBG-lacZ (wild-type) was injected intravenously into 16-week-old Aft4fl/fl mice previously fed the DIO diet for 13 weeks. After three further weeks on the DIO diet, VAT from these mice was immunostained for F4/80 to identify macrophages, the percentage of macrophages in CLS was quantified, and the VAT was assayed for Adgre1 and the indicated inflammatory mRNAs. Twelve wild-type and 11 H-ATF4(KO) mice was analysed for CLS macrophages, and a randomly selected subset of five wild-type and five H-ATF4(KO) mice was analysed for the VAT mRNAs. Mean ± s.e.m., *P < 0.05 by two-tailed Student’s t-test.

Source data

Extended Data Figure 2 DPP4 in the plasma of DIO mice induces Mcp1 and Il6 in SVF cells from the VAT of obese mice and in macrophages.

a, Representative images of haemotoxylin and eosin-stained VAT sections from lean and DIO mice and Adgre1 mRNA levels in SVF from lean and DIO mice. n = 4 mice per group; mean ± s.e.m.; *P < 0.05. b, SVF cells from the VAT of lean mice were incubated for 4 h in medium containing 10% (v/v) plasma from lean or DIO mice and then assayed for Mcp1 and Il6 mRNA. n = 4 mice per group; mean ± s.e.m.; *P < 0.05; n.s., not significant. c, Mcp1 mRNA was assayed in mouse peritoneal macrophages (Mϕs) or bone marrow-derived macrophages that were incubated for 4 h with medium containing 10% (v/v) plasma from lean or DIO mice. n = 5 technical replicates per group; mean ± s.e.m.; *P < 0.05. d, Mcp1 mRNA levels in SVF cells that were incubated for 4 h with medium containing 10% (v/v) control or heat-inactivated (heat) plasma from the indicated groups of mice. n = 6 mice per group; mean ± s.e.m.; *P < 0.05. e, UV protein chromatogram obtained after fractionation of DIO mice plasma using gel-filtration FPLC; vertical grey bar depicts peak of activity shown in f. f, Obese mouse SVF cells were incubated with medium containing 10% lean or DIO mouse plasma or the indicated FPLC fractions from e and assayed for Mcp1 mRNA. n.d., Mcp1 mRNA not detected. Arrows indicate the fractions that were selected for LC–MS/MS analysis. g, LC–MS/MS normalized spectral counts corresponding to DPP4 in the FPLC fractions from f. h, DPP4 activity in the plasma of lean and DIO mice. n = 5 mice per group; mean ± s.e.m.; *P < 0.05. i, SVF cells from DIO mice were incubated for 4 h with medium containing 10% (v/v) lean or DIO mouse plasma that was pre-treated for 1 h with or without 10 μM DPP4 inhibitor KR62436. The cells were then assayed for Il6 mRNA. n = 3 technical replicates per group; mean ± s.e.m.; *P < 0.05. j, VAT from the mice in Extended Data Fig. 1f, g was assayed for Dpp4 mRNA (n = 4). Data in a–c and h were analysed by two-tailed Student’s t-test; data in i and j were analysed by one-way ANOVA (g, h); data in d were analysed by two-way ANOVA.

Source data

Extended Data Figure 3 Restoration of DPP4 in livers of H-CaMKII(KO) mice abrogates suppression of VAT inflammation in DIO mice; ATF4 ChIP of the Dpp4 gene; and AAV8-H1-shDpp4 treatment lowers hepatic DPP4.

Wild-type and H-CaMKII(KO) mice and a third group in which Camk2gfl/fl mice were injected intravenously with adeno-Dpp4 together with the AAV8-TBG-cre (H-CaMKII(KO) + adeno-Dpp4) were analysed as follows. a, Body weight, plasma DPP4 activity and liver and VAT DPP4 protein. b, Representative images of VAT immunostained with F4/80 antibody, with quantification of CLS macrophages and Tnfa and Mcp1 mRNA in VAT. Note that the first two groups of mice received adeno-lacZ instead of adeno-DPP4. In a and b, n = 3–4 mice per group; mean ± s.e.m.; *P < 0.05 by one-way ANOVA; n.s., non-significant. For gel source data, see Supplementary Fig. 1. c, Top, ChIP was performed with liver extracts from the indicated groups of mice using anti-ATF4 or control IgG antibodies. The region spanning the predicted ATF4-binding sequence in exon 1 of Dpp4 was amplified by RT–qPCR and normalized to the values obtained from the input. n = 3 ChIP assays for wild-type, H-CaMKII(KO), H-CaMKII(KO) + adeno-Atf4; n = 1 for H-ATF4(KO); n = 10 for control IgG; mean ± s.e.m.; #,*P < 0.05 by one-way ANOVA for groups 1–3. Bottom, per cent input for ATF4 ChIP using liver extracts from the indicated DIO mice and PCR primers for a region in the Dpp4 gene that does not contain a consensus sequence for ATF4 binding. n = 3 ChIP assays for wild type, H-CaMKII(KO), H-CaMKII(KO) + adeno-Atf4; n = 1 for H-ATF4(KO); n = 10 for control IgG; mean ± s.e.m.; n.s., non-significant by one-way ANOVA. d, e, Sixteen-week-old mice previously fed with the DIO diet for 10 weeks were injected intravenously with hepatocyte-specific AAV8-H1-shDpp4 (H-shDpp4) or control AAV8-H1 vector (con). After four additional weeks on DIO diet, the mice were analysed as follows. d, DPP4 immunoblot, with densitometric quantification of DPP4 protein in the indicated tissues; representative of three independent experiments. e, Dpp4 mRNA in liver and VAT. In d and e, n = 5 mice per group; mean ± s.e.m.; *P < 0.05; n.s., non-significant by two-tailed Student’s t-test. See Supplementary Fig. 1 for gel source data.

Source data

Extended Data Figure 4 Silencing of DPP4 in liver suppresses VAT inflammation and improves metabolism without increasing plasma incretins.

a, Top, 16-week-old mice previously fed with the DIO diet for 10 weeks were injected intravenously with AAV8-H1-shDpp4 (H-shDpp4) or control AAV8-H1 vector (con). After four weeks, the mice were assayed for Il6, Mcp1, Tnfa and Il1b mRNA in VAT. Bottom, control and H-shDpp4-treated mice similar to those above were analysed nine days after adenovirus injections for Il6, Mcp1, Tnfa and Il1b mRNA in ATMs. b–g, H-shDPP4 and control mice were analysed after four weeks. b–d, Representative images of adipose tissue immunostained with F4/80 antibody, with quantification of CLS macrophages, Adgre1 and inflammatory gene mRNA expression in perirenal, inguinal and brown fat. e, Plasma MCP1, IL6 and TNFα. f, Body weight and food intake. g, Weights of liver and the indicated adipose tissue depots. In a–g, n = 5–6 mice per group; mean ± s.e.m.; *P < 0.05; n.s., non-significant by two-tailed Student’s t-test. h–j, Mice similar to those in b–g were assayed. h, Blood glucose and plasma insulin. i, p-AKT and total AKT in VAT and liver after insulin injection into the portal vein. n = 1 PBS-injected, n = 4 insulin-injected mice per group; blots are representative of three independent experiments. Gel source data are shown in Supplementary Fig. 1. j, Plasma active GIP (1–42) and GLP1 (7–36). In h and j, n = 6 mice per group; mean ± s.e.m.; *P < 0.05; n.s., non-significant by two-tailed Student’s t-test.

Source data

Extended Data Figure 5 Hepatocyte-specific silencing of DPP4 improves glucose metabolism in ob/ob mice without increasing plasma incretins and does not affect VAT inflammation or glucose metabolism in lean mice.

a–h, Five-week-old chow-fed ob/ob mice were injected intravenously with AAV8-H1-shDpp4 (H-shDpp4) or AAV8-H1-control (con), and were assayed four weeks later. a, Body weight. b, Immunoblot of liver and VAT DPP4. c, Representative images of VAT immunostained with F4/80 antibody, with quantification of CLS macrophages and Adgre1 mRNA in VAT. d, Il6, Mcp1, Tnfa and Il1b mRNA in VAT. e, Blood glucose after challenge with intraperitoneal glucose or insulin. f, p-AKT and total AKT in VAT and liver extracts after portal vein insulin injection. g, Blood glucose and plasma insulin 5 h after food withdrawal. h, Plasma active GIP (1–42) and GLP1 (7–36). i–m, Sixteen-week-old chow-fed wild-type lean mice were injected intravenously with AAV8-H1-shDpp4 (H-shDpp4) or AAV8-H1-control (Con) and were analysed as follows. i, Body weight. j, Immunoblot of DPP4 in liver and VAT. k, p-AKT and total AKT in VAT and liver extracts after portal vein insulin injection. l, Blood glucose and plasma insulin 5 h after food withdrawal. m, Blood glucose after challenge with intraperitoneal glucose or insulin. In all panels, n = 5–6 mice per group; mean ± s.e.m.; n.s., non-significant by two-tailed Student's t-test. For gel source data, see Supplementary Fig. 1.

Source data

Extended Data Figure 6 Effect of DPP4 silencing on insulin-induced p-AKT in primary hepatocytes, on NEFA in obese or lean mice and on metabolism in comparison with sitagliptin in obese mice.

a, Wild-type or DPP4-silenced primary hepatocytes were treated with or without 50 μM palmitate for 10 h with the last 5 h in serum-free medium, and then stimulated with 100 nM of insulin for 5 min. p-AKT and total AKT and DPP4 were assayed by immunoblot. The data are representative of two independent experiments. b, Plasma samples from the following mice were assayed for non-esterified fatty acids (NEFA) four weeks after intravenous injection with AAV8-H1-shDpp4 (H-shDpp4) or control AAV8-H1 vector (con): 16-week-old mice fed the DIO diet for the last 10 weeks, 5-week-old chow-fed ob/ob mice, and 6-week-old chow-fed wild-type lean mice. n = 5–6 mice per group; mean ± s.e.m.; *P < 0.05 and n.s., non-significant by two-tailed Student's t-test. c–h, After 10 weeks on high-fat diet, control DIO mice, H-shDpp4 DIO mice and DIO mice were administered sitagliptin (sita) in drinking water to achieve a dose of ~30–45 mg/kg/day. After 4 weeks of treatment, the mice were analysed as follows. c, Body weight. d, Plasma and VAT DPP4 activity. e, Hepatic DPP4 immunoblot and hepatocyte DPP4 activity. f, Blood glucose and plasma insulin 5 h after food withdrawal. g, p-AKT and total AKT in VAT and liver after portal vein insulin injection. h, Plasma non-esterified fatty acids (NEFA). For all quantified data panels except e, n = 6–8 mice per group; for e, n = 4 mice per group; mean ± s.e.m.; *,#P < 0.05 and n.s., non-significant by one-way ANOVA. For gel source data, see Supplementary Fig. 1. i, DIO mice were treated with AAV8-H1-con, AAV8-H1-shDpp4 or sitagliptin (+ AAV8-H1-con) exactly as above, except the mice were analysed after 11 weeks of treatment instead of after 4 weeks. n = 5 mice per group; mean ± s.e.m.; *,#P < 0.05 and n.s., non-significant by one-way ANOVA.

Source data

Extended Data Figure 7 DPP4 requires a plasma factor to induce inflammation in SVF.

a, Mcp1 mRNA was assayed in SVF cells that were incubated for 4 h with medium containing 10% (v/v) DIO mouse plasma; DIO mouse plasma immunodepleted of DPP4; recombinant DPP4 (rDPP4) alone; or rDPP4 plus DIO mouse plasma immunodepleted of DPP4. n = 3 technical replicates per group;. mean ± s.e.m. b, As a, except plasma from H-shDPP4 DIO mice were used instead of DPP4-depleted obese mouse plasma, and both Mcp1 and Il6 were assayed (n = 5 technical replicates per group; mean ± s.e.m.). In a and b, groups with different symbols are different from each other (P < 0.05 by one-way ANOVA). c, DPP4-depleted plasma from DIO mice was fractionated by Superdex-200 FPLC. Each fraction, as well as unfractionated DIO mouse plasma, was incubated with SVF cells with or without rDPP4, followed by assay of Mcp1 mRNA. n.d., Mcp1 mRNA not detected. The fraction numbers in red (arrows) were selected for LC–MS/MS analysis. The data are from a single experiment.

Source data

Extended Data Figure 8 DPP4 and FXa synergistically promote VAT inflammation, and inhibition of FXa improves glucose metabolism.

a, LC–MS/MS normalized spectral counts corresponding to FX in the indicated FPLC fractions from Extended Fig. 7c. b, SVF cells or BMDMs were pre-incubated for 1 h with or without 10 μM FXa inhibitor rivaroxaban (riv) and then incubated with medium containing 10% (v/v) plasma from lean or DIO mice and assayed for Mcp1 mRNA. n = 3 technical replicates per group; mean ± s.e.m.; *,#P < 0.05 by two-way ANOVA. c, BMDMs were pretreated with or without 10 μM FXa inhibitor rivaroxaban (riv) or 10 μM DPP4 inhibitor KR62436, followed by incubation for 4 h with rFX or rDPP4 alone or both together. Il6 mRNA was then quantified. n = 4 technical replicates per group; mean ± s.e.m.; #,*P < 0.05 by one-way ANOVA. d, SVF cells from lean or DIO mice were treated with rFX or rDPP4 alone or in combination for 4 h, and then Mcp1 and Il6 mRNA levels were assayed (n = 4 technical replicates per group; mean ± s.e.m.; *P < 0.05 and n.s., non-significant by one-way ANOVA). e–j, The control and rivaroxaban-treated mice from Fig. 3b, c were assayed. e, Body weight. f, Adgre1, Mcp1, Il6, Tnfa and Il1b mRNA in VAT. g, h, Blood glucose and plasma insulin 5 h after food withdrawal. i, p-AKT and total AKT in VAT and liver after portal vein insulin injection. j, Plasma non-esterified fatty acids (NEFA). In e–h and j, n = 5 mice per group; mean ± s.e.m.; *P < 0.05 and n.s., non-significant by two-tailed Student’s t-test. In i, the mean fold increases of the plus-insulin values relative to the minus-insulin value, based on the densitometric ratio of p-AKT:total AKT, are shown below the blots (n = 1 PBS-injected and n = 4-insulin injected mice per group; mean ± s.e.m.; *P < 0.05). For gel source data, see Supplementary Fig. 1.

Extended Data Figure 9 DPP4 and FXa synergistically induce inflammatory signalling pathways in macrophages.

a, BMDMs were pre-treated for 4 h with or without 0.25 U rFXa, medium was then removed, cells were washed and treated with 10 μM rivaroxaban (riv) to inhibit residual FXa activity. The cells were then incubated for 4 h with or without 70 ng rDPP4 and assayed for Mcp1 and Il6 mRNA. b, BMDMs were pre-treated for 4 h with or without 70 ng rDPP4, medium was then removed, cells were washed and treated with 10 μM KR62436 to inhibit residual DPP4 activity. The cells were then incubated for 4 h with or without 0.25 U rFXa and assayed for Mcp1 and Il6 mRNA. In a and b, n = 3 technical replicates per group; mean ± s.e.m.; *P < 0.05 versus all other groups by one-way ANOVA. c, ATMs were pre-treated for 1 h with or without 10 μM of the PAR2 inhibitor GB83, 25 μM of the CAV1 inhibitor daidzein, 10 μM of the MEK inhibitor PD98059 or 10 μM of the IKK inhibitor PS-1145. The cells were then incubated for 4 h with or without rFXa and rDPP4 and assayed for Mcp1 and Il6 mRNA. n = 4 technical replicates per group; mean ± s.e.m.; *,#P < 0.05 by one-way ANOVA. d, Human monocyte-derived macrophages were pre-treated for 1 h with or without 10 μM PAR2 inhibitor (GB83) or 25 μM CAV1 inhibitor (daidzein) and then incubated for 4 h with or without rFXa, rDPP4 or both and assayed for MCP1 and IL6 mRNA. n = 3 technical replicates; mean ± s.e.m.; #,*P < 0.05 versus all other groups by one-way ANOVA. e–g, Bone marrow-derived macrophages were incubated for 10 min with or without 0.25 U rFXa, 70 ng rDPP4 or rFXa plus rDPP4 and then assayed by immunoblot for phosphorylated and total IRAK1, TAK1, RAF1, ERK1/2 and P65, followed by densitometric quantification. The data are representative of two (e, f) or three (g) independent experiments. For all panels, n = 3 technical replicates per group; mean ± s.e.m.; *P < 0.05 versus other groups and n.s., not significant by one-way ANOVA. h–j, Bone marrow-derived macrophages were pretreated for 1 h with or without the following inhibitors. h, IRAK-1/4 inhibitor-I, 0.5 μM. i, TAK1 MAPKKK inhibitor Z-7-oxozeaenol, 0.1 μM. j, RAF1 inhibitor GW5074, 1 μM. Cells were then incubated for 4 h with or without rFXa plus rDPP4 and assayed for Mcp1 and Il6 mRNA. n = 4 technical replicates per group; mean ± s.e.m.; *P < 0.05 by one-way ANOVA.

Source data

Extended Data Figure 10 Supporting data for GERP experiments.

a, Five-week-old chow-fed ob/ob mice were treated daily by intraperitoneal injection of FITC-labelled glucan shells for 12 days and then assayed by DAPI staining, F4/80 immunofluorescence and glucan shell fluorescence in the indicated tissues. b–e, Five-week-old chow-fed ob/ob mice were injected intraperitoneally once daily for 12 days with 0.2 mg GERPs containing scrambled siRNA (control), Par2 siRNA or Cav1 siRNA. The mice were analysed 24 h after the last injection. b, Body weight. c, Immunoblots of PAR2 and CAV1 in ATMs and splenic extracts. d, Il6, Mcp1, Tnfa and Il1b mRNA in VAT. e, Plasma non-esterified fatty acids (NEFA). In b, d and e, n = 5–6 mice per group; *P < 0.05 and n.s., non-significant by two-tailed Student’s t-test for each siRNA GERP versus control GERP. For gel source data, see Supplementary Fig. 1.

Source data

Supplementary information

Life Sciences Reporting Summary (PDF 96 kb)

Supplementary Data

This file contains Supplementary Figures 1-16: Uncropped versions of blots. Uncropped blots are shown for the indicated cropped blots in the main and Extended Data figures. (PDF 14544 kb)

Supplementary Tables

This file contains Supplementary Table 1: Gene primers used for RT-qPCR. Primer sets are shown for the R-qPCR assays used in the study. Supplementary Table 2: LC-MS/MS spectral analyses. Supplementary Table 2a shows the normalized LC-MS/MS spectral counts of selected FPLC fractions of plasma from DIO mice (see Extended Data Figure 2f). The first set of data show proteins with higher normalized spectral counts in FPLC fraction 44 (F44; active in inducing Mcp1 in macrophages) than in fractions F42 and F46, which were inactive in this assay. The second set of data show normalized spectral counts of other proteins identified fractions 42, 44, and/or 46. Supplementary Table 2b shows normalized LC-MS/MS spectral counts in selected FPLC fractions of plasma from DIO mice that was immunodepleted of DPP4 (see Extended Data Figure 7c). The first set of data show proteins with higher normalized spectral counts in FPLC fraction 44 (F44; active in inducing Mcp1 in macrophages) than in fractions F42 and F46, which were inactive in this assay. The second set of data show normalized spectral counts of other proteins identified fractions 42, 44, and/or 46. (PDF 727 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghorpade, D., Ozcan, L., Zheng, Z. et al. Hepatocyte-secreted DPP4 in obesity promotes adipose inflammation and insulin resistance. Nature 555, 673–677 (2018). https://doi.org/10.1038/nature26138

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature26138

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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