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:

Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance

Nature Metabolism volume 4, pages 476–494 (2022) Cite this article

Subjects

Abstract

Resulting from impaired collagen turnover, fibrosis is a hallmark of adipose tissue (AT) dysfunction and obesity-associated insulin resistance (IR). Prolidase, also known as peptidase D (PEPD), plays a vital role in collagen turnover by degrading proline-containing dipeptides but its specific functional relevance in AT is unknown. Here we show that in human and mouse obesity, PEPD expression and activity decrease in AT, and PEPD is released into the systemic circulation, which promotes fibrosis and AT IR. Loss of the enzymatic function of PEPD by genetic ablation or pharmacological inhibition causes AT fibrosis in mice. In addition to its intracellular enzymatic role, secreted extracellular PEPD protein enhances macrophage and adipocyte fibro-inflammatory responses via EGFR signalling, thereby promoting AT fibrosis and IR. We further show that decreased prolidase activity is coupled with increased systemic levels of PEPD that act as a pathogenic trigger of AT fibrosis and IR. Thus, PEPD produced by macrophages might serve as a biomarker of AT fibro-inflammation and could represent a therapeutic target for AT fibrosis and obesity-associated IR and type 2 diabetes.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Obesity reduces adipose tissue PEPD activity and promotes PEPD release associated with fibrosis and insulin resistance.
The alternative text for this image may have been generated using AI.
Fig. 2: Pharmacological inhibition of PEPD promotes adipose tissue fibro-inflammation and insulin resistance in lean mice.
The alternative text for this image may have been generated using AI.
Fig. 3: Pepd silencing exacerbates adipose tissue fibro-inflammation and metabolic dysfunctions in diet-induced obese mice.
The alternative text for this image may have been generated using AI.
Fig. 4: ‘Fibro-inflammatory’ and ‘adipose tissue dysfunction’-related pathways are enriched in the gonadal white adipose tissue from Pepd HET mice.
The alternative text for this image may have been generated using AI.
Fig. 5: Haematopoietic-specific Pepd silencing reduced adipose tissue fibro-inflammation and improved insulin sensitivity in obese mice.
The alternative text for this image may have been generated using AI.
Fig. 6: Purified PEPD protein induces fibro-inflammation and adipose tissue insulin resistance.
The alternative text for this image may have been generated using AI.
Fig. 7: High PEPD serum levels are associated with adipose tissue insulin resistance and drives the differences between the pharmacological and genetic animal models of PEPD downregulation.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

DEGs and pathways from the RNA-seq analysis are available in Supplementary Tables 1–10. The RNA-seq dataset is deposited in the Gene Expression Omnibus under accession number GSE198358. Source data are provided with this paper. All other raw data are available upon request.

Code availability

Code is available at https://github.com/bpucker/RNA-Seq_analysis/. An archived version is available at https://zenodo.org/record/6192463/.

References

  1. Sun, K., Tordjman, J., Clément, K. & Scherer, P. E. Fibrosis and adipose tissue dysfunction. Cell Metab. 18, 470–477 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Vidal-Puig, A. Adipose tissue expandability, lipotoxicity and the metabolic syndrome. Endocrinol. Nutr. 60, 39–43 (2013).

    Article  PubMed  Google Scholar 

  3. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sorisky, A., Molgat, A. S. D. & Gagnon, A. Macrophage-induced adipose tissue dysfunction and the preadipocyte: should I stay (and differentiate) or should I go? Adv. Nutr. 4, 67–75 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Abdennour, M. et al. Association of adipose tissue and liver fibrosis with tissue stiffness in morbid obesity: links with diabetes and BMI loss after gastric bypass. J. Clin. Endocrinol. Metab. https://doi.org/10.1210/jc.2013-3253 (2014).

  6. Pellegrinelli, V. et al. Human adipocyte function is impacted by mechanical cues. J. Pathol. 233, 183–195 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Lavie, C. J., De Schutter, A. & Milani, R. V. Healthy obese versus unhealthy lean: the obesity paradox. Nat. Rev. Endocrinol. 11, 55–62 (2015).

    Article  PubMed  Google Scholar 

  8. Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kluth, O. et al. Differential transcriptome analysis of diabetes-resistant and -sensitive mouse islets reveals significant overlap with human diabetes susceptibility genes. Diabetes 63, 4230–4238 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Yaghootkar, H. et al. Genetic evidence for a normal-weight ‘metabolically obese’ phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes. Diabetes 63, 4369–4377 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kitchener, R. L. & Grunden, A. M. Prolidase function in proline metabolism and its medical and biotechnological applications. J. Appl. Microbiol. 113, 233–247 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Rayment, J. H., Jobling, R., Bowdin, S., Cutz, E. & Dell, S. D. Prolidase deficiency diagnosed by whole-exome sequencing in a child with pulmonary capillaritis. ERJ Open Res. 5, 00205–02018 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Olivares, O. et al. Collagen-derived proline promotes pancreatic ductal adenocarcinoma cell survival under nutrient limited conditions. Nat. Commun. 8, 16031 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yang, L. et al. Prolidase directly binds and activates epidermal growth factor receptor and stimulates downstream signaling. J. Biol. Chem. 288, 2365–2375 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. Yang, L., Li, Y. & Zhang, Y. Identification of prolidase as a high affinity ligand of the ErbB2 receptor and its regulation of ErbB2 signaling and cell growth. Cell Death Dis. 5, e1211 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yang, L., Li, Y., Bhattacharya, A. & Zhang, Y. A recombinant human protein targeting HER2 overcomes drug resistance in HER2-positive breast cancer. Sci. Transl. Med. 11, eaav1620 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Choi, H. et al. Plasma protein and microRNA miomarkers of insulin resistance: a network-based integrative -omics analysis. Front. Physiol. 10, 379 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lupi, A. et al. N-benzyloxycarbonyl-l-proline: an in vitro and in vivo inhibitor of prolidase. Biochim. Biophys. Acta 1744, 157–163 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Small, L., Brandon, A. E., Turner, N. & Cooney, G. J. Modeling insulin resistance in rodents by alterations in diet: what have high-fat and high-calorie diets revealed. Am. J. Physiol. Endocrinol. Metab. 314, E251–E265 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Søndergaard, E., Espinosa De Ycaza, A. E., Morgan-Bathke, M. & Jensen, M. D. How to measure adipose tissue insulin sensitivity. J. Clin. Endocrinol. Metab. 102, 1193–1199 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Blohmke, C. J. et al. Interferon-driven alterations of the host’s amino acid metabolism in the pathogenesis of typhoid fever. J. Exp. Med. 213, 1061–1077 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Murray, P. J. et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity 41, 14–20 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Alasoo, K. et al. Transcriptional profiling of macrophages derived from monocytes and iPS cells identifies a conserved response to LPS and novel alternative transcription. Sci. Rep. 5, 12524 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vila, I. K. et al. Immune cell Toll-like receptor 4 mediates the development of obesity- and endotoxemia-associated adipose tissue fibrosis. Cell Rep. 7, 1116–1129 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Petkevicius, K. et al. Accelerated phosphatidylcholine turnover in macrophages promotes adipose tissue inflammation in obesity. Elife 8, e47990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sharif, O. et al. Beneficial metabolic effects of TREM2 in obesity are uncoupled from its expression on macrophages. Diabetes 70, 2042–2057 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  CAS  Google Scholar 

  30. Iwayama, T. et al. PDGFRα signaling drives adipose tissue fibrosis by targeting progenitor cell plasticity. Genes Dev. 29, 1106–1119 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Keophiphath, M. et al. Macrophage-secreted factors promote a profibrotic phenotype in human preadipocytes. Mol. Endocrinol. 23, 11–24 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liang, B. et al. Characterization and proteomic analysis of ovarian cancer-derived exosomes. J. Proteom. 80, 171–182 (2013).

    Article  CAS  Google Scholar 

  33. Gonzales, P. A. et al. Large-scale proteomics and phosphoproteomics of urinary exosomes. J. Am. Soc. Nephrol. 20, 363–379 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kharaziha, P. et al. Molecular profiling of prostate cancer derived exosomes may reveal a predictive signature for response to docetaxel. Oncotarget 6, 21740–21754 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lazar, I. et al. Proteome characterization of melanoma exosomes reveals a specific signature for metastatic cell lines. Pigment Cell Melanoma Res. 28, 464–475 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Yang, L., Li, Y., Bhattacharya, A. & Zhang, Y. PEPD is a pivotal regulator of p53 tumor suppressor. Nat. Commun. 8, 2052 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Surazynski, A., Liu, Y., Miltyk, W. & Phang, J. M. Nitric oxide regulates prolidase activity by serine/threonine phosphorylation. J. Cell. Biochem. 96, 1086–1094 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Aslan, M., Duzenli, U., Esen, R. & Soyoral, Y. U. Serum prolidase enzyme activity in obese subjects and its relationship with oxidative stress markers. Clin. Chim. Acta 473, 186–190 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Bel Lassen, P. et al. The FAT score, a fibrosis score of adipose tissue: predicting weight-loss outcome after gastric bypass. J. Clin. Endocrinol. Metab. 102, 2443–2453 (2017).

    Article  PubMed  Google Scholar 

  40. Yang, L., Li, Y., Bhattacharya, A. & Zhang, Y. Inhibition of ERBB2-overexpressing tumors by recombinant human prolidase and its enzymatically inactive mutant. EBioMedicine 2, 396–405 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Roberts, A. W. G-CSF: a key regulator of neutrophil production, but that’s not all! Growth Factors 23, 33–41 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Lacasa, D., Taleb, S., Keophiphath, M., Miranville, A. & Clement, K. Macrophage-secreted factors impair human adipogenesis: involvement of proinflammatory state in preadipocytes. Endocrinology 148, 868–877 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Besio, R. et al. Improved prolidase activity assay allowed enzyme kinetic characterization and faster prolidase deficiency diagnosis. Clin. Chim. Acta 412, 1814–1820 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Myara, I., Charpentier, C. & Lemonnier, A. Optimal conditions for prolidase assay by proline colorimetric determination: application to iminodipeptiduria. Clin. Chim. Acta 125, 193–205 (1982).

    Article  CAS  PubMed  Google Scholar 

  45. Marcelin, G. et al. A PDGFRα-mediated switch toward CD9high adipocyte progenitors controls obesity-induced adipose tissue fibrosis. Cell Metab. 25, 673–685 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Moreno-Navarrete, J. M. et al. Insulin resistance modulates iron-related proteins in adipose tissue. Dia Care 37, 1092–1100 (2014).

    Article  CAS  Google Scholar 

  47. Reggio, S. et al. Increased basement membrane components in adipose tissue during obesity: links with TGFβ and metabolic phenotypes. J. Clin. Endocrinol. Metab. 101, 2578–2587 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Haak, M. et al. High quality de novo transcriptome assembly of Croton tiglium. Front Mol. Biosci. 5, 62 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Väremo, L., Nielsen, J. & Nookaew, I. Enriching the gene-set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Grund, B. & Sabin, C. Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves. Curr. Opin. HIV AIDS 5, 473–479 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Goksuluk, D., Korkmaz, S., Zararsiz, G. & Karaagaoglu, A. E. easyROC: an interactive web-tool for ROC curve analysis using R language environment. R. J. 8, 213–226 (2016).

  57. Yong, A. G. & Pearce, S. A beginner’s guide to factor analysis: focusing on exploratory factor. Quant. Methods Psychol. 9, 79–94 (2013).

  58. Revelle, W. Procedures for psychological, psychometric and personality research (R package psych version 2.1.9). https://CRAN.R-project.org/package=psych (2021).

  59. Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Soft. 25, 10 (2008).

  60. R Core Team (2020). R: a language and environment for statistical computing (R Foundation for Statistical Computing). https://www.R-project.org/ (2020).

Download references

Acknowledgements

This work was funded by a Wellcome Trust strategic award (100574/Z/12/Z), MRC MDU (MC_UU_12012/2), H2020 EPoS (Elucidating Pathways of Steatohepatitis grant agreement 634413) and the British Heart Foundation (RG/18/7/33636). The Disease Model Core, Biochemistry Assay Lab, the Histology Core and the Genomics and Transcriptomics Core are funded by MRC_MC_UU_12012/5 and a Wellcome Trust Strategic Award (208363/Z/17/Z). We thank the Wellcome Trust Sanger Institute Mouse Genetics Project (Sanger MGP) and its funders for providing the mutant mouse line (Pepdtm1a(KOMP)Wtsi). We thank the Disease Model Core from the Wellcome-MRC Institute of Metabolic Science and A. Lukasik for their technical assistance in animal work. All animal work was carried out in the Disease Model Core (MRC Metabolic Diseases Unit (MRC_MC_UU_12012/5); Wellcome Trust Strategic Award (100574/Z/12/Z)). We also thank Genomics and Transcriptomics core, the Histology core and G. Strachan from the Imaging core for their technical assistance. All serum biochemistry was conducted by the Biochemistry Assay Laboratory (MRC Metabolic Diseases Unit (MRC_MC_UU_12012/5)). Clinical studies in France were supported by ‘Contrat de Recherche Clinic’ (CRC APHP, Fibrota to J.A.-W. and K.C.), by the National Agency of Research (ANR-Captor to C.R. and K.C.) and by EFSD (to K.C.). H2020 EPoS funded K.C. (Elucidating Pathways of Steatohepatitis grant agreement 634413). A.R.D., Y.H.L., M.D. and M.C. were funded by MRC MDU (MRC_MC_UU_00014/5). M.D. also receives funding from the National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). D.C. was supported by MRC MDU (MRC_MC_UU_12012/4). S.C. was supported by the ERC Senior Investigator award (669879). We also thank all the patients and their physicians, L. Genser for the surgical procedures, C. Poitou for patient recruitment and F. Marcheli for data management. R. J. F. Loos is supported by a grant from the National Institutes of Health (NIH R01DK107786). M.d.H. is a fellow of the Swedish Heart-Lung Foundation (20170872), is a Kjell and Märta Beijer Foundation researcher and is supported by project grants from the Swedish Heart-Lung Foundation (20140543, 20170678 and 20180706) and the Swedish Research Council (2015-03657 and 2019-01417). We also acknowledge the FATBANK platform promoted by the CIBEROBN and the IDIBGI Biobank (Biobanc IDIBGI, B.0000872), integrated into the Spanish National Biobanks Network, for their collaboration and coordination. The funders had no role in study design, data collection and interpretation, or submitting the work for publication. The authors’ views are their own and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Author information

Authors and Affiliations

  1. Wellcome-MRC Institute of Metabolic Science and MRC Metabolic Diseases Unit, University of Cambridge, Cambridge, UK

    V. Pellegrinelli, S. Rodriguez-Cuenca, E. Figueroa-Juarez, S. Virtue, G. Bidault, M. C. Vázquez-Borrego, A. R. Dias, M. Dale, M. Campbell, S. Carobbio, Y. H. Lin, M. Vacca, D. Chiarugi & A. Vidal-Puig

  2. Cambridge University Nanjing Centre of Technology and Innovation, Nanjing, P. R. China

    S. Rodriguez-Cuenca, M. Campbell & A. Vidal-Puig

  3. Sorbonne University, INSERM, NutriOmique Research Unit, Paris, France

    C. Rouault, J. Aron-Wisnewsky & K. Clément

  4. Genetics and Genomics of Plants, Centre for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany

    H. Schilbert & B. Pucker

  5. Department of Diabetes, Endocrinology and Nutrition, Girona Biomedical Research Institute (IDIBGI), University Hospital of Girona Dr Josep Trueta, Girona, Spain

    J. M. Moreno-Navarrete & J. M. Fernández-Real

  6. Department of Medicine, University of Girona, Girona, Spain

    J. M. Moreno-Navarrete & J. M. Fernández-Real

  7. CIBERobn Pathophysiology of Obesity and Nutrition, Institut of Salud Carlos III, Madrid, Spain

    J. M. Moreno-Navarrete & J. M. Fernández-Real

  8. Maimonides Institute for Biomedical Research of Cordoba (IMIBIC), Cordoba, Spain

    M. C. Vázquez-Borrego

  9. Evolution and Diversity, Department of Plant Sciences, University of Cambridge, Cambridge, UK

    B. Pucker

  10. Centro de Investigacion Principe Felipe, Valencia, Spain

    S. Carobbio & A. Vidal-Puig

  11. Department of Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan

    Y. H. Lin

  12. Insterdisciplinary Department of Medicine, Università degli Studi di Bari ‘Aldo Moro’, Bari, Italy

    M. Vacca

  13. Assistance-Publique Hôpitaux de Paris, Nutrition department, Pitié-Salpêtrière hospital, Paris, France

    J. Aron-Wisnewsky & K. Clément

  14. Dept Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain

    S. Mora

  15. Institute of Biomedicine, University of Barcelona (IBUB), Barcelona, Spain

    S. Mora

  16. The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden

    M. M. Masiero, A. Emmanouilidou & M. den Hoed

  17. MRC Centre for Transplantation Peter Gorer Department of Immunobiology School of Immunology & Microbial Sciences King’s College, London, UK

    S. Mukhopadhyay

  18. Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

    G. Dougan

  19. Division of Infectious Diseases, Department of Medicine, University of Cambridge, Cambridge, UK

    G. Dougan

  20. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    R. J. F. Loos

  21. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    R. J. F. Loos

  22. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark

    R. J. F. Loos

Authors
  1. V. Pellegrinelli
    View author publications

    Search author on:PubMed Google Scholar

  2. S. Rodriguez-Cuenca
    View author publications

    Search author on:PubMed Google Scholar

  3. C. Rouault
    View author publications

    Search author on:PubMed Google Scholar

  4. E. Figueroa-Juarez
    View author publications

    Search author on:PubMed Google Scholar

  5. H. Schilbert
    View author publications

    Search author on:PubMed Google Scholar

  6. S. Virtue
    View author publications

    Search author on:PubMed Google Scholar

  7. J. M. Moreno-Navarrete
    View author publications

    Search author on:PubMed Google Scholar

  8. G. Bidault
    View author publications

    Search author on:PubMed Google Scholar

  9. M. C. Vázquez-Borrego
    View author publications

    Search author on:PubMed Google Scholar

  10. A. R. Dias
    View author publications

    Search author on:PubMed Google Scholar

  11. B. Pucker
    View author publications

    Search author on:PubMed Google Scholar

  12. M. Dale
    View author publications

    Search author on:PubMed Google Scholar

  13. M. Campbell
    View author publications

    Search author on:PubMed Google Scholar

  14. S. Carobbio
    View author publications

    Search author on:PubMed Google Scholar

  15. Y. H. Lin
    View author publications

    Search author on:PubMed Google Scholar

  16. M. Vacca
    View author publications

    Search author on:PubMed Google Scholar

  17. J. Aron-Wisnewsky
    View author publications

    Search author on:PubMed Google Scholar

  18. S. Mora
    View author publications

    Search author on:PubMed Google Scholar

  19. M. M. Masiero
    View author publications

    Search author on:PubMed Google Scholar

  20. A. Emmanouilidou
    View author publications

    Search author on:PubMed Google Scholar

  21. S. Mukhopadhyay
    View author publications

    Search author on:PubMed Google Scholar

  22. G. Dougan
    View author publications

    Search author on:PubMed Google Scholar

  23. M. den Hoed
    View author publications

    Search author on:PubMed Google Scholar

  24. R. J. F. Loos
    View author publications

    Search author on:PubMed Google Scholar

  25. J. M. Fernández-Real
    View author publications

    Search author on:PubMed Google Scholar

  26. D. Chiarugi
    View author publications

    Search author on:PubMed Google Scholar

  27. K. Clément
    View author publications

    Search author on:PubMed Google Scholar

  28. A. Vidal-Puig
    View author publications

    Search author on:PubMed Google Scholar

Contributions

V.P. developed the hypothesis, designed the experiments, performed the experimental work, collected and analysed the data, coordinated and directed the project, created the images and wrote the manuscript. S.R.C. designed and conducted part of the experimental work and edited the manuscript. C.R. and J.-M.M.-N. performed and analysed human experiments. S.V. performed the BMT in mice and the GC–MS for the detection of imidopeptides. H.S., G.B., M.C.V.-B., A.R.D., M.D., M.C., S.C., S. Mora, M.M.M., A.E., S. Mukhopadhyay and M.d.H. conducted experiments. J.A.-W. and K.C. supervised enrolment and clinical phenotyping of obese participants. H.S., B.P. and D.C. performed bioinformatics analysis. D.C. also edited the manuscript. G.D., R.L., J.M.F. and K.C. provided access to human data and discussed the manuscript. A.V.P. developed the hypothesis, coordinated and directed the project, wrote the manuscript, is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors critically reviewed and edited the manuscript.

Corresponding authors

Correspondence to V. Pellegrinelli or A. Vidal-Puig.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Kai Sun, Anthony Ferrante and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt, in collaboration with the Nature Metabolism team.

Additional information

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

Extended data

Extended Data Fig. 1 Obesity reduces AT PEPD activity and promotes PEPD release in association with AT fibrosis and insulin resistance.

a, Age, BMI, glycaemic and lipidic status in the different human cohorts. b, PEPD serum/ScW levels, BMI and metabolic parameters in obese subjects from cohort cohort 2e (n = 9) with low vs. high VsW PEPD levels. c, PEPD activity/ecplants levels, BMI, blood chemistry parameters and liver Actitest score in obese subjects (cohort 2e,f) with high (>2.065 nmol pro/mg prot/min) vs. low (<2.065 nmol pro/mg prot/min) PEPD peptidase activity in VsW. d, Area under the receiver operating curve (AUC) values (95% CI) for VsW and ScW PEPD peptidase activity (PA) to discriminate subjects with type 2 diabetes (cohort 2e,f).e, Pearson correlation between PEPD serum levels and, ScW and VsW ECM remodelling markers and metabolic parameters in obese subjects from cohort 2b (n = 14 biologically independent samples). f, g, Pepd mRNA relative expression (f), and PEPD prolidase activity (g) in ScW, GnW and liver from C57Bl/6 mice in chow (n = 6) and 58% HFD (n = 8, 20 weeks) conditions.* compared to chow diet. h, ELISA analysis of PEPD protein in serum from C57/Bl6 mice fed chow (n = 6) or 20 weeks 58% HFD (n = 8). I-k. ScW prolidase activity (i), PEPD serum level (j) and ScW peri-ad collagen in C57Bl/6 mice fed 2, 8, 16 or 28 weeks (n = 8/group) with chow diet (ch) of HFD 45% (HF). l, Prolidase activity in the serum of C57Bl6 mice fed chow (2, 28 weeks) or HFD 58% (28 weeks). m, Pearson correlation matrix between ScW and GnW ECM remodelling markers and metabolic parameters in chow and HFD conditions (n = 22). Data presented as mean values +/− SEM. Data was analysed using a two-tailed Student’s t-test (b, c, h) or a 2-way ANOVA followed by a Sidak post-hoc multiple comparisons test (f, g, i-k).

Extended Data Fig. 2 PEPD pharmacological inhibition promotes AT fibro-inflammation independently from obesity.

a-d. ALAT and ASAT serum levels (a), body weight (b), and fat mass % (c), and tissue weight (d) in mice treated 10 weeks or not (control) with CBZ-Pro (n = 8 biologically independent animals per group). e, f. Fasting insulin and FFA blood levels from CBZ-Pro-treated mice compared to control mice (n = 8 biologically independent animals per group) fed chow and HFD 58% for 16 weeks. g. Fasting insulin blood level before and 30 min after glucose injection (2 g/kg) in CBZ-Pro-treated mice compared to controls (n = 8 biologically independent animals per group). h,i. Representative images of blots and quantification of total and basal phosphorylated (Ser473) AKT in GnW (h) and gastrocnemius muscle (i) of CBZ-Pro- treated mice compared to controls (n = 8 biologically independent animals per group). j. Heat map representing the four factors extracted through exploratory factor analysis. The columns report the factors loadings of the observed variables. k. Representative images of red Sirius staining in ScW and GnW of control and CBZ-Pro treated mice (n = 8 biologically independent animals per group) fed HFD 58%, and corresponding quantification of peri-adipocyte collagen content (peri-AD) represented in % Area (excluding perivascular staining). l, m. Blood glucose levels up to 120 min. after an intraperitoneal injection of glucose (2 g/kg) in a glucose tolerance test (l) or insulin (0.75 IU/kg) in an insulin tolerance test (m) in control and CBZ-Pro treated mice (n = 8 biologically independent animals per group) fed HFD 58%. Respective AUC are represented. Data is presented as mean values +/− SEM. Data was analysed using a two-tailed Student’s t-test (a-d, h, i l, m) or a 2-way ANOVA followed by a Turkey (e-g) or Sidak (k-m) post-hoc multiple comparisons test.

Source data

Extended Data Fig. 3 Pepd silencing promotes AT fibrosis but does not affect metabolic parameters in chow fed mice.

a. Pepd RNA relative expression in ScW, GnW, liver and gastrocnemius (SKM) from pepd WT (n = 6), HET (n = 8) and KO mice (n = 5). b. Level of amino acids related to proline metabolism in the serum or GnW explant medium from pepd WT (n = 6), HET (n = 8) and KO mice (n = 5). c-f, h, k. Body length (c), body weight (d), fat mass % (e), tissue weight (f), fed glucose, fasting glucose and FFA (blood levels h), and fasting insulin blood level (k) in pepd WT (n = 6), HET (n = 8) and KO (n = 5) mice fed chow diet. g. Pearson correlations matrix between ScW and GnW ECM remodelling markers, PEPD serum levels and metabolic parameters in pepd mice fed chow (n = 19 biologically independent samples). i, j. Blood glucose levels up to 120 minutes after an intraperitoneal injection of glucose (2 g/kg) in a glucose tolerance test (i, IP-GTT) or insulin (0.75 IU/kg) in an insulin tolerance test (j, IP-ITT), and respective AUC adjusted to basal, in pepd WT (n = 6), HET (n = 8) and KO (n = 5)mice fed chow diet. l-n. Representative images of blots and quantification of total and phosphorylated (Ser473) AKT in GnW (l), liver (m) and gastrocnemius (n) of pepd WT (n = 6-11), HET (n = 6-12) and KO n = 6-8) mice fed chow diet after i.p injection of saline or insulin. 2way ANOVA with Dunnett’s (a) or Sidak’s (I, k, l-n) post-hoc multiple comparisons test; G, genotype; X, interaction. Data is presented as mean values +/− SEM. Data was analysed using a One way ANOVA followed by a Dunnett (b-f, h, k) or Sidak (i, j, l-n) post-hoc multiple comparisons test.

Source data

Extended Data Fig. 4 Pepd silencing exacerbates metabolic disturbances in HFD 58%-fed mice.

Body weight curves and fat mass % curves of pepd WT, HET and KO mice after 20 weeks HFD 45% (n = 8, 11, 9 biologically independent animals per group, respectively) and HFD 58% (n = 8, 11, 8 biologically independent animals per group, respectively). b, c. Blood glucose levels up to 120 min. after an intraperitoneal injection of glucose in a glucose tolerance test (b) or insulin (0.75 IU/kg) in an insulin tolerance test (c) with the representative AUC in pepd WT (n = 9), HET (n-11) and KO (n = 9) mice fed HFD 45%. d. Fasting glucose, insulin and FFA blood levels in pepd WT (n = 8), HET (n = 11) and KO (n = 8) mice fed HFD 45%. e. Representative images of red Sirius staining in ScW and GnW from C57Bl/6 mice fed chow (n = 6), 45%HFD (n = 8) and HFD 58% (n = 8, 20 weeks) conditions and quantification of peri-adipocyte collagen content (peri-AD) represented in % Area (excluding perivascular staining).f. Heat map of gene expression in GnW from C57Bl/6 mice fed 20 weeks with chow (n = 6), 45%HFD (n = 8) and HFD 58% (n = 8). Results are expressed as fold change over chow diet. g. Representative images of blots and quantification of total and basal phosphorylated (Ser473) AKT in gastrocnemius muscle of pepd WT, HET and KO mice fed chow (n = 5, 6, 5 biologically independent animals per group, respectively), and HFD 58% conditions (n = 5, 5, 7 biologically independent animals per group, respectively). h. Heat map of gene expression of fibro-inflammatory and functional markers in gastrocnemius of pepd WT, HET and KO mice fed chow (n = 6, 7, 6 biologically independent animals per group, respectively), and HFD 58% conditions (n = 8, 7, 8 biologically independent animals per group, respectively). i. Heat map of gene expression in GnW from pepd WT, HET and KO fed HFD 45% (n = 6, 10, 9 biologically independent animals per group, respectively) and HFD 58% (n = 8, 9, 9 biologically independent animals per group, respectively) expressed as fold change variation over chow diet; *p < 0.05 compared to chow, #p < 0.05 compared to WT. j, k. Pathway enrichment analysis of the DEGs in GnW (i) and liver (j) from pepd KO mice (n = 9) compared to WT mice (n = 8) fed HFD 45%, using different data bases (KEGG, Reactome, Biocarta, NABA and PID). The heat maps indicate the level of significant changes (false discovery rate-adjusted p-value). Data is presented as mean values +/− SEM. Data was analysed using a 2-way ANOVA followed by a Turkey post-hoc multiple comparisons test; G, genotype; X, interaction (a-c, i), or a one way ANOVA followed by a Sidak (b, c, e) or Dunnett (d, g, h) post-hoc multiple comparisons test. A two-tailed Student’s t-test was also used to analyse the data (f).

Source data

Extended Data Fig. 5 Pepd silencing in hematopoietic cells prevent obesity and associated metabolic disturbances.

a. Prolidase activity in BMDMs during differentiation (n = 4 biologically independent samples). b. Pepd mRNA relative expression in adipocytes (AD), Mɸ (CD11b positive cells) and negative stroma-vascular fraction (SVF−) isolated from GnW of WT (n = 6) and LepOb/Ob mice (n = 9). *compared to AD WT, # compared to Mφ WT. c. Abundance of PEPD measured by mass spectrometry in unstimulated human iPS-derived Mɸ differentiated from FPS10C iPS line. Relative abundance of PEPD in comparison of other detected protein in the same sample is plotted in the graph- where actin and cyclin are representing examples of high and low abundant proteins respectively. d, e. Pepd mRNA expression (d) or PEPD ELISA in culture media from BMDMs treated or not (M0) 6 h (d) or 24 (e) with LPS, dexamethasone (GC) or IL4 (n = 4 biologically independent samples). BMDMs were treated with LPS for 1,6 or 24 h: f, g. PEPD level in culture media (f) and prolidase activity in BMDMs (g). h. Prolidase activity in culture media from BMDMs treated without (control) or with LPS (100 ng/ml, 24 h) (n = 4 biologically independent samples). I, j. Blood glucose levels up to 120 min. after an intraperitoneal injection of glucose (2 g/kg) in a glucose tolerance test (i) or insulin (0.75 IU/kg) in an insulin tolerance test (j) in BMT WT (n = 8) and KO mice (n = 6) fed chow. Respective AUC are represented. k-n. Fasting glucose (k), fasting insulin (l), fasting FFA (m) and fed glucose (n) blood levels in BMT-WT (n = 8) mice compared to BMTKO mice (n = 6) fed chow and HFD 58% (20 weeks).. o, p. Tissue weight (o) and Fat mass % (p) in BMT + / + (n = 8) and −/− (n = 6) mice fed chow and HFD 58% for 20 weeks. q. Body weight curve in BMT-WT (n = 8) mice compared to BMTKO mice (n = 6) mice between 0 and 20 weeks HFD 58%. r. Representative images of red Sirius staining ScW and GnW from BMT-WT (n = 8) mice compared to BMTKO mice (n = 6) fed HFD 58% and quantification of peri-AD collagen represented as %. s. Representative images of blots and quantification of total and basal phosphorylated (Ser473) AKT in GnW of of BMT WT and KO mice fed HFD 58% (n = 6/group). Data is presented as mean values +/− SEM. Data was analysed using a One way ANOVA followed by a Dunnett (a, f, g) or Tuckey (d, e) post-hoc multiple comparisons test, or using a 2-way ANOVA followed by a Tukey (b, k—p, r) or Sidak (I, j, q) post-hoc multiple comparisons test; G, genotype; X, interaction. A two-tailed Student’s t-test was also used to analyse the data (h-j, s).

Source data

Extended Data Fig. 6 Purified PEPD protein promotes fibro-inflammation in macrophages through EGFR signaling.

a. Cox2 mRNA relative expression in BMDMs treated or not (control) 4 h with purified PEPD protein active or denaturated (denat PEPD) (n = 3 biologically independent samples).b. Dose response effect of 4 h treatment with purified PEPD protein on cox2 mRNA relative expression in BMDMs (n = 4 biologically independent samples). c. % of cytotoxicity in BMDMs after 4 h treatment with purified PEPD protein (n = 4 biologically independent samples). d. Representative images of blots of total and phosphorylated (Ser 536) NF−kB protein and beta-tubulin in BMDMs (n = 4 biologically independent samples) treated with purified PEPD protein (0, 10, 60 min or 24 h, 250 nM). e, f. Tyrosine-kinase receptor phospho-Array of multiple analytes (e) and quantification (f) of phosphorylation levels in cell extract from BMDMs treated without (control) or with purified PEPD protein (10 min, 250 nM). Data from one experiment of a pool of 4 independent macrophage preparations. g. Cox2 mRNA relative expression in BMDMs (n = 4 biologically independent samples) pre-treated (Erlo) or not (PBS) with Erlotinib 5 µM, prior 24 h treatment with (PEPD) or without (control) purified PEPD protein. * compared to control PBS, # compared to PEPD PBS. h-j. mRNA expression of Egfr (h), Cox2 (i) and Il1β (j) in RAW macrophages transfected with Si-EGFR or its negative control (Si-NEG) prior treatment with (PEPD) or without (control) purified PEPD protein (250 nM,24 h). k. Tissue and cell distribution of egfr expression from Tabula muris DataBase; l, m. Heat map of Cox2 mRNA expression (l) and prolidase activity (m) in different tissues from C57Bl/6 mice injected with saline or purified PEPD (n = 11 biologically independent animals per group). n-p. Fat mass (n), fasting glucose (o), free fatty acids (FFA, p) and insulin levels (p), and fed glucose level (p) in PEPD-injected mice compared to controls (n = 11 biologically independent animals per group). q, r. Blood glucose levels up to 120 minutes after an intraperitoneal injection of glucose (2 g/kg) in a glucose tolerance test (q, IP-GTT) or insulin (0.75 IU/kg) in an insulin tolerance test (r, IP-ITT) in PEPD-injected mice compared to controls (n = 11 biologically independent animals per group). Data is presented as mean values +/− SEM. Data was analysed using a One-way ANOVA followed by a Dunnett (b, c) or Tuckey (a) post-hoc multiple comparisons test, or using a 2-way ANOVA followed by a Tukey (g, I, j) or Sidak (l-n, q, r) post-hoc multiple comparisons test. A two-tailed Student’s t-test was also used to analyse the data (h, o, p).

Source data

Extended Data Fig. 7 Purified PEPD protein promotes fibro-inflammation in pre-adipocytes and stellate cells through EGFR signaling.

a. Representative images of confocal analysis of anti-Collagen type I (in red,) and Bodipy staining (lipid accumulation in green) (i) and corresponding quantifications of collagen I staining (j) in 3T3-L1 adipocytes (n = 4 biologically independent samples) pre-treated (Erlo) or not (PBS) with Erlotinib (5 µM) prior treatment with (PEPD) or without (control) purified PEPD protein (250 nM) during the first 5 days of adipogenic differentiation. * compared to control PBS, # compared to PEPD PBS. X, interaction. b. Il-6 level in culture media of mature adipocytes isolated from ScW of C57Bl/6 mice treated or not (control) with purified PEPD protein (250 nM, 24 h, n = 6 biologically independent animals per group). c. Representative images of confocal analysis of anti-Collagen type IV (in green,) and Actin staining (in red) and corresponding quantifications of collagen IV staining in stellate cells (n = 3 biologically independent samples) pre-treated (Erlo) or not (PBS) with Erlotinib (5 µM) prior treatment with (PEPD) or without (control) purified PEPD protein (250 nM). d. Gene expression profile in stellate cells (n = 4) pre-treated (Erlo) or not (PBS) with Erlotinib (5 µM) prior treatment with (PEPD) or without (control) purified PEPD protein (250 nM). e. AST serum level in PEPD-injected mice compared to controls (n = 11/group). f. Representative images of H&E and Sirius staining in the liver from PEPD-injected mice compaired to control (n = 11 biologically independent animals per group, a), and quantification of lipid droplet (steatosis) and collagen contents (fibrosis). g. Liver gene expression profile in PEPD-injected mice compared to controls (n = 11 biologically independent animals per group). h. Representative images of red Sirius staining in gastrocnemius from PEPD-injected mice compaired to control (n = 11 biologically independent animals per group), and quantification of collagen content (fibrosis). i. Gastrocnemius gene expression profile in PEPD-injected mice compared to controls (n = 11 biologically independent animals per group). j. Gene expression profile in muscle fibroblasts pre-treated (Erlo) or not (PBS) with Erlotinib (5 µM) prior treatment with (PEPD) or without (control) purified PEPD protein (250 nM, 5 days). k. Representative images of confocal analysis of anti-Collagen type I (in green,) and αSMA staining (in red), and corresponding quantifications of collagen I and αSMA stainings in muscle fibroblasts (n = 4 biologically independent samples) pre-treated (Erlo) or not (PBS) with Erlotinib (5 µM) prior treatment with (PEPD) or without (control) purified PEPD protein (250 nM, 5 days). Data is presented as mean values +/− SEM. Data was analysed using a 2-way ANOVA followed by aTukey post-hoc multiple comparisons test (a, c, d, j, k), or using a two-tailed Student’s t-test (b, e-i).

Extended Data Fig. 8 High PEPD serum levels is associated with AT insulin resistance and drives the differences between the pharmacologic and genetic animal models of PEPD down-regulation.

a. Heat map representing the four factors extracted through EFA performed among mice fed chow. The columns report the factors loadings of the observed variables. b, c. Pearson correlation matrix between ScW and GnW ECM remodelling markers, PEPD serum levels and metabolic parameters in the mice from the four models (PEPD, CBZ-Pro, BMT and PEPD-injection) fed chow (b) or chow+HFD (c). Metabolic/fibro-inflammatory parameters from the four animal models (that is CBZ-Pro, PEPD, BMT and PEPD-injection) were plotted according to factor 1 and 3.

Supplementary information

Source data

Source Data Fig. 1 (download XLSX )

Statistical source data.

Source Data Fig. 2 (download XLSX )

Statistical source data.

Source Data Fig. 3 (download XLSX )

Statistical source data.

Source Data Fig. 3 (download PDF )

Unprocessed western blot.

Source Data Fig. 4 (download XLSX )

Statistical source data.

Source Data Fig. 5 (download XLSX )

Statistical source data.

Source Data Fig. 5 (download PDF )

Unprocessed western blot.

Source Data Fig. 6 (download XLSX )

Statistical source data.

Source Data Fig. 6 (download PDF )

Unprocessed western blot.

Source Data Extended Data Fig. 2 (download PDF )

Unprocessed western blot.

Source Data Extended Data Fig. 3 (download PDF )

Unprocessed western blot.

Source Data Extended Data Fig. 4 (download PDF )

Unprocessed western blot.

Source Data Extended Data Fig. 5 (download PDF )

Unprocessed western blot.

Source Data Extended Data Fig. 6 (download PDF )

Unprocessed western blot.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pellegrinelli, V., Rodriguez-Cuenca, S., Rouault, C. et al. Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance. Nat Metab 4, 476–494 (2022). https://doi.org/10.1038/s42255-022-00561-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s42255-022-00561-5

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