Loss-of-function mutations in MRAP2 are pathogenic in hyperphagic obesity with hyperglycemia and hypertension

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Abstract

The G-protein-coupled receptor accessory protein MRAP2 is implicated in energy control in rodents, notably via the melanocortin-4 receptor1. Although some MRAP2 mutations have been described in people with obesity1,2,3, their functional consequences on adiposity remain elusive. Using large-scale sequencing of MRAP2 in 9,418 people, we identified 23 rare heterozygous variants associated with increased obesity risk in both adults and children. Functional assessment of each variant shows that loss-of-function MRAP2 variants are pathogenic for monogenic hyperphagic obesity, hyperglycemia and hypertension. This contrasts with other monogenic forms of obesity characterized by excessive hunger, including melanocortin-4 receptor deficiency, that present with low blood pressure and normal glucose tolerance4. The pleiotropic metabolic effect of loss-of-function mutations in MRAP2 might be due to the failure of different MRAP2-regulated G-protein-coupled receptors in various tissues including pancreatic islets.

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Fig. 1: Location of the MRAP2 variants detected in the present sequencing study.
Fig. 2: Rates of hyperglycemia, hypertension, low HDL and high TG in patients deficient for LEP, LEPR, MC4R, PCSK1, POMC, SIM1 or MRAP2.

Data availability

All relevant data have been included in the manuscript and/or in its supplementary tables and figures. Source data are available online for Extended Data Figs. 1, 2, 4 and 5. Targeted DNA-seq data of patients deficient in MRAP2 were deposited in the NCBI Sequence Read Archive under PRJNA564478.

Code availability

Code to perform analyses in this manuscript are available from the authors upon reasonable request (A.B., M.D. and M.C.).

References

  1. 1.

    Asai, M. et al. Loss of function of the melanocortin 2 receptor accessory protein 2 is associated with mammalian obesity. Science 341, 275–278 (2013).

  2. 2.

    Geets, E. et al. Copy number variation (CNV) analysis and mutation analysis of the 6q14.1-6q16.3 genes SIM1 and MRAP2 in Prader Willi-like patients. Mol. Genet. Metab. 117, 383–388 (2016).

  3. 3.

    Schonnop, L. et al. Decreased melanocortin-4 receptor function conferred by an infrequent variant at the human melanocortin receptor accessory protein 2 gene. Obesity 24, 1976–1982 (2016).

  4. 4.

    Greenfield, J. R. et al. Modulation of blood pressure by central melanocortinergic pathways. N. Engl. J. Med. 360, 44–52 (2009).

  5. 5.

    El-Sayed Moustafa, J. S. & Froguel, P. From obesity genetics to the future of personalized obesity therapy. Nat. Rev. Endocrinol. 9, 402–413 (2013).

  6. 6.

    Kühnen, P. et al. Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. N. Engl. J. Med. 375, 240–246 (2016).

  7. 7.

    Clément, K. et al. MC4R agonism promotes durable weight loss in patients with leptin receptor deficiency. Nat. Med. 24, 551–555 (2018).

  8. 8.

    Soletto, L. et al. Melanocortin receptor accessory protein 2-induced adrenocorticotropic hormone response of human melanocortin 4 receptor. J. Endocr. Soc. 3, 314–323 (2019).

  9. 9.

    Josep Agulleiro, M. et al. Melanocortin 4 receptor becomes an ACTH receptor by coexpression of melanocortin receptor accessory protein 2. Mol. Endocrinol. 27, 1934–1945 (2013).

  10. 10.

    Zhang, J. et al. The interaction of MC3R and MC4R with MRAP2, ACTH, α-MSH and AgRP in chickens. J. Endocrinol. 234, 155–174 (2017).

  11. 11.

    Bradshaw, P. T., Monda, K. L. & Stevens, J. Metabolic syndrome in healthy obese, overweight, and normal weight individuals: the Atherosclerosis Risk in Communities study. Obesity 21, 203–209 (2013).

  12. 12.

    Stanley, C. A. Perspective on the genetics and diagnosis of congenital hyperinsulinism disorders. J. Clin. Endocrinol. Metab. 101, 815–826 (2016).

  13. 13.

    Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).

  14. 14.

    Ndiaye, F. K. et al. Expression and functional assessment of candidate type 2 diabetes susceptibility genes identify four new genes contributing to human insulin secretion. Mol. Metab. 6, 459–470 (2017).

  15. 15.

    Ravassard, P. et al. A genetically engineered human pancreatic beta cell line exhibiting glucose-inducible insulin secretion. J. Clin. Invest. 121, 3589–3597 (2011).

  16. 16.

    Rouault, A. A. J., Srinivasan, D. K., Yin, T. C., Lee, A. A. & Sebag, J. A. Melanocortin receptor accessory proteins (MRAPs): functions in the melanocortin system and beyond. Biochim. Biophys. Acta Mol. Basis Dis. 1863, 2462–2467 (2017).

  17. 17.

    Novoselova, T. V. et al. Loss of MRAP2 is associated with Sim1 deficiency and increased circulating cholesterol. J. Endocrinol. 230, 13–26 (2016).

  18. 18.

    Chaly, A. L., Srisai, D., Gardner, E. E. & Sebag, J. A. The melanocortin receptor accessory protein 2 promotes food intake through inhibition of the prokineticin receptor-1. eLife 5, e12397 (2016).

  19. 19.

    Srisai, D. et al. MRAP2 regulates ghrelin receptor signaling and hunger sensing. Nat. Commun. 8, 713 (2017).

  20. 20.

    Mao, Y., Tokudome, T. & Kishimoto, I. Ghrelin and blood pressure regulation. Curr. Hypertens. Rep. 18, 15 (2016).

  21. 21.

    Chan, L. F. et al. MRAP and MRAP2 are bidirectional regulators of the melanocortin receptor family. Proc. Natl Acad. Sci. USA 106, 6146–6151 (2009).

  22. 22.

    Balkau, B. [An epidemiologic survey from a network of French Health Examination Centres, (D.E.S.I.R.): epidemiologic data on the insulin resistance syndrome]. Rev. Epidemiol. Sante Publique 44, 373–375 (1996).

  23. 23.

    Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).

  24. 24.

    Meyre, D. et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat. Genet. 41, 157–159 (2009).

  25. 25.

    Leger, J. et al. Reduced final height and indications for insulin resistance in 20 year olds born small for gestational age: regional cohort study. BMJ 315, 341–347 (1997).

  26. 26.

    Romon, M. et al. Relationships between physical activity and plasma leptin levels in healthy children: the Fleurbaix-Laventie Ville Santé II Study. Int. J. Obes. Relat. Metab. Disord. 28, 1227–1232 (2004).

  27. 27.

    American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2019. Diabetes Care 42, S13–S28 (2019).

  28. 28.

    Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 285, 2486–2497 (2001).

  29. 29.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  30. 30.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  31. 31.

    Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

  32. 32.

    Liu, X., Wu, C., Li, C. & Boerwinkle, E. dbNSFP v.3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Hum. Mutat. 37, 235–241 (2016).

  33. 33.

    Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

  34. 34.

    Sun, J., Zheng, Y. & Hsu, L. A unified mixed-effects model for rare-variant association in sequencing studies. Genet. Epidemiol. 37, 334–344 (2013).

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Acknowledgements

We are grateful to all individuals included in the different cohort studies. We thank A. Abderrahmani (University of Lille) for his technical advice. We thank L. Chan (Queen Mary University of London) for providing the plasmids including MRAP2 and MC4R. We thank F. Allegaert and N. Larcher for DNA collection and storage. We thank Endocells for providing the pancreatic beta cell line EndoC-βH1. We thank the Type 2 Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org/gene/geneInfo/MRAP2) and the groups that provided data to this resource.

This work was supported by grants from the French-speaking Society of Diabetes (Société Française du Diabète) to A.B., from the European Foundation for the Study of Diabetes/Lilly (to A.B.), from the French National Research Agency (ANR-10-LABX-46 (European Genomics Institute for Diabetes) and ANR-10-EQPX-07-01 (LIGAN-PM) to P.F.), from the European Research Council (ERC GEPIDIAB-294785 to P.F. and ERC Reg-Seq-715575 to A.B.), from FEDER (to P.F.) and from the ‘Région Nord Pas-de-Calais’ (to P.F.). A.B. was supported by Inserm.

Author information

P.F. and A.B. conceived the idea for the study and supervised the analyses. M.B., J.M., M.H., A.D., R.B., H.L., E.D., B.T., E.V., J.P., J.T., A.G., M.B., M.D., S.G., M.C. and A.B. performed the experiments and/or analyses. M.B. and A.B. wrote the first draft of the paper. P.F. revised the paper. S.F., G.C., J.-M.B., C.L.-M., M.T., R.S., J.W., C.A., J.K.-C., F.P., R.B., B.B., M.M. and P.F. contributed data (cohort studies or beta cell models). Furthermore, all authors critically reviewed the paper and approved the report for submission.

Correspondence to Philippe Froguel or Amélie Bonnefond.

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Competing interests

The authors declare no competing interests.

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Design of the functional assessment of each MRAP2 mutation and its validation.

α-MSH, α-melanocyte-stimulating hormone; ACTH, adrenocorticotropic hormone; βGal, β-galactosidase; CHO, Chinese hamster ovary cells; pβGal, plasmid including the β-galactosidase gene; pCreLuc, plasmid including the firefly luciferase (Luc) gene under the control of cAMP response element (CRE); pMC4R, plasmid including MC4R; pMRAP2, plasmid including MRAP2; WT, wild-type. For the control of pMRAP2 transfection into CHO cells, we performed Western blot analyses in wild-type CHO cells and CHO cells transfected with pMRAP2. We confirmed that wild-type CHO cells did not endogenously express MRAP2 protein, whereas CHO cells transfected with pMRAP2 expressed MRAP2 protein (27 kDa, bottom asterisk), as well as its glycosylated form (29 kDa, top asterisk). For the negative control, data are cAMP reporter activity (CRE-Luc normalized with β-galactosidase), expressed as fold change after 0–30,000 pM α-MSH (left) or ACTH (right), relative to 0 pM. Data are the mean ± sem of three independent experiments in technical triplicates. Fold change was computed by dividing normalized luciferase (L*) by the mean of the baseline luciferase measures. This normalized luciferase fold change (FCL*) was analyzed using a linear regression model. The mutation (M), the agonist concentration (C), as an orthogonal polynomial function of degree 3 (PC) to enable possible nonlinear relations between FCL* and C and the interaction term (MPC) between M and PC were included in the model as covariates. The model was defined as follows: \(FC_{L^ \ast } = \beta _0 + \beta _1M + MP_C + P_C + {\it{\epsilon }}\) with, \(P_C = \alpha _1C^1 + \alpha _2C^2 + \alpha _3C^3\) \(MP_C = \theta _1C^1M + \theta _2C^2M + \theta _3C^3M\) ***P < 0.001, MC4R + p.Q4* MRAP2 (red) versus MC4R + wild-type MRAP2 (black). Source data

Extended Data Fig. 2 Effect of MRAP2 variants on MC4R activity in response to α-MSH and ACTH in CHO cells.

Data are cAMP reporter activity (CRE-Luc normalized with β-galactosidase) in CHO cells cotransfected with MC4R plasmid and wild-type or mutated MRAP2 plasmid, expressed as fold change after 0–30,000 pM α-MSH or ACTH, relative to 0 pM. Data are the mean ± sem of three independent experiments in technical triplicates. Fold change was computed by dividing normalized luciferase (L*) by the mean of the baseline luciferase measures. This normalized luciferase fold change (FCL*) was analyzed using a linear regression model. The mutation (M), the agonist concentration (C), as an orthogonal polynomial function of degree 3 (PC) to enable possible nonlinear relations between FCL* and C, and the interaction term (MPC) between M and PC were included in the model as covariates. The model was defined as follows: \(FC_{L^ \ast } = \beta _0 + \beta _1M + MP_C + P_C + {\it{\epsilon }}\) with, \(P_C = \alpha _1C^1 + \alpha _2C^2 + \alpha _3C^3\) \(MP_C = \theta _1C^1M + \theta _2C^2M + \theta _3C^3M\) *P < 0.05, **P < 0.01, ***P < 0.001, MC4R + mutated MRAP2 (colors) versus MC4R + wild-type MRAP2 (black). Source data

Extended Data Fig. 3 Co-segregation of p.N77S (carried by participants no. 4 and 9) and p.P195L (carried by participants no. 6 and 10) with obesity in two families.

HDL, high-density lipoprotein; HT, hypertension; MS, metabolic syndrome; strikethrough MS, no metabolic syndrome; NBP, normal blood pressure; NG, normal fasting glucose; NM, mutation carrier; NN, wild type; NW, normal weight; Ob, obese; PD, prediabetes; SOb, severely obese; TG, triglycerides.

Extended Data Fig. 4 Expression of MRAP2 in human pancreatic islets and beta cells.

a, PCR-free quantification of MRAP2 mRNA levels in a panel of human tissues. b, Western blot analyses of human islets and EndoC-βH1 cells using MRAP2 antibody. FACS-sorted beta cell, pancreatic beta cells sorted by flow cytometry; LCM beta cell, pancreatic beta cells obtained by laser capture microdissection. Three independent experiments showed similar results for Extended Data Fig. 4b. Source data

Extended Data Fig. 5 Impaired insulin secretion from EndoC-βH1 cells treated with siRNA targeting MRAP2.

EndoC-βH1 cells were transfected with control nontargeting pool siRNA (siNTP) or MRAP2 siRNA (siMRAP2) and were analyzed 72 h thereafter. Insulin secretion (percentage of secretion of the total insulin content) was analyzed in response to 60 min of incubation with 0.5 mM glucose, followed by 60 min of incubation with 16.7 mM glucose. Data are box and whisker plots (with the minimum and the maximum) of four independent experiments (Left). Fold change data are mean values ± sem of four independent experiments (right). Fold change of insulin secretion for siMRAP2 was analyzed using a linear regression adjusted for experimental conditions (operator and date). Glc, glucose. Source data

Supplementary information

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Unprocessed Western Blot

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Baron, M., Maillet, J., Huyvaert, M. et al. Loss-of-function mutations in MRAP2 are pathogenic in hyperphagic obesity with hyperglycemia and hypertension. Nat Med 25, 1733–1738 (2019) doi:10.1038/s41591-019-0622-0

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