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MC3R links nutritional state to childhood growth and the timing of puberty

  • 1.

    Friedman, J. M. The function of leptin in nutrition, weight, and physiology. Nutr. Rev. 60, S1–S14; discussion S68–S84, S85–S87 (2002).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 2.

    Cone, R. D. Anatomy and regulation of the central melanocortin system. Nat. Neurosci. 8, 571–578 (2005).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 3.

    Cowley, M. A. et al. Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus. Nature 411, 480–484 (2001).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 4.

    Hill, J. W. et al. Direct insulin and leptin action on pro-opiomelanocortin neurons is required for normal glucose homeostasis and fertility. Cell Metab. 11, 286–297 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 5.

    Varela, L. & Horvath, T. L. Leptin and insulin pathways in POMC and AgRP neurons that modulate energy balance and glucose homeostasis. EMBO Rep. 13, 1079–1086 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 6.

    Chen, A. S. et al. Role of the melanocortin-4 receptor in metabolic rate and food intake in mice. Transgenic Res. 9, 145–154 (2000).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 7.

    Fan, W., Boston, B. A., Kesterson, R. A., Hruby, V. J. & Cone, R. D. Role of melanocortinergic neurons in feeding and the agouti obesity syndrome. Nature 385, 165–168 (1997).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 8.

    Vaisse, C., Clement, K., Guy-Grand, B. & Froguel, P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat. Genet. 20, 113–114 (1998).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 9.

    Yeo, G. S. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 20, 111–112 (1998).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 10.

    Huszar, D. et al. Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88, 131–141 (1997).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 11.

    Farooqi, I. S. et al. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N. Engl. J. Med. 348, 1085–1095 (2003).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 12.

    Krakoff, J. et al. Lower metabolic rate in individuals heterozygous for either a frameshift or a functional missense MC4R variant. Diabetes 57, 3267–3272 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 13.

    Brown, P. I. & Brasel, J. in The Malnourished Child Nestlé Nutrition Workshop Series (eds Lewinter-Suskind, L. & Suskind, R. M.) 213–228 (Nestlé Nutrition Institute and Vevey/Raven Press, 1990).

  • 14.

    Clement, K. et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392, 398–401 (1998).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 15.

    Strobel, A., Issad, T., Camoin, L., Ozata, M. & Strosberg, A. D. A leptin missense mutation associated with hypogonadism and morbid obesity. Nat. Genet. 18, 213–215 (1998).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 16.

    Roselli-Rehfuss, L. et al. Identification of a receptor for gamma melanotropin and other proopiomelanocortin peptides in the hypothalamus and limbic system. Proc. Natl Acad. Sci. USA 90, 8856–8860 (1993).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 17.

    Gantz, I. et al. Molecular cloning of a novel melanocortin receptor. J. Biol. Chem. 268, 8246–8250 (1993).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 18.

    Butler, A. A. et al. A unique metabolic syndrome causes obesity in the melanocortin-3 receptor-deficient mouse. Endocrinology 141, 3518–3521 (2000).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 19.

    Chen, A. S. et al. Inactivation of the mouse melanocortin-3 receptor results in increased fat mass and reduced lean body mass. Nat. Genet. 26, 97–102 (2000).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 20.

    Renquist, B. J. et al. Melanocortin-3 receptor regulates the normal fasting response. Proc. Natl Acad. Sci. USA 109, E1489–E1498 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 21.

    Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 22.

    Day, F. R. et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat. Genet. 49, 834–841 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 23.

    Demidowich, A. P., Jun, J. Y. & Yanovski, J. A. Polymorphisms and mutations in the melanocortin-3 receptor and their relation to human obesity. Biochim. Biophys. Acta Mol. Basis Dis. 1863, 2468–2476 (2017).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 24.

    Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 25.

    Mencarelli, M. et al. Rare melanocortin-3 receptor mutations with in vitro functional consequences are associated with human obesity. Hum. Mol. Genet. 20, 392–399 (2011).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 26.

    Zegers, D. et al. Identification of three novel genetic variants in the melanocortin-3 receptor of obese children. Obesity (Silver Spring) 19, 152–159 (2011).

    CAS 
    Article 

    Google Scholar
     

  • 27.

    Lee, Y. S., Poh, L. K. & Loke, K. Y. A novel melanocortin 3 receptor gene (MC3R) mutation associated with severe obesity. J. Clin. Endocrinol. Metab. 87, 1423–1426 (2002).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 28.

    Studenski, S. A. et al. The FNIH Sarcopenia Project: rationale, study description, conference recommendations, and final estimates. J. Gerontol. A Biol. Sci. Med. Sci. 69, 547–558 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 29.

    Kim, T. N. et al. Comparisons of three different methods for defining sarcopenia: an aspect of cardiometabolic risk. Sci. Rep. 7, 6491 (2017).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 30.

    Boyd, A. et al. Cohort profile: the ‘children of the 90s’-the index offspring of the Avon Longitudinal Study of Parents and Children. Int. J. Epidemiol. 42, 111–127 (2013).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 31.

    Wade, K. H. et al. Loss-of-function mutations in the melanocortin 4 receptor in a UK birth cohort. Nat. Med. 27, 1088–1096 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 32.

    Lotta, L. A. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat. Genet. 53, 54–64 (2021).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 33.

    Khaw, K. T. et al. Combined impact of health behaviours and mortality in men and women: the EPIC-Norfolk prospective population study. PLoS Med. 5, e12 (2008).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 34.

    Pietzner, M. et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat. Med. 27, 471–479 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 35.

    Tapanainen, J. et al. Short and long term effects of growth hormone on circulating levels of insulin-like growth factor-I (IGF-I), IGF-binding protein-1, and insulin: a placebo-controlled study. J. Clin. Endocrinol. Metab. 73, 71–74 (1991).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 36.

    Finer, S. et al. Cohort profile: East London Genes & Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int. J. Epidemiol. 49, 20–21i (2020).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 37.

    de Onis, M. et al. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ. 85, 660–667 (2007).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 38.

    Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 39.

    Sweeney, P. et al. The melanocortin-3 receptor is a pharmacological target for the regulation of anorexia. Sci. Transl. Med. 13, eabd6434 (2021).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 40.

    Lam, B. Y. H. et al. Heterogeneity of hypothalamic pro-opiomelanocortin-expressing neurons revealed by single-cell RNA sequencing. Mol. Metab. 6, 383–392 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 41.

    Romanov, R. A. et al. Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes. Nat. Neurosci. 20, 176–188 (2017).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 42.

    Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Rep. 18, 3227–3241 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 43.

    Backholer, K. et al. Kisspeptin cells in the ewe brain respond to leptin and communicate with neuropeptide Y and proopiomelanocortin cells. Endocrinology 151, 2233–2243 (2010).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 44.

    Cocchi, D., De Gennaro Colonna, V., Bagnasco, M., Bonacci, D. & Muller, E. E. Leptin regulates GH secretion in the rat by acting on GHRH and somatostatinergic functions. J. Endocrinol. 162, 95–99 (1999).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 45.

    Tannenbaum, G. S., Gurd, W. & Lapointe, M. Leptin is a potent stimulator of spontaneous pulsatile growth hormone (GH) secretion and the GH response to GH-releasing hormone. Endocrinology 139, 3871–3875 (1998).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 46.

    Wang, L. & Moenter, S. M. Differential roles of hypothalamic AVPV and arcuate kisspeptin neurons in estradiol feedback regulation of female reproduction. Neuroendocrinology 110, 172–184 (2020).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 47.

    Dunger, D. B., Ahmed, M. L. & Ong, K. K. Effects of obesity on growth and puberty. Best Pract. Res. Clin. Endocrinol. Metab. 19, 375–390 (2005).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 48.

    Hauspie, R. C., Vercauteren, M. & Susanne, C. Secular changes in growth and maturation: an update. Acta Paediatr. Suppl. 423, 20–27 (1997).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 49.

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

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar
     

  • 50.

    Roa, J. & Herbison, A. E. Direct regulation of GnRH neuron excitability by arcuate nucleus POMC and NPY neuron neuropeptides in female mice. Endocrinology 153, 5587–5599 (2012).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 51.

    Manfredi-Lozano, M. et al. Defining a novel leptin–melanocortin–kisspeptin pathway involved in the metabolic control of puberty. Mol. Metab. 5, 844–857 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 52.

    Salomon, F., Cuneo, R. C., Hesp, R. & Sonksen, P. H. The effects of treatment with recombinant human growth hormone on body composition and metabolism in adults with growth hormone deficiency. N. Engl. J. Med. 321, 1797–1803 (1989).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 53.

    Doherty, T. J. Invited review: aging and sarcopenia. J. Appl. Physiol. 95, 1717–1727 (2003).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 54.

    McCance, R. A. & Widdowson, E. M. The determinants of growth and form. Proc. R. Soc. Lond. B Biol. Sci. 185, 1–17 (1974).

    ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 55.

    Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 56.

    Eastwood, S. V. et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS ONE 11, e0162388 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 57.

    Powell, R. M. et al. Development and validation of total and regional body composition prediction equations from anthropometry and single frequency segmental bioelectrical impedance with DEXA. Preprint at medRxiv https://doi.org/10.1101/2020.12.16.20248330 (2020).

  • 58.

    Zhao, Y. et al. GIGYF1 loss of function is associated with clonal mosaicism and adverse metabolic health. Nat. Commun. 12, 4178 (2021).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 59.

    McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 60.

    Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 61.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 62.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 63.

    Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 64.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 65.

    Millard, L. A. C., Davies, N. M., Gaunt, T. R., Davey Smith, G. & Tilling, K. Software application profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. Int. J. Epidemiol. 47, 29–35 (2018).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 66.

    Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311–D1320 (2021).

  • 67.

    Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.244293 (2020).

  • 68.

    McInnes, G. et al. Global Biobank Engine: enabling genotype–phenotype browsing for biobank summary statistics. Bioinformatics 35, 2495–2497 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 69.

    Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 35, 4851–4853 (2019).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 70.

    Howe, L. D. et al. Changes in ponderal index and body mass index across childhood and their associations with fat mass and cardiovascular risk factors at age 15. PLoS ONE 5, e15186 (2010).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 71.

    Frysz, M., Howe, L. D., Tobias, J. H. & Paternoster, L. Using SITAR (superimposition by translation and rotation) to estimate age at peak height velocity in Avon Longitudinal Study of Parents and Children. Wellcome Open Res. 3, 90 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 72.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://export.arxiv.org/abs/1303.3997 (2013).

  • 73.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 74.

    Wade, K. H. et al. Loss-of-function mutations in the melanocortin 4 receptor in a UK birth cohort. Nat. Med. 27, 1088–1096 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 75.

    Lindsay, T. et al. Descriptive epidemiology of physical activity energy expenditure in UK adults (the Fenland study). Int. J. Behav. Nutr. Phys. Act. 16, 126 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 76.

    Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 77.

    Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 6397 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 78.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 79.

    Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80 95–103 (1999).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 80.

    Narasimhan, V. M. et al. Health and population effects of rare gene knockouts in adult humans with related parents. Science 352, 474–477 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 81.

    Bayraktar, O. A. et al. Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map. Nat. Neurosci. 23, 500–509 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 82.

    Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 83.

    Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons in MICCAI 2018265–273 (Springer Nature Switzerland, 2018)

  • 84.

    Widmann, J. et al. RNASTAR: an RNA structural alignment repository that provides insight into the evolution of natural and artificial RNAs. RNA 18, 1319–1327 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 85.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

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