Abstract

Quantitative changes in leptin concentration lead to alterations in food intake and body weight, but the regulatory mechanisms that control leptin gene expression are poorly understood. Here we report that fat-specific and quantitative leptin expression is controlled by redundant cis elements and trans factors interacting with the proximal promoter together with a long noncoding RNA (lncOb). Diet-induced obese mice lacking lncOb show increased fat mass with reduced plasma leptin levels and lose weight after leptin treatment, whereas control mice do not. Consistent with this finding, large-scale genetic studies of humans reveal a significant association of single-nucleotide polymorphisms (SNPs) in the region of human lncOb with lower plasma leptin levels and obesity. These results show that reduced leptin gene expression can lead to a hypoleptinemic, leptin-responsive form of obesity and provide a framework for elucidating the pathogenic mechanism in the subset of obese patients with low endogenous leptin levels.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. For the CoLaus study, owing to restrictions by the cantonal ethical committee (CER-VD) no individual data for the CoLaus study can be made publicly available. However, such data can be requested for research purposes from the scientific committee of the CoLaus study (peter.vollenweider@chuv.ch). For the Inter99 study, the source data are available from authors at the Novo Nordisk Foundation Center for Basic Metabolic Research, who may be contacted at tuomas.kilpelainen@sund.ku.dk.

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Acknowledgements

We thank S. Malik and R. G. Roeder for valuable discussions. We acknowledge technical support from S. Schmidt and R. Sarnoff. We also thank the Gene Targeting Core at Rockefeller University for assistance with CRISPR targeting. O.S.D. acknowledges support from the Swedish Research Council, the Swedish Medical Research Society, and the Sweden-America Foundation. This work was supported by NIH grant R01-DK49780 (M.A.L.), the JPB Foundation (M.A.L.), and CEN 5402133 (J.M.F.). The study was supported in part by the NCATS CTSA program (UL1-TR001866) (R.V.). The Sohn Conference Foundation and the Leona M. and Harry B. Helmsley Charitable Trust are acknowledged for mass spectrometry instrumentation. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (https://www.metabol.ku.dk).

Author information

Affiliations

  1. Laboratory of Molecular Genetics, The Rockefeller University, New York, NY, USA

    • Olof S. Dallner
    • , Yi-Hsueh. Lu
    • , Emory Werner
    • , Gulya Fayzikhodjaeva
    • , Yinxin Zhang
    •  & Jeffrey M. Friedman
  2. Division of Endocrinology, Diabetes, and Metabolism and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    • Jill M. Marinis
    •  & Mitchell A. Lazar
  3. Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA

    • Kivanc Birsoy
  4. Proteomics Resource Center, The Rockefeller University, New York, NY, USA

    • Brian D. Dill
    •  & Henrik Molina
  5. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Arden Moscati
    •  & Ruth J. F. Loos
  6. Institute of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland

    • Zoltán Kutalik
  7. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    • Zoltán Kutalik
  8. Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland

    • Pedro Marques-Vidal
  9. Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Tuomas O. Kilpeläinen
    •  & Niels Grarup
  10. Centre for Clinical Research and Prevention, Frederiksberg-Bispebjerg Hospital, Copenhagen, Denmark

    • Allan Linneberg
  11. Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark

    • Allan Linneberg
  12. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Allan Linneberg
  13. Department of Biostatistics, The Rockefeller University, New York, NY, USA

    • Roger Vaughan
  14. The Mindich Childhood and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Ruth J. F. Loos
  15. Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA

    • Jeffrey M. Friedman

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Contributions

O.S.D. performed the majority of the experiments. O.S.D. planned experiments and drafted the manuscript with J.M.F. J.M.M. performed experiments (LNA and GRO-seq). Additional experiments were performed by Y.L. and K.B. E.W. provided support for animal husbandry and some mouse experiments. G.F. performed pronuclear injections for BACTGs and CRISPR, and B.D.D. and H.M. performed mass spectrometry and subsequent analysis. Z.K. and P.M.-V. contributed the CoLaus data, and T.O.K., N.G., and A.L. provided the Inter99 data. Y.Z. provided some experimental advice. R.V. performed a major part of the statistical analysis. A.M. and R.J.F.L. performed the CoLaus and Inter99 analysis. M.A.L. conceived part of the study and supervised the LNA and GRO-seq experiments. J.M.F. conceived the majority of the study and drafted the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Jeffrey M. Friedman.

Extended data

  1. Extended Data Fig. 1 Initial BACTG reporter lines and additional data.

    a, The first two BACTG lines covering 304 kb were imaged with IVIS comparing fasted or fed mice or mice crossed to Lepob/Lepob mice. Tissues were analyzed for luciferase reporter activity (−156 kb to +18 kb, n = 4; −22 kb to +148 kb, n = 4). Sp, spleen; St, stomach; Br, brain; Li, liver; Ki, kidney; In, intestine; eW, eWAT; iW, iWAT. b, Additional luciferase biochemistry for Fig. 1a,d for mice fasted for 48 h, fed, or crossed to Lepob/Lepob mice for the −16.5 to +8.8 kb BACTG (fasted, n = 7; fed, n = 6; ob, n = 6) and the −762 bp to +13.9 kb BACTG (fasted, n = 14; fed, n = 13; ob iWAT, n = 4; ob eWAT, n = 6. Values are means ± s.e.m. Source Data

  2. Extended Data Fig. 2 Overview of BACTGs.

    An overview of the BACTG deletion series generated indicating the length of the reporter gene and the measured luciferase activity for mice crossed to Lepob/Lepob mice, fed, or fasted for 48 h. ob eWAT/iWAT: −22 to +8.8 (n = 3), −17.4 to +8.8 (n = 7), −16.5 to +8.8 (n = 10), +762 to +13.9 (n = 3), +762 to +14.2 (n = 4), +762 to +14.8 (n = 3), +762 to +15.8 (n = 6), +762 to +18 (n = 2 or 3); fed eWAT/iWAT: −22 to +8.8 (n = 8), −17.4 to +8.8 (n = 13), −16.5 to +8.8 (n = 12), −16.4 to +8.8 (n = 2), −16.1 to +8.8 (n = 11), −762 to +8.8 (n = 7), +762 to +13.6 (n = 14), +762 to +13.9 (n = 3), +762 to +14.2 (n = 9), +762 to +14.8 (n = 3), +762 to +15.8 (n = 3), +762 to +18 (n = 5 or); fasted eWAT/iWAT: −22 to +8.8 (n = 6), −17.4 to +8.8 (n = 4), −16.5 to +8.8 (n = 11), +762 to +13.9 (n = 2), +762 to +14.2 (n = 11), +762 to +14.8 (n = 3), +762 to +15.8 (n = 3), +762 to +18 (n = 3). Values are means ± s.e.m. Source Data Source Data

  3. Extended Data Fig. 3 Predicted interactions of identified proteins.

    String interaction prediction was performed (https://string-db.org/) indicating interactions between RNA-binding proteins (red) and DNA-binding proteins (blue).

  4. Extended Data Fig. 4 RNA expression of leptin and lncOb in human subcutaneous adipose tissue.

    RNA levels of leptin and lncOb were measured by TaqMan qPCR in human samples (Sm, skeletal muscle; Li, liver; Lu, lung; AdiMix, mixed human fat samples; BMI25–30, three separate samples of human subcutaneous adipose tissue with known BMI). n = 7. Data were analyzed by linear regression. Source Data

  5. Extended Data Fig. 5 A mutation in lncOb causes dysregulation of quantitative leptin mRNA expression in mice.

    Leptin mRNA levels were measured in subcutaneous adipose tissue with a TaqMan assay and are plotted against fat mass (grams) for HFD-fed mice (WT, n = 10; lncOb–/–, n = 9) (squares, males; circles, females). Data were analyzed by linear regression. Source Data

  6. Extended Data Fig. 6 A mutation in lncOb causes dysregulation of quantitative leptin expression (regular diet).

    a, Seven-week-old male (WT, n = 9; lncOb–/–, n = 7) and female (WT, n = 6; lncOb–/–, n = 9) mice were fed a regular diet for 20 weeks and body weight was monitored (shaded regions represent means ± s.e.m). b, Fat mass was measured by EchoMRI in male (WT, n = 9; lncOb–/–, n = 7) and female (WT, n = 6; lncOb–/–, n = 9) mice after 20 weeks of a regular diet. Values are means ± s.e.m. P values were calculated by two-tailed Student’s t-test. c, Leptin levels (ng/ml) were measured by ELISA and are plotted against fat mass (grams) for regular diet (RD)-fed mice (WT, n = 13; lncOb–/–, n = 14) (squares, males; circles, females). For a data were analyzed by a linear mixed model with an AR(1) correlation structure, and for c data were analyzed by linear regression. Source Data

  7. Extended Data Fig. 7 Regional plots for the leptin locus.

    a,b, Regional plots for ‘comparative body size at age 10 years’ in UK Biobank (n 450,000) (a) and BMI-adjusted leptin levels from Kilpelainen et al.23 at the LEP locus (n > 46,000) (b). P values were calculated using a linear regression model.

  8. Extended Data Fig. 8 rs10487505 is a significant eQTL in the GTEx portal.

    a,b, The GTEx portal (https://gtexportal.org/) was queried for rs10487505, indicating a single eQTL (a) that impacts expression of IMPDH1 in thyroid (b) (n = 103 reference homozygotes, n = 204 heterozygotes, n = 92 alternative homozygotes). Box plots depict the interquartile range (IQR) and mean, and whiskers depict minimum and maximum values (1.5 times IQR). P value was calculated with a linear regression model. The normalized effect size (NES) of the eQTL is defined as the slope of the linear regression.

Supplementary information

  1. Supplementary Information

    Supplementary Tables 1–6

  2. Reporting Summary

  3. Supplementary Table 7

    Proteins identified by mass spectrometry in Fig. 3a

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https://doi.org/10.1038/s41591-019-0370-1