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Genomic and epigenomic mapping of leptin-responsive neuronal populations involved in body weight regulation

Nature Metabolism volume 1, pages 475–484 (2019)Cite this article

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Abstract

Genome-wide association studies in obesity have identified a large number of non-coding loci located near genes expressed in the central nervous system. However, due to the difficulties in isolating and characterizing specific neuronal subpopulations, few obesity-associated single-nucleotide polymorphisms have been functionally characterized. Leptin-responsive neurons in the hypothalamus are essential in controlling energy homoeostasis and body weight. Here, we combine fluorescence-activated cell sorting of leptin-responsive hypothalamic neuron nuclei with genomic and epigenomic approaches (RNA sequencing, chromatin immunoprecipitation sequencing, assay for transposase-accessible chromatin sequencing) to generate a comprehensive map of leptin response-specific regulatory elements, several of which overlap obesity-associated genome-wide association study variants. We demonstrate the usefulness of our leptin response neuron regulome, by functionally characterizing an enhancer near Socs3, a leptin response-associated transcription factor. We envision our data to serve as a useful resource and a blueprint for functionally characterizing obesity-associated single-nucleotide polymorphisms in the hypothalamus.

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Fig. 1: Isolation of LepRb-GFP+ nuclei.
Fig. 2: The LepRb-GFP+ nuclei transcriptome.
Fig. 3: Leptin-responsive transcriptome.
Fig. 4: The LepRb-GFP+ regulome.
Fig. 5: Functional characterization of a Socs3 enhancer.
Fig. 6: Obesity GWAS loci.

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Data availability

The RNA-seq data are available in NCBI BioProject PRJNA439388. ChIP-seq and ATAC-seq data are available in NCBI Bioproject PRJNA439388. The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We thank J. Tollkuhn (CSHL) for critical advice and M. Myers (University of Michigan) for sharing the LepRb TRAP-seq data. We also thank A. Hardin and S. Rattanasopha for help with the dissections and M. Cavrois and M. Maiti for assistance with FACS. This article was supported in part by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (grant no. 1R01DK090382 to N.A. and C.V.) and the University of California, San Francisco Nutrition Obesity Research Center funded by the National Institute of Health (grant no. P30DK098722). N.A. is also supported by grants from the National Human Genome Research Institute (NHGRI) and Division of Cancer Prevention, National Cancer Institute grant no. 1R01CA197139, National Institute of Mental Health grant no. 1R01MH109907, National Institute of Child and Human Development grant no. 1P01HD084387, NHGRI grant no. 1UM1HG009408 and National Heart, Lung, and Blood Institute grant no. 1R01HL138424. The Gladstone Institute Flow Cytometry Core is supported by National Institutes of Health (NIH) grant no. P30 AI027763 for using LSR2, Calibur, VYB, Aria, HTFC and ImageStream, NIH grant no. S10 RR028962 and the James B. Pendleton Charitable Trust for using the FACSAria cell sorter, and Department of Defense grant no. W81XWH-11-1-0562 for using ImageStream.

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Author notes

  1. These authors contributed equally: Fumitaka Inoue, Walter L. Eckalbar.

Authors and Affiliations

  1. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    Fumitaka Inoue, Walter L. Eckalbar, Karl K. Murphy, Navneet Matharu & Nadav Ahituv

  2. Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

    Fumitaka Inoue, Walter L. Eckalbar, Karl K. Murphy, Navneet Matharu & Nadav Ahituv

  3. Diabetes Center, University of California, San Francisco, San Francisco, CA, USA

    Yi Wang & Christian Vaisse

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Contributions

F.I., C.V. and N.A. conceived and designed the study. F.I. performed the mouse, genomic and enhancer experiments. K.K.M. and N.M. helped with the mouse dissections. Y.W. carried out the immunostaining and W.L.E. performed the computational analyses. F.I., W.L.E., C.V. and N.A. analysed the data. C.V. and N.A. provided resources and critical suggestions. F.I., W.L.E., C.V. and N.A. wrote the manuscript.

Corresponding authors

Correspondence to Christian Vaisse or Nadav Ahituv.

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Supplementary information

Supplementary Information

Supplementary Figures 1–6 and Supplementary Tables 1 and 4

Reporting Summary

Supplementary Table 2

Transcription factor binding site analyses of H3K27ac ChIP-seq and ATAC-seq peaks

Supplementary Table 3

Obesity GWAS SNPs overlapping mouse ChIP-seq and ATAC-seq lifted-over peak regions

Supplementary Data 1

RNA-seq results. This file contains a table of RNA-seq results for all genes in the Ensembl annotation and all lncRNAs, including gene annotation information, location, average expression, expression per sample and differential expression test results for all comparisons.

Supplementary Data 2

ChIP-seq and ATAC-seq results. This file contains a table of ChIP-seq and ATAC-seq results for all peaks including annotation information, location, average enrichment, enrichment per sample and differential expression test results for all comparisons.

Supplementary Data 3

eQTL analyses of H3K27ac ChIP-seq and ATAC-seq peaks. This file contains a table of ChIP-seq and ATAC-seq results for all peaks and includes nearest gene annotation information, and obesity-associated variant overlap information, plus peak location, average enrichment, enrichment per sample and differential expression test results for all comparisons.

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Inoue, F., Eckalbar, W.L., Wang, Y. et al. Genomic and epigenomic mapping of leptin-responsive neuronal populations involved in body weight regulation. Nat Metab 1, 475–484 (2019). https://doi.org/10.1038/s42255-019-0051-x

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