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A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes

Nature Metabolism volume 5, pages 861–879 (2023)Cite this article

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Abstract

Recent large-scale genomic association studies found evidence for a genetic link between increased risk of type 2 diabetes and decreased risk for adiposity-related traits, reminiscent of metabolically obese normal weight (MONW) association signatures. However, the target genes and cellular mechanisms driving such MONW associations remain to be identified. Here, we systematically identify the cellular programmes of one of the top-scoring MONW risk loci, the 2q24.3 risk locus, in subcutaneous adipocytes. We identify a causal genetic variant, rs6712203, an intronic single-nucleotide polymorphism in the COBLL1 gene, which changes the conserved transcription factor motif of POU domain, class 2, transcription factor 2, and leads to differential COBLL1 gene expression by altering the enhancer activity at the locus in subcutaneous adipocytes. We then establish the cellular programme under the genetic control of the 2q24.3 MONW risk locus and the effector gene COBLL1, which is characterized by impaired actin cytoskeleton remodelling in differentiating subcutaneous adipocytes and subsequent failure of these cells to accumulate lipids and develop into metabolically active and insulin-sensitive adipocytes. Finally, we show that perturbations of the effector gene Cobll1 in a mouse model result in organismal phenotypes matching the MONW association signature, including decreased subcutaneous body fat mass and body weight along with impaired glucose tolerance. Taken together, our results provide a mechanistic link between the genetic risk for insulin resistance and low adiposity, providing a potential therapeutic hypothesis and a framework for future identification of causal relationships between genome associations and cellular programmes in other disorders.

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Fig. 1: The pleiotropic 2q24.3 MONW locus is associated with increased risk for T2D and decreased adiposity-related traits, and maps to sparse enhancer signatures in adipocytes.
Fig. 2: rs6712203 is a functional variant at the 2q24.3 MONW locus.
Fig. 3: The 2q24.3 effector gene COBLL1 affects actin remodelling processes in differentiating adipocytes.
Fig. 4: The rs6712203 MONW risk haplotype affects actin remodelling in adipocytes and adipocyte lipid storage capacity.
Fig. 5: Cobll1-deficient mice are leaner and display metabolically dysfunctional phenotypes.

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Data availability

The ATAC–seq data for the immortalized human adipocytes are deposited in the database of Genotypes and Phenotypes under accession no. PRJNA539992. The high-content imaging data are available at the Cell Painting Gallery on the Registry of Open Data on Amazon Web Services (https://registry.opendata.aws/cellpainting-gallery/) under accession no. cpg0011. Source data are provided with this paper.

Code availability

The code used is publicly available on GitHub (https://github.com/ClaussnitzerLab/). The GitHub LP-2q24.3-metabolic-risk-locus repository containing the code and files used to generate LipocyteProfiles can be accessed at https://github.com/sophiestrobel/LP-2q24.3-metabolic-risk-locus.git.

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Acknowledgements

Work in the Claussnitzer lab is supported by the Foundation for the National Institutes of Health (NIH) (AMP-T2D RFB8b), the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (UM1 DK126185 and NIDDK DK102173), the Novo Nordisk Foundation (NNF21SA0072102) and by a Next Generation Award from the Broad Institute of MIT and Harvard. We acknowledge support from the Center for Advanced Light Microscopy of the Technical University of Munich School of Life Sciences, T. Kufer at the University of Hohenheim for access to microscopes, the Clinical Cooperation Group ‘Nutrigenomics and Type 2 Diabetes’ from the Helmholtz Center Munich and the German Center for Diabetes Research, the Else Kröner-Fresenius-Foundation, a Stanford Graduate Fellowship, a Center for Computational, Evolutionary and Human Genomics Graduate Fellowship, the Novo Nordisk Foundation Challenge Grant (no. NNF18OC0033754), NIH grant nos. R01HL128075 and P30DK020595, the Federal Ministry of Education and Research (award no. 0315674), the German Research Foundation (award no. 338582098) and the Federal Ministry of Education and Research (grant no. 0315674). We thank A. Flaccus, A. Kozza, D. Mvondo, M. Hubersberger, E. Hofmair, J. Röttgen, C. Deuschle, A. Saadat and S. Harken for technical support.

Author information

Author notes

  1. These authors contributed equally: Viktoria Glunk, Samantha Laber, Nasa Sinnott-Armstrong, Debora R. Sobreira, Sophie M. Strobel.

  2. These authors jointly supervised this work: Marcelo A. Nobrega, Melina Claussnitzer.

Authors and Affiliations

  1. Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany

    Viktoria Glunk, Sophie M. Strobel, Phil Kubitz, Bahareh Nemati Moud, Beate Brandl, Julius Honecker, Thomas Skurk & Hans Hauner

  2. ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany

    Viktoria Glunk, Sophie M. Strobel, Phil Kubitz, Bahareh Nemati Moud, Beate Brandl, Julius Honecker, Thomas Skurk & Hans Hauner

  3. Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA

    Samantha Laber, Nasa Sinnott-Armstrong, Sophie M. Strobel, Thiago M. Batista, Phil Kubitz, Yi Huang, Garrett Garbo, Virtu Calabuig-Navarro, Cecilia M. Lindgren & Melina Claussnitzer

  4. Department of Genetics, Stanford University, Stanford, CA, USA

    Nasa Sinnott-Armstrong

  5. Herbold Computational Biology Program, Publich Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Nasa Sinnott-Armstrong

  6. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    Debora R. Sobreira & Marcelo A. Nobrega

  7. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Thiago M. Batista, Phil Kubitz, Yi Huang & Melina Claussnitzer

  8. Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany

    Hannah Ebert & Virtu Calabuig-Navarro

  9. Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    David R. Stirling, Beth A. Cimini & Anne E. Carpenter

  10. Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Nezar Abdennur

  11. Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Soeren Ocvirk

  12. Intestinal Microbiology Research Group, Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany

    Soeren Ocvirk

  13. Molecular Cell Biology, Institute for Theoretical Medicine, University of Augsburg, Augsburg, Germany

    Kerstin Stemmer

  14. Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany

    Kerstin Stemmer

  15. German Center for Diabetes Research, Neuherberg, Germany

    Kerstin Stemmer

  16. Department of Clinical Science, University of Bergen, Bergen, Norway

    Simon N. Dankel

  17. Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

    Cecilia M. Lindgren

  18. Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Melina Claussnitzer

  19. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Melina Claussnitzer

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  1. Viktoria Glunk
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  2. Samantha Laber
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  10. Yi Huang
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Contributions

V.G. and M.C. conceptualized the study. V.G., S.L., N.S.-A., D.R.Sobreira, S.M.S., T.M.B., P.K., B.N.M., H.E., Y.H., B.B., G.G., J.H., D.R.S., N.A., V.C.-N., T.S., S.O., K.S., B.A.C., A.E.C., S.N.D., C.M.L., H.H., M.A.N. and M.C. devised the methodology. V.G., S.L., N.S.-A., D.R.Sobreira, S.M.S., T.M.B., P.K., B.N.M., H.E., Y.H., B.B., D.R.Stirling and B.A.C. carried out the formal analysis. V.G., S.L., N.S.-A., D.R.Sobreira, S.M.S., T.M.B., P.K., B.N.M., H.E., Y.H., B.B., D.R.Stirling and B.C. carried out the investigation. H.H., C.M.L., M.A.N. and M.C. obtained the resources. A.E.C., H.H., C.M.L., M.A.N. and M.C. supervised the study. H.H., C.M.L., M.A.N. and M.C. acquired the funding. V.G., N.S.-A., D.R.Sobreira, S.M.S. and M.C. wrote the original draft. V.G., S.L., N.S.-A., D.R.Sobreira, S.M.S., T.M.B., P.K., B.N.M., H.E., Y.H., B.B., G.G., J.H., D.R.Stirling, N.A., V.C.-N., T.S., S.O., K.S., B.C., A.E.C., S.N.D., C.M.L., H.H., M.A.N. and M.C. reviewed and edited the manuscript.

Corresponding author

Correspondence to Melina Claussnitzer.

Ethics declarations

Competing interests

M.C. holds equity in Waypoint Bio, consults for Pfizer, and is a member of the Nestle scientific advisory board. The authors have filed a provisional patent application (63/218,656).

Peer review

Peer review information

Nature Metabolism thanks Inês Cebola, Hannah Maude, Camilla Scheele and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ashley Castellanos-Jankiewicz, 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 The pleiotropic 2q24.3 MONW locus is associated with increased risk for type 2 diabetes and decreased adiposity related traits and maps to sparse enhancer signatures in adipocytes.

(a) Schematic overview for the 2q24.3 metabolic risk locus dissection. Aim of step (top, bold); methods/experiments used (middle); key finding/result of each step (bottom). (b) PheWAS of trait associations at the rs3923113-tagged haplotype of a meta-analysis https://t2d.hugeamp.org/. Colors represent trait classes while individual rs3923113 variant association p-values are shown on the Y axis. Direction of effect is indicated by orientation of triangles, upward: increase, downward: decrease (c) The 2q24.3 MONW locus spans 19 non-coding SNPs in high linkage disequilibrium with rs3923113 (LD r2 > 0.8). The region of association localizes to a >55 kb interval in an intergenic region between COBLL1 and GRB14. (d) Annotation panel and color key for the twenty-five state chromatin model70. Rows represent chromatin states abbreviations, columns are emission parameters, corresponding to the frequency with which each mark is expected in each state (left table) and genome coverage and median enrichments of relevant genomic annotations (right panel). TssA: Active TSS, TssAFlnk: Flanking Active TSS, TxFlnk: Transcription at gene 5’ and 3’, Tx: Strong Transcription, TxWk: Weak Transcription, EnhG: Genic enhancers: Enh: Enhancers, ZNF/Rpts: ZNF genes & repeats, Het: Heterochromatin, TssBiv: Bivalent/Poised TSS, BivFlnk: Flanking Bivalent TSS/Enhancer, EnhBiv: Bivalent Enhancer, ReprPC: Repressed Polycomb, ReprPCWk: Weak Repressed Polycomb, Quies: Quiescent/Low. (e) Stranded allele-specific chromatin accessibility measures at the haplotype using ATAC-seq data in differentiating adipocytes from a heterozygous individual. For each day of differentiation of an individual heterozygous, the number of reads overlapping with 20 non-coding SNPs in the haplotype, ordered by their start position and strand relative to the position of the variant, are shown. More reads indicate higher activity in haplotype 1 (non-risk, blue) compared to haplotype 2 (risk yellow). x-axis: offset from SNP position (bp), y-axis: stranded read count. (f) Replication of the effect at time 0 (mesenchymal stem cells) with ATAC-seq. (g) BMI-dependent variant association analysis. Bar plots represent the beta of the rs6712203 association with type 2 diabetes following BMI stratification. The cohort analysed is the UK Biobank self-identified white British individuals (total N = 327,960; N = 109198 with BMI < 25, N = 140539 with BMI between 25 and 30, and N = 78223 with BMI > = 30), and overlay of data points is not practical. Betas and 95% confidence intervals are shown, derived from a two-sided generalized linear model on outcome adjusted for demographic covariates (age, sex, genotyping array, 40 PCs).

Extended data Fig. 2 Conditional ana- lyses implicating rs6712203 in the genetic control of anthropometric traits and type 2 diabetes.

a, Conditional analyses implicating rs6712203 in the genetic control of anthropometric traits and type 2 diabetes. Each panel represents a different trait/sex/conditional analysis window, and all panels have an X axis corresponding to 100 kb on either side of the rs6712203 variant. The Y axis shows, for each variant in the window, the association strength for the given trait conditioned on the variants noted in White British participants in UK Biobank with the sex shown, and red lines indicate the significance threshold 5 × 10-8). −log10 p-values are shown, derived from a two-sided generalized linear model on outcome adjusted for demographic covariates (age, sex, genotyping array, 40 PCs).

Extended data Fig. 3 Chromatin inter- actions and CRISPRi of 2q24.3 locus identify COBLL1 as target genes.

(a) Cross-cell type conserved genome-wide higher order chromatin interactions for the 2q24.3 locus analyzed by Hi-C assays in human fibroblasts (left) and NHEK primary normal human epidermal keratinocytes (right), chr2: 163,556,000 - 167,558,000 (hg19), binned at 2 kb resolution. (b) Cas9 protein expression in dCas9 hWAT compared to the parental hWAT cell line. (c) mRNA expression of COBLL1 and GRB14 in response to increasing amounts of lentiviral sgRNA vectors (2 sgRNAs, virus volume 50 μl and 500 μl) targeting TSS regions of each gene compared to non-targeting controls (NT, 2 sgRNAs). Columns are means of individual sgRNAs indicated by different symbols. (d) COBLL1 protein expression normalized to b-actin in dCas9 hWATs transduced with sgRNAs targeting COBLL1 or GRB14 compared to controls. Top panel: Image of gel of representative sgRNA targeting NT, COBLL1 or GRB14. Bottom panel: plot of protein expression; 2 sgRNA for each target in 2 replicates. (e) Representation of 1,181 bp region flanking the COBLL1 intronic variant rs6712203 at the 2q24.3 MONW locus showing individual sgRNAs (n = 6) targeting the rs6712203 flanking regulatory region used in the CRISPRi experiments. (f, g) mRNA expression of (f) COBLL1 and (g) GRB14 in undifferentiated dCas9-hWAT preadipocytes at 6 days post lentiviral transduction with sgRNAs targeting TSS regions (red: COBLL1 TSS; blue GRB14 TSS) and the rs6712203-flanking regulatory element at position 1 to 6 as depicted in (e). Data are mean +/− SEM of 3 independent experiments. **** P < 0.0001, *** P = 0.0004, ** P = 0.006, * P = 0.013 – 0.036, two-tailed Student’s t test. (h) Predicted binding of POU2F2 between the two alleles using the Intragenomic Replicate Method (Cowper-Sal lari et al. 2012). As in Fig. 2d with different kmer counts.

Extended data Fig. 4 COBLL1 regulates actin cytoskeleton remodeling.

(a) COBLL1 expression in subcutaneous and visceral AMSCs throughout adipogenic differentiation, N = 4 biologically independent experiments, t-test two-sided, data represent median + 95% CI. (b) COBLL1 gene expression enrichment across 142 tissues (A-D) from enrichment profiler36. COBLL1 probes 203641_s_at and 203642_s_at were used for coregulation analysis (E-F). (c) Correlation with COBLL1 probe ILMN_1761260 using microarray data from lean and individuals with obesity. (d) Enrichment of pathways in the HCI (upper panel) and WikiPathways (lower panel) gene set lists from Enrichr, plotted as in Fig. 3A (KEGG), with p-value thresholds corresponding to the FDR cutoffs in those data. p-values are derived from a hypergeometric test. (e) COBLL1 expression in subcutaneous adipose tissue before and after a very low caloric diet (VLCD, upper panel, n = 18), corresponding body weight (lower panel), Wilcoxon signed-rank test.

Extended data Fig. 5 Knockdown of COBLL1 affects actin remodeling processes in differentiating adipocytes along with adipocyte differentiation, insulin sensitivity and lipolysis rate.

(a) COBLL1 expression in siCOBLL1 and siNT at day 0, 3 and 14 of differentiation, N = 3 biologically independent experiments, t-test two-sided. knock-down efficiency 80%, mean values + SEM. (b–d) Morphological profiles of siCOBLL compared to siNT AMSCs at day 0 (b) day 3 (c) and day 9 (d) of differentiation, t-test two-sided, significance level < 5%FDR. (e)ActinandCOBLL1staininginsiCOBLL1comparedtosiNT subcutaneous adipocytes at day 9 using phalloidin and COBLL1 antibody staining (HPA053344, Alexa-Fluor 488), magnification x63/oil. Scale bar = 52.8 um. Representative results from N = 3 independent experiments. (f) Cells_Children_LargeBODIPY_objects_count in siCOBLL1- and siNT AMSCs at day 3, 9, 14, N = 3 biologically independent experiments, t-test two-sided, significance level < 5% FDR. (G) qPCR-based gene expression of COBLL1 and adipocyte marker genes GLUT4, FASN, LIPE, PPARG, PLIN1, FABP4, CEBPA, ADIPOQ in siCOBLL1 and siNT AMSCs at day 14 of differentiation, t-test two-sided, N = 4 biologically independent experiments, mean values +/− SEM. (h) qPCR-based leptin gene expression in shCOBLL1 compared to shEV adipocytes. Data are represented as median + 95% CI, one-way ANOVA with Tukey’s HSD test, N = 4 biologically independent experiments (i) Correlation of COBLL1 mRNA with LEP mRNA in subcutaneous adipose tissue from 24 lean individuals measured by Illumina microarrays. The pearson’s correlation coefficient r and p-value are depicted (j) Schematic of siCOBLL1 KD and AMSCs differentiation. (k) UMAP-based dimensionality reduction of LipocyteProfiler features in siCOBLL1 and siNT AMSCs. (l) Actin and COBLL1 staining in siCOBLL1 and siNT visceral adipocytes at day 14 using phalloidin and COBLL1 antibody staining (HPA053344, Alexa-Fluor 488), magnification x63/oil. Representative result from N = 2 independent experiments, scale bar = 52,8um (m) Representative Oil-Red-O lipid staining in SGBS adipocytes following lentiviral COBLL1 knock-down (shCOBLL1, knock-down efficiency 69%) and GRB14 (shGRB14, knock-down efficiency 61%) compared to empty vector control (shEV), scale bar = 15 mm. (n) GPDH metabolic activity in shCOBLL1, shGRB14 and shEV SGBS adipocytes, one-way ANOVA with Tukey’s HSD test, mean + 95% CI, N = 4 biologically independent experiments (o) Basal and insulin-stimulated 3H-2-deoxyglucose uptake in shCOBLL1, shGRB14 and shEV SGBS adipocytes, one-way ANOVA with Tukey’s HSD test, mean + 95% CI, N = 4 biologically independent experiments, 1st and 3rd quartiles (box) and median (middle line) are indicated, p = 4.3 × 10-8. (p) qPCR-based GLUT4 gene expression in shCOBLL1, shGRB14 and shEV adipocytes, one-way ANOVA with Tukey’s HSD test, mean + 95% CI, N = 4 biologically independent experiments.

Source data

Extended data Fig. 6 Lipocyte profiles of risk versus non-risk haplotype carriers.

(a–c) Differences in morphological profiles between TT (n = 7) and CC (n = 6) allele carriers at day 0 (a), day 3 (b) and day 8 (c) in subcutaneous AMSCs (multi-way ANOVA, significance level < 5% FDR). (d–f) Differences in morphological profiles between TT (n = 7) and CC (n = 6) allele carriers at (d) day 0, (e) day 3 and (f) day 8 in visceral AMSCs (multi-way ANOVA, significance level < 5% FDR).

Extended data Fig. 7 Generation of COBLL1 mutant mice using CRISPR/Cas9 editing.

(a) Overview of the CRISPR/- Cas9 strategy to delete ~20 kb of the Cobll1 gene. The gRNA-targeting sequences (gRNAs) are underlined, and the PAM sequences are indicated in bold. Exons are represented as thick black boxes, introns are indicated as black lines with arrows, and the yellow boxes indicate the DNA-targeting region. Red hexagon indicates a stop codon gen- erating a Cobll1 truncated protein. Agarose gel showing the PCR products generated from DNA containing success- fully targeted Cobll1 from F0 mouse tail genomic DNA. The 308 bp band corresponds to the genomic deletion. (b) A real-time quantitative PCR of levels of Cobll1 mRNA in white adipose tissue (WAT), liver and kidney of Cobll1 WT, Cobll1 heterozygous (+/−) and null knockout Cobll1 (−/−) animals to confirm the Cobll1 ablation in knockout animals. Each group was analyzed using 5 different mice and the values were expressed as the mean ± s.e.m and P values by Student’s t-test. the experiment was repeated independently two times with similar results. (c) Pie chart illustrating non-redundant differential features per channel and class of measurement at day 8 of subcutaneous adipocyte differentiation in rs6712003 homozygous risk compared to non-risk carriers. (d, e) Differences in morphological profiles between AMSCs from Cobll1−/− mice (n = 3) and WT (n = 4) at (h) day 0 (i) day 2 (t-test two-sided, significance level < 5%FDR).

Supplementary information

Source data

Source Data Fig. 3

The source data for Fig1a is related to Fig. 3j: Images of Oil Red O lipid staining in SGBS adipocytes after lentiviral COBLL1 and GRB14 knockdown; the source data in Fig1a is related to Fig. 3n: western blots for lipolysis-relevant proteins assayed in basal or isoproterenol/IBMX-stimulated differentiated shCOBLL1 compared to shEV SGBS adipocytes.

Source Data Extended Data Fig. 5

The source data for Fig2a is related to Extended Data Fig. 5m: Images of Oil Red O lipid staining in SGBS adipocytes after lentiviral COBLL1 and GRB14 knockdown; the source data for Fig2b–h is related to Extended Data Fig. 5i: microscopy images of COBLL1 antibody (green), actin (phalloidin, red) and nuclei (Hoechst, blue) staining in siCOBLL1 and siNT visceral adipocytes.

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Glunk, V., Laber, S., Sinnott-Armstrong, N. et al. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat Metab 5, 861–879 (2023). https://doi.org/10.1038/s42255-023-00807-w

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