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. 2019 Feb 1;10(1):540.
doi: 10.1038/s41467-019-08492-8.

Genome-wide gene-based analyses of weight loss interventions identify a potential role for NKX6.3 in metabolism

Armand Valsesia  1 , Qiao-Ping Wang  2   3 , Nele Gheldof  4 , Jérôme Carayol  4 , Hélène Ruffieux  4   5 , Teleri Clark  2 , Victoria Shenton  2 , Lisa J Oyston  2 , Gregory Lefebvre  4 , Sylviane Metairon  4 , Christian Chabert  4 , Ondine Walter  4 , Polina Mironova  4 , Paulina Lau  6 , Patrick Descombes  4 , Nathalie Viguerie  7 , Dominique Langin  7   8 , Mary-Ellen Harper  9 , Arne Astrup  10 , Wim H Saris  11 , Robert Dent  5 , Greg G Neely  2 , Jörg Hager  4
Affiliations

Genome-wide gene-based analyses of weight loss interventions identify a potential role for NKX6.3 in metabolism

Armand Valsesia et al. Nat Commun. .

Abstract

Hundreds of genetic variants have been associated with Body Mass Index (BMI) through genome-wide association studies (GWAS) using observational cohorts. However, the genetic contribution to efficient weight loss in response to dietary intervention remains unknown. We perform a GWAS in two large low-caloric diet intervention cohorts of obese participants. Two loci close to NKX6.3/MIR486 and RBSG4 are identified in the Canadian discovery cohort (n = 1166) and replicated in the DiOGenes cohort (n = 789). Modulation of HGTX (NKX6.3 ortholog) levels in Drosophila melanogaster leads to significantly altered triglyceride levels. Additional tissue-specific experiments demonstrate an action through the oenocytes, fly hepatocyte-like cells that regulate lipid metabolism. Our results identify genetic variants associated with the efficacy of weight loss in obese subjects and identify a role for NKX6.3 in lipid metabolism, and thereby possibly weight control.

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Conflict of interest statement

A.V., J.C., N.G., H.R., G.L., S.M., C.C., O.W., P.M., P.D. and J.H. are full-time employees at Nestlé Institute of Health Sciences SA. D.L. is a member of Institut Universitaire de France. W.H.S. reports having received research support from several food companies such as Nestle, DSM, Unilever, Nutrition et Sante and Danone as well as Pharmaceutical companies such as GSK, Novartis and Novo Nordisk. He is an unpaid scientific advisor for the International Life Science Institute, ILSI Europe. A.A. reports grants or personal fees from McCain Foods, USA, personal fees from McDonald’s, USA, personal fees from Basic Research, USA, personal fees from Nestlé, Lausanne, personal fees from Dutch Beer Knowledge Institute, NL, personal fees from Gelesis, USA, personal fees from Novo Nordisk, DK, personal fees from S-Biotek, DK, grants from Arla Foods, DK, grants from Danish Dairy Research Council, grants from Nordea Foundation, DK, outside the submitted work; and Royalties received for the book first published in Danish as Verdens Bedste Kur (Politiken, Copenhagen) and subsequently published in Dutch as Het beste dieet ter wereld (Kosmos Uitgevers, Utrecht/Antwerpen), and in English as World’s Best Diet (Penguin, Australia). G.N. is supported by an NHMRC career development fellowship II CDF1111940. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Manhattan plot: Gene-based association results for the discovery cohort (Optifast900), Highlighted genes (in red) correspond to loci with genome-wide significant association signals (FDR < 5%). Source data are provided as a Source Data file
Fig. 2
Fig. 2
LocusZoom plots for the RBSG4 (LINC01363) and the MIR486/NKX6.3 loci. Left (right) panel corresponds to RBSG4 (MIR486/NKX6.3/ANK1). Panels from top to bottom correspond to results from the meta-analysis, the Optifast900 cohort and the DiOGenes cohort. Source data are provided as a Source Data file
Fig. 3
Fig. 3
Bayesian risk variant inference for SNPs within the RBSG4 and the MIR486/NKX6.3 loci. Panel (a) corresponds to RBSG4 (LINC01363) and panel (b) to MIR486/NKX6.3. Tracks from top to bottom display: (1) gene track, (2) −log10 association p-values from the meta-analysis, (3) posterior probability of disease association (PPA), (4) the number of overlapping epigenomic annotation at a given marker, (5) the detail of the epigenomic marks that overlap a given variant. The risk inference analyses are limited to variants with p-values smaller than 10−3; all other variants are not displayed. Due to a large number of DNase annotation tracks (>100 for specific variants), these tracks have been reduced to a single one (referred as “DNase”). Source data are provided as a Source Data file
Fig. 4
Fig. 4
HGTX/NKX6.3 regulates triglyceride (TAG) in Drosophila. a Whole-body HGTX RNAi decreased TAG level in adult flies, n = 4–8 groups, 10 flies each. b TAG levels in adult inducible of whole-body HGTX RNAi flies, n = 7 groups, 5 flies each. c HGTX mRNA was significantly reduced in adult inducible RNAi flies, n = 4–5 groups, 5 flies each. d HGTX mRNA overexpression detected by qPCR, n = 4 groups, 5 flies each. e TAG levels in adult inducible whole-body HGTX over-expression flies, n = 6 groups, 5 flies each. f TAG levels in tissue-specific HGTX RNAi flies, n = 6–7 groups, 5 flies each. g TAG levels in oenocyte-specific HGTX over-expression flies, n = 6 groups, 5 flies each. Data are represented as means ± SEM. One-way ANOVA with Bonferroni’s multiple comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, NS, not significant. Source data are provided as a Source Data file
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