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. 2021 Jul 21;7(30):eabg0108.
doi: 10.1126/sciadv.abg0108. Print 2021 Jul.

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo

Samantha Laber  1   2   3   4   5 , Sara Forcisi  6   7 , Liz Bentley  8 , Julia Petzold  9 , Franco Moritz  10 , Kirill S Smirnov  10 , Loubna Al Sadat  7   11 , Iain Williamson  12 , Sophie Strobel  4   9 , Thomas Agnew  8 , Shahana Sengupta  8 , Tom Nicol  8 , Harald Grallert  7   13 , Margit Heier  14   15 , Julius Honecker  11 , Joffrey Mianne  16 , Lydia Teboul  16 , Rebecca Dumbell  8 , Helen Long  8   17 , Michelle Simon  8 , Cecilia Lindgren  3   18   19   20 , Wendy A Bickmore  12 , Hans Hauner  7   9   11 , Philippe Schmitt-Kopplin  10   7   21 , Melina Claussnitzer  22   5   23 , Roger D Cox  1
Affiliations

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo

Samantha Laber et al. Sci Adv. .

Abstract

Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot-dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet-induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant's action.

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Figures

Fig. 1
Fig. 1. Evolutionary conservation of the chromatin landscape between human and mouse at the region surrounding the FTO/Fto locus.
Hi-C data from human H1-ESC (top track) (16) and mouse embryonic stem cell (mESC) (bottom track) (74) are visualized in the 3D Genome Browser (http://3dgenome.org) (15). The heatmap values on a color scale correspond to the number of times that reads in two 40-kb bins were sequenced together (red, strong interaction; white, weak interaction). TADs are indicated at the base of the heatmaps, and the red vertical lines indicate the boundaries of the TAD containing the BMI-associated region in FTO. Histone modification signals (H3K27ac and H3K4me1) for human (75) and mouse (76) preadipocytes as well as DNase I hypersensitive sites (DHS) peaks for human (67) and mouse (55) adipose were overlapped and visualized using the 3D Genome Browser (http://3dgenome.org) (15). The blue vertical line indicates the position of BMI-associated variant rs1421085 and its orthologous site in mouse.
Fig. 2
Fig. 2. rs1421085-DEL82 results in an adipose depot–specific metabolic phenotype in mouse.
(A) Schematic of the intron-1 orthologous rs1421085 region in mouse and the 82-nt deletion highlighted in gray. CRMs of TFBSs, conserved in 80% of human, mouse, rhesus, chimp, rabbit, rat, horse, dog, cow, and opossum species, were identified by scanning (+4/−8-nt windows) using Genomatix (Munich, Germany) and are highlighted in pink. (B to D) Body weight (B), fat mass (C), and lean mass (D) were assessed in WT and rs1421085-DEL82 mice on LFD or HFD in the same animals over a time course. Numbers for each group are male WT HFD (n = 17), rs1421085-DEL82 HFD (n = 17), male WT LFD (n = 13), and rs1421085-DEL82 LFD (n = 15). Statistical significance was determined using two-way repeated-measures analysis of variance (ANOVA) and Bonferroni’s multiple comparisons test adjustment. (E and F) Skin was excised from mice on HFD at 6 months of age for histological processing. (E) Representative histological images of dermal WAT (dWAT). (F) Relative dermal adipocyte layer (dWAT) thickness for skin samples obtained from ventral/abdominal area (adWAT) and the dorsal area/back (sdWAT) calculated from histological assay. Statistical significance was determined using multiple Student’s t tests. All data are expressed as means ± SEM.
Fig. 3
Fig. 3. rs1421085 CRM has an adipose depot–specific effect on Irx3 and Irx5 expression in adipocytes.
(A to C) Rpgrip1l, Fto, Irx3, Irx5, and Irx6 gene expression (qPCR) normalized to Canx in iWAT (A) and gWAT (B) preadipocytes and in hypothalamus (C) at 6 to 8 weeks. Statistical analysis using Student’s t test; means ± SEM. (D) Location of genes in 1.6-Mb mouse genomic locus. Tracks 4 to 7 ChromHMM (77) 3T3-L1 preadipocyte annotations (75). Track 8 ATAC-seq in iWAT preadipocytes at day 1 adipogenic induction. Bottom track position of FISH fosmids. (E) Representative FISH nuclei images from iWAT- and gWAT-derived male WT and rs1421085-DEL82 undifferentiated proliferating (D0) or 1 day post-adipogenic stimulation (D1) primary preadipocytes (n = 3 animals each). Fluorescent probes for rs1421085-en, Irx3, and Irx5. Probe distances measured in iWAT D0 WT n = 71, rs1421085-DEL82 n = 70; iWAT D1 WT n = 90, rs1421085-DEL82 n = 90; gWAT D0 WT n = 71, rs1421085-DEL82 n = 62; gWAT D1 WT n = 82, rs1421085-DEL82 n = 70 nuclei. (F) Probe proximity (percentage of colocalized pairs, d < 200 nm). Statistical analysis using Fisher’s exact two-tailed tests. (G) Violin plots (median and interquartile range) of interprobe distances (nm) between different probe combinations (rs1421085-en/Irx3; Irx3/Irx5; rs1421085-en/Irx5). Horizontal line proportion of alleles that are colocalized <200 nm. Statistical analysis using Mann-Whitney U tests.
Fig. 4
Fig. 4. Metabolic profiling reveals a change of steroids in rs1421085-DEL82 male mice under HFD.
(A) Experimental design. (B and C) Mass difference analysis (MDiA) to assign unambiguous molecular formulas and map a metabolic mass difference network (MDiN) structure to UHR-MS metabolic features (78). Mapping of HMDB database compounds to MDiNs shows clustering of compound classes (legend top right) on the networks of sc-iWAT (B) and vc-gWAT (C) of rs1421085-DEL82 mice. ORA on rs1421085-DEL82 features revealed enrichment of steroids and derivatives in sc-iWAT (P = 0.0062) and not in vc-gWAT. However, the topology of the steroid cluster in vc-gWAT implied high chemical diversity demanding more refined analyses. (D) A schematic of a generic biotransformation supporting (E) and (F). (E) Forward reactions (vertical axis) between down-regulated (consumed) source compound classes and up-regulated (produced) target compound classes on the horizontal axis. Statistical significance of a compound class to be over-represented as a source/target in a reaction was determined using hypergeometric test. (F) Backward reactions between up-regulated (produced) source compound classes and down-regulated (consumed) target compound classes. Statistical significance of a compound class to be over-represented as a source/target in a reaction was determined using a hypergeometric test.
Fig. 5
Fig. 5. Steroid-related annotations cluster on the MDiN of human plasma metabolic features showing concerted responses to OGTT challenge for the rs1421085 CC risk compared to the nonrisk genotype.
(A) Experimental design. We examined the immediate OGTT response (the metabolic change between Time 0 and Time 1h) and the short-term OGTT response (the metabolic change between Time 1h and Time 2h). We successively performed MDiN analysis once for the assignment of high-confidence molecular formula (MF) and once for bioinformatic inference, where appropriate (27, 31). (B) Example mapping of a biochemical reaction (cholesterol and oleoyl-CoA (coenzyme A) react to give oleoyl-cholesterol and CoA) in the compositional space (i.e., masses and molecular formulas) as the principle of MDiN. The Δm of 264.245316 atomic mass units (amu) is observed as oleic acid is condensed onto a substrate. (C) Description of the overall metabolic network by MDiN analysis, colored for HMDB compound class (legend, top left) annotation by exact formula matching or assignment of similar compositions (79). (D) To appreciate the behavior of this compound class, during the OGTT responses, we extracted this subnetwork from (C). We colored the nodes following up- and down-regulations in the risk class.
Fig. 6
Fig. 6. Tile maps describing the main steroid effect in male subjects in response to an OGTT for two independent clinical cohorts.
(A and B) Each panel depicts a set of steroid-associated compositions in CHNO chemical space and the difference in responses of the corresponding compounds to an OGTT between risk-allele carriers versus nonrisk individuals. The left-side columns depict the immediate OGTT response (Time 1h–Time 0) and the short-term OGTT response (Time 2h–Time 1h) by means of a difference in average values of log2 fold changes in intensity levels. The right-side columns depict the static differences in average values of log2 intensity levels for each time considered. The stronger the green color of the filled circle, the higher the mean value associated with the risk group, compared to the mean value associated with the nonrisk group. In the opposite case, the stronger the blue color of the filled circle, the lower is the mean value. The size of the circles corresponds to a P value, from a two-sample Student’s t test assuming equal variance (risk and nonrisk). For P value lower than 0.05, the corresponding tiles are highlighted in yellow. (A) Tile maps for the KORA F4 cohort. (B) Tile maps for the replication cohort.
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