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Adipocyte NR1D1 dictates adipose tissue expansion during obesity

  1. Ann Louise Hunter
  2. Charlotte E Pelekanou
  3. Nichola J Barron
  4. Rebecca C Northeast
  5. Magdalena Grudzien
  6. Antony D Adamson
  7. Polly Downton
  8. Thomas Cornfield
  9. Peter S Cunningham
  10. Jean-Noel Billaud
  11. Leanne Hodson
  12. Andrew SI Loudon
  13. Richard D Unwin
  14. Mudassar Iqbal
  15. David W Ray
  16. David A Bechtold  Is a corresponding author
  1. Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
  2. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, and NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, United Kingdom
  3. Digital Insights, Qiagen, United States
  4. Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
  5. Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
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Cite this article as: eLife 2021;10:e63324 doi: 10.7554/eLife.63324

Abstract

The circadian clock component NR1D1 (REVERBα) is considered a dominant regulator of lipid metabolism, with global Nr1d1 deletion driving dysregulation of white adipose tissue (WAT) lipogenesis and obesity. However, a similar phenotype is not observed under adipocyte-selective deletion (Nr1d1Flox2-6:AdipoqCre), and transcriptional profiling demonstrates that, under basal conditions, direct targets of NR1D1 regulation are limited, and include the circadian clock and collagen dynamics. Under high-fat diet (HFD) feeding, Nr1d1Flox2-6:AdipoqCre mice do manifest profound obesity, yet without the accompanying WAT inflammation and fibrosis exhibited by controls. Integration of the WAT NR1D1 cistrome with differential gene expression reveals broad control of metabolic processes by NR1D1 which is unmasked in the obese state. Adipocyte NR1D1 does not drive an anticipatory daily rhythm in WAT lipogenesis, but rather modulates WAT activity in response to alterations in metabolic state. Importantly, NR1D1 action in adipocytes is critical to the development of obesity-related WAT pathology and insulin resistance.

Introduction

The mammalian circadian clock directs rhythms in behaviour and physiology to coordinate our biology with predictable changes in food availability and daily alternations between fasted and fed states. In this way, profound cycles in nutrient availability and internal energy state can be managed across multiple organ systems. A central circadian clock in the suprachiasmatic nuclei (SCN) drives daily rhythms in our behaviour (e.g. sleep/wake cycles) and physiology (e.g. body temperature), and orchestrates rhythmic processes in tissue systems across the body (Dibner et al., 2010; West and Bechtold, 2015). The molecular clock mechanism is also present in most cell types. The rhythmic transcriptome that defines cells and tissues is shaped by both local tissue clock activity and input from the central clock and rhythmic systemic signals (Guo et al., 2005; Hughes et al., 2012; Kornmann et al., 2007; Koronowski et al., 2019; Lamia et al., 2008; Hunter et al., 2020). The relative importance of these intrinsic and systemic factors remains ill-defined. Mounting evidence suggests that systemic signalling (e.g. SCN and/or behaviour-driven rhythmicity) is highly dominant in setting daily rhythms, while local clocks serve to buffer tissue/cell responses based on time of day. Nevertheless, it is clear that our rhythmic physiology and metabolic status reflects the interaction of clocks across the brain and body (West and Bechtold, 2015). Disturbance of this interaction, as occurs with shift work and irregular eating patterns, is increasingly recognised as a risk factor for metabolic disease and obesity (Broussard and Van Cauter, 2016Kim et al., 2020).

Extensive work over the past 20 years has demonstrated that circadian clock function and its component factors are closely tied into energy metabolism (Bass and Takahashi, 2010Reinke and Asher, 2019), with strong rhythmicity evident in cellular and systemic metabolic processes. Clock-metabolic coupling in peripheral tissues is adaptable, as demonstrated by classical food-entrainment studies (Damiola et al., 2000Mistlberger, 1994), and by recent work showing that systemic perturbations such as cancer and high-fat diet (HFD) feeding can reprogramme circadian control over liver metabolism (Dyar et al., 2018Masri et al., 2016). Plasticity therefore exists within the system, and the role of the clock in tissue and systemic responses to acute and chronic metabolic perturbation remains a critical question. The nuclear receptor NR1D1 (REVERBα) is a core clock component, and has been highlighted as a key link between the clock and metabolism. NR1D1 is a constitutive repressor, with peak expression in the latter half of the inactive phase (daytime in the nocturnal animal). In liver, NR1D1 exerts repressive control over programmes of lipogenesis by recruiting the NCOR/HDAC3 co-repressor complex to metabolic target genes, such that global loss of NR1D1 or liver-specific deletion of HDAC3 results in hepatosteatosis (Feng et al., 2011Zhang et al., 2015); (Zhang et al., 2016). Importantly, we recently showed that NR1D1 regulation of hepatic metabolism is state-dependent, with minimal impact under basal conditions yet increased transcriptional influence in response to mistimed feeding (Hunter et al., 2020). The selective functions of NR1D1 in white adipose tissue (WAT) are not well established and remain poorly understood. Early studies implicated an essential role of Nr1d1 in adipocyte differentiation (Chawla and Lazar, 1993Kumar et al., 2010); however, these findings are difficult to align with in vivo evidence. Indeed, pronounced adiposity and adipocyte hypertrophy are evident in Nr1d1-/- mice, even under normal feeding conditions (Delezie et al., 2012Hand et al., 2015); (Zhang et al., 2015). Daily administration of NR1D1 agonists has also been shown to reduce fat mass and WAT lipogenic gene expression in mice (Solt et al., 2012), although these agents do have significant off-target actions (Dierickx et al., 2019). Given the links between circadian disruption and obesity, and the potential of NR1D1 as a pharmacological target, we now define the role of NR1D1 in dictating WAT metabolism.

Transcriptomic and proteomic profiling of WAT in global Nr1d1-/- mice revealed an expected de-repression of lipid synthesis and storage programmes. However, in contrast, selective deletion of Nr1d1 in adipocytes did not result in dysregulation of WAT metabolic pathways. Loss of NR1D1 activity in WAT did, however, significantly enhance adipose tissue expansion in response to HFD feeding; yet despite exaggerated obesity, adipocyte-specific knockout (KO) mice were spared the anticipated obesity-related pathology. Integration of transcriptomic data with the WAT NR1D1 cistrome demonstrates that, under basal conditions, NR1D1 activity is limited to a small set of direct target genes (enriched for extracellular matrix [ECM] processes). However, NR1D1 regulatory control broadens to include lipid and mitochondrial metabolism pathways under conditions of obesity. Our data recast the role of NR1D1 as a regulator responsive to the metabolic state of the tissue, rather than one which delivers an anticipatory daily oscillation to the WAT metabolic programme.

Results

Adiposity and up-regulation of WAT lipogenic pathways in Nr1d1-/- mice

We first examined the body composition of age-matched Nr1d1 global KO (Nr1d1-/-) mice and littermate controls (wild type [WT]). In keeping with previous reports (Delezie et al., 2012Hand et al., 2015), Nr1d1-/- mice are of similar body weight to littermate controls (Figure 1A), yet carry an increased proportion of fat mass (KO: 24.2 ± 3.0% of body weight; WT: 10.8 ± 1.4%; mean ± SEM, p<0.01 Student’s t-test, n=12–14/group) and display adipocyte hypertrophy (Figure 1B), even when maintained on a standard chow diet. Metabolic phenotyping demonstrated expected day-night differences in food intake, energy expenditure, activity, and body temperature in both KO and WT controls, although genotype differences in day/night activity and temperature levels suggest some dampening of rhythmicity in the Nr1d1-/- mice (Figure 1—figure supplement 1A–E). However, this is unlikely to account for the increased adiposity in these animals, and a previous study did not report significant genotype differences in these parameters (Delezie et al., 2012). This favours instead an altered energy partitioning within these mice, with a clear bias towards storing energy as lipid.

Figure 1 with 1 supplement see all
Global deletion of Nr1d1 results in obesity and increased adipose lipid synthesis.

(ANr1d1-/- mice exhibit significantly greater fat mass relative to wild-type (WT) littermate controls. Body weight, fat mass, and lean mass of 13-week-old males (n=12–14/group). (B) Increased fat mass in Nr1d1-/- mice is reflected in adipocyte hypertrophy in gonadal white adipose tissue (gWAT) (representative 10× H and E images shown). (C, D) gWAT from Nr1d1-/- mice exhibits a programme of increased lipid synthesis. Proteomic profiling of gWAT depots (Nr1d1-/- mice plotted relative to their respective weight-matched littermate controls, n=6/group (C)) shows deregulation of metabolic regulators and enrichment (D) of metabolic pathways (up- and down-regulated proteins shown in blue and red, respectively). Top five (by protein count) significantly enriched Reactome terms shown. (E) Analyses of fatty acid (FA) composition reveal increased de novo lipogenesis (reflected by C16:0/C18:2 n ratio) and FA desaturation (reflected by C16:1 n-7/C16:0 ratio) in gWAT of Nr1d1-/-. n=6/group. Data presented as mean with individual data points (A, E). *p<0.05, **p<0.01, unpaired two-tailed t-test (A, E). Source data for panels C, D available in Figure 1—source data 1.

To explore further the lipid storage phenotype, we undertook proteomic analysis of gonadal white adipose tissue (gWAT) collected at ZT8 (zeitgeber time, 8 hr after lights on), the time of normal peak in NR1D1 expression in this tissue (Figure 1—figure supplement 1F). Isobaric tag (iTRAQ) labelled LC-MS/MS identified 2257 proteins, of which 182 demonstrated differential regulation (FDR<0.05) between WT and Nr1d1-/- gWAT samples (n=6 weight-matched, 13-week-old male mice/group) (Figure 1C). Differentially expressed (DE) proteins included influential metabolic enzymes, with up-regulation of metabolic processes detected on pathway enrichment analysis (Figure 1D). Importantly, and in line with the phenotype observed, increased NADPH regeneration (e.g. ME1, G6PDX), enhancement of glucose metabolism (also likely reflecting increased glyceroneogenesis, e.g. PFKL, ALDOA), and up-regulation of fatty acid synthesis (e.g. ACYL, FAS, ACACA) all support a shunt towards synthesis and storage of fatty acids and triglyceride in the KO mice. To validate this putative increase in local lipid synthesis, we quantified fatty acid species in gWAT, and indeed, the ratio of palmitic to linoleic acid (C16:0/C18:2n6), a marker of de novo lipogenesis, was significantly elevated in Nr1d1-/- samples (Figure 1E, Figure 1—figure supplement 1G). Fatty acid profiling also revealed evidence of increased SCD1 activity (C16:1 n-7/C16:0). Enhanced fatty acid synthesis in gWAT of mice lacking NR1D1 may be in part driven by increased glucose availability and adipose tissue uptake as previously suggested (Delezie et al., 2012), although we do not observe elevated blood glucose levels in the Nr1d1-/- animals (Figure 1—figure supplement 1H). The propensity to lipid storage is further highlighted by the substantial obesity, compared to littermate controls, displayed by Nr1d1-/- mice when challenged with 10 weeks of HFD (Figure 1—figure supplement 1I; Delezie et al., 2012Hand et al., 2015). Interestingly, we observed a strong positive correlation between body weight and daily intake of HFD in the Nr1d1-/- mice (Figure 1—figure supplement 1J), suggesting that HFD-induced hyperphagia exacerbates weight gain and obesity in the Nr1d1-/- mice.

Limited impact of adipocyte-selective Nr1d1 deletion under basal conditions

To define the role of NR1D1 specifically within WAT, we generated a new mouse line with loxP sites flanking Nr1d1 exons 2–6 (Nr1d1Flox2-6), competent for Cre-mediated conditional deletion (Hunter et al., 2020). We crossed this mouse with the well-established adiponectin Cre-driver line (AdipoCre; Eguchi et al., 2011Jeffery et al., 2014) to delete Nr1d1 selectively in adipocytes. This new line results in loss of Nr1d1 mRNA (Figure 2A) and protein (Figure 2B) expression in adipose tissue depots, as well as coordinate de-repression of Arntl (Bmal1), upon Cre-mediated recombination. In marked contrast to global Nr1d1-/- mice, adult Nr1d1Flox2-6:AdipoqCre mice did not show an increase in adiposity when maintained on a standard chow diet (Figure 2C; n=7/group), with no differences in mean body weight, fat, and lean mass observed. In parallel with this, we saw no differences in daily patterns of food intake, energy expenditure, activity levels, or body temperature in matched Nr1d1Flox2-6:AdipoqCre and control (Nr1d1Flox2-6) mice (Figure 2D–F). As brown adipose tissue (BAT) makes an important contribution to whole body energy metabolism, we studied thermoregulation in Nr1d1Flox2-6:AdipoqCre mice in greater detail. It has previously been proposed that NR1D1 is key in conferring circadian control over thermogenesis, through its repression of uncoupling protein 1 (UCP1) expression (Gerhart-Hines et al., 2013). However, we saw no evidence of genotype differences in thermoregulation between Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 mice (Figure 2—figure supplement 1A–E). Despite increased BAT UCP1 expression in the Cre+ve mice, no differences in body temperature profiles were observed between Cre-ve and Cre+ve mice when housed under normal laboratory temperature (22°C) nor when placed under thermoneutral conditions (29°C) for >14 days. Moreover, Cre-ve and Cre+ve mice did not differ in their thermogenic response to an acute cold challenge (4°C for 6 hr) (Figure 2—figure supplement 1F–H). These data support our overall findings that adipocyte-selective deletion of Nr1d1 does not have a significant impact on metabolic phenotype under normal chow (NC) feeding conditions. Together, our data also suggest that the lack of adiposity phenotype in the Cre+ve mice is not driven by an increase in locomotor activity, energy expenditure, or thermogenesis.

Figure 2 with 1 supplement see all
Impact of adipose Nr1d1 deletion is limited under normal conditions.

(A) Nr1d1 and Arntl (Bmal1) gene expression in gonadal white adipose tissue (gWAT), brown adipose (BAT), liver and lung in Nr1d1Flox2-6 (Cre-ve), and Nr1d1Flox2-6:AdipoqCre (Cre+ve) mice (n=4–5/group). (B) NR1D1 protein expression (arrowhead) in Cre-ve and targeted Cre+ve mice. Lower blot shows Ponceau S protein staining. (C) Body weight, fat mass, and lean mass in 13-week-old Cre-ve and Cre+ve male mice (n=7/group). (D–F) Both Nr1d1Flox2-6:AdipoqCre Cre+ve and Cre-ve mice demonstrate diurnal rhythms in behaviour and physiology, with no genotype differences observed in food intake (D), energy expenditure and daily activity (E) or body temperature (F) in 13-week-old males (n=4–7/group). Data presented as mean ± SEM (A) or as mean with individual data points (C–F). *p<0.05, **p<0.01, unpaired t-tests corrected for multiple comparisons (A), unpaired t-tests (C), two-way ANOVA with Tukey’s multiple comparisons tests (D–F).

Figure 2—source data 1

Raw uncropped and annotated blot images for Figure 2, panel B.

https://cdn.elifesciences.org/articles/63324/elife-63324-fig2-data1-v2.zip

In normal WAT, NR1D1 regulated targets are limited to clock and collagen genes

To investigate adipocyte-specific Nr1d1 activity, we performed RNA-seq at ZT8 (n=6/group) in both Nr1d1-/- and Nr1d1Flox2-6:AdipoqCre mouse lines. Global Nr1d1 deletion had a large effect on the gWAT transcriptome, with 4163 genes showing significant differential expression (FDR¡0.05) between Nr1d1-/- mice and age- and weight-matched WT littermate controls (Figure 3A). Pathway enrichment analysis demonstrated that these changes are dominated by metabolic genes (Figure 3B), with lipid metabolism and the TCA cycle emerging as prominent processes (Figure 3B). Thus, the gWAT transcriptome in Nr1d1-/- mice is concordant with the phenotype, and the gWAT proteome, in demonstrating up-regulation of lipid accumulation and storage processes. By contrast, and consistent with the absence of an overt phenotype, only a small genotype effect on the transcriptome was observed when comparing gWAT RNA-seq from Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 littermates (Figure 3C; n=6/group). Here, 231 genes showed significant differential expression between genotypes, of which 126 were also differentially regulated in the WAT analysis of global Nr1d1-/- mice (Figure 3D). These 126 common genes included circadian clock components (Arntl, Clock, Cry2, Nfil3), whilst pathway analysis also revealed collagen formation/biosynthesis processes to be significantly enriched (Figure 3E). Regulation of the molecular clock is expected, but the discovery of collagen dynamics as a target of NR1D1 regulatory action in adipocytes has not been previously recognised. We validated consistent up-regulation of collagen and collagen-modifying genes in Nr1d1-/- and Nr1d1Flox2-6:AdipoqCre gWAT by qPCR (Figure 3F). It is notable that Nr1d1Flox2-6:AdipoqCre mouse gWAT exhibits neither enrichment of lipid metabolic pathways nor de-regulation of individual key lipogenic genes, previously identified as NR1D1 targets (Figure 3E,GFeng et al., 2011Zhang et al., 2015Zhang et al., 2016). These findings suggest that lipogenic gene regulation may be a response to system-wide changes in energy metabolism in the Nr1d1-/- animals and challenge current understanding of NR1D1 action. These findings parallel our recent observations in hepatocyte-selective deletion of Nr1d1 (Hunter et al., 2020).

Figure 3 with 1 supplement see all
Global or adipose-specific Nr1d1 deletion produces distinctive gene expression profiles.

(A, B) Nr1d1-/- gonadal white adipose tissue (gWAT) demonstrates extensive remodelling of the transcriptome and up-regulation of metabolic pathways. Mean difference (MD) plot (A) showing significantly (FDR<0.05) up- (blue) and down- (red) regulated genes in gWAT of Nr1d1-/- mice compared to littermate controls (n=6/group). Pathway analysis (B) of significantly differentially expressed (DE) genes (FDR<0.05): top five (by gene count) significantly enriched Reactome terms shown (left panel), top five (by gene count) metabolic pathways shown (right panel). Up-regulated genes in blue, down-regulated in red. ODC = ornithine decarboxylase. (C) By contrast, RNA-seq demonstrates modest remodelling of the transcriptome in gWAT of Nr1d1Flox2-6:AdipoqCre mice. MD plot, n=6/group. (D) Venn diagram showing overlap of DE genes in Nr1d1Flox2-6:AdipoqCre and Nr1d1-/- gWAT. (E) Pathway analysis of 126 commonly DE genes. Top five (by gene count) significantly enriched Reactome terms shown. (F, G) Collagen genes are commonly up-regulated in both genotypes (F), whilst genes of lipid metabolism are not DE in Nr1d1Flox2-6:AdipoqCre (G). gWAT qPCR, n=6–7/group. Data presented as mean ± SEM (F, G). *p<0.05, **p<0.01, unpaired t-tests corrected for multiple comparisons (F, G). Source data for panels A–E available in Figure 3—source data 1.

Figure 3—source data 1

Source data (lists of differentially expressed genes, pathway lists) for Figure 3, panels A–E.

https://cdn.elifesciences.org/articles/63324/elife-63324-fig3-data1-v2.xlsx

Work in liver has suggested that the NR1D1 paralogue, NR1D2 (REVERBβ), contributes to the suppression of lipogenesis, and that concurrent NR1D2 deletion amplifies the impact of NR1D1 loss (Bugge et al., 2012). We therefore performed double knockdown of Nr1d1 and Nr1d2 in differentiated 3T3-L1 cells (Figure 3—figure supplement 1A). Whilst double knockdown produced greater Arntl de-repression than either Nr1d1 or Nr1d2 knockdown alone, it did not lead to de-repression of lipogenic genes previously considered NR1D1 targets (Figure 3—figure supplement 1B). We observed a similar pattern when comparing the liver transcriptome of liver-specific Nr1d1/Nr1d2 dual-deletion mice with that of global Nr1d1-/- KO; double knockdown produces a greater effect than loss of Nr1d1 alone, but does not lead to de-repression of metabolic genes (Hunter et al., 2020). Moreover, any potential compensation by NR1D2 does not prevent adipose lipid accumulation in the global Nr1d1-/- mice. Thus, although it cannot be ruled out, these findings suggest that compensation by NR1D2 does not underlie the mild phenotype observed in the Nr1d1Flox2-6:AdipoqCre mice.

Therefore, whilst global NR1D1 targeting produces an adiposity phenotype with up-regulation of WAT lipogenesis and lipid storage, this is not seen when NR1D1 is selectively targeted in adipose alone. The distinction is not due to loss of Nr1d1 expression in brown adipose, and is not due to compensatory NR1D2 action. Taken together, our data suggest that under a basal metabolic state, the adipose transcriptional targets under direct NR1D1 control are in fact limited to core clock function and collagen dynamics. NR1D1 is not a major repressor of lipid metabolism in this setting. This also suggests that the enhanced lipid accumulation phenotype of Nr1d1-/- adipose tissue is either independent from adipose NR1D1 entirely or that the action of NR1D1 in adipose is context-dependent.

Diet-induced obesity reveals a broader WAT phenotype in tissue-specific NR1D1 deletion

Studies in liver tissue have demonstrated reprogramming of both nuclear receptor and circadian clock factor activity by metabolic challenge (Eckel-Mahan et al., 2013Goldstein et al., 2017Guan et al., 2018Quagliarini et al., 2019). Both our data here, and previous reports (Delezie et al., 2012Feng et al., 2011Hand et al., 2015Le Martelot et al., 2009Preitner et al., 2002), highlight that the NC-fed Nr1d1-/- mouse is metabolically abnormal. The emergence of the collagen dynamics as a direct NR1D1 target and exaggerated diet-induced obesity evident in Nr1d1-/- mice supports a role for NR1D1 in regulating adipose tissue expansion under obesogenic conditions. To test this, Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 mice were provided with HFD for 16 weeks to drive obesity and WAT expansion. Indeed, compared to their controls, Nr1d1Flox2-6:AdipoqCre mice exhibited greater weight gain and adiposity in response to HFD feeding (Figure 4A,B). Of note, divergence between control and Nr1d1Flox2-6:AdipoqCre mice became clear only after long-term HFD feeding (beyond 13 weeks), a time at which body weight gain plateaus in control mice. This contrasts substantially with Nr1d1-/- mice, which show rapid and profound weight gain from the start of HFD feeding (Hand et al., 2015). The stark difference in progression and severity of diet-induced obesity is likely due (at least in part) to the HFD-induced hyperphagia, which is observed in Nr1d1-/- mice (WT food intake 2.92±0.10 g HFD/day/mouse; KO 3.74±0.21 g, p=0.0014, Student’s t-test, n=21/genotype), but not in Nr1d1Flox2-6:AdipoqCre mice (Cre-ve 2.99±0.61 g HFD/day/mouse; Cre+ve 3.01±0.60 g, p>0.05, n=8/genotype). Nevertheless, both models highlight that loss of NR1D1 increases capacity for increased lipid storage and adipose tissue expansion under obesogenic conditions. Despite the enhanced diet-induced obesity, HFD-fed Nr1d1Flox2-6:AdipoqCre mice showed little evidence of typical obesity-related pathology. Histological assessment of gWAT after 16 weeks of HFD feeding revealed widespread adipose tissue fibrosis (Picrosirius Red staining of collagen deposition under normal and polarised light) and macrophage infiltration (F4/80 immunohistochemistry) in obese control mice, but these features were not seen in Nr1d1Flox2-6:AdipoqCre mice (Figure 4C,D). Furthermore, we saw evidence of preserved insulin sensitivity in the HFD-fed Nr1d1Flox2-6:AdipoqCre mice, with neither circulating glucose nor insulin being higher than Nr1d1Flox2-6 littermate controls (Figure 4E), despite carrying significantly greater fat mass. Indeed, on insulin tolerance testing, HFD-fed Nr1d1Flox2-6:AdipoqCre mice demonstrated a significantly greater hypoglycaemic response than that observed in HFD-fed controls (Figure 4F). We saw no differences in adipocyte size between the two genotypes, indicating that our observations did not simply reflect greater adipocyte hypertrophy in the Nr1d1Flox2-6:AdipoqCre mice (Figure 4—figure supplement 1A). In line with a pronounced increase in fat mass, both gWAT and inguinal white adipose tissue depots (iWAT) were substantially larger in obese Nr1d1Flox2-6:AdipoqCre mice than in obese Nr1d1Flox2-6 controls (Figure 4—figure supplement 1B).

Figure 4 with 1 supplement see all
Diet-induced obesity unmasks a role for NR1D1 in the regulation of adipose expansion.

(A, B) High-fat diet (HFD) leads to exaggerated adiposity in Nr1d1Flox2-6:AdipoqCre mice. Body weight track of Cre-ve and Cre+ve male mice on HFD (solid line) or normal chow (NC) (dashed line) (A) (ap<0.05: Cre+ve NC vs. HFD; bp<0.05, Cre-ve NC vs. HFD; cp<0.05, Cre+ve HFD vs. Cre-ve HFD); total body, fat, and lean weight after 16 weeks in the high-fat diet group (B). (C, D) On histological examination of gonadal white adipose tissue (gWAT), HFD-fed Cre+ve mice display less fibrosis and inflammation than Cre-ve littermates. Representative Picrosirius Red and F4/80 immunohistochemistry images (20× magnification) (C), quantification of staining across groups, each data point represents the mean value for each individual animal (D). (E, F) Despite increased adiposity, HFD-fed Cre+ve mice display greater insulin sensitivity than Cre-ve controls. Terminal blood glucose and insulin levels (animals culled 2 hr after food withdrawal) in NC and HFD-fed in Nr1d1Flox2-6:AdipoqCre Cre-ve (black) and Cre-ve (orange) mice (E). Blood glucose values for individual animals and area under curve (change from baseline) for 16-week HFD-fed Nr1d1Flox2-6:AdipoqCre Cre-ve and Cre-ve mice undergoing insulin tolerance testing (ITT) (F). Data presented as mean ± SEM (A) or as individual data points with mean (B, D, E, F). *p<0.05, **p<0.01, two-way repeated-measures ANOVA with Tukey’s multiple comparisons tests (A), two-way ANOVA with Sidak’s multiple comparisons tests (D, E), unpaired two-tailed t-test (B, F). n=4–11/group for all panels. Picrosirius Red images for each animal available in Figure 4—source data 1.

Figure 4—source data 1

Source data (Picrosirius Red images, one per animal) for Figure 4, panel C.

https://cdn.elifesciences.org/articles/63324/elife-63324-fig4-data1-v2.pdf

Therefore, under long-term HFD-feeding conditions, adipose-targeted Nr1d1 deletion results in continued adipose tissue expansion accompanied by a healthier metabolic phenotype with reduced adipose inflammation and fibrosis, and preserved systemic insulin sensitivity. Importantly, these findings also suggest that the regulatory influence of NR1D1 is context-dependent, with the metabolic impact of adipose-targeted Nr1d1 deletion revealed by transition to an obese state.

NR1D1-dependent gene regulation is reprogrammed by obesity

We next performed RNA-seq on gWAT collected at ZT8 from Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 littermate controls fed either NC or HFD for 16 weeks (NC, n=4/group; HFD, n=6/group). As expected, HFD feeding had a substantial impact on the gWAT transcriptome in both Cre-ve and Cre+ve animals (i.e. NC vs. HFD comparison within each genotype; Figure 5A). Under NC feeding conditions, we again observed only a small genotype effect on the transcriptome, and as before, DE genes included core clock genes (Arntl, Nfil3, Npas2, Clock) and those of collagen synthesis pathways (Figure 5B). However, obesity revealed a substantial genotype effect, with 3061 genes DE (1706 up, 1355 down) in HFD-fed Nr1d1Flox2-6:AdipoqCre mice vs. HFD-fed Nr1d1Flox2-6 controls (Figure 5B), and 1704 genes showing a significant (α<0.05) diet-genotype interaction (stageR-specific interaction analysis; Van den Berge et al., 2017). Of these 1704 genes, those up-regulated in obese Nr1d1-deficient adipose were strongly enriched for metabolic pathways, whilst down-regulated genes showed weak enrichment of ECM organisation processes (Figure 5C). To examine how loss of Nr1d1 alters adipose tissue response to diet-induced obesity, we compared directly those processes which showed significant obesity-related dysregulation in control mice (Figure 5D). While HFD feeding caused a profound down-regulation (vs. NC conditions) of metabolic pathways in the WAT of control mice, this was not observed in Nr1d1Flox2-6:AdipoqCre mice. HFD feeding led to an up-regulation of immune pathways in both genotypes (Figure 5D); however, as highlighted by Ingenuity Pathway Analysis (IPA) of differentially regulated genes in gWAT, inflammation and immune-related processes were widely and markedly attenuated in the HFD-fed Cre+ve mice when compared to HFD-fed controls (Figure 5—source data 1). Thus, transcriptomic profiling correlates with phenotype in suggesting that WAT function and metabolic activity is protected from obesity-related dysfunction in the Nr1d1Flox2-6:AdipoqCre mice, and that the impact of adipose Nr1d1 deletion is dependent on system-wide metabolic state.

Under conditions of obesity, a broader programme of NR1D1 repression is seen.

(A) High-fat diet (HFD) dramatically remodels the white adipose tissue (WAT) transcriptome. RNA-seq (n=4–6/group) was performed in gonadal WAT (gWAT) from Cre-ve and Cre+ve male mice fed normal chow (NC) or HFD for 16 weeks. Mean difference (MD) plots show genes significantly (FDR<0.05) up-regulated (blue) or down-regulated (red) by HFD in each genotype. (B) With HFD, the NR1D1-responsive gWAT transcriptome broadens. MD plots show effect of genotype in NC (top panel) and HFD (lower panel) feeding conditions. Genes where stageR detects a significant (α=0.05) genotype-diet interaction highlighted in orange. (C) Reactome pathway analysis of genes up- or down-regulated in Cre+ve gWAT under HFD conditions, where this diet-genotype interaction is also detected. Top five (by gene count) significantly enriched terms shown. (D) Adipose-targeted deletion of Nr1d1 attenuates the normal HFD-induced down-regulation of metabolic pathways. Heatmaps show enrichment (-log10(padj)) of Reactome pathways in genes up-regulated (left) or down-regulated (right) by HFD feeding in Cre-ve and Cre+ve gWAT. Top 20 (by gene count in Cre-ve group) significantly enriched terms shown. Lines indicate related terms. Source data for panels A–D available in Figure 5—source data 1.

Figure 5—source data 1

Source data (lists of differentially expressed genes, pathway lists) for Figure 5, panels A–D, plus IPA.

https://cdn.elifesciences.org/articles/63324/elife-63324-fig5-data1-v2.xlsx

Integration of differential gene expression with the WAT cistrome reveals state-dependent regulation of metabolic targets by NR1D1

To understand this protective effect of Nr1d1 deletion, we profiled the gWAT NR1D1 cistrome using HaloTag-based technology. This permits antibody-independent capture of the NR1D1 cistrome, offering superior sensitivity and specificity to antibody-based approaches (Hunter et al., 2020). HaloChIP-seq was performed in gWAT tissue collected from NC-fed HaloNr1d1 mice at ZT8 and at ZT20, and from WT mice at ZT8. ZT8 and ZT20 are the expected peak and nadir of NR1D1 recruitment to the genome, respectively (Cho et al., 2012Feng et al., 2011). We identified 4474 ZT8 HaloNR1D1 peaks (MACS2, q<0.01) commonly called against both Halo ZT20 and WT ZT8 libraries (Figure 6A). As anticipated, we saw clear evidence of NR1D1 binding in proximity to core clock genes (Figure 6—figure supplement 1A), with this signal not seen in controls. Motif analysis of this high-confidence peak set detected strong enrichment of RORE and RevDR2 motifs (Figure 6—figure supplement 1B), again supporting the specificity of our cistrome.

Figure 6 with 1 supplement see all
NR1D1 binding sites associate with genes of lipid and mitochondrial metabolism normally repressed in obese adipose.

(A) Calling peaks against both wild-type (WT) ZT8 (zeitgeber time, 8 hr after lights on) and HaloNr1d1 ZT20 samples detects 4474 high-confidence regions of HaloNR1D1 binding in gonadal white adipose tissue (gWAT). (B, C) Genes commonly up-regulated in normal chow (NC)-fed Nr1d1Flox2-6:AdipoqCre and Nr1d1-/- gWAT (compared to littermate controls) are significantly enriched in proximity to HaloNR1D1 ChIP-seq peaks (B), as too are genes commonly up-regulated in high-fat diet (HFD)-fed Nr1d1Flox2-6:AdipoqCre and Nr1d1-/- gWAT (C). Genes commonly down-regulated are not. Venn diagrams show intersection of up- and down-regulated genes in the two models of Nr1d1 deletion. Plots shows enrichment (over all genes in the genome) of gene clusters of interest at increasing distances (λ) from HaloNR1D1 peaks. Up-regulated genes in blue, down-regulated in red. (D) Integrative Genomics Viewer (IGV) visualisations show HaloNR1D1 peaks in proximity to exemplar genes only up-regulated by Nr1d1 deletion in obese adipose. Uniform y-axes within each panel. (E) NR1D1 targets are also down-regulated in obesity. Volcano plot highlighting effect of HFD (in intact (Cre-ve) animals) of the 863 NR1D1 target genes from (C). NR1D1 target genes shown in purple; 495 NR1D1 target genes also within 100 kbp of a HaloNR1D1 peak outlined in black. (F) Metabolic map illustrating NR1D1 targets. Genes with a transcription start site (TSS) within 100 kbp of a HaloNR1D1 ChIP-seq peak are starred*. Source data for panels A–C available in Figure 6—source data 1.

Figure 6—source data 1

Source data (peak list, gene lists underlying Venn diagrams) for Figure 6, panels A–C.

https://cdn.elifesciences.org/articles/63324/elife-63324-fig6-data1-v2.xlsx

The NR1D1 cistrome spanning >4000 binding sites contrasts with the small number of DE genes observed between Nr1d1Flox2-6:AdipoqCre and control gWAT under NC conditions (231 genes in the first RNA-seq experiment; Figure 3C, and 138 genes in the second; Figure 5B), but is compatible with the larger number of DE genes revealed under HFD conditions (Figure 3A, Figure 5A). We next used this NR1D1 cistrome to define the relationship between direct chromatin binding and altered gene expression under different genetic and/or dietary challenge. For this, we employed a custom Python script that calculates the enrichment of DE gene sets in spatial relation to identified transcription factor binding sites, over all genes in the genome (Briggs et al., 2021bHunter et al., 2020Yang et al., 2019). We identified putative NR1D1 target genes by comparing gene sets which changed in both Nr1d1Flox2-6:AdipoqCre gWAT (relative to control mice, under both NC and HFD conditions) and in Nr1d1-/- gWAT (relative to WT littermates). Under NC conditions, only small sets of genes were up- or down-regulated in both Nr1d1Flox2-6:AdipoqCre and Nr1d1-/- tissues (vs. their respective controls) (Figure 6B). Extending out from NR1D1 ChIP-seq peaks by increasing distances (λ), we found transcription start sites (TSSs) of genes up-regulated in Nr1d1Flox2-6:AdipoqCre and Nr1d1-/- gWAT to be significantly enriched (above all genes in the genome) at distances up to 100 kbp (Figure 6B). This is consistent with repression mediated directly by DNA-bound NR1D1 and strongly suggests that this gene cluster (clock and collagen genes) represents direct targets of NR1D1 repression in WAT under NC conditions. In contrast, no enrichment of genes with decreased expression in Nr1d1Flox2-6:AdipoqCre or Nr1d1-/- WAT was evident at any distance from NR1D1 peaks (Figure 6B). Thus, NR1D1 activation of transcription involves a different mechanism of regulation, likely involving secondary or indirect mechanisms (e.g. de-repression of another repressor), as previously proposed to explain NR1D1 transactivation (Le Martelot et al., 2009).

HFD -feeding of Nr1d1Flox2-6:AdipoqCre mice greatly increased the overlap of DE genes with those DE in Nr1d1-/- WAT (Figure 6C). Critically, we observed a highly significant proximity enrichment of these commonly up-regulated genes (863) to sites of NR1D1 chromatin binding (Figure 6C,D), but again, saw no enrichment of the commonly down-regulated genes (Figure 6C). Thus, this integration of transcriptome and cistrome profiling suggests that these commonly up-regulated genes represent direct NR1D1 targets unmasked by the abnormal environment of obese adipose, and that NR1D1’s exertion of direct repressive control is dependent on metabolic state.

Consistent with the healthier metabolic phenotype observed in obese Nr1d1Flox2-6:AdipoqCre mice, we found that the large majority of the 863 NR1D1 gene targets unmasked by HFD feeding are normally repressed in obesity (Figure 6E), with 551 (63.8%) showing a significant down-regulation in obese control (Cre-ve) animals (chronic HFD compared to NC-fed state). Furthermore, 495 of these genes were found to lie within 100 kbp of a NR1D1 binding site (Figure 6E). These genes include important regulators of lipid and mitochondrial metabolism (Figure 6F) – including Fasn, Scd1, Acsl1, Cs – and FGF-21 co-receptor Klb, a previously identified NR1D1 target gene (Jager et al., 2016). Importantly, obesity-dependent de-repression of these genes was also observed in isolated mature adipocytes (MA) collected from NC or HFD-fed mice (Figure 6—figure supplement 1C,D). This provides further evidence that they are regulated by adipocyte NR1D1, and that this regulation is direct.

Considered together, these findings suggest that the healthy adiposity phenotype in HFD-fed Nr1d1Flox2-6:AdipoqCre mice results from de-repression of NR1D1-controlled adipocyte pathways which allow continued and efficient lipid synthesis and storage, thus permitting greater expansion of the adipose bed, and attenuation of obesity-related dysfunction.

Discussion

We set out to define the role of NR1D1 in the regulation of WAT metabolism and subsequently reveal a new understanding of NR1D1 function. Together, our data show NR1D1 to be a state-dependent regulator of WAT metabolism, with its widespread repressive action only unmasked by diet-induced obesity. Surprisingly, Nr1d1 expression in WAT appears to limit the energy-buffering function of the tissue. This finding parallels our recent work in the liver (Hunter et al., 2020). Hepatic-selective loss of NR1D1 carries no metabolic consequence, with NR1D1-dependent control over hepatic energy metabolism revealed only upon altered feeding conditions. Contrary to current understanding, our findings therefore suggest that NR1D1 (and potentially other components of the peripheral clock) does not impose rhythmic repression of metabolic circuits under basal conditions but rather determines tissue responses to altered metabolic state. As reported previously by us and others (Delezie et al., 2012Hand et al., 2015), global deletion of Nr1d1 leads to an increase in lipogenesis, adipose tissue expansion, and an exaggerated response to diet-induced obesity. How the loss of NR1D1 specifically in WAT contributes to this phenotype has not previously been addressed. Here, we use proteomic, transcriptomic, and lipid profiling studies to show a clear bias towards fatty acid synthesis and triglyceride storage within Nr1d1-/- WAT. Adipocyte-targeted deletion of Nr1d1 reveals only a modest phenotype, and a relatively selective set of gene targets, limited to clock processes and collagen dynamics. These genes are concurrently de-regulated in Nr1d1-/- adipose, and are found in proximity to NR1D1 binding sites, strongly implicating them as direct targets of NR1D1 repressive activity.

The reduced inflammation seen in obese Nr1d1Flox2-6:AdipoqCre mice is likely multifactorial, but may be secondary to a reduction in the pro-inflammatory free fatty acid pool, resulting from de-repression of lipogenic and mitochondrial metabolism pathways, or the absence of signals from dead/dying adipocytes. It is of note that improved metabolic flexibility is beneficial in other mouse models of metabolic disease (Jonker et al., 2012Kim et al., 2007Virtue et al., 2018) and in human obesity (Aucouturier et al., 2011Begaye et al., 2020). Whilst the clock has been linked to ECM remodelling in other tissues (Chang et al., 2020Dudek et al., 2016Sherratt et al., 2019), where it is thought to coordinate ECM dynamics, collagen turnover, and secretory processes (Chang et al., 2020), we now identify ECM as a direct target of adipose NR1D1 action; altered regulation of WAT collagen production and modification likely contributing to the rapid and continued adipose tissue expansion observed in Nr1d1-targeted adipose. The diminished adipose fibrosis seen in Nr1d1Flox2-6:AdipoqCre mice may well reflect alteration in multiple processes, some of which may be under direct/indirect NR1D1 control. Adipose-specific deletion of Nr1d1 thus provides a unique model to explore the complex ECM responses which accompany obesity-related tissue hypertrophy and development of fibrosis.

A role for the clock in the regulation of WAT function has been reported in the literature (Barnea et al., 2015Paschos et al., 2012Shostak et al., 2013), perhaps implying that it is the rhythmicity conferred by the clock which is important for WAT metabolism. However, despite robust rhythms of clock genes persisting, rhythmic gene expression in gWAT is largely attenuated following genetic disruption of SCN function (Kolbe et al., 2016). This supports the alternative notion that an intact local clock is not the primary driver of rhythmic peripheral tissue metabolism. Indeed, metabolic processes, including lipid biosynthesis, were highly enriched in the cohort of SCN-dependent rhythmic genes from this study (Kolbe et al., 2016), implying that feeding behaviour and WAT responses to energy flux are more important than locally generated rhythmicity for adipose function.

The modest impact of adipose-selective Nr1d1 deletion is both at odds with the large effect of global Nr1d1 deletion, and with the extensive WAT cistrome we have identified for NR1D1. This suggests that the tissue-specific actions of NR1D1 are necessary, but not sufficient, and require additional regulation from the metabolic state. Although not explored to the same extent, a lipogenic phenotype of liver-specific RORα/γ deletion has previously been shown to be unmasked by HFD feeding (Zhang et al., 2017). By driving adipose tissue hypertrophy through HFD feeding of the Nr1d1Flox2-6:AdipoqCre, we observed a stark difference in the adipose phenotype of targeted mice and littermate controls. WAT tissue lacking Nr1d1 showed significantly increased tissue expansion, but little evidence of normal obesity-related pathology (tissue fibrosis and immune cell infiltration/inflammation). Genes controlling mitochondrial activity, lipogenesis, and lipid storage were relatively spared from the obesity-related down-regulation observed in control mouse tissue, and were associated with the WAT NR1D1 cistrome. Thus, in response to HFD feeding, NR1D1 acts to repress metabolic activity in the adipocyte and limit tissue expansion (albeit at the eventual cost of tissue dysfunction, inflammation, and development of adipose fibrosis).

The broadening of NR1D1’s regulatory influence in response to obesity likely reflects a change to the chromatin environment in which NR1D1 operates (Figure 6—figure supplement 1E). The majority of emergent NR1D1 target genes are repressed in obese adipose when Nr1d1 expression is intact (Figure 6E). As these genes are not de-repressed by Nr1d1 loss in normal adipose, NR1D1 activity must be redundant or ineffective in a ‘basal’ metabolic state. Subsequent emergence of NR1D1’s transcriptional control may reflect alterations in chromatin accessibility or organisation, and/or the presence of transcriptional repressors and accessory factors required for full activity. As NR1D1 is itself proposed to regulate enhancer-promoter loop formation (Kim et al., 2018), modulation of Nr1d1 expression would be a further important variable here. Such reshaping of the regulatory landscape likely occurs across tissues, and may explain why, with metabolic challenge, emergent circadian rhythmicity is observed in gene expression (Eckel-Mahan et al., 2013Kinouchi et al., 2018Tognini et al., 2017), and in circulating and tissue metabolites (Dyar et al., 2018Eckel-Mahan et al., 2013).

Here, we now uncover a role for NR1D1 in limiting the energy-buffering role of WAT, a discovery which may present therapeutic opportunity as we cope with an epidemic of human obesity. Despite recent findings which have cast doubt on the utility of some of the small molecule NR1D1 ligands (Dierickx et al., 2019), antagonising WAT NR1D1 now emerges as a potential target in metabolic disease. Finally, our study suggests that a functioning circadian clock may be beneficial in coping with acute mistimed metabolic cues but, that under chronic energy excess, may contribute to metabolic dysfunction and obesity-related pathology.

Materials and methods

Animal experiments

All experiments described here were conducted in accordance with local requirements and licenced under the UK Animals (Scientific Procedures) Act 1986, project licence number 70/8558 (DAB). Procedures were approved by the University of Manchester Animal Welfare and Ethical Review Body (AWERB). Unless otherwise specified, all animals had ad libitum access to standard laboratory chow and water, and were group-housed on 12 hr:12 hr light:dark cycles and ambient temperature of 22±1.5°C. Unless otherwise stated, male mice (Mus musculus) were used for all experimental procedures. All proteomics studies were carried out on 13-week-old weight-matched males. RNA-seq studies for Figure 3 were carried out on 12- to 14-week-old weight-matched males; the RNA-seq study for Figure 5 was carried out on 28-week-old males (following 16 weeks of HFD or NC feeding). HaloChIP-seq was carried out on males aged 12–21 weeks.

For group allocation, we employed a timed breeding approach, so that cohorts of mice were produced within a narrow time window. All studies compared littermate control and transgenic mice, which inherently confers group allocation and randomisation in cage housing (and thus diet regime). The n numbers, and overall experimental design, were determined on the basis of extensive experience with the models, and power analyses incorporating previous results (based on achieving 80% power with 5% type I error). Blinding was facilitated by animal numbering, numbered coding of samples at collection, and use of automated analyses where possible.

Nr1d1-/-

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Nr1d1-/- mice were originally generated by Ueli Schibler (University of Geneva) (Preitner et al., 2002). These mice were created by replacing exons 2–5 of the Nr1d1 gene by an in-frame LacZ allele. Mice were then imported to the University of Manchester and backcrossed to C57BL/6J mice.

Nr1d1Flox2-6

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A CRISPR-Cas9 approach was used to generate a conditional knock allele for Nr1d1 -, as described (Hunter et al., 2020). LoxP sites were integrated, in a two-step process, at intron 2 and intron 6, taking care to avoid any previously described transcriptional regulatory sites (Yamamoto et al., 2004). A founder animal with successful integration of both the 5' and 3' loxP sites, transmitting to the germline, was identified and bred forward to establish a colony.

Nr1d1Flox2-6:AdipoqCre

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Adiponectin-driven Cre-recombinase mice (Eguchi et al., 2011Jeffery et al., 2014) were purchased from the Jackson Laboratory and subsequently bred against the Nr1d1Flox2-6at the University of Manchester.

HaloNr1d1

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HaloNr1d1 mice were generated by the University of Manchester Genome Editing Unit, as described (Hunter et al., 2020).

In vivo phenotyping

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Body composition of mice was analysed prior to cull by quantitative magnetic resonance (EchoMRI 900). Energy expenditure was measured via indirect calorimetry using CLAMS (Columbus Instruments) for 10- to 12-week-old male mice. Mice were allowed to acclimatise to the cages for 2 days, prior to an average of 5 days of recordings being collected. Recording of body temperature and activity was carried out via surgically implanted radiotelemetry devices (TA-F10, Data Sciences International). Data is shown as a representative day average of single-housed age-matched males. For the diet challenge, male mice were fed HFD (60% energy from fat; DIO Rodent Purified Diet, IPS Ltd) for a period of 10–16 weeks from 12 weeks of age. Blood glucose was measured from tail blood using the Aviva Accuchek meter (Roche). For the insulin tolerance test, mice were fasted from ZT0, then injected with 0.75 IU/kg human recombinant insulin (I2643, Sigma-Aldrich) at ZT6 (time ‘0 min’).

Insulin ELISA

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Insulin concentrations were measured by ELISA (EZRMI-13K Rat/Mouse insulin ELISA, Merck Millipore) according to the manufacturer’s instructions. Samples were diluted in matrix solution to fall within the range of the assay. Internal controls supplied with the kit were run alongside the samples and were in the expected range.

Histology

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gWAT was collected and immediately fixed in 4% paraformaldehyde for 24 hr, transferred into 70% ethanol, and processed using a Leica ASP300 S tissue processor; 5 μm sections underwent H and E staining (Alcoholic Eosin Y solution [HT110116] and Harris Haematoxylin solution [HHS16], Sigma-Aldrich), Picrosirius Red staining (see below), or F4/80 immunohistochemistry (see below). Images were collected on an Olympus BX63 upright microscope using 10×/0.40 UPlan SAPo and 20×/0.75 UApo/340 objectives. Percentage area stained was quantified using ImageJ (version 1.52a) as detailed in the online ImageJ documentation, with 5–12 images quantified per animal. Adipocyte area was quantified using the Adiposoft ImageJ plug-in (version 1.16).

For Picrosirius Red staining, sections were dewaxed and rehydrated using the Leica ST5010 Autostainer XL. Sections were washed in distilled water and then transferred to Picrosirius Red (Direct Red 80, Sigma-Aldrich) (without the counterstain) for 1 hr. Sections were then washed briefly in 1% acetic acid. Sections were then dehydrated, cleared, and mounted using the Leica ST5010 Autostainer XL.

For F4/80 immunohistochemistry, sections were dewaxed and rehydrated prior to enzymatic antigen retrieval (trypsin from porcine pancreas [T7168, Sigma]). Sections were treated with 3% hydrogen peroxide to block endogenous peroxidase activity followed by further blocking with 5% goat serum. Rat mAb to F4/80 (1:500) (ab6640, Abcam) was added and sections were incubated overnight at 4°C. Sections were washed before addition of the biotinylated anti-rat IgG (BA-9400, H and L) secondary antibody (1:1500) for 1 hr. Sections were developed using VECTAstain Elite ABC kit peroxidase, followed by DAB Peroxidase substrate (Vector Labs) and counterstained with haematoxylin. Slides were then dehydrated, cleared, and mounted.

Lipid extraction and gas chromatography

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Total lipid was extracted from tissue lysates using chloroform-methanol (2:1; v/v) according to the Folch method (Folch et al., 1957). An internal standard (tripentadecanoin glycerol [15:0]) of known concentration was added to samples for quantification of total triacylglyceride. Lipid fractions were separated by solid-phase extraction and fatty acid methyl esters (FAMEs) were prepared as previously described (Heath et al., 2003). Separation and detection of total triglyceride FAMEs was achieved using a 6890N Network GC System (Agilent Technologies, Santa Clara, CA) with flame ionisation detection. FAMEs were identified by their retention times compared to a standard containing 31 known fatty acids and quantified in micromolar from the peak area based on their molecular weight. The micromolar quantities were then totalled and each fatty acid was expressed as a percentage of this value (molar percentage, mol%).

Proteomics

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Mice were culled by cervical dislocation and the gWAT was immediately removed and washed twice in ice-cold PBS and then once in ice-cold 0.25 M sucrose, prior to samples being snap-frozen in liquid nitrogen and stored at −80°C. To extract the protein, the samples were briefly defrosted on ice and then cut into 50 mg pieces and washed again in ice-cold PBS. The sample was then lysed in 200 μl of 1 M triethylammonium bicarbonate buffer (Sigma) with 0.1% (w/v) sodium dodecyl sulphate with a Tissue Ruptor (Qiagen). Samples were centrifuged for 5 min, full speed, at 4°C and the supernatant collected into a clean tube. A Bio-Rad Protein Assay (Bio-Rad) was used to quantify the protein and Coomassie protein stain (InstantBlue Protein Stain Instant Blue, Expedeon) to check the quality of extraction. Full methods of subsequent iTRAQ proteomic analysis including bioinformatic analysis has been published previously (Kassab et al., 2019Xu et al., 2019). Here, the raw data was searched against the mouse Swissprot database (release October 2017) using the paragon algorithm on Protein-Pilot (version 5.0.1, AB SCIEX). A total of 33,847 proteins were searched. As described (Xu et al., 2019), Bayesian protein-level differential quantification was performed by Andrew Dowsey (University of Bristol) using their own BayesProt (version 1.0.0), with default choice of priors and MCMC settings. Expression fold change relative to the control groups was determined and proteins with a global false discovery rate of <0.05 were deemed significant.

Adipose tissue fractionation

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Following a method adapted from Collins et al., 2010, gWAT was collected from adult male mice and washed in Hanks’ Balanced Salt Solution (Sigma). Next, tissue was minced and digested in 1 mg/ml collagenase (Collagenase H, Sigma) for 30 min in a shaking incubator at 170 rpm, 37°C. The sample was then centrifuged at 1000 rpm for 5 min at 4°C. MA (floating layer) and stromal vascular fraction (SVF) (cell pellet) were collected separately, lysed in TRIzol Reagent (Invitrogen), and stored at −80°C before proceeding to RNA extraction.

3T3-L1 cells

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The 3T3-L1 cell line was purchased from ATCC (authentication and mycoplasma testing status as per ATCC documentation). Cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) – high glucose (D6429, Sigma-Aldrich) supplemented with 10% foetal bovine serum (FBS) and 1% penicillin/streptomycin (P/S) at 37°C/5% CO2. Cells were grown until confluent, passaged and plated into 12-well tissue culture plates for differentiation. The differentiation protocol was initiated 5 days later. Cells were treated with 10 μg/ml insulin (Sigma-Aldrich), 1 μM dexamethasone (Sigma-Aldrich), 1 μM rosiglitazone (AdooQ Bioscience), and 0.5 mM IBMX (Sigma-Aldrich) prepared in DMEM + 10% FBS + 1% P/S for 3 days. On day 3 and day 5, the cell culture media was changed to 10 μg/ml insulin and 1 μM rosiglitazone in DMEM + 10% FBS + 1% P/S. On day 7, the cell culture media was changed to 10 μg/ml insulin in DMEM + 10% FBS + 1% P/S. Finally on day 10, the cell culture media was changed to DMEM + 10% FBS + 1% P/S without any additional differentiation mediators. Cells were used from day 11 onwards. Lipid droplets were visible by day 5.

For knockdown studies, mature 3T3-L1 adipocytes were transfected with SiControl (Control ON-TARGETplus siRNA, Dharmacon), SiNr1d1 (Mouse NR1D1 ON-TARGETplus siRNA, Dharmacon), or SiNr1d2 (Mouse NR1D2 ON-TARGETplus siRNA, Dharmacon) at 50 nM concentration using Lipofectamine RNAiMAX (Invitrogen) as a transfection reagent. Briefly, 12-well plates were coated with poly-L-lysine hydrobromide (Sigma) and incubated for 20–30 min prior to excess poly-L-lysine being removed and the plates allowed to dry. SiRNAs and RNAiMAX transfection reagent were separately mixed with reduced serum media (Opti-MEM, Gibco). The control or Nr1d1/β siRNA was then added to each well and mixed with an equal quantity of RNAiMAX and then incubated for 5 min at room temperature. Mature 3T3-L1 adipocytes were trypsinised (trypsin-EDTA solution, Sigma) and resuspended in FBS without P/S prior to being re-plated into the wells containing the SiRNA. After 24 hr the transfection mix was removed and replaced with DMEM without FBS or P/S. The cells were then collected 48 hr after transfection.

RNA extraction (cells)

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RNA was extracted from cells using the ReliaPrep RNA Cell Miniprep system (Promega UK), following manufacturer’s instructions. RNA concentration and quality was determined with the use of a NanoDrop spectrophotometer and then stored at −80°C.

RNA extraction (tissue)

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Frozen adipose tissue was homogenised in TRIzol Reagent (Invitrogen) using Lysing Matrix D tubes (MP Biomedicals) and total RNA extracted according to the manufacturer’s TRIzol protocol. To remove excess lipid, samples underwent an additional centrifugation (full speed, 5 min, room temperature) prior to chloroform addition. For the RNA sequencing samples, the isopropanol phase of TRIzol extraction was transferred to Reliaprep tissue Miniprep kit (Promega, Madison, WI) columns to ensure high-quality RNA samples were used. The column was then washed, DNAse treated, and RNA eluted as per protocol. RNA concentration and quality was determined with the use of a NanoDrop spectrophotometer and then stored at −80°C. For RNA-seq, RNA was diluted to 1000 ng in nuclease-free water to a final volume of 20 μl.

RNA extraction (adipose fractions)

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MA and SVF were suspended in TRIzol Reagent (Invitrogen), and pipetted up and down to ensure cells were fully lysed. To remove excess lipid from MA fractions, samples were centrifuged (full speed, 5 min, room temperature) prior to chloroform addition. RNA extraction was then carried out as per the manufacturer’s TRIzol protocol, up to the stage of removing the isopropranol phase, which was transferred to Reliaprep columns (Promega) for on-column DNase treatment, clean-up, and elution as per manufacturer’s protocol.

RT-qPCR

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For RT-qPCR, samples were DNase-treated (RQ1 RNase-Free DNase, Promega, Madison, WI) prior to cDNA conversion High Capacity RNA-to-cDNA kit (Applied Biosystems). qPCR was performed using a GoTaq qPCR Master Mix (Promega, Madison, WI) and primers listed in Appendix Adipocyte NR1D1 dictates adipose tissue expansion during obesity using a Step One Plus (Applied Biosystems) qPCR machine. Relative quantities of gene expression were determined using the [delta][delta] Ct method and normalised with the use of a geometric mean of the housekeeping genes Hprt, Ppib, and Actb (housekeeping gene Ppia used for Figure 4—figure supplement 1B,D, Figure 6—figure supplement 1D). The fold difference of expression was calculated relative to the values of control groups.

RNA-seq

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Adipose tissue was collected from adult male mice (n=6–8/group) at ZT8 and flash-frozen. Total RNA was extracted and DNase-treated as described above. Biological replicates were taken forward individually to library preparation and sequencing. For library preparation, total RNA was submitted to the Genomic Technologies Core Facility (GTCF). Quality and integrity of the RNA samples were assessed using a 2200 TapeStation (Agilent Technologies) and then libraries generated using the TruSeq Stranded mRNA assay (Illumina, Inc) according to the manufacturer’s protocol. Briefly, total RNA (0.1–4 μg) was used as input material from which polyadenylated mRNA was purified using poly-T, oligo-attached, magnetic beads. The mRNA was then fragmented using divalent cations under elevated temperature and then reverse-transcribed into first strand cDNA using random primers. Second strand cDNA was then synthesised using DNA Polymerase I and RNase H. Following a single ‘A’ base addition, adapters were ligated to the cDNA fragments, and the products then purified and enriched by PCR to create the final cDNA library. Adapter indices were used to multiplex libraries, which were pooled prior to cluster generation using a cBot instrument. The loaded flow cell was then paired-end sequenced (76 + 76 cycles, plus indices) on an Illumina HiSeq4000 instrument. Finally, the output data was demultiplexed (allowing one mismatch) and BCL-to-Fastq conversion performed using Illumina’s bcl2fastq software, version 2.17.1.14.

RNA-seq data processing and differential gene expression analysis

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Paired-end RNA-seq reads were quality-assessed using FastQC (version 0.11.3), FastQ Screen (version 0.9.2) (Wingett and Andrews, 2018). Reads were processed with Trimmomatic (version 0.36) (Bolger et al., 2014) to remove any remaining sequencing adapters and poor quality bases. RNA-seq reads were then mapped against the reference genome (mm10) using STAR (version 2.5.3a) (Dobin et al., 2013). Counts per gene (exons) were calculated by STAR using the genome annotation from GENCODEM16. Differential expression analysis was then performed with edgeR (Robinson et al., 2010) using QLF tests based on published code (Chen et al., 2016). Changes were considered significant if they reached an FDR cut-off of <0.05. Transcripts with ‘NA’ gene ID are not included in gene numbers described. Interaction analysis was performed with stageR (Van den Berge et al., 2017) in conjunction with Limma voom (Law et al., 2014), setting alpha at 0.05.

HaloChIP-seq

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NR1D1 HaloChIP-seq was performed using a protocol based on Hunter et al., 2020, with modifications informed by Castellano-Castillo et al., 2018. Reagents were from the ChIP-IT High Sensitivity Chromatin Preparation kit (Active Motif 53046) and the HaloCHIP System (Promega G9410), unless otherwise specified. Freshly harvested gWAT was finely minced and dual-crosslinked in 10 ml 0.5 M disuccinimidyl glutarate (Thermo Fisher Scientific 20593) followed by 10 ml 1% formaldehyde-PBS. After quenching with glycine, tissue pieces were washed 2× in ice-cold ChIP wash buffer (Active Motif), then resuspended in 5 ml freshly made adipose lysis buffer (10 mM Tris HCl, 140 mM NaCl, 5 mM EDTA, 1% NP-40) supplemented with a HaloChIP-compatible protease inhibitor cocktail (Promega G6521). Fixation and wash solutions were changed by centrifuging tissue suspensions at 1250 g for 3 min (4°C), and removing the infranatant from below the floating adipose tissue pieces with glass Pasteur pipettes. Tissue was disrupted mechanically with the Qiagen TissueRuptor, then incubated on ice for 30 min to lyse cells. Following centrifugation (2780 g for 3 min, 4°C), the lipid layer and supernatant were carefully removed, and the nuclei pellet resuspended in 650 µl Mammalian Lysis Buffer (Promega), supplemented with protease inhibitor cocktail. Suspensions were once again incubated on ice (15 min) prior to low-amplitude probe sonication (Active Motif) (8× 2 min cycles of 30 s on/30 s off, 20% amplitude). Cellular debris was pelleted by spinning at 21,000 g for 3 min (4°C), and 600 µl of the chromatin suspension added to HaloLink Resin, prepared as per manufacturer’s instructions. Pull-down reactions were rotated for 3 hr at room temperature, subsequent to wash and elution steps as per manufacturer’s HaloCHIP System instructions. Eluted ChIP DNA was purified with the ChIP DNA Clean and Concentrator kit (Zymo D5205). ChIP DNA was quantified with the Qubit dsDNA HS Assay Kit (Invitrogen Q32851). For experimental samples, tissue was collected from male homozygous HaloNr1d1 mice at ZT8; for control samples, tissue was collected from male homozygous HaloNr1d1 mice at ZT20, and from male WT mice (related colony) at ZT8. For each ChIP-seq library preparation, ChIP DNA from three to seven mice was pooled to total 5 ng ChIP DNA. ChIP DNA pools (2× ZT8 HaloNr1d1 pools, 1× ZT20 HaloNr1d1 pool, 1× ZT8 WT pool) were submitted to the University of Manchester GTCF for TruSeq ChIP library preparation (Illumina) as per manufacturer’s instructions and paired-end sequencing (HiSeq 4000).

HaloChIP-seq data processing

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Adapter trimming was performed with Trimmomatic v0.38.1 (5) using Galaxy version 20.01, specifying the following parameters: ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36. Paired reads were aligned to the mm10 genome with Bowtie 2 v2.3.4.3+galaxy0 (Langmead and Salzberg, 2012) using default settings. Duplicates were removed with Picard v2.18.2.1 (Broad Institute) with –REMOVE_DUPLICATES = true. Resulting mean library size was 89.5M reads.

HaloChIP-seq data analysis

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Peaks were called using MACS2 v2.2.7.1 (Zhang et al., 2008), using: macs2 callpeak –t BAM_FILES –c BAM_FILES –g mm –f BAMPE –q 0.01. Peaks were called from the ZT8 HaloNr1d1 libraries using either ZT8 WT or ZT20 HaloNr1d1 libraries as controls. Peaks commonly called by both strategies were identified using bedtools v2.19.1 (Quinlan, 2014) intersect tool. Motif analysis was performed using HOMER v4.9.1 (Heinz et al., 2010), running: findMotifsGenome.pl BED_FILE mm10 –size 200 –mask ChIP data was visualised with Integrative Genomics Viewer v2.4.6 (Thorvaldsdóttir et al., 2013).

Integrating RNA-seq and ChIP-seq

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In order to calculate enrichment of RNA-seq-based gene clusters with respect to ChIP-seq peaks, we used our in-house custom tool (Briggs et al., 2021bYang et al., 2019) which calculates gene cluster enrichment within specified distances from the centre of peaks (see also Code Availability statement below). This Peak-set Enrichment of Gene-sets (PEGS) tool extends peaks in both directions for the given distances and extracts all genes whose TSSs overlap with the extended peaks. Given these genes, the inputted RNA-seq-based gene cluster, and the overlap of these two groups, it performs a hypergeometric test with the total number of genes in the mm10 genomes as background. Parameters were set as: pegs refGene mm10_intervals.bed BED_FILES/ GENE_LISTS/ 100 500 1000 5000 10000 50000 100000 500000 1000000 5000000 10000000.

Pathway analysis

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Pathway enrichment analysis of gene identifiers, either extracted from RNA-seq or proteomics data, was carried out using IPA (Qiagen) as described in Krämer et al., 2014, or using the R Bioconductor package ReactomePA (Yu and He, 2016). For ReactomePA, the enrichPathway tool was used with the following parameters: organism = ‘mouse’, pAdjustMethod = ‘BH’, maxGSSize = 2000, readable = FALSE. We considered pathways with a padj<0.01 to be significantly enriched.

Protein extraction and Western blotting

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Small pieces (<100 mg) of tissue were homogenised with the FastPrep Lysing Matrix D system (MP Biomedicals) in T-PER (Thermo Fisher Scientific), supplemented with protease inhibitor cocktail (Promega) at 1:50 dilution. Benzonase nuclease (EMD Millipore) was added (2 μl), the homogenate briefly vortexed, then incubated on ice for 10 min. Homogenates were then centrifuged for 8 min at 10,000 g, at 4°C, and the supernatant removed (avoiding any lipid layer). Protein concentration was quantified using the Bio-Rad Protein Assay (Bio-Rad). For Western blotting, equal quantities (75 μg for detection of NR1D1) of protein were added to 4× NuPAGE LDS sample buffer (Invitrogen), NuPAGE sample reducing agent (dithiothreitol) (Invitrogen) and water, and denatured at 70°C for 10 min. Samples were run on 4–20% Mini-PROTEAN TGX Precast Protein Gels (Bio-Rad) before wet transfer to nitrocellulose membranes. Membranes were blocked with Protein-Free Blot Blocking Buffer (Azure Biosystems), and subsequent incubation and wash steps carried out following manufacturer’s instructions. Primary and secondary antibodies used were as listed in the Key resources table, with primary antibodies being used at a 1:1000 dilution and secondary antibodies at 1:10,000. Membranes were imaged using chemiluminescence or the LI-COR Odyssey system. Uncropped blot images are provided in the Source Data Files.

Statistics

To compare two or more groups, t-tests or ANOVAs were carried out using GraphPad Prism (v.8.4.0). For all of these, the exact statistical test used and n numbers are indicated in the figure legends. All n numbers refer to individual biological replicates (i.e. individual animals). Cell line experiments were replicated three times; animal experiments were typically performed only once. Notable exceptions included two independent assessments of HFD feeding in the Nr1d1Flox2-6:AdipoqCre mice, and the two independent RNA-seq experiments comparing Nr1d1Flox2-6:AdipoqCre Cre-ve and Cre+ve mice on NC (Figure 3C, Figure 5B). Unless otherwise specified, bar height is at mean, with error bars indicating ± SEM. In these tests, significance is defined as *p<0.05 or **p<0.01 (p-values below 0.01 were not categorised separately, i.e. no more than two stars were used, as we deemed this to be a meaningful significance cut-off). Statistical analyses of proteomics, RNA-seq, and ChIP-seq data were carried out as described above in Materials and methods, using the significance cut-offs mentioned. Plots were produced using GraphPad Prism or R package ggplot2.

Code availability

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The custom Python code (Briggs et al., 2021bYang et al., 2019) used to carry out the peaks-genes enrichment analysis in this study is available as the PEGS package at https://github.com/fls-bioinformatics-core/pegs (Briggs, 2021a) and through the Python Package Index (PyPI).

Appendix 1

qPCR primer sequences

GeneForward primer (5'−3')Reverse primer (5'−3')
AcacaTAATGGGCTGCTTCTGTGACTCTCAATATCGCCATCACTCTTG
Acsl1TGGGGTGGAAATCATCAGCCCACAGCATTACACACTGTACAACGG
Acss3AATGTCGCAAAGTAACAGGCGGTGGGTCTTGTACTCACCACC
ActbGGCTGTATTCCCCTCCATCGCCAGTTGGTAACAATGCCATGT
AldoaCGTGTGAATCCCTGCATTGGCAGCCCCTGGGTAGTTGTC
ArntlGTCGAATGATTGCCGAGGAAGGGAGGCGTACTTGTGATGTTC
Cd36CCACAGTTGGTGTGTTTTATCCTCAATTATGGCAACTTTGCTT
Col1a1TCCCAGAACATCACCTATCACCTGTTGCCTTCGCCTCTGAG
Col5a3TACCTCTGGTAACCGGGGTCTCCCTTTTGGTCCCTCATCACCC
Col6a1TGCCCTGTGGATCTATTCTTCGCTGTCTCTCAGGTTGTCAATG
Col6a2TGGTCAACAGGCTAGGTGCCATTAGACAGGGAGTTGACTCGCTC
Col6a3CTGTCGCCTGCATTCATCACAACCCTCTGCACAAAGTC
CsTGACTGGCACCCAACATTTGACAGCTTGAGGCACAGCAGGTATAG
Dio2CCAGACAACTAGCATGGCGTGAAAATTGGCTGCCCCACAC
Elovl6GAGCAGAGGCGCAGAGAACATGCCGACCACCAAAGATAA
FasnCCCAGAGGCTTGTGCTGACTCGAATGTGCTTGGCTTGGT
G6pdxAGACCTGCATGAGTCAGACGTGGTTCGACAGTTGATTGGA
HprtGTTGGATACAGGCCAGACTTTGTTGGATTCAACTTGCGCTCATCTTAGGC
Loxl4TTGCTCTCAAGGACACCTGGTAGCAGCGAACTCCACTCATCA
LplAGGGCTCTGCCTGAGTTGTACCATCCTCAGTCCCAGAAAA
Me1GGGATTGCTCACTTGGTTGTGTTCATGGGCAAACACCTCT
Pfk1TGCAGCCTACAATCTGCTCCGTCAAGTGTGCGTAGTTCTGA
Plin2AAGAGGCCAAACAAAAGAGCCAGGAGACCAACCCTGAATTTTCTGGTTGGCACTGTGCAT
Pnpla2TGTGGCCTCATTCCTCCTACTCGTGGATGTTGGTGGAGCT
PpiaTATCTGCACTGCCAAGACTGAGTGCTTCTTGCTGGTCTTGCCATTCC
PpibGGAGATGGCACAGGAGGAAACCGTAGTGCTTCAGTTTGAAGTTCT
Nr1d1GTCTCTCCGTTGGCATGTCTCCAAGTTCATGGCGCTCT
Nr1d2CAGGAGGTGTGATTGCCTACAGGACGAGGACTGGAAGCTAAT
Scd1CGCTGGTGCCCTGGTACTGCCAGCCAGGTGGCGTTGAGCA
Ucp1ACTGCCACACCTCCAGTCATTCTTTGCCTCACTCAGGATTGG

Data availability

RNA-seq data generated in the course of this study has been uploaded to ArrayExpress and is available at http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8840. ChIP-seq data generated in the course of this study has been uploaded to ArrayExpress and is available at http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10573. Raw proteomics data has been uploaded to Mendeley Data at https://data.mendeley.com/datasets/wskyz3rhsg/draft?a=ef40a1ec-36a4-4509-979d-32d494b96585. Output of 'omics analyses (proteomics, edgeR, stageR, ReactomePA outputs, peak calling) are provided in the Source Data Files.

The following data sets were generated

References

Decision letter

  1. Peter Tontonoz
    Reviewing Editor; University of California, Los Angeles, United States
  2. David E James
    Senior Editor; The University of Sydney, Australia

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This manuscript clarifies the role of NR1D1/REVERBa in transcriptional regulation in adipose tissue. The authors show that the phenotype resulting from adipose NRD1 knockout is distinct to what was previously reported in a global knockout model. The findings reveal that loss of adipose REVERBa leads to healthy adipose expandability with systemic metabolic benefits.

Decision letter after peer review:

Thank you for submitting your article "Adipocyte REVERBα dictates adipose tissue expansion during obesity" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by David James as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

The manuscript by Hunter et al., identifies a dominant metabolic role for REVERBa in adipose tissue. By using an adipose-specific REVERBa knockout mouse model, the authors describe a metabolic phenotype distinct from that previously reported in a global model. Convincing data is presented supporting a role for REVERBa in regulating the circadian clock and collagen dynamics, the latter of which was not previously recognized. The authors further show that loss of adipose REVERBa leads to healthy adipose expandability with added systemic metabolic benefit. Overall, the results supporting a broader role for REVERBa to include lipid and mitochondrial metabolic pathways in the context of obesity are of interest. However, the reviewers also identified a number of areas where the current data do not fully support the conclusions drawn.

Essential revisions:

1) The authors are encouraged to provide mechanistic insights detailing altered REVERBa repressive activity upon high fat-feeding. Is activity of REVERBa physiologically regulated in response to HFD, e.g., by altered post-translational modifications and/or by altered co-repressor complex interactions? Also, is adipose REVERBa expression by itself affected during obesity?

2) Prior publications have shown that the metabolic gene regulation mediated by REVERBa tends to also involve HDAC3. They should consider further mechanistic studies of REVERBa and HDAC3 in this mouse line.

3) One major issue with this study is the lack of cellular specificity. The authors generate mice with an adipocyte-specific knockout of REVERBa, but then do RNA-seq of the entire gonadal fat pad. Since only about 50% of the cells in the tissue are adipocytes, there is thus no way to know whether the gene expression changes seen are due to direct loss of REVERBa in adipocytes or secondary effects on other cell types within adipose tissue. The authors could partly address this concern by either (a) fractionating adipose tissue into mature adipocytes and stromal vascular fraction to verify which cell types are demonstrating the expression changes or (b) isolating SVF from control and adipocyte-specific knockout animals and differentiating them in vitro to determine cell intrinsic effects of REVERBa deletion.

4) The lack of specificity also becomes an issue with the analyses done in Figure 6 in which they have overlaid their RNA-Seq data with previously published ChIP-seq data. They then use bioinformatics to predict which genes are direct targets of REVERBa. This analysis would be strengthened if they first knew which of their transcriptional changes represented expression differences in adipocytes and if they then either did their own ChIP-seq study or at least validated some candidate genes by ChIP-PCR to actually confirm REVERBa binding.

5) In Figure 3, the authors highlight that adipocyte-specific knockout of REVERBa results in increased collagen gene expression (Figure 3). However, on a high fat diet, mutant animals actually have decreased fibrosis. How do the authors explain this seeming contradiction? At a minimum, they should measure the same panel of collagen genes they propose to be regulated by REVERBa to see if they are altered in high fat fed gWAT.

6) Some of the claims made on the metabolic phenotyping are overstatements:

a) The data presented on acute cold exposure and REVERBb compensation in 3T3-L1 adipocytes are not at all sufficient to claim that the phenotypes seen here are not due to effects in brown fat or due to compensatory action by REVERBb.

b) Isolated sections with F4/80 alone are not sufficient to make a broad claim about inflammation. This would be more convincing if supported by flow cytometry data and inflammatory gene expression.

c) The legend for Figure 4 says that mutant mice have "greater insulin sensitivity". However, the curves in Figure 4F look very similar. Moreover, they calculate the area within the curve. I am not sure if they mean the area under the curve, which is more typically done, but this is confusing and needs to be clarified.

https://doi.org/10.7554/eLife.63324.sa1

Author response

Summary:

The manuscript by Hunter et al., identifies a dominant metabolic role for REVERBa in adipose tissue. By using an adipose-specific REVERBa knockout mouse model, the authors describe a metabolic phenotype distinct from that previously reported in a global model. Convincing data is presented supporting a role for REVERBa in regulating the circadian clock and collagen dynamics, the latter of which was not previously recognized. The authors further show that loss of adipose REVERBa leads to healthy adipose expandability with added systemic metabolic benefit. Overall, the results supporting a broader role for REVERBa to include lipid and mitochondrial metabolic pathways in the context of obesity are of interest. However, the reviewers also identified a number of areas where the current data do not fully support the conclusions drawn.

We thank the reviewers and editors for their positive feedback on our work. As outlined below, we have added new data, analyses, and discussion to further support the main conclusions of the paper. Specifically, we have now undertaken and present: (i) HaloNR1D1 ChIP-seq studies; (ii) assessment of gene expression in mature adipocytes isolated from gWAT of NC- and HFD-fed mice; (iii) additional characterisation of energy expenditure in Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 mice; (iv) additional characterisation of immune/inflammation-related processes in the HFD-fed mice, and (v) extended analyses of relevant published datasets.

Overall, our studies demonstrate that NR1D1 (REVERBα) is highly influential over the adipose tissue response to high-fat diet (HFD) feeding, and the development of obesity-related pathology. Underlying this is a repression of metabolic function in response to chronic over-nutrition, which is mitigated upon genetic targeting of Nr1d1. Integration of RNA-seq and ChIP-seq studies indicates that these effects are due in large part to direct repressive action of NR1D1. Metabolic genes which are shut down in obese adipose show significant enrichment at sites of NR1D1 chromatin binding, and are significantly up-regulated in Nr1d1Flox2-6:AdipoqCre adipose. A similar proximity relationship was not observed for genes showing decreased expression in the Nr1d1 targeted mice, in line with the constitutive repressive function of NR1D1 (and supporting the specificity of our analyses).

In contrast, the impact of adipose Nr1d1 deletion on gene expression and phenotype was minimal under normal chow feeding conditions, despite a wide-reaching cistrome (as demonstrated by our adipose HaloNR1D1 ChIP-seq). Thus, NR1D1 has the potential to regulate a broad programme of gene expression, linked to widespread chromatin binding, but this does not occur in the basal state. It is only revealed under a state of obesity.

Essential revisions:

1) The authors are encouraged to provide mechanistic insights detailing altered REVERBa repressive activity upon high fat-feeding. Is activity of REVERBa physiologically regulated in response to HFD, e.g., by altered post-translational modifications and/or by altered co-repressor complex interactions? Also, is adipose REVERBa expression by itself affected during obesity?

We understand the reviewers/editors’ request for additional consideration of how NR1D1 (REVERBα) delivers obesity-dependent transcriptional control over metabolic processes. To this end, we have undertaken additional studies, including the generation of a novel, antibody-independent NR1D1 cistrome in adipose tissue, transcriptional analyses in mature adipocytes under normal and obese conditions, assessment of NR1D1 expression under chow and HFD-fed conditions, and additional integration with published literature (these additions are discussed in detail below). Overall, these data suggest that altered NR1D1 expression and/or interaction with co-repressors per se is unlikely to explain the emergence of increased repressive influence by NR1D1 under conditions of obesity.

NR1D1 expression. In our studies, we find a large effect of NR1D1 loss in obese adipose, which we do not see in the basal state. This is important, as it suggests that NR1D1 repressive activity increases in obesity. However, as we and others have shown, Nr1d1 gene expression is consistently decreased in white adipose tissue following long-term HFD feeding in mice (e.g. Cunningham et al., 2016). This was similarly observed in our RNA-seq studies in the current work (Nr1d1 expression showing log fold-change of -1.56 between HFD- and NC-fed Nr1d1Flox2-6 adipose, FDR 1.19E-06). Therefore, increased Nr1d1 gene expression cannot explain the increased influence of the nuclear receptor in the obese state. To examine whether there is a similar impact of chronic HFD-feeding on NR1D1 protein expression, we undertook Western blot analyses following 16 weeks of NC or HFD-feeding (see Author response image 1). Here, we similarly find no evidence of increased protein expression in obese gWAT. We therefore have no evidence that NR1D1 is expressed more highly in WAT of obese animal.

Author response image 1
Western blot (top panel) showing NR1D1 protein expression in NC and HFD gWAT.

Corresponding Ponceau stain (bottom panel).

Altered mode of action. DNA binding and subsequent repressive activity of NR1D1 involves two key mechanisms: firstly by competing with the ROR transcriptional activators at ROR-elements (ROREs; Forman et al., 1994), and secondly by recruiting co-repressors NCOR and HDAC3 to sites of NR1D1 binding at RevDR2 elements or co-located RORE motifs (Phelan et al., 2010; Zamir et al., 1996, 1997). Our new HaloNR1D1 cistrome reveals widespread chromatin binding in gWAT under basal state conditions (i.e. NC-fed mice), and significant enrichment of both RORE and RevDR2 motifs at NR1D1 binding sites. Classic examples of these two repressive mechanisms include clock genes Arntl (Bmal1) and Nfil3. NR1D1 repression of the core clock gene Arntl is mediated through the binding of NR1D1 to two closely situated RORE motifs in the Arntl promoter (Preitner et al., 2002; Yin and Lazar, 2005). By contrast, NR1D1 repression of clock gene Nfil3 (E4bp4) likely occurs by antagonization of ROR transactivation, with NR1D1 binding a promoter-located RORE motif as a monomer (Duez et al., 2008). In our studies, we observe significant de-repression of both Arntl and Nfil3 under both NC and obese conditions (Author response image 2). This suggests that both modes of NR1D1 repressive activity are evident in the basal (NC) state, and does not support an alteration in the functioning of NR1D1 in the obese state. Whilst it would be of interest to investigate whether there are more subtle differences in the NR1D1 protein under normal and obese conditions, studying the native NR1D1 protein is very challenging due to poor sensitivity of NR1D1 antibodies. We believe that such studies are beyond the scope of this current work.

Author response image 2
Gene expression (mean +/- SEM of raw counts) of core clock genes Arntl and Nfil3 in bulk gWAT RNA-seq from NC- or HFD-fed Nr1d1Flox2-6:AdipoqCre mice and Nr1d1Flox2-6controls.

A significant difference (FDR<0.05) between genotypes is detected for each comparison. N=4-6/group.

We can, however, gain mechanistic insight by examining the genome-wide DNA binding profile of NR1D1. The initial version of our manuscript made use of an existing NR1D1 cistrome (taken from Zhang et al., 2015), with stringent peak-calling parameters applied. Stringent criteria were applied because, as we have shown ourselves, overlap between antibody-dependent NR1D1 cistromes is limited, likely reflecting poor antibody sensitivity and specificity (Hunter et al., 2020). However, we have now been able to profile the gWAT NR1D1 cistrome in an antibody-independent manner, and thus substantially add to this study. We have used HaloNr1d1 mice which express (at the endogenous Nr1d1 locus) NR1D1 protein with a HaloTag fused to its N-terminus. HaloNR1D1 demonstrates normal DNA-binding domain function (no de-repression of NR1D1 target genes is seen in HaloNr1d1 tissue), and HaloNr1d1 mice show normal fertility, behaviour, and body composition (Hunter et al., 2020). By performing HaloNR1D1 ChIP-seq in gWAT collected from HaloNr1d1 mice at ZT8 (peak NR1D1 genomic recruitment), and calling peaks against two control libraries (WT ZT8, HaloNr1d1 ZT20), we mapped a NR1D1 cistrome of over 4,000 binding sites (Figure 6A).

Therefore, under basal conditions, the NR1D1 cistrome is far broader than the limited effects of Nr1d1 deletion would suggest. To support a functional role for this broad cistrome, we looked for evidence that the cistrome correlates with NR1D1 regulation of gene expression (Figure 6B,C). We examined the relationship between NR1D1 binding sites and the genes affected by Nr1d1 deletion, under both NC and HFD conditions, and looked for associations that are more significant than would occur by chance (i.e. that might occur for any gene in the genome). We did not see any association between NR1D1 binding sites and genes down-regulated with NR1D1 loss, in keeping with NR1D1’s constitutive repressor function, and supporting the specificity of our analysis. Importantly, we did see strong associations between NR1D1 binding sites and genes up-regulated with NR1D1 loss, not only in the basal (NC-fed) state, but under HFD conditions also. This suggests that NR1D1 has the potential to regulate a broad programme of gene expression, but this does not occur in the basal state. We propose that, in the basal state, chromatin organisation and the action of other transcriptional regulators combine to make NR1D1 repressive activity redundant. In the perturbed, obese state, rewiring of the regulatory landscape serves to make NR1D1 repression important. Exploration of this hypothesis is intended for ongoing and future studies. There is supportive data, however, from studies which demonstrate chromatin alterations in obese adipose (Roh et al., 2020; Zhang et al., 2018), and studies in mouse liver which have shown remodelling of the gene regulatory environment in response to high-fat diet (Dyar et al., 2018; Quagliarini et al., 2019).

2) Prior publications have shown that the metabolic gene regulation mediated by REVERBa tends to also involve HDAC3. They should consider further mechanistic studies of REVERBa and HDAC3 in this mouse line.

One of the mechanisms of NR1D1-mediated repression is indeed recruitment of the NCOR/HDAC3 co-repressor complex. An earlier report suggested that NR1D1 represses distinct programmes of genes (most notably core clock-related versus metabolic genes) through different mechanisms (Zhang et al., 2015). Specifically, this report suggested that NR1D1 repression of clock genes involved direct binding to DNA, whereas repression of metabolic genes was accomplished by tethering to lineage-determining factors (e.g. HNF6 in liver). This theory stemmed from experiments performed in the Nr1d1DBDm mouse, a conditional transgenic model expressing a DNA-binding domain mutant form of NR1D1. We have shown that the Nr1d1DBDm mouse is a phenocopy of the Nr1d1-/- mouse (Hunter et al., 2020); ie. it is not spared the metabolic abnormalities observed in the global knockout model. Furthermore, in HaloNR1D1 ChIP-seq in liver, there is no enrichment of lineage-determining factor motifs above and beyond what would be expected at any sites of open chromatin (Hunter et al., 2020). As we now also observe in our adipose HaloNR1D1 ChIP-seq (employing dual-crosslinking to maximise capture of any tethered interactions), the RORE and RevDR2 motifs are by far the dominant transcription factor binding motifs underlying NR1D1 DNA binding. Therefore, NR1D1 repression requires DNA-binding, and as such, may be mediated through competition with RORs, not only through NCOR/HDAC3 recruitment.

Existing evidence suggests that NCOR and HDAC3 activity extends far beyond NR1D1 action (Feng et al., 2011; Ferrari et al., 2017; Li et al., 2011), with these factors having wide-ranging repressive activity, including of critical metabolic regulators such as PPARγ. In further support is our report that, in liver, we find substantial overlap between NR1D1, HDAC3, and NCOR cistromes, but also large numbers of sites bound by only one of these factors (Hunter et al., 2020). We have been interested to examine HDAC3 activity in adipose, but HDAC3 does not directly bind the genome, thus HDAC3 ChIP has proven challenging (we attempted this in response to reviewer/editor feedback, but without success). We can use removal of the H3K27ac mark as a proxy measure of HDAC3 activity however (Nguyen et al., 2020). Here, we do see removal of the H3K27ac mark at NR1D1 binding sites (adipocyte H3K27ac ChIP-seq data taken from Roh et al., 2020) under both NC and HFD-feeding conditions (Author response image 3), at both established NR1D1 targets, and at NR1D1 targets unmasked by obesity. This suggests that altered interaction between NR1D1 and HDAC3/NCOR is not the driving factor in increased NR1D1 repression in obese mice, although we cannot exclude this possibility with current data. Given that NR1D1 does not mediate gene repression exclusively through HDAC3, and that HDAC3 does not interact exclusively with NR1D1, we believe that pursuing extensive, technically difficult studies of HDAC3-NR1D1 interaction state under different chronic feeding conditions is beyond the scope of the current manuscript.

Author response image 3
Adipocyte H3K27ac ChIP-seq signal, collected in NC (top row) and HFD (middle row) conditions, aligned with adipose HaloNR1D1 ChIP-seq signal (bottom row).

3) One major issue with this study is the lack of cellular specificity. The authors generate mice with an adipocyte-specific knockout of REVERBa, but then do RNA-seq of the entire gonadal fat pad. Since only about 50% of the cells in the tissue are adipocytes, there is thus no way to know whether the gene expression changes seen are due to direct loss of REVERBa in adipocytes or secondary effects on other cell types within adipose tissue. The authors could partly address this concern by either (a) fractionating adipose tissue into mature adipocytes and stromal vascular fraction to verify which cell types are demonstrating the expression changes or (b) isolating SVF from control and adipocyte-specific knockout animals and differentiating them in vitro to determine cell intrinsic effects of REVERBa deletion.

We recognise that this is a critical question, and have undertaken additional experiments and analyses to address this. Undoubtedly, gene expression changes observed on bulk analyses reflect effects across all adipose cell types. Therefore, as suggested by the reviewers, we have now completed an adipose tissue fractionation study, in which Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 mice were maintained on HFD or NC for 16 weeks, with subsequent isolation of mature adipocytes from freshly-collected gWAT. This method (adapted from Collins et al., 2010) clearly separated a population of adiponectin (Adipoq)-expressing mature adipocytes from the remaining stromal vascular fraction. These studies (now included in Figure 6 Supplemental) provide strong support for the conclusions drawn from the whole tissue RNA-seq (regarding lack of differential gene expression under NC conditions, and unmasking of differential gene expression with chronic HFD feeding in the adipocyte knockout mice). Specifically, we assessed expression of 7 genes, all of which show up-regulation in Nr1d1Flox2-6:AdipoqCregWAT under HFD but not NC conditions in bulk RNA-seq, and which all have a TSS within 100kbp of a NR1D1 ChIP-seq peak (Figure 6SD). In line with our previous studies, we saw no between-genotype differences in any of these genes in mature adipocytes isolated from NC-fed mice. In contrast, mature adipocytes isolated from HFD-fed mice showed a pronounced up-regulation in Nr1d1Flox2-6:AdipoqCre samples (compared to control HFD-fed mice), strongly suggesting that there is an effect specific to adipocyte gene expression with adipocyte-targeted Nr1d1 deletion. These results are described on lines 308-311 of the main manuscript.

Recent data from the literature also reveal a major impact of chronic HFD-feeding on the adipocyte transcriptome. In their recent study, Evan Rosen’s group (Roh et al., 2020) used the NuTRAP reporter to profile adipocyte-specific gene expression and H3K27 acetylation in NC and HFD-fed mice. We have analysed the raw RNA-seq data from this paper and compared it with our bulk adipose RNA-seq data. In line with our original conclusions, genes commonly (adipocyte and whole tissue) up-regulated by HFD-feeding are strongly enriched for immune pathways, whilst down-regulated genes enrich for lipid and mitochondrial metabolism pathways (Author response table 1). Overall, these data reinforce our earlier conclusions that metabolic pathways are shut down in adipocytes in response to long-term HFD feeding, and that genetic targeting of Nr1d1 expression in adipocytes mitigates this effect.

Author response table 1
ReactomePA pathway analysis for genes commonly dysregulated by HFD (FDR<0.05, HFD vs NC) in our study, and an adipocyte-only RNA-seq study.
Top 5 ReactomePA pathways (by gene count) for 749 genes up-regulated by HFD in both our data (Cre-) and Roh et al. adipocyte-only RNA-seq:
IDDescriptionGeneRatioBgRatiop.adjustCount
R-MMU-168256Immune System463/4381691/87331.90E-16163
R-MMU-168249Innate Immune System108/438911/87335.32E-16108
R-MMU-109582Hemostasis70/438505/87334.92E-1370
R-MMU-1280218Adaptive Immune System65/438667/87335.89E-0665
R-MMU-6798695Neutrophil degranulation62/438514/87336.32E-0962
Top 5 ReactomePA pathways (by gene count) for 826 genes down-regulated by HFD in both our data (Cre-) and Roh et al., adipocyte-only RNA-seq:
IDDescriptionGeneRatioBgRatiop.adjustCount
R-MMU-1430728Metabolism177/4211727/87334.85E-24177
R-MMU-556833Metabolism of lipids52/421594/87330.00099252
R-MMU-1428517The citric acid (TCA) cycle and respiratory electron transport37/421143/87333.61E-1537
R-MMU-71291Metabolism of amino acids and derivatives33/421247/87339.30E-0633
R-MMU-8978868Fatty acid metabolism25/421172/87335.83E-0525

Unfortunately, these events cannot be easily addressed in primary differentiated adipocyte cultures. Firstly, the impact of chronic HFD and adipose tissue hypertrophy is a principal variable in the broadening of Nr1d1’s repressive influence. Secondly, as has been shown in the literature, NR1D1 is required for in vitro differentiation of pre-adipocytes into mature adipocytes (Kumar et al., 2010; Wang and Lazar, 2008).

4) The lack of specificity also becomes an issue with the analyses done in Figure 6 in which they have overlaid their RNA-Seq data with previously published ChIP-seq data. They then use bioinformatics to predict which genes are direct targets of REVERBa. This analysis would be strengthened if they first knew which of their transcriptional changes represented expression differences in adipocytes and if they then either did their own ChIP-seq study or at least validated some candidate genes by ChIP-PCR to actually confirm REVERBa binding.

As discussed above in response to points 1 and 3, we have now performed our own ChIP-seq study with the robust HaloNR1D1 system. The results of this study are described in the main manuscript on lines 258-300. We have used this new ChIP-seq data to examine the relationship between differentially regulated genes and sites of NR1D1 chromatin binding, and so infer the scope of NR1D1 action. We understand the reviewers/editors’ suggestion that we extend these studies with candidate genes. Therefore, we have conducted an additional study in which mature adipocytes were isolated from Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 mouse under 16-week NC and HFD-feeding. Here, a set of candidate genes were chosen which (i) showed differential regulation in bulk adipose RNA-seq under HFD- but not NC-fed conditions; and (ii) were in close proximity to a HaloNR1D1 ChIP peak. Profiling gene expression in these adipocyte fractions confirmed both the absence of de-repression under the NC-feeding state, as well as an enhanced expression of target genes under HFD-feeding conditions in the Nr1d1Flox2-6:AdipoqCre mice.

5) In Figure 3, the authors highlight that adipocyte-specific knockout of REVERBa results in increased collagen gene expression (Figure 3). However, on a high fat diet, mutant animals actually have decreased fibrosis. How do the authors explain this seeming contradiction? At a minimum, they should measure the same panel of collagen genes they propose to be regulated by REVERBa to see if they are altered in high fat fed gWAT.

We apologise for any lack of clarity in the original manuscript regarding the potential role of NR1D1 in regulating collagen and extracellular matrix dynamics. Our data strongly suggest that several collagen genes and collagen modifying enzymes are direct targets of NR1D1 repressive activity in gWAT. This is based on increased gene expression in the Nr1d1Flox2-6:AdipoqCre mice compared to Nr1d1Flox2-6 controlled (Figure 3F), as well as close proximity of de-repressed genes to NR1D1 ChIP peaks (for example, on simple nearest-gene annotation with HOMER, the genes Col1a1, Col5a3, Col6a1, Col6a2, Col18a1, Loxl4 are found adjacent to NR1D1 peaks). These altered ECM dynamics may facilitate adipocyte/adipose tissue expansion under HFD-feeding conditions (discussed more below). However, this is likely to be quite a different process than the development of pronounced tissue fibrosis in the control animals under chronic HFD-feeding conditions.

The development of obesity-related WAT fibrosis is a complex process, with remodelling of the extracellular matrix (involving both collagen synthesis and breakdown) and contribution from both resident and infiltrating immune cells. Moreover, the fibrotic state of a tissue cannot be judged simply on collagen gene expression. The virtual absence of gWAT fibrosis in the HFD-fed mice lacking Nr1d1 expression in adipocytes is most likely a reflection of an attenuated inflammatory response and reduced adipocyte dysfunction in comparison to obese control animals. Indeed, under a pathological obese state, a major contribution to collagen production, modification and fibril deposition would come from non-adipocyte cells (where NR1D1 activity is intact).

As suggested, we have measured the same panel of collagen genes in HFD gWAT (Author response image 4), and find that obesity does modulate observed patterns of gene expression. Even in this limited examination, some important differences remain (such as the reduced induction of Col6a3 in response to chronic HFD-feeding in the adipocyte knockout mice). Col6a3 has been associated with obesity, and obesity-related WAT inflammation and fibrosis in both animal models and humans (Khan et al., 2009; Pasarica et al., 2009; Sun et al., 2014).

Author response image 4
Collagen gene expression in gWAT, as measured by qPCR.

2-way ANOVA with Tukey's multiple comparison tests. *P<0.05, **P<0.01.

Moreover, inspection of the RNA-seq data shows significant reduction in expression of numerous matrix metalloproteinase genes (e.g. Mmp2, Mmp14, Mmp23, Mmp27) (Figure 5 Source Data File 1 – “Figure 5B_edgeR_HFD_Cre+vsCre-sigDN” tab) in control HFD-fed samples compared to equivalent knockout samples, which suggests that ECM remodelling activity is different between the two genotypes. The same data show altered expression of fibulin and fibrillin genes between genotypes in the HFD state, indicating that the extracellular microfibrillar network is likely impacted as well.

Therefore, it is not contradictory to find that certain collagen genes are targets of NR1D1 repressive activity, but that Nr1d1-targeted adipose is less susceptible to fibrosis. Instead, the reduced fibrosis reflects differences in numerous processes, some of which are under direct NR1D1 control, and others which are indirectly affected by Nr1d1 deletion. We have now added further comment on this subject in the manuscript text (lines 346-348).

6) Some of the claims made on the metabolic phenotyping are overstatements:

a) The data presented on acute cold exposure and REVERBb compensation in 3T3-L1 adipocytes are not at all sufficient to claim that the phenotypes seen here are not due to effects in brown fat or due to compensatory action by REVERBb.

The key phenotypic finding revealed in our study is that loss of NR1D1 in adipocytes increases adipose tissue expansion in response to HFD-feeding. Therefore, it may be hypothesised that a decrease in energy expenditure and/or reduced thermogenesis could contribute to this phenotype. However, we and others have shown that daily mean energy expenditure and body temperature are not significantly altered in either the adipocyte-specific or the global Nr1d1 knockout line (Delezie et al., 2012; Hand et al., 2015). Moreover, in our original manuscript, we demonstrate that daily body temperature profiles are not different between adipocyte-specific Nr1d1 knockout mice under normal temperature, thermoneutral (~28-29°C), and cold-challenge conditions. These studies under different ambient temperature environments would be expected to reveal any major differences in inherent (thermoneutral) or stimulated brown adipose tissue (BAT) activity. We now also include assessment of metabolic gas exchange in Nr1d1Flox2-6:AdipoqCre and Nr1d1Flox2-6 mice, which similarly shows no change in energy expenditure between the two genotypes (Author response image 5). Taken together, these results strongly suggest that altered BAT activity does not significantly contribute to the adiposity phenotype of the mice under normal chow or chronic HFD feeding. Nevertheless, we have softened the relevant manuscript text (lines 142-146).

Author response image 5
No change in metabolic parameters in Nr1d1Flox2-6:AdipoqCre mice.

Representative two day recordings of metabolic gas exchange parameters of oxygen consumption (VO2), carbon dioxide production (VCO2), energy expenditure (kcal/hr) and respiratory exchange ratio (RER) in male mice. Average day and night readings from 5 days of recordings showed no alterations in metabolic parameters in Nr1d1Flox2-6:AdipoqCre mice compared to Nr1d1Flox2-6controls. Data shown as mean +/-SEM, or as individual values and mean. **P<0.01, indicating a difference due to time-of-day on 2-way ANOVA with Sidak’s multiple comparisons.

With regards to potential compensatory activity by NR1D2 (REVERBβ) in our Nr1d1 knockout models, we acknowledge that it is difficult to completely rule out this possibility. Specifically, it is the paucity of phenotypic or transcriptional impact in the Nr1d1Flox2-6:AdipoqCre mice under normal chow feeding conditions which raises a question of NR1D2 activity. However, several lines of evidence argue against this possibility: (i) expression of Nr1d2 in WAT is not altered by the loss of Nr1d1 in either the global or adipocyte-specific knockout mouse; (ii) if NR1D2 activity were sufficient to compensate for loss of Nr1d1, and in doing so mitigate transcriptional and/or phenotypic impact, then why is this not evident in the global Nr1d1-/- mice (under normal chow conditions), or in the Nr1d1Flox2-6:AdipoqCre mice under HFD-feeding conditions?; (iii) potential compensation by Nr1d2 does not extend to transcriptional regulation of the core circadian clock genes, including Arntl (Bmal1) and Nfil3, which show significant de-repression upon Nr1d1 deletion; (iv) finally, as we have shown in our in vitro studies (Figure 3 Supplemental), combined knockdown of both Nr1d1 and Nr1d2 in differentiated 3T3-L1 adipocytes does not cause an increase in the expression of putative metabolic targets. It is difficult to undertake similar studies in vivo. Dual deletion of Nr1d1 and Nr1d2 profoundly disrupts circadian clock function (core clock gene expression is rendered arrhythmic; e.g. Cho et al., 2012; Guan et al., 2020), and therefore, it is not possible to separate the impact of NR1D1/β loss from loss of circadian timing per se under these conditions.

Taken together, we do not believe that NR1D2 compensation is likely to play a major role in our models; nevertheless, we have softened the text in the main manuscript (lines 178-183).

b) Isolated sections with F4/80 alone are not sufficient to make a broad claim about inflammation. This would be more convincing if supported by flow cytometry data and inflammatory gene expression.

We appreciate that immunohistochemistry using a general macrophage marker has limitations with regards to describing overall immune response and inflammatory state within the adipose tissue of our different mice. Our aim was to broadly demonstrate that, despite showing enhanced obesity in response to HFD-feeding, mice lacking Nr1d1 expression in adipocytes do not exhibit increased macrophage infiltration into gWAT (an extremely well-characterised pathological process associated with obesity in humans and animals). The reduced appearance of inflammatory cuffs was pronounced and consistent across our animals. The main focus of our current manuscript is the altered metabolic state and transcriptional regulation delivered by NR1D1 under normal conditions and during obesity, rather than the detailed immune consequence of enhanced lipid storage. Therefore, we have not undertaken flow cytometry-based immune cell characterisation. Nonetheless, to increased clarity and to reinforce the attenuated immune response observed in gWAT collected from HFD-fed Nr1d1Flox2-6:AdipoqCre mice, we have more thoroughly analysed and presented immune-related gene expression within our RNA-seq studies. Using IPA (Qiagen), we assessed enrichment and overall direction of change in genes that showed differential expression between the two genotypes fed HFD for 16-weeks. Extensive enrichment of immune-related pathways were revealed by this analysis, and importantly, all of those showing significant enrichment were predicted to be strongly attenuated in the gWAT samples lacking Nr1d1 expression. We have now also included these analyses within Figure 5 Source Data File 1 accompanying the main manuscript.

c) The legend for Figure 4 says that mutant mice have "greater insulin sensitivity". However, the curves in Figure 4F look very similar. Moreover, they calculate the area within the curve. I am not sure if they mean the area under the curve, which is more typically done, but this is confusing and needs to be clarified.

We apologise if the data has been presented in a confusing way. To show insulin sensitivity more clearly, we have plotted the data in Figure 4F as blood glucose change from baseline. Area under the curve is then calculated from this. We used the term ‘area within curve’ as negative effect of insulin on blood glucose levels entails that the area of interest lies above the curve, but agree that the term ‘area under the curve’ is more conventional.

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https://doi.org/10.7554/eLife.63324.sa2

Article and author information

Author details

  1. Ann Louise Hunter

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Conceptualization, Software, Formal analysis, Funding acquisition, Investigation, Writing - original draft
    Contributed equally with
    Charlotte E Pelekanou
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3874-4852
  2. Charlotte E Pelekanou

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Writing - original draft
    Contributed equally with
    Ann Louise Hunter
    Competing interests
    No competing interests declared
  3. Nichola J Barron

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Rebecca C Northeast

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3121-2802
  5. Magdalena Grudzien

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Antony D Adamson

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  7. Polly Downton

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1617-6153
  8. Thomas Cornfield

    Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, and NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  9. Peter S Cunningham

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Jean-Noel Billaud

    Digital Insights, Qiagen, Redwood City, United States
    Contribution
    Formal analysis
    Competing interests
    J-N.B. is an employee of Qiagen.
  11. Leanne Hodson

    Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, and NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared
  12. Andrew SI Loudon

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  13. Richard D Unwin

    Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
  14. Mudassar Iqbal

    Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Software, Formal analysis
    Competing interests
    No competing interests declared
  15. David W Ray

    Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, and NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  16. David A Bechtold

    Centre for Biological Timing, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    david.bechtold@manchester.ac.uk
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8676-8704

Funding

Biotechnology and Biological Sciences Research Council (BB/I018654/1)

  • David A Bechtold

Medical Research Council (MR/N021479/1)

  • Ann Louise Hunter

Medical Research Council (MR/P00279X/1)

  • David A Bechtold

Medical Research Council (MR/P011853/1)

  • David W Ray

Medical Research Council (MR/P023576/1)

  • David W Ray

Wellcome Trust (107849/Z/15/Z)

  • David W Ray

Wellcome Trust (107851/Z/15/Z)

  • David W Ray

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Rachel Scholey, I-Hsuan Lin, Ping Wang, and Peter Briggs (Bioinformatics Core Facility, UoM), Emma Smith and Thea Danby (Faculty of Biology, Medicine and Health, UoM) for statistical and technical assistance, and acknowledge support of core facilities at the University of Manchester: Genomic Technologies Core Facility, Biological Services Unit, and Histological Services Unit. We thank Argentina-Gabriela Baluta (Faculty of Biology, Medicine and Health, UoM) for useful insights into the RNA-seq data. We also acknowledge and thank the support of our funders: the BBSRC (BB/I018654/1 to DAB), the MRC (Clinical Research Training Fellowship MR/N021479/1 to ALH; MR/P00279X/1 to DAB; MR/P011853/1 and MR/P023576/1 to DWR), and the Wellcome Trust (107849/Z/15/Z, 107851/Z/15/Z).

Ethics

Animal experimentation: All experiments described here were conducted in accordance with local requirements and licenced under the UK Animals (Scientific Procedures) Act 1986, project licence number 70/8558 (licence holder Dr. David A Bechtold). Procedures were approved by the University of Manchester Animal Welfare and Ethical Review Body (AWERB).

Senior Editor

  1. David E James, The University of Sydney, Australia

Reviewing Editor

  1. Peter Tontonoz, University of California, Los Angeles, United States

Publication history

  1. Received: September 22, 2020
  2. Preprint posted: September 25, 2020 (view preprint)
  3. Accepted: July 30, 2021
  4. Accepted Manuscript published: August 5, 2021 (version 1)
  5. Version of Record published: August 12, 2021 (version 2)

Copyright

© 2021, Hunter et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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