# BMAL2 controls adipose tissue inflammation and metabolic adaptation during obesity

Morgane A Philippe, Blandine Fruchet, Lucie Cagninacci, Lucie Beaudoin, Alexis Gadault, Bastien Aznar, Nicolas Venteclef, Etienne Challet, Agnès Lehuen, Ute C Rogner, et al.

► To cite this version:

Morgane A Philippe, Blandine Fruchet, Lucie Cagninacci, Lucie Beaudoin, Alexis Gadault, et al. BMAL2 controls adipose tissue inflammation and metabolic adaptation during obesity. Metabolism, 2025, 174, pp.156396. 10.1016/j.metabol.2025.156396. hal-05372302

HAL Id: hal-05372302

https://hal.science/hal-05372302v1

Submitted on 19 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# BMAL2 controls adipose tissue inflammation and metabolic adaptation during obesity

Morgane A. Philippe<sup>a,b</sup>, Blandine Fruchet<sup>a</sup>, Lucie Cagninacci<sup>a</sup>, Lucie Beaudoin<sup>a</sup>, Alexis Gadault<sup>c</sup>, Bastien Aznar<sup>c</sup>, Nicolas Venteclef<sup>c</sup>, Etienne Challet<sup>d</sup>, Agnès Lehuen<sup>a</sup>, Ute C. Rogner<sup>a,*,1</sup>, Amine Toubal<sup>a,c,*,1</sup>

## ARTICLE INFO

### Keywords:

- Obesity

- Preadipocytes

- Adipose tissue

- Inflammation

- Circadian rhythm

## ABSTRACT

Contemporary lifestyle modifications such as changes in nutritional and sleep/wake rhythms increase the risk of metabolic and inflammatory complications linked to obesity, including type 2 diabetes (T2D) and metabolic dysfunction-associated steatohepatitis (MASH). BMAL2 (Brain and Muscle ARNT Like Protein 2) is a transcription factor belonging to the circadian clock transcriptional feedback loop which synchronizes internal biological rhythms to environment. In humans, reduced expression in white adipose tissue (WAT) and specific polymorphisms of BMAL2 are associated with obesity and T2D. In this study we report that *Bmal2* deletion in mice leads to increased body weight gain during diet-induced obesity. Loss of BMAL2 triggers the inflammatory response by increasing *Tnfa* expression and modifying adipocyte progenitor fate. This results in reduced lipid storage capacity within the WAT and increased ectopic storage in the liver. These functional and structural alterations culminate in the onset of hepatic steatosis and insulin resistance in liver and WAT. Overall, our investigations underscore the role of BMAL2 in the development and function of adipocytes, as well as in their inflammatory potential within the WAT. Our findings contribute to the understanding of the role of circadian clock genes in obesity and interconnected metabolic complications.

## 1. Introduction

The global emergence of obesity is now considered pandemic [1]. Primary drivers of this swift upsurge in obesity are the alterations of environment and lifestyle, notably manifested as disturbances in biological rhythms such as shifted work schedules, sleep patterns, and dietary habits [2]. In mammals, biological rhythms are controlled by the circadian clock, an intricate transcriptional feedback loop involving repressor (PER1/2:CRY1/2) and activator (BMAL1/2:CLOCK) elements [3,4]. The central clock, situated in the hypothalamus responds mainly to light, and controls downstream clocks in peripheral tissues that are sensitive to meal time [4,5]. Thus, rhythm disruption or mutations affecting circadian clock genes lead to modifications of behavior, but also of internal metabolic functions [3,4,6]. Notably, desynchronization of the circadian system promotes obesity [3-6]. Circadian disruptions

have for example been described in pancreatic islets [7], peripheral immune cells [8] and white adipose tissue [9,10] of individuals afflicted by obesity and/or type 2 diabetes (T2D).

Obesity associated dysfunction of adipose tissue and chronic low-grade inflammation are causal to insulin resistance leading to the development of T2D [11,12]. The enduring low-grade inflammation arises from immuno-metabolic dysfunctions in mature adipocytes and adipose progenitors, alongside with the activation of tissue resident and infiltration of pro-inflammatory immune cells [13]. Adipocytes, with their ability to secrete adipokines governing metabolism and inflammation (such as leptin or adiponectin) in addition to chemokines, play a critical role in orchestrating the recruitment and activation of inflammatory cells into the tissue [14]. Under pathological conditions, adipocytes and their progenitors also produce inflammatory cytokines such as the tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) that

<footnote><sup>a</sup> Institut Cochin, INSERM UMR-S1016, CNRS UMR-S8104, Université Paris Cité, Paris, France</footnote>

<footnote><sup>b</sup> University of Southern Denmark, Center for Adipocyte signaling - ADIPOSIGN, Campusvej 55, 5230, Odense M, Denmark</footnote>

<footnote><sup>c</sup> Institut Necker-Enfants Malades (INEM), INSERM UMR-S1151, CNRS UMR-S8253, Université Paris Cité, Paris, France</footnote>

<footnote><sup>d</sup> Institute for Cellular and Integrative Neurosciences, CNRS and University of Strasbourg, Strasbourg, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: ute.rogner@inserm.fr (U.C. Rogner), amine.toubal@inserm.fr (A. Toubal).</footnote>

<footnote n="1">¹ These authors contributed equally to this work.</footnote>
inhibit adipocyte metabolic functions [15,16] and have been directly linked to insulin resistance leading to T2D and hepatic steatosis [17,18]. Notably, TNF-α has first been identified as the major cytokine involved in adipose tissue low-grade inflammation [16,19]. Further studies highlighted the role of TNF-α in adipocyte dysfunctions [15,20,21]. Murine *Tnfa* knock-out models showed a pivotal role of TNF-α in the distribution of lipid deposition between adipose tissues and liver. *Tnfa* invalidation in mice enhances adipose tissue storage capacity, hepatic steatosis and therefore insulin sensitivity [22–26].

As such, increased inflammation is associated with circadian dysfunction in mature adipocytes and adipose progenitors from obese patients [9]. While it has been established that circadian systems exert control over the storage capacity of adipose tissue [27], the precise mechanisms governing circadian regulation of the inflammatory response in adipose tissue, particularly within adipocytes during obesity, remain poorly described. As a major clock transcription factor, BMAL1 (Brain and Muscle ARNT Like Protein 1, Basic Helix-Loop-Helix ARNT Like 1) related mechanisms are well known. BMAL1 is involved in glucose homeostasis control [28,29] and metabolic adaptation during obesity and insulin resistance via the regulation of adipogenesis [30–32], adipose tissue metabolic functions [33–36] and compensatory pancreatic beta cells expansion [37,38]. *Bmal1* is an important regulator of inflammatory responses [39–41], taking part in NFκB signaling pathway [9,42,43] and in TNF-α production control in macrophages [44–46].

*Bmal2* (Basic Helix-Loop-Helix ARNT Like 2) is a paralog of *Bmal1* [47–49]. Like BMAL1, BMAL2 forms a transcriptionally active heterodimer with CLOCK and hypoxia-inducible factors [47] and is involved in circadian transcriptional feedback loops [50]. Murine *Bmal2* is able to restore partly *Bmal1* functions in the control of obesity [51]. The role of *Bmal2* in physiology and disease may therefore be more important than has been appreciated to date.

In humans, several *BMAL2* polymorphisms have been associated with obesity and type 2 diabetes [52,53], and *BMAL2* expression is downregulated in white adipose tissue of T2D patients [10]. *BMAL2* expression is regulated during weight-loss interventions in humans and has been shown to inhibit adipogenesis [54]. Our previous studies have delineated the role of *Bmal2* in inflammation during type 1 diabetes through the control of interleukin 21 expression in mice [55–59]. Hence, it is conceivable that BMAL2 might directly influence the regulation of inflammation associated with obesity. In recent mice studies, we have reported that a lack of *Bmal2* alters the circadian regulation of food consumption and energy expenditure [60]. Yet, the specific mechanisms by which impairment of BMAL2 expression or activity could lead to obesity and type 2 diabetes (T2D) are still to be thoroughly examined.

In the present study, using a C57BL/6 *Bmal2*<sup>-/-</sup> mouse model and the corresponding wildtype mice, we investigated metabolic and inflammatory responses in the liver and white adipose tissue during either a normal diet (ND) or high fat diet (HFD) feeding. We report that *Bmal2* deficiency impairs immune-metabolic functions in both mature adipocytes and their progenitors, leading to reduced lipid storage in epididymal white adipose tissue (EpiWAT). Concurrently, excess lipids accumulate ectopically in the liver, driving insulin resistance in both organs. Together, our findings underscore a key role of *Bmal2* in adipose tissue biology and highlight the broader importance of circadian regulation in obesity.

# 2. Materials and methods

## 2.1. Mice

The *Bmal2* mutant strain (designated by *Bmal2*<sup>-/-</sup> or KO mice) carries the allele *Arntl2*<sup>tm1a(KOMP)Wtsi</sup>, has been imported from the UCDavis KOMP Repository (Davis, CA), and has been crossed to the C57BL/6 J strain to obtain wildtype control (WT) and *Bmal2*<sup>-/-</sup> strains [58]. Mice were fed a standard chow diet (ND, 12,6 kJ% fat, SAFE 150

SP-25). At 10 weeks of age, male mice were either fed with the standard chow diet or high fat diet (HFD, 60 kJ% fat, SSNIFF 813 ref.# E15742–347) for 12 weeks. All mice were bred and maintained in the mouse facility of the Institut Pasteur, Paris under controlled temperature (21–23 °C) and light (12-h light/12-h dark) conditions: lights were turned on at Zeitgeber time (ZT) 0 and turned off at ZT12. Mice were sacrificed in the ad-libitum fed state (except where explicitly stated for metabolic tests) and all tissues were collected in the morning at ZT4 or alternatively at 6 time points over a complete 24-h light-dark cycle (ZT0, ZT4, ZT8, ZT12, ZT16, ZT20) for measure of circadian rhythm in gene expression.

## 2.2. Metabolic analysis

Oral glucose tolerance tests (OGTT) were performed after 10–12 weeks of diet as follows: overnight fasted mice were injected with glucose by gavage (2 g/kg body weight of glucose, 20 % glucose solution; G8270, Sigma). Blood glucose concentration was determined at 0, 15, 30, 60, 90 and 120 min following the glucose load with a glucometer (Accu-Chek® Performa, Roche). Blood samples from the tip of the tail vein were collected at 0, 15 and 30 min following glucose load using Microvette CB 300 K2E (16.444, Sarstedt). Serum samples were collected through centrifugation to measure insulin level using Ultra-sensitive mouse insulin ELISA kit (90,080, Crystal Chem).

Intraperitoneal (i.p.) insulin tolerance tests (ITT) were performed after 10–12 weeks of diet as follows: overnight fasted mice were injected with 0.00075 U of insulin/g of body weight (0.075 U/ml in PBS 1 % BSA solution; Humalog 100 UI/ml, Lispro, Lilly). Blood glucose concentration was determined at 0, 15, 30, 60, 90 and 120 min with a glucometer (Accu-Chek® Performa, Roche) on blood from the tip of the tail vein. The area under the curve (AUC, 0–120 min) was calculated for each group of mice.

For insulin-signaling assays, mice were fasted overnight and then i.p. injected with 1.00 U/kg insulin. Mice were euthanized 15 min after insulin injection, and liver and EpiWAT were collected for Western Blot analysis.

Lean tissue and fat mass of mice and liver biopsies were measured by nuclear magnetic resonance (NMR) using MiniSpec LF90 and miniSpec mq60, Bruker, according to the manufacturer's instructions.

## 2.3. Histology

EpiWAT and liver samples were fixed in 4 % formaldehyde solution overnight and embedded in paraffin. EpiWAT and liver slides were stained with haematoxylin/eosin or Sirius red for the evaluation of the tissue morphology, following standardized protocols. Adipocyte size was measured by the diameters of the adipocytes in light-microscopy images (×20) of EpiWAT sections (100–400 adipocytes per section, 1 section per mouse, 7–15 mice per group), and analyzed using ImageJ software.

## 2.4. Western blot

Samples were lysed in RIPA buffer (Sigma) supplemented with protease (SIGMAFAST protease inhibitor tablets, Sigma) and phosphatase (PhosSTOP EASYpack, Roche) inhibitors, and were diluted with RIPA buffer, and Laemmli buffer (4× Laemmli sample buffer, Bio-Rad) and beta-mercaptoethanol to a concentration of 20 µg of protein and heated at 95 °C for 5 min. Proteins were separated by SDS–PAGE electrophoresis (4–12 % Bis-Tris-Gel, NuPage Invitrogen) and transferred to 0.2 µm nitrocellulose membranes (Bio-Rad). Blocking reagent (TBS with 0.5 % Tween 20 and 3 % BSA) were incubated for 1 h, and primary antibodies were incubated overnight at 4 °C in the blocking solution. The antibodies (listed in Supp. Table 1) and their concentrations were as follows: anti-phospho-AKT (P-AKT(S473) XP(R) Rabbit mAb, Cell Signaling 1:2000) and anti-beta-actin (Beta Actin Rabbit mAb, Cell Signaling
1:1000). After several washes in PBS with 0.5 % Tween 20, horseradish peroxidase (HRP)-labeled secondary antibodies (Anti-rabbit IgG HRP-linked Antibody, Cell Signaling 1:2000) were incubated for 1 h at room temperature in the blocking solution. Membranes were incubated with ECL western-blotting substrate (ECL Prime Western Blotting Detection Reagents, Amersham, Cytiva) and imaged by the myECL Imager (ThermoFisher) or FUSION FX (Vilber). Blots were semi-quantified using ImageJ software.

## 2.5. Immune cell isolation and flow cytometry

The EpiWAT fat pad was isolated from mice and digested with 5 ml of collagenase H (2 mg/ml, Roche) and BSA (2.5 %, Sigma) solution at 37 °C for 30 min with shaking (150 rpm). After digestion, adipocytes were removed by filtering through a 100 µm cell strainer, and cell suspension was centrifuged for 5 min at 300g to pellet the stroma-vascular fraction (SVF). Red blood cells in the SVF pellet were lysed by brief incubation in 1 ml RBC lysis buffer (Sigma). SVF was washed with PBS containing 5 % fetal calf serum (FCS) and 0.1 % sodium azide in PBS. Macrophages were directly analyzed from SVF, whereas other immune cells were further enriched on Percoll density gradients of 40 and 80 % (GE Healthcare). The interface between the layers was collected and suspended in PBS containing 5 % FCS and 0.1 % sodium azide, to retrieve immune cells.

Liver was perfused with RPMI 1640 medium (Gibco) supplemented with 5 % FCS to remove circulating blood cells and then harvested. Liver was passed through 70 µm cell strainer. Cells suspension was collected after 2 min centrifugation at 48g to avoid parenchymal cells and was centrifuge again at 440g. Red blood cells were lysed by brief incubation in 1 ml RBC lysis buffer (Sigma), centrifuge again at 440 g and the pellet was then resuspended in 40 % Percoll layered onto 80 % Percoll and centrifuged for 15 min at 780g at room temperature. The immune cell fraction was collected at the interface and suspended in PBS containing 5 % FCS and 0.1 % sodium azide, to retrieve immune cells.

Cell suspensions prepared from various tissues were labeled at 4 °C in PBS containing 5 % FCS and 0.1 % sodium azide. Surface labeling was performed at 4 °C during 30 min with the following antibodies: CD45 APC-Cy7, Ly6C BV510, CD206 FITC, F4/80 PE-Cy7, CD11c APC, TCRb BV711 BD, CD11b BV785, TCRgd Percp-Cy5.5, CD4 BV510, CD8 BV605 (listed in Supp Table 1).

Intra-cytoplasmic labeling for cytokine analysis were performed after PMA (25 ng/ml) and ionomycin (1 µg/ml) stimulation for 4 h at 37 °C in presence of Brefeldin A (10 µg/ml), all reagents from Sigma-Aldrich. After surface labeling, cells were fixed and permeabilized with Cytofix/Cytoperm kit (BD Biosciences), washed, and incubated at 4 °C with antibodies: TNF-α BV421, IFNγ AF700, IL17-A Percp-Cy5.5 (listed in Supp Table 1). Data acquisition was performed using a BD Biosciences LSR Fortessa cytometer then the results were analyzed using FlowJo analysis software (Tree Star).

## 2.6. Adipose progenitor isolation and flow cytometry

Adipose progenitor preparation and isolation were adapted from Hepler et al. 2018 [61]. The EpiWAT fat pad was isolated from mice and digested with 5 ml of collagenase H (2 mg/ml, Roche) and BSA (2.5 %, Sigma) solution at 37 °C for 45–60 min with shaking (180 rpm). The solution of digested tissue was passed through a 100 µm cell strainer, diluted to 30 ml with RPMI 1640 medium (Gibco) supplemented with 5 % FCS, and centrifuged at 300g for 5 min. The supernatant was aspirated and red blood cells in the SVF pellet were lysed by brief incubation in 1 ml RBC lysis buffer (Sigma). Next, the mixture was diluted to 10 ml with RPMI 1640 medium (Gibco) supplemented with 5 % FCS, passed through a 40 µm cell strainer, and then centrifuged at 500 g for 5 min. The supernatant was aspirated, and cells were resuspended in PBS containing 5 % FCS, 0.1 % sodium azide and 1 % BSA. Cell suspensions were stained at 4 °C in PBS containing 5 % FCS, 0.1 % sodium azide and

1 % BSA. Surface staining was performed at 4 °C during 20 min with the following antibodies (listed in Supp Table 1): CD31 Percp-Cy5.5 1/400, PDGFRβ PE 1/50, CD9 APC 1/400, CD45 APC-Cy7 1/200, Ly6C BV510 1/100.

Data acquisition was performed using a BD Biosciences LSR Fortessa cytometer, and cell sorting was performed using BD Biosciences FACSAria III, then the results were analyzed using FlowJo analysis software (Tree Star). After excluding immune (CD45⁺) and endothelial (CD31⁺) cells, we gated on PDGFRβ⁺ cells to define the adipose progenitor cells pool (see Supp. Fig. 5B).

## 2.7. Adipose progenitor cell isolation, siRNA transfection and 3D spheroid differentiation

Adipose progenitor cells were isolated from the EpiWAT of C57BL/6 mice. Briefly, the tissue was digested in collagenase H, and the resulting SVF was collected by centrifugation. The SVF was then seeded in DMEM (Thermo Fisher #10566016) supplemented with 10 % fetal bovine serum (FBS) and penicillin/streptomycin (PS) and cultured for 7 days to allow cell expansion.

Following this expansion phase, cells were harvested and counted. For each well, 50,000 cells were resuspended in antibiotic-free DMEM and transfected with 50 nM *siBmal2* (5'-AAGCTGGCCTCTGAATGTTGT-3', Eurofins) or non-targeting control siRNA (*siCtrl*, 5'- AGGUAGUGAAUCGCCUUG-3', Eurofins) using Lipofectamine™ 3000 (Thermo Fisher #L3000001) at a 1:3 reagent-to-siRNA ratio, according to the manufacturer's protocol. After 2 h of incubation with the transfection complexes, the medium was replaced with fresh DMEM (10 % FBS, 1 % PS). Subsequently, the transfected cells were transferred to 96-well low-attachment plates (In-Sphero #CS-PB15) at 50,000 cells per well. Cells in control condition, without treatment, were directly seeded without transfection. Cells were then cultured for 48 h in a seeding medium composed of DMEM, 10 % FBS, 1 % PS, and 0.1 % fungizone. During this period, they aggregated into spheroidal clusters by virtue of the non-adherent conditions, without the use of any gel matrix. To initiate adipocyte differentiation in spheroids, the medium was replaced with an adipogenic induction medium (DMEM, 10 % FBS, 1 % PS, 0.1 % fungizone, 5 µg/ml insulin, 50 µM isobutylmethylxan-thine, 1 µM dexamethasone, 1 µM rosiglitazone) and maintained for 72 h. Following this induction period, the cells were switched to a maintenance medium (DMEM, 10 % FBS, 1 % PS, 0.1 % fungizone, 5 µg/ml insulin) for an additional 5 days.

In total, the cells underwent 8 days of differentiation, resulting in the formation of 3D spheroids, which were then harvested for further analyses. Knockdown efficiency was verified by quantitative RT-PCR (qRT-PCR) for target gene expression 48 h post-transfection, confirming a robust reduction in *Bmal2* mRNA levels. Non-targeting control siRNA-treated cells were used to account for non-specific effects of the transfection reagent.

## 2.8. RT-qPCR

RNA was extracted from tissues or cultured cells using the RNeasy RNA Mini Kit (RNeasy mini Kit, Qiagen). RNA from cell sorted adipose progenitors was extracted with RNeasy RNA Plus micro Kit (RNeasy Plus micro Kit, Qiagen). Complementary DNAs were synthesized usingSUPERScript III reverse-transcriptase kit (Invitrogen). Quantitative PCR analysis was performed with SYBR Green Master Mix (Roche) using a LightCycler 480 (Roche). 18S was used for normalization to quantify relative mRNA expression levels. Relative changes in mRNA expression were calculated using the comparative cycle method (2⁻ΔΔCt). Primers are listed in Supp Table 2.

## 2.9. Bulk RNA-seq: preparation, sequencing and analysis

Adipose progenitors defined as CD45⁻CD31⁻PDGFRβ⁺ were flow
sorted and frozen following RNeasy RNA Plus micro Kit (Qiagen) in-
structions to extract RNA. Quality of the RNA was measured using a
Bioanalyzer 2100 (Agilent) as per the manufacturer's instructions using
pico chip kit, RIN > 6,5 were kept for sequencing.

Non-strand-oriented libraries were prepared following the NEBNext Single Cell/Low Input RNA Library Prep Kit (New England BioLabs), paired end (2 × 75 bp) sequencing was performed on an Illumina Nextseq 500 platform.

Fastq files were then aligned using STAR algorithm (version 2.7.6a), on the Ensembl release 101 GRCm38 reference. Reads were then count using RSEM (v1.3.1) and the statistical analyses on the read counts were performed with R (version 3.6.3) and the DESeq2 package (DESeq2_1.26.0) to determine the proportion of differentially expressed genes between two conditions. We used the standard DESeq2 normalization method (DESeq2's median of ratios with the DESeq function), with a pre-filter of reads and genes (reads uniquely mapped on the genome, or up to 10 different loci with a count adjustment, and genes with at least 10 reads in at least 3 different samples).

Following the package recommendations, we used the Wald test with the contrast function and the Benjamini-Hochberg FDR control procedure to identify the differentially expressed genes. R scripts and parameters are available on the platform https://github.com/GENOM-IC-Cochin/RNA-Seq_analysis. Differentially expressed genes were defined with log2FC > 0.6 and p-adj < 0.05.

2.10. GSEA analysis

Gene set enrichment analysis (GSEA, Broad Institute) was done on
hallmark genes. Database used: Mh.all.v2023.1 using mouse symbols;
1000 permutations, no collapse, permutation type was phenotype; max
size of data set 500, min size 15.

2.11. Statistics

Statistical analyses were performed with GraphPad Prism 8.3.0 (GraphPad Software, Inc., La Jolla, CA) or R software 4.1.0. by two-tailed Mann-Whitney test and Spearman correlation test. To analyze and compare circadian gene expression rhythms in mice tissues, we used Circacompare package using R software 4.1.0. to assess rhythmicity for each data set and statistical differences between cosinor parameters (Phase, MESOR, Amplitude) of *Bmal2*⁻⁻ and control mice. All data were represented as mean ± S.E.M, *P* < 0.05 was defined as significant.

## 2.12. Study approval

All animal studies have been approved by the relevant institutional
review boards (Comité d'Ethique en Expérimentation Animale CETEA,
IDF, Paris and C2EA 89, Paris) and the French ministry of higher education
and research (Ministère de l'enseignement supérieur et de la recherche)
under the reference number APAFIS#31813-202,105,271,511,696 v1.

3. Results

3.1. *Bmal2* deletion enhances body weight gain and reshapes lipid storage distribution during diet-induced obesity

We subjected ten-week-old male C57BL/6 Bmal2<sup>-/-</sup> (KO) mice and age-matched wild type controls (WT) to either ND or HFD.

In order to first explore whether obesity impacts *Bmal2* expression in WT controls, we measured its expression over 24-h-light-dark cycle in liver and EpiWAT from both ND and HFD WT fed mice. In HFD condition, *Bmal2* displayed a significantly altered expression pattern across the 24-h cycle compared to ND. Specifically, the midline estimation statistics of the rhythm (MESOR) increased significantly in the liver while in EpiWAT, *Bmal2* gained a marked 24-h expression rhythm. This was accompanied by significant increase in both the MESOR and

amplitude of its rhythmic profile (Fig. 1A).

Then, we evaluated the phenotype of *Bmal2*⁻⁻ mice to investigate the role of *Bmal2* in obesity and evaluate its influence on the disease. *Bmal2*⁻⁻ mice exhibited greater weight gain than WT mice when fed HFD (Fig. 1B and Supp. Fig. 1A). NMR analyses further indicated an increase in total body fat mass in *Bmal2*⁻⁻ mice compared to WT mice (Fig. 1C). Liver weight measurements under HFD condition were significantly higher in *Bmal2*⁻⁻, whereas EpiWAT weight was lower relative to WT (Fig. 1D). In contrast, subcutaneous white adipose tissue (ScWAT) weight was significantly increased in *Bmal2*⁻⁻ mice compared to WT mice under HFD (Supp. Fig. 1B). Notably, no differences in organ weights were detected between *Bmal2*⁻⁻ and WT mice fed with ND (Fig. 1D and Supp. Fig. 1B).

To probe the structural alterations induced by *Bmal2* deficiency, we next examined the liver and EpiWAT. Histological and NMR analyses of the liver revealed marked ectopic lipid accumulation in *Bmal2*⁻⁻ mice under HFD conditions (Fig. 1E-H). In contrast, there was no significant increase in muscle fat mass (Supp. Fig. 2E). Adipocyte diameters in EpiWAT were measured using histological sections (Fig. 1G-H). Under HFD, *Bmal2*⁻⁻ mice showed a significantly higher percentage of large adipocytes (>100 µm diameter) and a lower percentage of intermediate-sized adipocytes (50-100 µm diameter) than WT mice, indicating EpiWAT adipocyte hypertrophy in *Bmal2*⁻⁻ mice (Fig. 1H). Both adipocyte diameter and fat pad mass were elevated in ScWAT of *Bmal2*⁻⁻ mice on HFD, suggesting both hypertrophic and hyperplastic growth in this depot (Supp. Fig. 1B-D) and a redistribution of fat toward the subcutaneous depot in the absence of *Bmal2*.

A correlation analysis between liver weight and EpiWAT weight revealed a negative association in *Bmal2*⁻⁻ mice fed HFD, but not in WT mice (Fig. 1I). This finding suggests a direct role of *Bmal2* in regulating lipid storage capacity within adipose tissues and in preventing ectopic lipid deposition in the liver under obesogenic conditions. Importantly, no such correlations were observed under ND (Supp. Fig. 1F-G).

3.2. *Bmal2* deletion promotes insulin resistance in the context of obesity

Next, we conducted oral glucose tolerance tests (OGTT) and insulin
tolerance tests (ITT) in *Bmal2*⁻⁻ and WT mice fed ND and HFD. Under
HFD conditions, *Bmal2*⁻⁻ mice exhibited significantly impaired glucose
tolerance was observed in compared to WT, whereas no differences were
detected under ND (Fig. 2A). The observed glucose intolerance in
*Bmal2*⁻⁻ mice was accompanied by elevated insulin secretion (Fig. 2B)
indicating decreased insulin sensitivity in *Bmal2*⁻⁻ mice under HFD. In
agreement, obese *Bmal2*⁻⁻ mice showed higher blood glucose levels
during ITT than WT mice (Fig. 2C), and a concomitant increase in the
homeostasis model assessment of insulin resistance (HOMA-IR) index
(Fig. 2D).

To further assess insulin signaling, we measured Ser473-Akt phos-
phorylation (pAKT-Ser473), a key read-out for intracellular insulin
signaling. Under HFD, *Bmal2*⁻⁻ mice exhibited significantly reduced
pAKT-Ser473 levels in both liver and EpiWAT compared to WT (Fig. 2E).
Consistent with these findings, obese *Bmal2*⁻⁻ mice displayed elevated
circulating triglyceride levels (Fig. 2F), potentially reflecting impaired
insulin-mediated suppression of lipolysis.

To determine whether *Bmal2* deficiency further affected EpiWAT function, we measured the expression of genes involved in adipogenesis (*Pparg*), adipose tissue homeostasis function (*Leptin*, *Adipoq*), and lipolysis (*Hsl*, *Atgl*). While no statistically differences were observed (Supp. Fig. 2A), *Pparg* and *Adipoq* were slightly decreased and *Leptin* slightly increased in the EpiWAT of *Bmal2*⁻⁻ mice on HFD. Similarly, the expression of key hepatic fatty acid synthesis genes (*Acly*, *Acaca*, *Scd1*, *Elovl6*) was not markedly altered in liver by *Bmal2* deficiency (Supp. Fig. 2B). Taken together these findings suggest that despite limited changes in metabolic gene expression, *Bmal2* deletion in obesogenic conditions leads to increased insulin resistance and altered lipid handling.
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /></figure>

D

<figure><img src="image_6.png" /></figure>

F

<figure><img src="image_7.png" /></figure>

H

<figure><img src="image_8.png" /></figure>

M**

<figure><img src="image_9.png" /></figure>

C

<figure><img src="image_10.png" /></figure>

E

<figure><img src="image_11.png" /></figure>

G

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

(caption on next page)
**Fig. 1.** *Bmal2* invalidation induces increased body weight gain and modified lipid storage distribution during diet-induced obesity. A) Expression of *Bmal2* across a 24 h cycle in liver and EpiWAT of control mice (WT) fed by ND or 12 weeks of HFD (n = 3–8 per group). Circaccompare package was used to assess rhythmicity for each data set and statistical differences between cosinor parameters (M: Mesor; A: Amplitude; \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001). Data are represented as mean ± S.E.M with a line representing the estimated cosinor model. Solid line = significant rhythmicity, dashed line = no rhythmicity. B) Body weight (KO-ND n = 3–5, WT-ND n = 6, KO-HFD n = 6, WT-HFD n = 7), C) NMR-evaluated body fat and lean mass (KO-HFD n = 6, WT-HFD n = 6), D) Weight of liver and EpiWAT (KO-ND n = 11, WT-ND n = 12, KO-HFD n = 22, WT-HFD n = 17) relative to body weight (%), E) NMR-evaluated liver fat mass (KO-HFD n = 6, WT-HFD n = 6), E) Representative haematoxylin/eosin staining (scale bars, 100 µm) and pictures of liver and F) NMR-evaluated liver fat and lean mass (KO-HFD n = 6, WT-HFD n = 6), G) Representative haematoxylin & eosin staining of EpiWAT (scale bars, 200 µm) and representative pictures of EpiWAT, H) Quantification of adipocyte size in EpiWAT (100–400 adipocytes per section, 1 section per mouse, 7–15 mice per group), and I) Spearman correlation between weight of EpiWAT and liver relative to body weight (%) from *Bmal2*⁻/⁻ mice (KO) and controls (WT) fed with ND and/or HFD during 12 weeks, as detailed on the figure. All data are represented as mean ± S.E.M. All statistical analyses were performed by two-tailed Mann-Whitney test. \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001.

### 3.3. *Bmal2* deletion exacerbates inflammation in the liver and visceral adipose tissue during diet-induced obesity

We next investigated the expression of key genes associated with metabolic inflammation in *Bmal2*⁻/⁻ and WT mice. In liver of *Bmal2*⁻/⁻ ND-fed mice, *Tnfa* expression was increased compared to WT mice (Fig. 3A). The difference became even more pronounced with HFD

feeding reaching 6-fold increase in obese *Bmal2*⁻/⁻ mice compared to obese WT mice.

Similarly, *Il6* expression was significantly elevated in the liver of *Bmal2*⁻/⁻ mice under HFD, while *Il17* was only slightly increased compared to WT. Under ND, no significant differences were observed in *Il6* and *Il17* expression (Fig. 3A). In EpiWAT, *Tnfa* and *Il6* expression was substantially increased in *Bmal2*⁻/⁻ mice fed HFD compared to WT

<figure><img src="image_17.png" /></figure>

**Fig. 2.** *Bmal2* invalidation enhances insulin resistance during diet-induced obesity. A) OGTT (KO-ND n = 7, WT-ND n = 5, KO-HFD n = 12, WT-HFD n = 9). Pooled data from two independent experiments are represented, B) Plasma insulin during OGTT (n = 9–12 per group). Pooled data from two independent experiments are represented, C) ITT (KO-ND n = 6, WT-ND n = 6, KO-HFD n = 6, WT-HFD n = 3), D) HOMA-IR index (WT-HFD n = 4, KO-HFD n = 5), E) Western blot (left) and quantification (right) of AKT phosphorylation (p-AKT-S473) and β-actin in liver and EpiWAT after intraperitoneal insulin administration (1 UI/kg) (quantification n = 6–8 mice per group), and F) Serum triglycerides (mg/dL) (WT-HFD n = 4, KO-HFD n = 6) in *Bmal2*⁻/⁻ mice (KO) and controls (WT) fed with ND and/or HFD during 12 weeks. All data are represented as mean ± S.E.M. All statistical analyses were performed by two-tailed Mann-Whitney test. \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001.
A Liver

<figure><img src="image_18.png" /></figure>

B EpiWAT

<figure><img src="image_19.png" /></figure>

C Liver *Tnfa*

<figure><img src="image_20.png" /></figure>

E EpiWAT *Tnfa*

<figure><img src="image_21.png" /></figure>

D Liver *Il6*

<figure><img src="image_22.png" /></figure>

F EpiWAT *Il6*

<figure><img src="image_23.png" /></figure>

• WT - ND ■ KO - ND

• WT - HFD ■ KO - HFD

(caption on next page)
**Fig. 3.** Increased inflammatory gene expression in liver and EpiWAT of obese *Bmal2*<sup>-/-</sup> mice. A-B) RT-qPCR analysis of inflammatory (*Tnfa*, *Il6*, *Il17*) gene expression in (A) liver (n = 5-16 per group) and (B) EpiWAT (n = 6-18 per group) from *Bmal2*<sup>-/-</sup> mice (KO) and controls (WT) fed with ND or HFD during 12 weeks). Data are represented as mean ± S.E.M. Statistical analyses were performed by two-tailed Mann-Whitney test. \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001. B) RT-qPCR analysis of inflammatory gene expression (*Tnfa*, *Il6*, *Il17*) and C-F) of *Tnfa*, *Il6* across a 24 h cycle in liver (C,D) EpiWAT (E,F) from *Bmal2*<sup>-/-</sup> mice (KO) and controls (WT) fed with ND or HFD during 12 weeks (n = 3-8 per group). Circacompare package was used to assess rhythmicity for each data set and statistical differences between cosinor parameters (M: Mesor; A: Amplitude; Φ: Phase, \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001). Data are represented as mean ± S.E.M with lines representing the estimated cosinor model. Solid line = significant rhythmicity, dashed line = no rhythmicity.

(Fig. 3B). These results indicate that *Bmal2* deletion amplifies the expression of specific inflammatory markers during diet-induced obesity in both liver and EpiWAT.

To further assess potential circadian regulation of these inflammatory genes, we monitored their expression over 24-h-light-dark cycle in both ND and HFD fed mice (Fig. 3C-F). In the liver, only minor changes were observed in the circadian expression profiles of *Tnfa* and *Il6* between *Bmal2*<sup>-/-</sup> and WT mice under both diets (Fig. 3C, D). In contrast, *Tnfa* circadian expression in EpiWAT was altered in *Bmal2*<sup>-/-</sup> mice when compared to WT even under ND, evidenced by significant modifications in MESOR and phase cosinor parameters (Fig. 3E). Under HFD, the *Tnfa* circadian expression was further disrupted in *Bmal2*<sup>-/-</sup> mice compared to WT. Meanwhile, the *Il6* circadian expression pattern in EpiWAT was not significantly disrupted by *Bmal2* deletion (Fig. 3F).

Overall, these findings demonstrate that the absence of *Bmal2* increases inflammatory responses in both liver and EpiWAT, particularly under HFD-induced metabolic stress, likely contributing to the altered metabolic phenotype observed in obese *Bmal2*<sup>-/-</sup> mice.

### 3.4. *Bmal2* deletion promotes CD8⁺ T cell infiltration in liver and visceral adipose tissue under high fat diet

Given the exacerbated inflammatory state observed in *Bmal2*<sup>-/-</sup> mice fed an HFD, we next assessed whether alterations in immune cell composition underlie this phenotype. We quantified the frequencies of macrophages and T lymphocytes in both liver and EpiWAT of *Bmal2*<sup>-/-</sup> and WT mice fed either a ND or HFD. Under ND, *Bmal2*<sup>-/-</sup> mice exhibited a significant increase in double-negative CD4<sup>-</sup>CD8<sup>-</sup> (DN) T cells in the liver (Supp. Fig. 3A) and in CD4<sup>+</sup> T cells in EpiWAT (Supp. Fig. 3B), relative to WT. However, no significant difference in macrophage frequency was observed between *Bmal2*<sup>-/-</sup> and WT in either tissue (Supp. Fig. 3C-G). In HFD condition, total macrophage (F4/80<sup>+</sup>) frequency did not differ between in *Bmal2*<sup>-/-</sup> WT mice in either liver or EpiWAT (Fig. 4A-B).

Nevertheless, the proportion of F4/80<sup>+</sup> Ly6C<sup>+</sup> cells was increased in liver whereas it was decreased in the EpiWAT of *Bmal2*<sup>-/-</sup> HFD-fed mice when compared to WT mice (Fig. 4A-B). T lymphocytes profiling in the liver showed a reduction in CD4<sup>+</sup> and DN T cells concomitant with an increase in CD8<sup>+</sup> T cells in *Bmal2*<sup>-/-</sup> mice fed HFD (Fig. 4C). Meanwhile in EpiWAT, both CD4<sup>+</sup> and CD8<sup>+</sup> T cell frequencies were elevated in *Bmal2*<sup>-/-</sup> mice relative to WT (Fig. 4D).

To elucidate whether these shifts in T cell populations correlate with changes in their inflammatory potential, we measured cytokine production by conventional T lymphocyte subsets in both liver and EpiWAT. In the liver of HFD-fed *Bmal2*<sup>-/-</sup> mice, TNFα- and IFNγ-producing CD4<sup>+</sup>, CD8<sup>+</sup> and DN T cells were significantly more abundant compared to WT, whereas IL-17-producing cells did not differ (Fig. 4E, Supp. Fig. 4A). However, the frequencies of TNFα, IFNγ or IL-17-producing T cells (either CD4<sup>+</sup> T, CD8<sup>+</sup> or DN) were decreased or unchanged in EpiWAT *Bmal2*<sup>-/-</sup> HFD-fed mice relative to WT (Fig. 4F, Supp. Fig. 4B). These results support the notion that the characteristic inflammatory environment in the liver of *Bmal2*<sup>-/-</sup> mice in obesogenic condition is predominantly driven by inflammatory T lymphocytes, whereas the adipose tissue inflammation may largely originate from the non-immune fraction itself with immune cell infiltration playing a comparatively smaller role in EpiWAT.

### 3.5. Loss of *Bmal2* enhances loss of adipose progenitor identity during diet-induced obesity

To clarify whether *Bmal2* deficiency impacts adipose tissue at the level of mature adipocytes, we isolated EpiWAT adipocytes from *Bmal2*<sup>-/-</sup> and WT mice under ND and HFD conditions. Transcript analysis of inflammatory genes (*Tnfa*, *Il6*) (Fig. 5A-B) revealed that even under ND, *Tnfa* expression was significantly upregulated in *Bmal2*<sup>-/-</sup> adipocytes compared to WT (Fig. 5B). Under HFD, both *Tnfa* and *Il6* levels were markedly higher in *Bmal2*<sup>-/-</sup> adipocytes relative to WT (Fig. 5B). Notably, the transcript level of *Nfkb1*, which encodes a central transcription factor for *Tnfa*, was also elevated in *Bmal2*<sup>-/-</sup> adipocytes under HFD (Supp. Fig. 5A).

Analyses of metabolic markers expression indicated that *Bmal2*<sup>-/-</sup> adipocytes exhibit functional disruptions even in basal conditions: *Hsl* (hormone-sensitive lipase) expression was slightly but significantly increased (Fig. 5D), and both *Leptin* and *Pparg* expression tended to be higher in *Bmal2*<sup>-/-</sup> adipocytes than in WT (Fig. 5C-D), suggesting an enhanced lipogenic and lipolytic potential linked to chronic inflammation.

Next, we investigated whether a similar metabolic and inflammatory dysfunction could be measured in adipose progenitors prior to differentiation. As previously described [61], we isolated CD4<sup>-</sup>CD31<sup>-</sup>PDGFRβ<sup>+</sup> adipose progenitor cells from the EpiWAT (see Fig. 5A). Previous studies have shown that HFD generally reduces the frequency of adipose progenitors [61,62] but increases total cell count per fat pad in C57BL/6 mice [62]. In agreement with these findings, we observed a decreased proportion of PDGFRβ<sup>+</sup> progenitors per gram of tissue in HFD versus ND-fed WT mice, as well as in *Bmal2*<sup>-/-</sup> ND fed mice compared to WT-ND fed mice (Fig. 5E; Supp. Fig. 5B). The decline in PDGFRβ<sup>+</sup> progenitor frequency was even more pronounced during HFD in *Bmal2*<sup>-/-</sup> mice compared to WT (Fig. 5E), suggesting that *Bmal2* deficiency mirrors the detrimental effect of HFD on progenitor cells and that combining *Bmal2* deletion with HFD exacerbates these impairments. Consistent with this, *Bmal2*<sup>-/-</sup> HFD-fed mice showed a significant reduction in PDGFRβ<sup>+</sup> Ki67<sup>+</sup> cell frequency, indicative of decreased proliferation capacity (Fig. 5F).

We further examined whether these progenitor cells exhibit early inflammatory alterations. PDGFRβ<sup>+</sup> progenitors from HFD fed *Bmal2*<sup>-/-</sup> mice expressed higher *Tnfa* levels than those from WT (Fig. 5G), indicating an inflammatory shift at a pre-adipocyte stage. No significant difference in *Tnfa* expression was observed in ND condition (Supp. Fig. 5C).

To gain deeper insights into progenitor subpopulations, we assessed CD9/Ly6C markers, classifying cells into adipogenic pre-adipocytes (APC: CD9<sup>-</sup>Ly6C<sup>-</sup>), fibrogenic and inflammatory progenitors (FIP: CD9<sup>+</sup>Ly6C<sup>+</sup>), and mesothelial-like cells (MLC: CD9<sup>+</sup>Ly6C<sup>-</sup>) as previously described [61]. Under ND, WT mice displayed distinct progenitor subsets, whereas HFD-fed WT mice had more homogeneous progenitor population (Fig. 5H). Remarkably, *Bmal2*<sup>-/-</sup> HFD-fed mice showed even greater alteration in these progenitor subpopulations compared to WT controls, suggesting that *Bmal2* deficiency compounds the HFD-driven shift in progenitor identity.

Finally, to test the ability of *Bmal2*<sup>-/-</sup> progenitors to sustain normal adipose tissue function, we employed an in vitro spheroid model using primary adipose tissue progenitors combined with *siBmal2* (Fig. 5I). Silencing *Bmal2* markedly reduced spheroid formation compared to both the untreated control and *siControl*, alongside decreased
<figure><img src="image_24.png" /><figcaption>**Fig. 4.** Impaired immune cell phenotypes in liver and EpiWAT of *obese Bmal2*−/− mice. A) Frequency of F4/80⁺ cells among CD45⁺ cells and Ly6C⁺ among F4/80⁺ cells in liver (%, n = 5–6 per group), B) Frequency of F4/80⁺ cells among CD45⁺ cells, CD11c⁻/CD206⁺ and CD11c⁺/CD206⁻ among F4/80⁺ cells, and Ly6C⁺ among F4/80⁺ cells, in EpiWAT (%, n = 5–6 per group); Frequency of CD4⁺, CD8⁺ and CD4⁻/CD8⁻ T cells among CD45⁺ cells in (C) liver and (D) EpiWAT (n = 5 per group), and frequency of TNFα, IFNγ and IL-17-producing CD4⁺, CD8⁺ and CD4⁻/CD8⁻ T cells in (E) liver and (F) EpiWAT (n = 5 per group) from *Bmal2*−/− mice (KO) and controls (WT) fed with HFD during 12 weeks. All data are represented as mean ± S.E.M. All statistical analyses were performed by two-tailed Mann-Whitney test. \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001.</figcaption></figure>

expression of key adipocytes functional markers (*Adipoq*, *Leptin*, *Pparg*) and a pronounced elevation of *Tnfa* and *Il6* expression.

Overall, *Bmal2* deletion enhances both inflammation and metabolic dysfunction in EpiWAT adipocytes and their progenitors, likely impairing the tissue capacity to adapt to diet-induced obesity. These findings strongly suggest that BMAL2 is a critical regulator of adipocytes

inflammatory response and of adipose progenitor identity maintenance.

### 3.6. Deletion of *Bmal2* activates apoptosis and inflammation transcriptional programs in adipose progenitors during diet-induced obesity

To delineate the transcriptomic changes in adipose progenitors due
A

① Digestion of EpiWAT

Mature adipocytes

<figure><img src="image_25.png" /></figure>

② Staining of SVF

<figure><img src="image_26.png" /></figure>

• WT - ND

KO - ND

• WT - HFD

KO - HFD

③ Cell sorting of adipose progenitors

B

Tnfa

Il6

C

Leptin

Adipoq

D

Pparg

Hsl

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

F

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

E

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

G

<figure><img src="image_37.png" /></figure>

H

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

APC

<figure><img src="image_40.png" /></figure>

FIP

<figure><img src="image_41.png" /></figure>

MLC

I

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

(caption on next page)
**Fig. 5.** *Bmal2* invalidation enhances inflammation and impairs proliferation in PDGFRβ⁺ adipose progenitors during diet-induced obesity. A) Experimental design used to isolate mature adipocytes and adipose progenitors from murine EpiWAT. Adipocytes and PDGFRβ⁺ adipose progenitors were isolated from EpiWAT of *Bmal2*⁻/⁻ mice (KO) and controls (WT) fed by ND or 12 weeks HFD. RT-qPCR analysis of (B) *Tnfa* and *Il6*, (C) *Leptin* and *Adipoq*, and (D) *Pparg* and *Hsl* expression in mature adipocytes (n = 9–11 per group). E) Representative dot plots and quantification of PDGFRβ⁺ cells per gram of tissue (upper panel) and PDGFRβ⁺ frequency among CD31⁻CD45⁻ cells (lower panel) (n = 4–6 per group). F) Representative dot plots and quantification of PDGFRβ⁺Ki67⁺ cells among PDGFRβ⁺ cells (n = 5–6 per group). G) RT-qPCR analysis of *Tnfa* and *Il6* expression in PDGFRβ⁺ progenitors (n = 5–10 per group). H) Representative dot plots and quantifications of PDGFRβ⁺ adipose progenitors: Ly6C⁻CD9⁻cells (APC), Ly6C⁺CD9⁺ cells (FIP) and Ly6C⁻CD9⁺cells (MLC) (n = 4–6 per group). I) In vitro spheroid model of adipose tissue based on primary EpiWAT progenitors without treatment (Ctrl), treated with siRNA control (siCtrl) or siRNA targeting *Bmal2* (*siBmal2*): representative pictures (left) and RT-qPCR analysis (right) of *Adipoq*, *Leptin*, *Pparg*, *Tnfa* and *Il6* expression. Data are represented as mean ± S.E.M. Statistical analyses were performed by two-tailed Mann-Whitney test. *P* < 0.05, **P* < 0.01, ***P* < 0.001.

to *Bmal2* deficiency, we performed bulk RNA sequencing of PDGFRβ⁺ progenitors isolated from EpiWAT. Principal component analysis (PCA) of these samples revealed a clear two-dimensional separation between *Bmal2*⁻/⁻ and WT groups in both ND and HFD conditions (Fig. 6A). Despite this consistent segregation, the genes driving the divergence differed notably between ND and HFD cohorts (Fig. 6B), indicating distinct molecular signatures shaped by diet.

In ND condition, 1040 genes were differentially expressed (log2FC > 0.6 and p-adj < 0.05) between *Bmal2*⁻/⁻ and WT adipose progenitors (587 upregulated, 454 downregulated) (Fig. 6B). In HFD condition, a total of 1348 genes were differentially expressed (814 upregulated, 534 downregulated) (Fig. 6B). Notably, *Tnfa* ranked among the most upregulated genes in PDGFRβ⁺ progenitors from *Bmal2*⁻/⁻ HFD-fed mice. The larger number of differentially expressed genes in HFD-fed mice underscores the interaction between *Bmal2*⁻/⁻ deletion and obesogenic stress.

A comparative analysis of genes differentially expressed in *Bmal2*⁻/⁻ progenitors under ND and HFD revealed limited overlap. Specifically, 35 genes were commonly upregulated irrespective of diet, 551 were uniquely upregulated under ND, and 779 genes were uniquely upregulated in HFD (Fig. 6C). Likewise, 15 genes were commonly downregulated irrespective of diet, 439 genes were uniquely downregulated under ND and 519 genes were uniquely downregulated under HFD (Fig. 6C). This pattern indicates that *Bmal2*⁻/⁻ adipose progenitors develop distinct transcriptional responses depending on dietary conditions.

Comparison of the top 1000 differentially expressed genes showed markedly different transcriptional signature between *Bmal2*⁻/⁻ versus WT progenitors in HFD condition (Fig. 6D). Gene ontology analysis indicated an up-regulation of inflammatory mediators, apoptotic pathways, lipolysis, and lipid metabolism in *Bmal2*⁻/⁻ progenitors (Fig. 6E, F). Conversely, genes involved in PI3K-Akt signaling axis, cellular development and maintenance were downregulated in *Bmal2*⁻/⁻ mice under HFD (Fig. 6E, F). Those pathways were not differentially regulated in ND condition (Supp. Fig. 6A-C). Gene set enrichment analysis (GSEA, FDR 25 %) validated 19 enriched upregulated pathways in the *Bmal2*⁻/⁻ mice, including inflammatory responses (e.g. TNFα signaling via NFκB, IL6-JAK-STAT3 signaling) and apoptosis, as well as one downregulated metabolic pathway (oxidative phosphorylation) under HFD (Supp. Fig. 6D).

Since increased inflammation can drive fibrosis and that adipose progenitors have been found to shift to a profibrogenic phenotype during obesity [62], we investigated fibrosis markers in *Bmal2*⁻/⁻ EpiWAT progenitors. Neither RNA-seq data (Fig. 6G) nor histological analyses of EpiWAT (Fig. 6H) showed significant differences in expression of profibrogenic genes by PDGFRβ⁺ progenitors or tissue fibrosis between *Bmal2*⁻/⁻ and WT mice, under either ND or HFD.

Overall, these findings demonstrate that *Bmal2* deletion strongly reshapes the transcriptome of adipose progenitors and, in conjunction with an obesogenic diet, amplifies deleterious effects on progenitor function. Specifically, loss of *Bmal2* enhanced inflammatory and apoptotic transcriptional programs while compromising metabolic and maintenance programs in adipose progenitors, which might limit their adaptive capacity during diet-induced obesity.

## 4. Discussion

Chronic disruption of circadian rhythms is increasingly recognized as a key contributor to obesity and its associated comorbidities, highlighting the need for a deeper understanding of how these biological clocks regulate energy homeostasis, inflammation and tissue remodeling. In the present study, we investigated the role of the circadian clock gene *Bmal2* in modulating the immune-metabolic adaptation of adipose tissue during obesity. Our findings reveal that *Bmal2* regulates inflammatory processes in EpiWAT through adipocytes, adipose progenitors, and immune cells, exerting systemic effects that also compromise liver metabolism and insulin sensitivity.

In the obesity-prone C57BL/6 mouse model, we observed an increased *Bmal2* expression in the EpiWAT of WT mice during HFD feeding. By contrast, *Bmal2*⁻/⁻ mice displayed exacerbated weight gain under the same obesogenic condition, accompanied by extensive immune-metabolic dysfunction in EpiWAT, particularly marked by elevated *Tnfa* expression, as well as hepatic steatosis and insulin resistance. These data suggest that *Bmal2* acts as a crucial regulatory node, linking adipose tissue inflammation to liver metabolism. In addition to the previously reported changes in circadian regulation of feeding and energy balance in *Bmal2*⁻/⁻ mice [60], the significant disruption of the *Tnfa* circadian expression pattern in the EpiWAT of *Bmal2*⁻/⁻ lean and obese mice suggests that disturbances in the circadian system at both the adipose tissue and central nervous system levels may cause or at least contribute to their altered phenotype.

Interestingly, the phenotype of *Bmal2*⁻/⁻ mice under ND was comparatively mild, notably in terms of tissue-specific inflammatory gene expression. This findings echoes our earlier work on the role of *Bmal2* in transcriptional regulation of *Il21* in type 1 diabetes context, in which *Il21* induction by BMAL2 was highly dependent on inflammatory and developmental cues in younger animals [59]. The modest phenotype we observed in ND-fed *Bmal2*⁻/⁻ mice implies that BMAL2-dependent transcription may require specific chromatin or metabolic configurations to activate inflammatory pathways [63]. In line with this concept, we detected no prominent difference in *Il21* expression under HFD in the current study, suggesting that the influence of BMAL2 on *Il21* expression may be age or context dependent.

Our data further indicate that BMAL2 helps to maintain EpiWAT homeostasis by preventing excessive inflammation and safeguarding the progenitor niche. The pronounced upregulation of *Tnfa* in *Bmal2*⁻/⁻ adipocytes and progenitors points to the potential for direct or indirect transcriptional repression of *Tnfa* by BMAL2. Notably, previous in vitro studies have shown that BMAL2 expression is induced by TNF-α [64,65], supporting the hypothesis of a reciprocal relationship in which BMAL2 and TNF-α form a regulatory loop controlling adipose tissue inflammation. Consequently, the insulin resistance observed in *Bmal2*⁻/⁻ mice under HFD conditions may stem predominately from enhanced inflammatory signaling, mediated by TNF-α, IL-6 and related cytokines, which disrupt the insulin signaling cascade.

The limited alterations in immune cells phenotype within the EpiWAT of *Bmal2*⁻/⁻ mice shifted our attention toward adipocytes and adipose progenitors. We found that *Bmal2*⁻/⁻ EpiWAT displayed reduced lipid storage, concurrent with hypertrophic adipocytes and impaired adipogenesis. Mature adipocytes and PDGFRβ⁺ progenitors
A

ND

<figure><img src="image_45.png" /></figure>

HFD

<figure><img src="image_46.png" /></figure>

B

ND

<figure><img src="image_47.png" /></figure>

HFD

<figure><img src="image_48.png" /></figure>

C

UP REGULATED

<figure><img src="image_49.png" /></figure>

DOWN REGULATED

<figure><img src="image_50.png" /></figure>

F

Upregulated genes in *Bmal2* KO - HFD (log2fc > 0.6, adj p-value < 0.05)

<table><thead><tr><th>Pathway</th><th>Genes</th><th>Statistics (FDR)</th></tr></thead><tbody><tr><td>Chemokine signaling pathway</td><td>29</td><td>6,44E-07</td></tr><tr><td>Cytokine-cytokine receptor interaction</td><td>30</td><td>2,79E-04</td></tr><tr><td>Antigen processing and presentation</td><td>14</td><td>9,21E-04</td></tr><tr><td>NF-kappa B signaling pathway</td><td>15</td><td>1,19E-03</td></tr><tr><td>Apoptosis</td><td>14</td><td>2,39E-02</td></tr></tbody></table>

Downregulated genes in *Bmal2* KO - HFD (log2fc < -0.6, adj p-value < 0.05)

<table><thead><tr><th>Pathway</th><th>Genes</th><th>Statistics (FDR)</th></tr></thead><tbody><tr><td>TGF-beta signaling pathway</td><td>13</td><td>7,1E-04</td></tr><tr><td>Pathways in cancer</td><td>28</td><td>2,0E-02</td></tr><tr><td>PI3K-Akt signaling pathway</td><td>21</td><td>2,4E-02</td></tr><tr><td>AGE-RAGE signaling pathway in diabetic complications</td><td>10</td><td>3,4E-02</td></tr><tr><td>Relaxin signaling pathway</td><td>11</td><td>4,4E-02</td></tr></tbody></table>

D

Top 1000
genes

<figure><img src="image_51.png" /></figure>

E

Selected
genes

<figure><img src="image_52.png" /></figure>

G

Fibrosis associated genes

ND

<figure><img src="image_53.png" /></figure>

HFD

<figure><img src="image_54.png" /></figure>

H

WT - HFD

<figure><img src="image_55.png" /></figure>

KO - HFD

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

(caption on next page)
**Fig. 6.** *Bmal2* invalidation induces changes of the transcriptome of PDGFRβ⁺ adipose progenitors during diet-induced obesity toward inflammatory and apoptotic gene signaling pathways. A) PCA plots showing distance between PDGFRβ⁺ adipose progenitors between WT and *Bmal2*⁻/⁻ mice (KO) fed by ND (upper panel) or 12 weeks HFD (lower panel). B) Volcano plots highlighting significant differentially expressed genes in PDGFRβ⁺ adipose progenitors of WT and *Bmal2*⁻/⁻ mice (KO) fed by ND (left) or 12 weeks HFD (right). C) Venn-diagram showing common up and down regulated genes PDGFRβ⁺ adipose progenitors of WT and *Bmal2*⁻/⁻ mice (KO) fed by ND or 12 weeks HFD. D) Heatmaps showing the top 1000 differentially expressed genes and (E) selection of 31 differentially expressed genes in PDGFRβ⁺ adipose progenitors of WT and *Bmal2*⁻/⁻ mice (KO) fed 12 weeks with HFD. F) Selection of DAVID pathways analysis of significantly upregulated and downregulated genes (log2fc > 0.6, adj *p*-value < 0.05), G) selected fibrosis associated genes expressed in PDGFRβ⁺ adipose progenitors of WT and *Bmal2*⁻/⁻ mice (KO) fed 12 weeks with HFD. Differentially expressed genes were defined with log2FC > 0.6 and p-adj < 0.05. n = 3 per group. H) Histology analysis of the EpiWAT of HFD fed mice using Sirius red staining and quantification (n = 8–11). Data are represented as mean ± S.E.M. All statistical analyses were performed by two-tailed Mann-Whitney test. *P* < 0.05, **P** < 0.01, ***P** < 0.001. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

from *Bmal2*⁻/⁻ mice showed elevated expression of proinflammatory cytokines, especially TNF-α, aligning with previous reports linking circadian disruption in adipocytes and progenitors to increased inflammation during obesity [9]. Other studies have reported an expansion of fibrogenic and inflammatory progenitors (FIP) and a decreased adipogenic progenitors (APC) in diet-induced obesity [61,62]. In contrast, *Bmal2* deficiency reshaped adipose progenitor populations differently, diminishing FIP cells while modifying APC population toward a more uniform state with reduced proliferative and metabolic capacities. This remodeling potentially denotes a loss of ability to diversify into distinct functional lineages, a phenomenon reminiscent of disrupted circadian regulation of adipose progenitor proliferation [27]. In light of the observed reduction in progenitor proliferation and adipogenic gene expression, we speculate that *Bmal2* deletion may induce early over-proliferation, as previously shown in response to acute HFD feeding [61,66,67], followed by progenitor exhaustion or epigenetic reprogramming, ultimately impairing the tissue adaptive capacity. We hypothesize that BMAL2 might contribute to control chromatin accessibility or activity of enhancers and promoters at adipogenesis and immune related genes. BMAL2 deficiency could therefore “lock in” a pro-inflammatory or progenitor-exhaustion phenotype via chromatin changes. Future lineage tracing and chromatin accessibility studies would be necessary to test this hypothesis.

An emerging body of work underscores circadian regulation of adipogenesis via multiple pathways [30,32,54,68–70]. For instance, *Bmal2* expression reportedly increase during early adipocyte differentiation in vitro, yet can also repress adipogenesis by targeting transcription factors such as BMAL1, KLF15 and C/EBPβ [54]. Consistent with these findings, we detected increased *Bmal2* expression in EpiWAT of HFD-fed WT mice, pointing to its involvement in adipogenesis under obesogenic conditions. Further investigations, including in vitro differentiation of preadipocyte cell line (3 T3-L1, 3 T3-F442A) with *Bmal2* knockdown or overexpression could help delineate the mechanisms through which BMAL2 integrates circadian control with adipocyte differentiation. Moreover, our spheroid-based in vitro differentiation model revealed a strong decrease in adipogenic markers upon *Bmal2* knockdown, contrasting with increased *Pparg* expression in mature KO adipocytes in vivo. This discrepancy likely reflects stage- and context-dependent regulation: *Bmal2* is likely crucial for initiating adipogenesis, while mature adipocytes may compensate for inflammatory stress by upregulating *Pparg*.

Obesity induces a state of chronic stress in adipocytes, shifting them toward a pro-inflammatory profile and impairing their capacity for lipogenesis [71]. In line with this, *Bmal2*⁻/⁻ adipocytes exhibited heightened inflammation and diminished lipid storage. Understanding how BMAL2 modulates immune and metabolic gene networks in discrete adipocyte subpopulations will provide a more granular view of the role of circadian regulation in adipose tissue. We also found that while total macrophage numbers were not significantly increased in *Bmal2*⁻/⁻ mice adipose tissue, the inflammatory cytokine milieu was elevated. This suggests that adipocyte and progenitor-derived inflammation can drive tissue dysfunction independently of massive macrophage infiltration. Whether BMAL2 regulates macrophage polarization in a cell-autonomous manner remains to be explored.

Notably BMAL1 also exerts significant control over adipogenesis and lipid handling. However, whether BMAL1 primarily activates or represses adipocyte differentiation remains debated. Although BMAL1 regulates inflammatory mechanisms in immune cells and adipose tissue [9,41,44,72] and that *Bmal2*⁻/⁻ and *Bmal1*⁻/⁻ mice share certain phenotypic features, they also exhibit distinct alterations in weight gain, lipid storage distribution, and insulin secretion capacity [30,31,34]. The present findings emphasize the necessity of discussing the individual contributions of these clock genes in orchestrating adipose tissue plasticity via progenitors and mature adipocytes, especially in the context of obesity.

Finally, while *BMAL2* has been linked to metabolic disease in human genetic association studies [52,53], our mouse findings strongly suggest that *Bmal2* deletion disrupts adipose-liver crosstalk, leading to ectopic lipid deposition in the liver and the onset of insulin resistance [73,74]. Although *Bmal2*⁻/⁻ mice exhibit exacerbated liver steatosis, the precise contribution of adipose tissue dysfunction versus direct hepatic effects remains unclear. The absence of direct measurements of hepatic β-oxidation and VLDL secretion limits our ability to fully attribute the liver phenotype to adipose overflow. Future studies using tissue specific *Bmal2* deletion in adipocytes or hepatocytes and metabolic flux analysis are required to disentangle how BMAL2 modulates systemic metabolism by coordinating the interplay between adipose and hepatic tissues.

Additionally, our study focused primarily on visceral fat depots while we also observed increased subcutaneous adipose mass in *Bmal2*⁻/⁻ obese mice. Future comparisons of depot-specific progenitor behavior may uncover depot-selective roles for BMAL2. Despite increased inflammation in visceral fat, fibrosis was not enhanced in KO mice, possibly due to impaired progenitor commitment to fibrogenic lineages. Detailed progenitor subset analyses may contribute to distinguishing impact of *Bmal2* deletion on both inflammation and fibrosis.

The strengths of our study lie in its integrative approach and translational implications. By employing a combination of histological, flow cytometric, and transcriptomic analysis in a well-established model of diet-induced obesity, we robustly demonstrated the multifaceted role of BMAL2 in regulating adipose tissue homeostasis, inflammation, and systemic metabolic adaptation. Importantly, our study identifies BMAL2 as a novel regulator of adipocyte progenitor function and inflammatory programming, positioning it alongside BMAL1 as a circadian factor of clinical interest in metabolic diseases. Given existing associations between *BMAL2* expression and variants, and type 2 diabetes or obesity in humans, our findings have potential translational relevance and open new perspectives for targeting circadian regulators in obesity-related complications.

However, several limitations warrant consideration. First, we used a global *Bmal2* knockout model, precluding the ability to dissect tissue- or cell-type-specific contributions to the observed phenotype. Second, although we observed marked liver steatosis in *Bmal2*⁻/⁻ mice, we did not directly assess hepatic β-oxidation capacity or lipid export pathways (VLDL secretion), leaving mechanisms of hepatic lipid accumulation partially unresolved. As our study focused on visceral adipose depot because of its established role in immune and metabolic complications, we also did not perform molecular or extensive cellular analysis on ScWAT. The impact of *Bmal2* deletion might vary across depots and it
would be worth investigating. Modified lipid storage distribution at the whole-body level and across tissues has been evaluated by NMR on live animals and tissue biopsies, as well as tissues weight. However, we did not perform a quantitative lipid extraction from tissues that could contribute to deeply characterize the modified lipid storage distribution across tissues. Finally, we did not perform lineage tracing or epigenetic profiling to delineate the fate and molecular reprogramming of adipose progenitors, which limits mechanistic insight into the differentiation blockade observed in *Bmal2*−/− cells. Addressing these limitations will provide deeper mechanistic resolution.

In conclusion, our study demonstrates that BMAL2 is an essential regulator of immune-metabolic adaptation in adipose tissue, particularly in obesogenic conditions. By modulating inflammatory gene expression and progenitor function, *Bmal2* deletion exacerbates adipose tissue dysfunction, reduces EpiWAT lipid storage capacity, and promotes insulin resistance in both adipose tissue and liver. These findings underscore the intricate relationship between circadian regulation, adipose tissue plasticity, and metabolic homeostasis, thereby opening new avenue for therapeutic strategies targeting circadian pathways to combat obesity and its complications.

### CRediT authorship contribution statement

**Morgane A. Philippe:** Writing – review & editing, Writing – original draft, Visualization, Software, Investigation, Formal analysis, Conceptualization. **Blandine Fruchet:** Investigation, Data curation. **Lucie Cagninacci:** Investigation, Data curation. **Lucie Beaudoin:** Investigation, Data curation. **Alexis Gadault:** Data curation. **Bastien Aznar:** Data curation. **Nicolas Venteclef:** Supervision, Resources. **Etienne Challet:** Resources, Investigation. **Agnès Lehuen:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Methodology, Funding acquisition. **Ute C. Rogner:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Investigation, Formal analysis. **Amine Toubal:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Fundings

This work was supported by current fundings from Inserm, CNRS, and Université Paris Cité, individual grants from Association Française pour l'Etude du Foie (AFEF 2023), Société Francophone du Diabète (SFD 2023) to A.T., European Foundation for the Study of Diabetes to A.L. and A.T., Laboratoire d'Excellence consortium Inflamex (ANR-11-IDEX-0005-02), Recherche Hospitalo-Universitaire (RHU) QUID-NASH program (ANR-17-RHUS-009), Fondation pour la Recherche Médicale (FRM EQU201903007779), Agence Nationale de la Recherche (ANR-19-CE14-0041-01 HEPADIMAIT) to A.L., DHU authors to U.R., M.P. and B. F. were supported by the French Ministry of Research.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Amine Toubal reports financial support was provided by French Association of Liver Research. Amine Toubal reports financial support was provided by Francophone Society for Diabetes. Amine Toubal reports financial support was provided by European Foundation for the Study of Diabetes. Agnes Lehuen reports financial support was provided by Laboratory of excellence Inflamex. Agnes Lehuen reports financial support was provided by Recherche Hospitalo-Universitaire (RHU) QUID-NASH program. Agnes Lehuen reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial

interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

We thank Cybio, HistIM and Genom'IC facilities of the Institut Cochin. We thank all the staff of Pasteur Animal Facility, and in particular Myriam Mattei for taking care of the animals during the COVID-19 lockdown. We thank Christian Boitard for support, and Sandrine Luce, Latif Rachdi and Masaya Oshima for their scientific and technical advice.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.metabol.2025.156396.

### Data and material availability

Bulk RNA-seq data have been deposited to GEO under accession code GSE289800.

### References

[1] Caballero B. The global epidemic of obesity: an overview. Epidemiol Rev 2007;29:1–5.

[2] Bae S-A, Fang MZ, Rustgi V, Zarbl H, Androulakis IP. At the interface of lifestyle, behavior, and circadian rhythms: metabolic implications. Front Nutr 2019;6.

[3] Albrecht U. The circadian clock, metabolism and obesity. Obes Rev 2017;18:25–33.

[4] Kumar V. Biological timekeeping: clocks, rhythms and behaviour. Springer; 2017.

[5] Panda S. Circadian physiology of metabolism. Science 2016;354:1008–15.

[6] Froy O. Metabolism and circadian rhythms—implications for obesity. Endocr Rev 2010;31:1–24.

[7] Petrenko V, et al. In pancreatic islets from type 2 diabetes patients, the dampened circadian oscillators lead to reduced insulin and glucagon exocytosis. Proc Natl Acad Sci U S A 2020;117:2484–95.

[8] Yu R, et al. Correlation between inflammatory markers and impaired circadian clock gene expression in type 2 diabetes mellitus. Diabetes Res Clin Pract 2019; 156:107831.

[9] Maury E, Navez B, Brichard SM. Circadian clock dysfunction in human omental fat links obesity to metabolic inflammation. Nat Commun 2021;12:2388.

[10] Stenvers DJ, et al. Diurnal rhythms in the white adipose tissue transcriptome are disturbed in obese individuals with type 2 diabetes compared with lean control individuals. Diabetologia 2019;62:704–16.

[11] Gregor MF, Hotamisligil GS. Inflammatory mechanisms in obesity. Annu Rev Immunol 2011;29:415–45.

[12] Roden M, Shulman GI. The integrative biology of type 2 diabetes. Nature 2019; 576:51–60.

[13] Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat Rev Mol Cell Biol 2008;9: 367–77.

[14] Tilg H, Moschen AR. Adipocytokines: mediators linking adipose tissue, inflammation and immunity. Nat Rev Immunol 2006;6:772–83.

[15] Cawthorn WP, Sethi JK. TNF-α and adipocyte biology. FEBS Lett 2008;582:117–31.

[16] Sethi JK, Hotamisligil GS. Metabolic messengers: tumour necrosis factor. Nat Metab 2021;3:1302–12.

[17] Dandona P, Aljada A, Bandyopadhyay A. Inflammation: the link between insulin resistance, obesity and diabetes. Trends Immunol 2004;25:4–7.

[18] Liu C, et al. Adiponectin, TNF-α and inflammatory cytokines and risk of type 2 diabetes: A systematic review and meta-analysis. Cytokine 2016;86:100–9.

[19] Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-α: direct role in obesity-linked insulin resistance. Science 1993;259:87–91.

[20] Ruan H, Hacohen N, Golub TR, Parijs LV, Lodish HF. Tumor necrosis factor-α suppresses adipocyte-specific genes and activates expression of preadipocyte genes in 3T3-L1 adipocytes: nuclear factor-κB activation by TNF-α is obligatory. Diabetes 2002;51:1319–36.

[21] Xu H, Sethi JK, Hotamisligil GS. Transmembrane tumor necrosis factor (TNF)-α inhibits adipocyte differentiation by selectively activating TNF receptor 1*. J Biol Chem 1999;274:26287–95.

[22] Bouter B, Geary N, Langhans W, Asarian L. Diet-genotype interactions in the early development of obesity and insulin resistance in mice with a genetic deficiency in tumor necrosis factor-α. Metabolism 2010;59:1065–73.

[23] De Taeye BM, et al. Macrophage TNF-α contributes to insulin resistance and hepatic steatosis in diet-induced obesity. Am J Physiol-Endocrinol Metab 2007; 293:E713–25.

[24] Kakino S, et al. Pivotal role of TNF-α in the development and progression of nonalcoholic fatty liver disease in a murine model. Horm Metab Res 2018;50:80–7.

[25] Salles J, et al. TNFα gene knockout differentially affects lipid deposition in liver and skeletal muscle of high-fat-diet mice. J Nutr Biochem 2012;23:1685–93.
[26] Uysal KT, Wiesbrock SM, Marino MW, Hotamisligil GS. Protection from obesity-induced insulin resistance in mice lacking TNF-α function. Nature 1997;389:610–4.

[27] Ribas-Latre A, et al. Cellular and physiological circadian mechanisms drive diurnal cell proliferation and expansion of white adipose tissue. Nat Commun 2021;12:3482.

[28] Marcheva B, et al. Disruption of the clock components CLOCK and BMAL1 leads to hypoinulinemia and diabetes. Nature 2010;466:627–31.

[29] Rudic RD, et al. BMAL1 and CLOCK, two essential components of the circadian clock, are involved in glucose homeostasis. PLoS Biol 2004;2:e377.

[30] Guo B, et al. The clock gene, brain and muscle Arnt-like 1, regulates adipogenesis via Wnt signaling pathway. FASEB J 2012;26:3453–63.

[31] Shimba S, et al. Deficient of a clock gene, brain and muscle Arnt-like protein-1 (BMAL1), induces dyslipidemia and ectopic fat formation. PloS One 2011;6:e25231.

[32] Shimba S, et al. Brain and muscle Arnt-like protein-1 (BMAL1), a component of the molecular clock, regulates adipogenesis. Proc Natl Acad Sci 2005;102:12071–6.

[33] Kennaway DJ, Varcoe TJ, Voultses A, Boden MJ. Global loss of Bmal1 expression alters adipose tissue hormones, gene expression and glucose metabolism. PloS One 2013;8.

[34] Paschos GK, et al. Obesity in mice with adipocyte-specific deletion of clock component Arntl. Nat Med 2012;18:1768–77.

[35] Shostak A, Meyer-Kovac J, Oster H. Circadian regulation of lipid mobilization in white adipose tissues. Diabetes 2013;62:2195–203.

[36] Wang S, et al. PPAR-γ integrates obesity and adipocyte clock through epigenetic regulation of Bmal1. Theranostics 2022;12:1589–606.

[37] Petrenko V, et al. The core clock transcription factor BMAL1 drives circadian β-cell proliferation during compensatory regeneration of the endocrine pancreas. Genes Dev 2020. https://doi.org/10.1101/gad.343137.120.

[38] Rakshit K, Hsu TW, Matveyenko AV. Bmal1 is required for beta cell compensatory expansion, survival and metabolic adaptation to diet-induced obesity in mice. Diabetologia 2016;59:734–43.

[39] Alexander RK, et al. Bmal1 integrates mitochondrial metabolism and macrophage activation. eLife 2020;9:e54090.

[40] Nguyen KD, et al. Circadian gene Bmal1 regulates diurnal oscillations of Ly6Chi inflammatory monocytes. Science 2013;341.

[41] Oishi Y, et al. Bmal1 regulates inflammatory responses in macrophages by modulating enhancer RNA transcription. Sci Rep 2017;7:1–14.

[42] Hong H-K, et al. Requirement for NF-κB in maintenance of molecular and behavioral circadian rhythms in mice. Genes Dev 2018;32:1367–79.

[43] Shen Y, et al. NF-κB modifies the mammalian circadian clock through interaction with the core clock protein BMAL1. bioRxiv 2020. https://doi.org/10.1101/2020.09.06.285254. 2020.09.06.285254.

[44] Keller M, et al. A circadian clock in macrophages controls inflammatory immune responses. Proc Natl Acad Sci 2009;106:21407–12.

[45] Castanon-Cervantes O, et al. Dysregulation of inflammatory responses by chronic circadian disruption. J Immunol 2010;185:5796–805.

[46] Curtis AM, et al. Circadian control of innate immunity in macrophages by miR-155 targeting Bmal1. Proc Natl Acad Sci 2015;112:7231–6.

[47] Hogenesch JB, et al. The basic helix-loop-helix-PAS protein MOP9 is a brain-specific heterodimeric partner of circadian and hypoxia factors. J Neurosci 2000; 20. RC83–RC83.

[48] Ikeda M, et al. cDNA cloning of a novel bHLH-PAS transcription factor superfamily gene, BMAL2: its mRNA expression, subcellular distribution, and chromosomal localization. Biochem Biophys Res Commun 2000;275:493–502.

[49] Okano T, Sasaki M, Fukada Y. Cloning of mouse BMAL2 and its daily expression profile in the suprachiasmatic nucleus: a remarkable acceleration of Bmal2 sequence divergence after Bmal gene duplication. Neurosci Lett 2001;300:111–4.

[50] Sasaki M, Yoshitane H, Du N-H, Okano T, Fukada Y. Preferential inhibition of BMAL2-CLOCK activity by PER2 reemphasizes its negative role and a positive role of BMAL2 in the circadian transcription. J Biol Chem 2009;284:25149–59.

[51] Shi S, et al. Circadian clock gene Bmal1 is not essential after all; functional replacement with its paralog, Bmal2. Curr Biol 2010;20:316–21.

[52] Costa-Urrutia P, et al. Genome-wide association study of body mass index and body fat in Mexican-Mestizo children. Genes 2019;10:945.

[53] Yamaguchi M, et al. Association between brain-muscle-ARNT-like protein-2 (BMAL2) gene polymorphism and type 2 diabetes mellitus in obese Japanese individuals: a cross-sectional analysis of the Japan Multi-institutional Collaborative Cohort Study. Diabetes Res Clin Pract 2015;110:301–8.

[54] Mandl M, et al. The circadian transcription factor ARNTL2 is regulated by weight-loss interventions in human white adipose tissue and inhibits adipogenesis. Cell Death Dis 2022;8:1–12.

[55] He C-X, Avner P, Boitard C, Rogner UC. Downregulation of the circadian rhythm related gene Arntl2 suppresses diabetes protection in Idd6 NOD.C3H congenic mice. Clin Exp Pharmacol Physiol 2010;37:1154–8.

[56] He C-X, Prevot N, Boitard C, Avner P, Rogner UC. Inhibition of type 1 diabetes by upregulation of the circadian rhythm-related aryl hydrocarbon receptor nuclear translocator-like 2. Immunogenetics 2010;62:585–92.

[57] Hung M-S, Avner P, Rogner UC. Identification of the transcription factor ARNTL2 as a candidate gene for the type 1 diabetes locus Idd6. Hum Mol Genet 2006;15: 2732–42.

[58] Lebailly B, Langa F, Boitard C, Avner P, Rogner UC. The circadian gene Arntl2 on distal mouse chromosome 6 controls thymocyte apoptosis. Mamm Genome 2017; 28:1–12.

[59] Lebailly B, He C, Rogner UC. Linking the circadian rhythm gene Arntl2 to interleukin 21 expression in type 1 diabetes. Diabetes 2014;63:2148–57.

[60] Dantas-Ferreira R, et al. Deletion of the clock gene *Bmal2* leads to alterations in hypothalamic clocks, circadian regulation of feeding and energy balance. J Neurosci 2024:e188623. https://doi.org/10.1523/jneurosci.1886-23.2024.

[61] Hepler C, et al. Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice. eLife 2018;7:e39636.

[62] Marcelin G, et al. A PDGFRα-mediated switch toward CD9high adipocyte progenitors controls obesity-induced adipose tissue fibrosis. Cell Metab 2017;25: 673–85.

[63] Leung A, Trac C, Du J, Natarajan R, Schones DE. Persistent chromatin modifications induced by high fat diet. J Biol Chem 2016;291:10446–55.

[64] Kouri V-P, et al. Circadian timekeeping is disturbed in rheumatoid arthritis at molecular level. PloS One 2013;8:e54049.

[65] Olkkonen J, et al. DEC2 blocks the effect of the ARNTL2/NPAS2 dimer on the expression of PER3 and DBP. J Circadian Rhythms 2017;15:6.

[66] Jeffery E, Church CD, Holtrup B, Colman L, Rodeheffer MS. Rapid depot-specific activation of adipocyte precursor cells at the onset of obesity. Nat Cell Biol 2015; 17:376–85.

[67] Kulenkampf E, Wolfrum C. Proliferation of nutrition sensing preadipocytes upon short term HFD feeding. Adipocyte 2019;8:16–25.

[68] Zhang Z, Sinha J, Bahrami-Nejad Z, Teruel M. The circadian clock mediates daily bursts of cell differentiation by periodically restricting cell differentiation commitment. 2022.05.17.492365 Preprint at. 2022. https://doi.org/10.1101/ 2022.05.17.492365.

[69] Aggarwal A, et al. The circadian clock regulates adipogenesis by a Per3 crosstalk pathway to Klf15. Cell Rep 2017;21:2367–75.

[70] Fontaine C, et al. The orphan nuclear receptor Rev-Erbα is a peroxisome proliferator-activated receptor (PPAR) γ target gene and promotes PPARγ-induced adipocyte differentiation *. J Biol Chem 2003;278:37672–80.

[71] Sárvári AK, et al. Plasticity of epididymal adipose tissue in response to diet-induced obesity at single-nucleus resolution. Cell Metab 2021;33:437–453.e5.

[72] Xiao T, et al. Tregs in visceral adipose tissue up-regulate circadian-clock expression to promote fitness and enforce a diurnal rhythm of lipolysis. Sci Immunol 2022;7: eabl7641.

[73] Mulder P, et al. Reduction of obesity-associated white adipose tissue inflammation by rosiglitazone is associated with reduced non-alcoholic fatty liver disease in LDLR-deficient mice. Sci Rep 2016;6:31542.

[74] Duval C, et al. Adipose tissue dysfunction signals progression of hepatic steatosis towards nonalcoholic steatohepatitis in C57BL/6 mice. Diabetes 2010;59:3181–91.