# Unveiling the Molecular Legacy of Transient Insulin Resistance: Implications for Hepatic Metabolic Adaptability

Alexandre Berthier, Céline Gheeraert, Manjula Vinod, Manuel Johanns, Loïc Guille, Joel Haas, Julie Dubois-Chevalier, Jérôme Eeckhout, Bart Staels, Philippe Lefebvre

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## To cite this version:

Alexandre Berthier, Céline Gheeraert, Manjula Vinod, Manuel Johanns, Loïc Guille, et al., Unveiling the Molecular Legacy of Transient Insulin Resistance: Implications for Hepatic Metabolic Adaptability. Journal of Hepatology, 2025, 10.1016/j.jhep.2025.02.004. hal-05014492

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Submitted on 31 Mar 2025

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# Unveiling the Molecular Legacy of Transient Insulin Resistance:
## Implications for Hepatic Metabolic Adaptability

Alexandre Berthier¹,*, Céline Gheeraert¹, Manjula Vinod¹, Manuel Johanns¹, Loïc Guille¹, Joel T. Haas¹,
Julie Dubois-Chevalier¹, Jérôme Eeckhoute¹, Bart Staels¹ and Philippe Lefebvre¹.

¹ Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, UMR1011-EGID, F-59000 Lille, France.

* Corresponding author:
Alexandre Berthier
Inserm UMR1011, Bldg J&K
Faculté de Médecine Henri Warembourg, Pôle Recherche
Blvd du Prof Leclerc
59000, Lille, France
e-mail : alexandre.berthier@inserm.fr
Tel +33.3.20974220

### Declaration of interest

The authors declare no competing interests.

### Author contributions

Conceptualization: AB, JTH, PL; Methodology: AB, CG, MV, MJ, JTH, JDC; Software: JDC; Validation: AB, CG, MV, MJ, PL; Formal analysis: AB, LG, PL; Investigation: AB, MJ; Resource: JCD, PL; Data Curation: AB, LG, JDC; Writing: AB, PL; Visualization: AB, JDC, PL; Supervision: JCD, JE, BS, PL; Project administration: PL; Funding acquisition: BS, PL.

### Financial support

This work was supported by ANR (LABX EGID, ANR-10-LABX-0046). MetaToul-Lipidomique (I2MC, Inserm, Toulouse, France) is supported by A.N.R. (MetaboHUB-ANR-11-INBS-0010). MJ was supported by grants from Wallonie-Bruxelles International (WBI, Belgium, ref. SUB/2020/479801) and from European Association for the Study of the Liver (EASL, Sheila Sherlock fellowship). JTH holds a European Research Council (ERC) starting grant (Metabo3DC, #101042759). PL's and BS's teams are supported by grant from Fondation pour la Recherche Médicale (EQU202203014645 and #EQU202203014650, respectively).

### Data availability statement

The data supporting the findings of this study are openly available in the repositories listed in the Supplementary Information document

**Short title:** Molecular Legacy of Transient Insulin Resistance
Open Access statement:

For the purpose of Open Access, a CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.

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https://creativecommons.org/licenses/by/4.0/
**Background:** Metabolic flexibility (MetF) is an organism's ability to adjust to changing metabolic supplies and energy demands. Insulin plays a central role in coordinating MetF through molecular mechanisms such as signaling pathways, transcriptional responses, and circadian regulation. Insulin resistance (IR) can impair MetF, contributing to type 2 diabetes and obesity, often stemming from continuous challenges such as sedentary lifestyles, poor diets, and circadian disruptions. Transient IR episodes, like gestational diabetes or stress-induced hyperglycemia, also heighten the risk of later diabetes development. Yet, the molecular processes post-transient IR remain poorly understood despite their health significance.

**Aims and Methods:** Our aims were to characterize the hepatic response to a high fat diet challenge in mice previously exposed to a transient IR episode. We integrated transcriptomic, epigenomic, lipidomic, and molecular clock assessments to provide a molecular basis for the observed dysregulations.

**Results:** Our study shows that temporarily blocking the insulin receptor in young mice leads to later-life liver issues hindering PPARα-mediated adaptation to a high-fat diet. This is linked to decreased histone active marks at PPARα sites and reduced endogenous PPARα ligands. Transient insulin receptor blockade also altered the liver's molecular clock, particularly affecting PPARα transcriptional responsiveness.

**Conclusions:** Seemingly reversible and unnoticed metabolic challenges in early adulthood may predispose the liver to exacerbated metabolic dysfunctions when confronted with chronic challenges later in life.

KEYWORDS

Liver, metabolic flexibility, insulin resistance, circadian clock, PPARα, REV-ERBα
Metabolic flexibility (MetF) reflects the ability of an organism to adapt to changes in metabolic supplies and/or energy demands. Physiologically exemplified by the coordinated transition from fasting to feeding, MetF involves multiple organs amongst which skeletal muscles (SKM), white adipose tissues (WAT) and liver play a crucial role [1]. At the metabolic level, this translates into the ability of organs to shift in fuel selection which is impaired in type 2 diabetes (T2D) and obesity. A key, but not unique, coordinator of MetF is insulin. Consequently, insulin resistance (IR) has now emerged as a major impediment to MetF and overall metabolic health [2]. Paving the way towards obesity, T2D and cardiovascular diseases, IR is triggered by multiple behavioral and/or environmental factors, notwithstanding genetic predisposition factors. As expected, these chronic human ailments are predominantly observed in overly sedentary individuals feeding an unbalanced diet and also, very interestingly, in nightshift workers [3]. The latter observation underlines the relationship between circadian (dys)synchrony and IR, with human and rodent studies convincingly establishing a causative role of a disturbed molecular clock in IR [4]. At the molecular level, MetF is controlled by molecular rheostats which are part of intertwined networks promoting either rapid (seconds or minutes) adaptative responses through signaling cascades and protein post-translational modifications, or delayed responses (a few hours) reshaping the cellular phenotype through transcriptional responses in which lipid-sensing nuclear receptors such as peroxisome proliferator-activated receptors (PPARs) play a critical role, and which may be oscillating through additional regulation by the circadian clock. It bears noting that these cyclic variations observed in homeostatic conditions are dampened in IR or T2D [5].

IR and T2D are often thought of as resulting from a chronic, long-term exposure to highly caloric diets without compensatory exercise, as shown by multiple human epidemiological studies and animal models. However, T2D is also significantly correlated to transient and reversible bouts of IR/T2D in human pathology. Two relevant pathological conditions exemplify this correlation. First, gestational diabetes (GDM), which affects 1 out of 6 to 8 mothers worldwide (20 million births) is usually detected at week 26-28 of pregnancy and resolves after delivery. Second, stress-induced hyperglycemia (SIH), which is observed during several days in 17% of critically ill patients (total admissions ≈850,000/year in France, 5 million/year in the USA) spontaneously resolves after recovery. Strikingly, both conditions significantly increase the risk of developing T2D later in life by 2- to 20-fold [6, 7]. Thus, those exposed to a transient episode of IR/T2D form a high-risk group for which there is a compelling case for early detection and intervention. However, and in contrast to “classical” forms of IR/T2D, there is little or no understanding of molecular processes causative of impaired metabolic regulations later in life after these reversible insulin-resistant episodes. Importantly, we note however that early-life transient exposure to alcohol, inflammation and hyperoxia have disruptive effects on circadian regulation during
adulthood [8], suggesting that imprinting of the molecular clock is a relevant pathological process which
possibly impinges on metabolic homeostasis.

This knowledge gap is thus a major impediment in understanding the molecular programing of
metabolic inflexibility triggered by a transient bout of IR. In order to shed light on the molecular events
leading to metabolic dysregulation later in life, we set up a model of early, transient IR and explored
disturbances in liver functions later-in-life, occurring after a secondary dietary challenge inducing an
insulin resistant state.
## Material and methods

Detailed Materials and Methods are described in the Supplementary Information.
RESULTS

Developing and characterizing a transient insulin resistance model.

Ten-week-old C57Bl6/J male, ad libitum-fed mice were exposed to the insulin receptor antagonist S961 [9] (0.46mg/kg/day) or to vehicle (phosphate buffer saline, PBS) for 7 days. After removal of osmotic pumps, mice recovered for 2 weeks (D21), after which mice were fed either a chow (CD) or an HFD (60% fat) for 9 weeks (D85, **Fig. 1A**). HFD-induced weight gain was less marked in the S961-treated group (-8%) despite similar cumulative food intakes during the course of the HFD [2.61g (13.7kcal)/mouse/day] (Fig. 1B). The food intake was higher in CD-fed mice (3.49g/mouse/day, +25%), in agreement with the lower caloric content of CD (3.5kcal/g, -32%) and previous reports (see for example [10]). S961-treated mice displayed a reversible hyperglycemia starting from day (D) 3 peaking from D4 to D7 above 5g/L (**Fig. 1C**). Glycemia returned to normal at D14 and remained unchanged until completion of the experiment at D85, with the exception of the HFD-only group which showed moderately elevated glycemia at intermediate time points (D58, D71) (**Fig. 1C**). Fasting insulinemia was similar (≈1ng/mL, D85) in PBS-HFD and S961-HFD mice (data not shown).

We assessed cumulative glycemic damage (oxidative stress, AGEs) in all experimental groups. S961 treatment did not significantly change AGEs or lipid peroxidation in the liver at D7 and D21 compared to controls (Fig. 1D, 1E). At D85, AGE content remained low regardless of treatment (Fig. 1F). Blood urea nitrogen (BUN) levels decreased after HFD and were unaffected by S961 (Fig. 1G), indicative of abnormal liver function in the HFD condition. Thus, short-term S961 treatment caused a reversible hyperglycemic episode without typical features of cumulative glycemic damage such as oxidative stress and AGEs.

Transient insulin resistance triggers a later-in-life alteration of the hepatic lipidome.

Metabolic consequences of the transient exposure to S961 were explored at the level of the hepatic lipidome at D85. Cholesterol, FFAs, eicosanoids, phospholipids and neutral lipids were analyzed in D85 livers (Fig. 1H-N, Supp. Table 1). The total hepatic content in FFAs and, accordingly in saturated, mono- or poly-unsaturated fatty acids (SFAs, MUFAs and PUFAs), did not vary whatever the treatment (Fig. 1H). Only minor variations were noted when quantifying individual molecular species such as palmitate, stearate, oleate, linoleate and arachidonate (Supp. Fig. 1A). While the cholesterol content was altered neither by diet nor S961 pretreatment, cholesteryl esters (mostly conjugated to a stearic acid backbone, Supp. Fig. 1B) increased upon high fat feeding, but were insensitive to S961
pretreatment (Fig. 1I). C50 and C52 triglycerides (TGs) content variation (Supp. Fig. 1C) reflected the globally increased TG content upon HFD and its ~ 50% decrease after S961 treatment (Fig. 1J). The hepatic content in eicosanoids and prostaglandins, ceramides, phosphoinositides, phosphosphingosides, phosphoethanolamine, phosphatidylcholines and sphingolipids was assessed (Supp. Fig. 2A, 2B, 3A, 3B, 4A, 4B, 5 respectively). Among the 176 identified molecular species, HFD triggered an increase in 5-oxo-ETE and 5-HETE; Cer (d18:1/20:0); PE (38:3), PE (38:4); PC (34:1), PC (34:2); PC (36:2); PC (36:4); PC (38:4) which was significantly blunted by S961 pretreatment (Fig. 1K-N). Thus, several HFD-induced lipidome alterations can be blunted by S961 pretreatment, mostly affecting TG and phospholipid content. Intriguingly, known PPARα binders and/or agonists [PC 34:1, PC 34:2 and PC 36:2] belonged to this list, as well as 5-oxo-ETE, a PPARγ agonist [11]. Since PPARα transcriptional activity can be regulated by a wide variety of lipase-generated MUFAs and PUFAs of extra-hepatic origin, we assessed the plasma FFA content of D85, HFD-fed mice. Amongst the 38 tested molecular species including SFAs, MUFAs and PUFAs (Supp. Table 2), palmitic acid (C16:0), stearic acid (C18:0) and lignoceric acid (C24:0) were the only detectable SFAs. Detectable unsaturated FAs were oleic acid (C18:1ω9), linoleic acid (C18:2ω6) and arachidonic acid (C20:4ω6). The plasma content in FFAs was not affected 8 or 11 weeks after cessation of the S961 treatment, indicating that hypothetical PPARα ligands are unlikely to be of extra-hepatic origin (Supp. Fig. 6, Supp. Table 2). Additionally, TG, ALAT and ASAT plasma levels were unchanged in S961-HFD mice compared to PBS-HFD (Supp. Fig. 6), suggesting no major alterations of systemic lipid homeostasis and of hepatic functions after S961 pre-exposure. Collectively, a transient S961 exposure thus altered specific aspects of hepatic lipid metabolism, notably decreasing concentrations of PPARα endogenous agonists.

**Insulin receptor blockade induces immediate but reversible effects on hepatic gene expression.**

To investigate whether S961 infusion could reshape the liver transcriptome, we compared the gene expression profile after a 7-day exposure to S961 (D7) to that of control, PBS-exposed mice (Fig. 2A). A limited number of genes [n=246, Log₂FC (-)1.2, FDR<0.05, Supp. Table 3] were modulated after S961 infusion (Fig. 2A, top panel). Gene set enrichment analysis (GSEA) showed an increase in the expression of genes associated with non-essential amino acid metabolism and one-carbon metabolism. (Fig. 2A, lower panel). Notably, glycine N-methyltransferase (Gnmt), cystathionine beta-synthase (Cbs), cystathionine gamma-lyase (Cth), homocysteine S-methyltransferase (Bhmt) were upregulated together with nicotinamide N-methyltransferase (Nnmt), suggesting alterations of methyl donor pool [12]. In addition, a number of fasting-induced genes were upregulated by S961, such as *Got1* (encoding AST), *Ppargc1a* (encoding PGC1α), *Slc16a5* (encoding MCT6 [Monocarboxylate
Transporter 6]) and *Ddit4*, in agreement with the imposed insulin-resistant state. Conversely, the basal expression of a few PPARα-regulated genes was repressed at D7 (*Fabp1*, *Fabp5*, *Cyp4a12*).

Insulin entrains the liver molecular clock through the hepatocyte insulin receptor and affects core clock protein expression or activity [13, 14]. We assessed whether a 7-day S961 treatment interfered with circadian rhythmicity using liver explants from PER2::Luc(iferase) mice. This ensured that external cues did not impact on hepatic PER2 rhythmicity. PER2::Luc mice were thus treated as above and the bioluminescence from D7 liver explants was monitored for 3 days. A period increase of 2h of the PER2::Luc fusion protein activity (control period =24h, q=1.8e-18 vs S961 period = 26h, q=2.2e-8) was detected in S961-infused mice (Fig. 2B).

A similar analysis was carried out in PER2::Luc mice after the 2 week-recovery (D21, **Fig. 2C** and **2D**). It revealed that, as for other biological parameters, no difference in liver gene expression was observed between PBS- and S961-infused mice at D21, with the exception of a single transcript (*Gm24069*) of unknown function (**Fig. 2C**). PER2::LUC protein activity displayed no comparative period alteration (control period = 22h, q=1.7e-14 vs S961 period = 22h, q=4.6e-21) (**Fig. 2D**). The activation level of lipid- or hormone (insulin)-sensitive protein kinases (PKCα, AKT, PKA) was unchanged at D7 and D21 (**Supp. Fig. 7**). Taken together, these data show that circadian rhythmicity and other parameters returned to normal at D21.

**Transient insulin resistance triggers a significant later-in-life alteration of the hepatic PPARα-driven response to a high fat diet.**

D85 hepatic transcriptomes were analyzed by GSEA to predict altered molecular functions (**Fig. 3**, *Supp. Tables 4* and *5*). HFD feeding of the control (PBS) group for 9 weeks upregulated genes involved in branched-chain amino acid and peroxisomal fatty acid catabolism, most of the latter belonging to the PPARα signaling pathway (**Fig. 3A**). A direct comparison of gene expression patterns between untreated and S961-pretreated animals under HFD indicated a downregulation of the PPARα signaling pathway after S961 pretreatment (**Fig. 3B**), suggesting a decreased inducibility of bona-fide PPARα target genes. RT-qPCR assays performed to quantify the expression of genes coding for lipid transporters and metabolic enzymes confirmed the HFD-mediated induction of these direct PPARα target genes (**Fig. 3C**), as expected from previous reports [15, 16]. Of note, the decreased expression of the fatty acid translocase *Cd36* (**Fig. 3C**) and the loss of *Acot1* expression (*Supp. Table 4*), an acyl-CoA thioesterase required for PPARα activation [17] were noted in HFD-fed, S961-treated mice. Exposure to S961 did not alter significantly the basal expression of most genes (**Fig 3C** and *Supp. Tables*
4 and 5). Thus, a transient exposure to S961 10 weeks earlier severely compromised the PPARα-driven response to HFD.

The integrity of the molecular clock was assessed by monitoring the expression of core clock genes (CCGs) in D85 livers at ZT9 (Fig. 3D). HFD did not alter the expression level of CCGs in control, PBS-treated mice (Fig. 3D, left panel), in agreement with previous reports showing the resilience of the molecular clock to HFD feeding under conditions similar to ours (Supp. Fig. 8A and [18-20]). In contrast, the S961-HFD successive exposure led to a pronounced alteration of *Nr1d1* (encoding REV-ERBα), *Per1*, *Dbp*, *Cry1*, *Cry2*, *Arntl* (encoding BMAL1) and *Nfil3* transcript levels (Fig. 3D, right panel). These gene expression alterations were consistent with observed BMAL1 protein levels, whereas PER2 level remained constant throughout all groups (Supp. Fig. 8B). Thus, a transient IR episode triggered dysregulated CCGs expression.

**A transient insulin resistance episode does not affect PPARα intrinsic transcriptional activity.**

The decreased PPARα-related responsiveness could stem from factors beyond reduced endogenous ligand content, such as increased PPARα protein degradation or epigenomic alterations. The first hypothesis was ruled out since D85 whole liver extracts showed similar PPARα content and subcellular localization across all conditions (Supp. Fig. 9A-D). ChIP-PCR analysis indicated that neither diet (as previously reported by [21]) nor S961 pretreatment affected PPARα loading at five target genes (Supp. Fig. 9E). PPARα intrinsic functionality was tested by substituting HFD, known to trigger a PPARα-mediated transcriptional response [15, 22] with fenofibrate (FF), a synthetic PPARα agonist [23] (Fig. 4A). Mice infused with S961 for one week were then fed a control diet (CD) or CD with FF for another week after a two-week recovery period. S961 induced transient hyperglycemia that normalized (Fig. 4B). FF treatment caused hepatomegaly (not shown) and reduced glycemia in both control and S961-treated groups (Fig. 4C). In control mice, FF induced *Vnn1*, *Cyp4a10*, and *Cyp4a14* expression (Fig. 4D-F), known PPARα target genes [15, 24]. However, *Mfsd2a* expression, a PPARα-regulated gene with binding sites near its promoter, was not altered by FF (Fig. 4G). One plausible explanation for this phenomenon lies in fenofibrate's capacity to overcome epigenetic barriers through stabilization of PPARα-coactivator(s) complexes.

Epigenomic events potentially limiting PPARα's transcriptional activity were assessed by examining chromatin accessibility at PPARα binding sites using ATAC-Seq at D85 (Fig. 5A, Supp. Fig. 10A and 11). Comparing Tn5 accessibility in nuclei from HFD-fed mice, with or without prior S961 exposure, revealed no global changes in chromatin accessibility around chromatin PPARα binding sites [25].
Histone post-translational modifications (PTMs) were monitored by ChIP-Seq at PPARα loci (Fig. 5B-D, Supp. Fig. 10B-D, 11). Active histone marks, such as H3K27Ac (an active enhancer and promoter marks) and H3K4me1 (indicating active and poised enhancers), were decreased, while the repressive mark H3K27me3 (associated with repressed enhancers and promoters) was modestly increased. This epigenomic profiling of PPARα-associated loci suggests decreased transcriptional activation at PPARα loci.

Thus, the later-in-life compromised PPARα-driven response after a short, reversible IR bout correlated with decreased histone activation marks and increased repression marks, while PPARα chromatin loading (in agreement with [21]) and intrinsic transcriptional functionality remained unaffected.

Since transient IR triggered a circadian rhythm alteration at D7, which can specifically be perpetuated in HFD-fed mice, we asked whether circadian rhythmicity impinge on PPARα expression. The *Nr1c1* gene (encoding PPARα) expression showed slight but significant time-dependent variations peaking at ZT8-12, which was also reflected at the protein level (JTK cycle q value = 0.0047, **Supp. Fig. 12**) as initially reported [26]. Global analysis of liver transcript expression every 3 hours over a 24-hour period in ad libitum-fed mice [27] identified 3,273 (10.5%) 24h-cycling transcripts out of 31,059 identified mRNAs (Fig. 6A), a figure in agreement with several reports [28, 29]. A significant fraction of these cyclic transcripts related to circadian rhythmicity and importantly to the PPAR signaling pathway (Fig. 6B). We compiled a list of 42 bona-fide PPARα target genes from a recently curated collection [30], which were induced by HFD in control mice (Fig. 6C). Twenty-one out of 42 (50%) transcripts including *Cd36* and *Acot1* were cycling with a 24h-period with varying peaks and troughs.

PPARα-driven genes are thus controlled by the circadian clock, consistent with published reports describing an auto-regulatory feedback loop between PPARα and components of the molecular clock such as ARNTL/BMAL1, PER2 and REV-ERBα [31-33].

In S961-exposed livers, 85% of cyclic PPARα target genes became insensitive to HFD (Fig. 6D), correlating with decreased H3K27 acetylation at their TSS (Fig. 6E). We hypothesized that molecular clock pacing affects PPARα transcriptional activity and scanned PPARα-centered regions with significant epigenomic alterations for transcription factor binding sites. The top-ranking motif was a direct repeat 1 (DR1) sequence (Supp. Fig. 13A), where PPARα binds with RXRα, its obligate heterodimerization partner in mouse hepatocytes [27, 34]. Other motifs included binding sites for FOXJ, CEBP, and HNF family members, but not for REV-ERBα, CLOCK:BMAL1, or other clock components. Public ChIP-seq dataset mining confirmed not only PPARα and RXRα but also REV-ERBα
presence, suggesting a possible tethering of the latter to PPARα-RXRα heterodimers at these loci (Supp. Fig. 13B).

Histone marks densities (H3K27Ac, H3K4me1 and H3K27me3) at PPARα binding sites (Fig. 5B-D) were compared to those found at REV-ERBα, BMAL1, CLOCK or CTCF-centered loci (Fig. 7A). Qualitative and quantitative similarities between active histone marks H3K27Ac and H3K4me1 at PPARα-centered loci were exclusively detected for the REV-ERBα-centered cistrome (Fig. 7A, Supp. Fig. 14, 15). The repressive histone mark H3K4me3 showed no similarity between PPARα- and REV-ERBα-centered loci (Supp. Fig 16), suggesting that observed transcriptional (dys)regulations mostly stem from altered activation mechanisms.

ChIP-seq data in naïve livers showed equivalent PPARα and REV-ERBα binding sites (~56,000), which could be identified as putative gene regulatory sequences using the Genomic Regions Enrichment of Annotations Tool (Fig. 7B, C). The 65% overlap between the PPARα and REV-ERBα cistromes (Fig. 7B-D) was in line with previous reports [35, 36]. KEGG-based term enrichment linked these co-bound regions to lipid and detoxification pathways (Fig. 7D), exemplified by prototypic PPARα target genes (Fig. 7E). These findings suggested a co-regulatory function for PPARα and REV-ERBα at these loci which was reflected by a similar decrease of active histone marks.

**Agonist-mediated PPARα target gene activation is temporally gated.**

We thus investigated how PPARα responsiveness could be regulated in a REV-ERBα-dependent manner. The nontransformed hepatocyte cell line AML12, which maintain a partial hepatocyte-like phenotype, display regular circadian rhythmicity and mimic part of the hepatocyte response to PPARα agonists, was used. An AML12 subclone stably expressing a Bmal1-Luc reporter gene showed regular luciferase activity cycles with a 27-hour period (JTK_Cycle q value = 0.049) (Fig. 8A). Luciferase activity troughs matched maximal Nr1d1 transcript expression (Fig. 8A, inset), after which the REV-ERBα polypeptide is degraded via the proteasomal pathway [37]. PPARα-encoding transcript levels were unaffected by time and treatment (Fig. 8A, inset). AML12 cells treated with the PPARα agonist pemafibrate under “standard” conditions (24 hours, T0-24) showed upregulation of several PPARα target genes such as *Cpt1a*, *Pdk4*, and *Angptl4* (Fig. 8B). AML12 cells were treated with pemafibrate during a short timeframe (6 hours) either before or after the Bmal1 luciferase nadir, corresponding to high and low cellular levels in REV-ERBα respectively (Fig. 8C, 8D). PPARα target genes, such as *Hmgcs2*, showed higher inducibility during the REV-ERBα accumulation phase (T14-20) and lower responsiveness during the degradation phase. Other targets, like *Acot2*, *Creb3l1*, and *Hsd12*, exhibited similar patterns. Conversely, some genes (e.g., *Angptl4*, *Cpt1a*, *Hmox1*) were higher activated when
pemafibrate was added after the REV-ERBα peak (T24-30). More unexpectedly, several bona fide PPARα target genes (*Impa2*, *Pllp*, *Plat...*) were down-regulated when pemafibrate was added prior to (T14-20) or after the REV-ERBα expression peak (T24-30). Therefore, a time-dependent inducibility of key metabolic genes by pemafibrate can be observed (Fig. 8E) under conditions where PPARα levels remained constant. To determine whether REV-ERBα transcriptional activity plays a role in this phenomenon, a similar experiment was conducted in which pemafibrate-treated AML12 cells were co-treated with the REV-ERBα antagonist SR8278 at T14-20. (Fig. 8F). While leaving *Nr1d1* transcript levels unaffected, this pharmacological interference resulted in a significantly lower pemafibrate-induced expression of *Hmgcs2*, *Pdk4*, *Aldh1a1*, and of *Acot2* (higher induced at T14-T20). *Angptl4* (higher induced at T24-30) mirrored this response. The T14-20 inducible *Creb3l3* gene was not affected by REV-ERBα inhibition, suggesting that other clock components, like *Cry1&2*, or *RXRα* might be involved [38-41]. *Acot1*, with time-independent pemafibrate responsiveness, was unaffected by REV-ERBα antagonism. These results indicate that modulating REV-ERBα activity impacts PPARα responsiveness to agonist treatment through mechanisms unrelated to its intrinsic transrepressive activity.
Deleterious signals affecting neonates or infants such as hyperoxia or overfeeding trigger adverse effects in adulthood in various tissues such as lung [8] and liver [42]. Pediatric populations transiently exposed to adverse events such as acute kidney injury are also at higher risk of suffering from chronic kidney disease, hypertension and cardiovascular disease later-in-life [43]. Importantly, negative later-in-life metabolic consequences such as increased obesity and T2D were observed after transient gut microbiome alteration, GDM and SIH [44-47]. Hence, even though there is a consensus regarding the idea that a temporary disruption in metabolism during early life can have later-in-life effects on metabolic homeostasis, our understanding of the specific molecular mechanisms at play in this process remains limited. We asked in a rodent model whether and how a transient IR bout, partially mimicking metabolic alterations observed for example in GDM or SIH, could significantly alter the response to chronic high fat feeding and involve a so-called cellular memory.

The peptidic insulin receptor antagonist S961 was used to induce a systemic, transient blockade of insulin signaling. Our data (Fig. 1) and previous reports [48, 49] show that S961 administration causes reversible hyperglycemia, massive lipolysis, hyperinsulinemia, hyperglucagonemia, and ketonemia. Notably, fasting, which also induces lipolysis, hyperglucagonemia, ketonemia, and activates the hepatic PPARα pathway, does not elicit a memory effect [50]. Key instigators of metabolic memory, such as advanced glycation-end products and oxidative stress [51], as well as activation of intracellular signaling, are not involved in our model. Instead, lipidomic, transcriptomic, and epigenomic features indicate an altered hepatic PPARα response to HFD after a period of insulin resistance. While PPARα proteostasis and intrinsic transcriptional activity remained unaffected, the decreased hepatic content of endogenous PPARα ligands, hence activity (Fig. 1 and Supp. Fig. 1-6) may be due to decreased lipid uptake, as suggested by the reduced expression of Cd36 (Fig. 3C).

Interestingly, altered circadian rhythms at D7 post-S961 infusion and in S961-pretreated, HFD-fed mice correlated with reduced PPARα target gene responsiveness to HFD (Fig. 2, 3). Many PPARα target genes, including REV-ERBα, exhibit circadian rhythmicity (Fig. 6, reviewed in [11]), and insulin signaling is essential for hepatic molecular clock synchronization [13, 52, 53]. Thus, the hepatic insulin signaling pathway, circadian clock, and PPARα-regulated pathway are deeply interconnected.

Significant co-binding of REV-ERBα at PPARα-centered loci was observed (Fig. 7, Supp. Fig. 13B and [35, 36]). The yet-to-be-determined and complex nature of this potential functional interaction translated into marked variations of PPARα's ability to activate its cognate target genes depending on
circadian rhythmicity (Fig. 8). The hypothesis that PPARα's transcriptional properties are directly coupled to REV-ERBα was supported by the effects of the REV-ERBα antagonist SR8278. This effect nevertheless suggested that the mechanism(s) involved extend beyond the traditional transrepressive activity of REV-ERBα. In that respect, we note that REV-ERBs may act independently of HDAC3 and exert positive transcriptional activity [54-57]. In summary, these data suggest an alternative and original, but not exclusive, mechanism that provides a basis for later-in-life PPARα signaling disruption by an early alteration of circadian rhythmicity.

Interestingly, circadian disruptions during the neonatal period persist in adulthood [58], and may affect adult behavior [59] or response to viral infection [8]. These studies and others [60] do not allow to disambiguate developmental from somatic effects, a potential confounding factor. Such a confounder is unlikely here as mice older than 14 weeks were used. These reports and our results indicate that what is commonly termed "metabolic memory" might be linked to transient disruptions in circadian rhythms, which have the potential to manifest more prominently under pathological conditions during adulthood than under normal circumstances. Interestingly, we note that ICU patients show reduced rhythmic *Nr1d1* and *Per2* oscillations [61].

**Limitations:** Our study did not address the cumulative effects of transient IR on the overall response to HFD. In particular, the mechanisms underlying the observed reduced weight gain observed in S961-exposed mice on a high-fat diet (HFD) indeed remain to be elucidated. However, this observation could be considered in light of previous studies reporting that whole-body PPARα knockout (KO) mice exhibit reduced sensitivity to HFD-induced weight gain, contrasting with the outcome of liver-specific *Ppara* inactivation [62, 63]. PPARα deletion has also been shown to protect against HFD-induced insulin resistance [64-66]. Thus, global inactivation of *Ppara* may confer protection against at least some deleterious effects of an HFD. This underscores the importance of considering the pleiotropic effects of S961, which may influence PPARα and other signaling pathways across various insulin-sensitive tissues. S961 affect multiple cell types, including pancreatic β cells and adipose tissues [48, 67-69], and influences gut microbiota [70]. Although lipolysis observed at D7 normalized after recovery, we noted significant changes in the transcriptional response to HFD in S961-exposed epididymal white adipose tissue (eWAT), but not in skeletal muscle. Transcriptomic analysis indicated that HFD induced cellular proliferation in eWAT, aligning with its expandable nature, and lysosomal activation. Pre-exposure to S961 significantly blunted eWAT response to HFD (Supp. Figure 17). Despite some limitations, such as relying solely on gene expression to define pathway activities, further analysis suggested that transient IR significantly increased macrophage and T cell ingress and altered the eWAT secretome. These findings, while beyond this manuscript's scope, warrant further investigation.
IMPACT AND IMPLICATIONS

This study thus provides a circadian molecular paradigm for a later-in-life alteration of liver
MetF and call for particular attention for detecting transient IR episodes as they occur in GDM and SIH
ICU patients. By extension, any transient exposure to compounds altering circadian rhythmicity such
as anti-depressants might predispose to a compromised metabolic response to an unbalanced diet
later-in-life.
ACKNOWLEDGMENTS

We thank J. Bertrand-Michel, N. Geoffre and the lipidomic facility core "MetaToul-Lipidomique (I2MC, Inserm, Toulouse, France) for lipid analysis, E. Vallez for blood biochemistry and J. Alexandre for technical support during initial experiments.
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FIGURE LEGENDS

**Fig. 1:** Transient insulin resistance affects at long term the metabolic response of the liver to HFD.

**(A)** Experimental setup. Wild type C57Bl6/J male mice were exposed (S961) or not (PBS) to S961 for 7 days (from D0 to D7). After a 2-week recovery (from D7 to D21), animals were fed either a CD or HFD for 9 weeks. Blood and livers were collected as indicated. **(B)** Body mass evolution (left panel) from D21 to D85. Food intake (right panel) from D35 to D85. Adjusted p-values were calculated by an unpaired 2-way ANOVA followed by a post-hoc Bonferroni's multiple comparison test (MCT) (n=15 per group) $\$: p<0.0001 (vs PBS-CD); \*: p<0.05 (S961-HFD vs PBS-HFD). **(C)** Blood glucose. Glycemia was quantified at the indicated time. Liver advance glycation end products (D, F) or lipid peroxidation (E) were quantified at indicated days of treatment. **(G)** Blood urea nitrogen (BUN) amounts were determined after a 6h fasting. **(H-N)** Liver lipidomic analysis at D85. Fatty acid (FA, H), cholesterol and cholesterol esters (C and CE (I)), and TG (J) species content were determined by mass spectrometry as well as eicosanoids (K), ceramides (L) and phospholipids (M, N). **(H, I, J, L, M, N)** Data are means ± SEM of relative abundance (metabolite area/ STD area ratio; or in pg/mg total protein (K)). Adjusted p values were calculated by an unpaired 2-way ANOVA followed by a post-hoc Tukey's MCT (n=6 per group). \*: p<0.05; \*: p<0.01; \*: p<0.001; \*: p<0.0001. TG: triglyceride; PE: phosphatidylethanolamine; PC: phosphatidylcholine; 13-hydroxyoctadecadienoic acid: 13-HODE; 5-hydroxyeicosatetraenoic acid: 5-HETE; 5-Oxo-eicosatetraenoic acid: 5-oxo-ETE, ISTD: internal standard.

**Fig. 2:** An insulin resistance bout affects hepatic circadian rhythmicity. (A) Transcriptomic analysis at D7 of mouse livers after S961 treatment. Transcriptomic profiles of control and S961-treated mice are presented as Volcano plots (FDR< 0.05). Lower panel: GSEA against the KEGG database. Most significantly enriched pathways are shown for each list. (B) Hepatic circadian rhythmicity at D7. Liver explants from S961-infused Per2::Luc transgenic mice were collected at D7. Detrended bioluminescence signals from explants (n=4) are shown. Cyclicity was assessed using the JTK cycle R script. (C). Transcriptomic analysis of mouse livers at D21. Data were obtained and analyzed as in (A). (D) Hepatic circadian rhythmicity at D21. Biological samples and data collection and data analysis were performed as described in (B) (n=4). FC: fold change; GSEA: gene set enrichment analysis; NES: normalized enrichment score.
**Fig. 3: S961 pretreatment disrupts the PPARα-mediated response to a high fat diet and clock component gene expression later-in-life. (A)** Transcriptomic profiles from control mouse livers at D85 fed with HFD vs CD. (B) Transcriptomic profiles from HFD-fed mouse livers at D85 after S961 vs vehicle pretreatment. Volcano plots (A, B) were graphed from a LIMMA differential gene expression analysis (FDR<0.05). Lower panels: Gene set enrichment analysis. GSEA was carried out using a list of down- or upregulated genes (|FC|>2) after HFD in either PBS- or S961-exposed mice. (C) PPARα target gene expressions. RT-qPCR data are expressed as means ± SEM of normalized gene expression relative to control conditions (PBS-exposed, CD-fed: D85-PBS-CD). p values were determined by a 2-way ANOVA followed by a post-hoc Tukey's MCT (n=10). *: p<0.05; **: p<0.01; ****: p<0.0001. (D) Core clock gene expression levels at D85 in HFD- vs CD-fed animals initially exposed to PBS (left) or S961 (right).

**Fig. 4: Transient insulin resistance event did not affect fenofibrate-dependent PPARα activation. (A)** Experimental setup. Wild type C57Bl6/J male mice were continuously exposed or not to S961 for 7 days. After a 2-week recovery period, animals were fed for 1 week with a chow diet (CD) or a CD supplemented with fenofibrate (CD+FF). Blood and livers were collected as indicated. (B) Glycemia from D0 to D21. (C): Glycemia at completion of the experiment. (D-G) Gene expression levels of PPARα target genes. RT-qPCR results are expressed as means ± SEM of normalized gene expression relative to control conditions (PBS-exposed, CD-fed: PBS-CD). p values were determined by a 2-way ANOVA followed by a post-hoc Tukey's MCT (n=8). ****: p<0.0001.

**Fig. 5: An insulin resistance bout affects the chromatin landscape at PPARα DNA binding sites later-in-life. (A)** Average normalized ATAC-Seq signals around PPARα binding sites (from-0.5 kb to + 0.5 kb). (B) Average normalized H3K27Ac signals around PPARα binding sites (from-0.5 kb to + 0.5 kb). (C) Average normalized H3K4me1 signals around PPARα binding sites (from-0.5 kb to + 0.5 kb). (D) Average normalized H3K27me3 signals around PPARα binding sites (from-0.5 kb to + 0.5 kb). Corresponding heatmaps of signal distribution are shown in **Supp. Fig. 10**.

**Fig. 6: HFD-activated, circadianly-regulated PPARα target genes are preferentially affected by S961 treatment. (A)** Time-dependent gene expression in mouse liver. Livers were collected every 3 hours and transcriptomic profiles were re-processed from the GSE 224446 dataset [27]. Transcripts exhibiting a 24h periodicity (+/-1 hour) were identified using the Bio_Cycle portal [71] and their expression profiles were plotted as a heatmap. (B) Biological term enrichment analysis (BTEA). A BTEA
of circadianly-expressed genes was carried out using the DAVID NCBI web site and the KEGG database.

(C) Daily variation of 42 PPARα direct target genes taken from (A). (D) Relative expression levels of circadian PPARα target genes in HFD- and in S961, HFD-exposed mouse livers vs PBS, CD-exposed mice. Intracolumn FC values were tested with an unpaired t-test. ns: non-significant, all other p values are < 0.05. (E) Histone H3K27Ac density around the TSS (± 0.5 kb) of circadian PPARα target genes. Top panel: Average normalized H3K27Ac signals around PPARα binding sites (from-0.5 kb to + 0.5 kb). Bottom panel: Heatmap of H3K27Ac signals around PPARα binding sites (from-0.5 kb to + 0.5 kb). Signals between -50bp and +250bp were averaged and compared by a 2-tailed t test. *: p value<0.05.

**Fig. 7: REV-ERBα co-binds to PPARα chromatin interaction sites. (A)** Average normalized H3K27Ac, H3K4me1 and H3K27me3 signals around REV-ERBα, BMAL1, CLOCK and CTCF binding sites (from-0.5 kb to + 0.5 kb) in HFD-fed, vehicle or S961-exposed mouse livers. **(B)** Features of genomic regions binding PPARα. Chromatin binding sites for PPARα were associated to the closest gene TSS (1Mb bin) using the Genomic Regions Enrichment of Annotations Tool (GREAT). Numbers of PPARα peaks and distance to associated TSS are shown. **(C)** Features of genomic regions binding REV-ERBα. Chromatin binding sites for REV-ERBα were associated to the closest gene TSS (1Mb bin) using GREAT. Numbers of REV-ERBα peaks and distance to associated TSS are shown. **(D)** Biological term enrichment analysis. A BTEA of genes displaying REV-ERBα- and PPARα binding sites gene was carried out using the DAVID NCBI web site and the KEGG database. **(E)** Colocalization of PPARα and REV-ERBα at *Acot4*, *Cfd*, *Cyp2b9* and *Cyp4a14* loci. TSS: transcription starting site.

**Fig. 8: PPARα ability to transactivate bona fide target genes is dependent on circadian rhythmicity.**

(A) Synchronized AML12 *Bmal*-Luc cells were used to monitor luciferase activity in real time. Bioluminescence signal is presented as detrended data. Pemafibrate treatment windows are shown. Histograms represent *Rev-erba* (*Nr1d1*) or *Ppara* gene expression level at both time points after 6h exposure to pemafibrate. Results are expressed as means ± SEM of normalized gene expression relative to T14-20 conditions. p values were determined by a 2-way ANOVA followed by a post-hoc Tukey's MCT (n=9). **\*\*\***: p<0.001 (B-D) PPARα agonist treatment of AML12 cells. Synchronized AML12 cells were either treated with 100 nM pemafibrate for 24h after synchronization (B) or for 6 hours as indicated. (C, D) Transcripts were quantified by RNA-Seq. Volcano plots were graphed from differential gene expression analysis (LIMMA, FDR<0.2). PPARα target genes are indicated as dot with a thick line. (E) Differential expression of selected PPARα target genes at T14-20 and T24-30. All genes showed statistically significant FC differences (p<0.05, unpaired t-test). (F) Effect of REV-ERBα antagonism on
pemafibrate-induced gene expression at T14-20. All histograms represent means ± SEM of normalized gene expression relative to control condition (6h pemafibrate 100 nM). p values were determined by a two-tail t test (n=6). *: p<0.05, **: p<0.01, ***: p<0.001.
<figure><img src="image_4.png" /><figcaption>Berthier et al., Figure 1</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>D7-PBS vs S961, CD (ZT9)</figcaption></figure>

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

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

<figure><img src="image_8.png" /><figcaption>D20-PBS vs S961, CD (ZT9)</figcaption></figure>

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

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

<figure><img src="image_11.png" /><figcaption>Explants processing
and culture</figcaption></figure>

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

<figure><img src="image_13.png" /><figcaption>Explants processing
and culture</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Berthier et al., Figure 2</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>A</figcaption></figure>

D85-PBS-CD vs D85-PBS-HFD (ZT9)

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

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

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

D85-PBS-HFD vs D85-S961-HFD (ZT9)

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

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

TG turnover

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

Microsomal FAO

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

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

<figure><img src="image_24.png" /></figure>

Lipid transport

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

Peroxisomal FAO

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

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

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

Berthier et al., Figure 3
<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>

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

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

<figure><img src="image_35.png" /><figcaption>Berthier et al., Figure 4</figcaption></figure>
A

ATAC-seq

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

B

H3K27Ac

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

C

H3K4me1

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

D

H3K27me3

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

Berthier et al., Figure 5
<figure><img src="image_40.png" /><figcaption>B</figcaption></figure>

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

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

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

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

Berthier et al., Figure 6
A

H3K27Ac

D85-PBS-HFD D85-S961-HFD

REV-ERBα

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

H3K4me1

D85-PBS-HFD D85-S961-HFD

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

H3K27me3

D85-PBS-HFD D85-S961-HFD

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

BMAL1

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

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

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

CLOCK

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

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

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

CTCF

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

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

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

B

PPARα

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

Number of associated genes per peak

<figure><img src="image_58.png" /></figure>

Distance to TSS (kb)

D

Genes associated to
REV-ERBα peaks

Genes associated to
PPARα peaks

<figure><img src="image_59.png" /></figure>

C

REV-ERBα

<figure><img src="image_60.png" /></figure>

Number of associated genes per peak

<figure><img src="image_61.png" /></figure>

Distance to TSS (kb)

<figure><img src="image_62.png" /></figure>

E

ChIP-Seq:
PPARα

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

Acot4

Cfd

Cyp2b9

Cyp4a14

<figure><img src="image_67.png" /></figure>

500 bp window

Berthier et al., Figure 7
A)

<figure><img src="image_68.png" /></figure>

B)

<figure><img src="image_69.png" /></figure>

C)

<figure><img src="image_70.png" /></figure>

D)

<figure><img src="image_71.png" /></figure>

E)

<figure><img src="image_72.png" /></figure>

F)

<figure><img src="image_73.png" /></figure>

Berthier et al., Figure 8