PRMT3-mediated post-translational adaptation to fasting regulates metabolic flexibility

Domain: neuroscience
Published: 2026-02-02
Editorial outcome: major_revision

Abstract
Obesity impairs metabolic flexibility—the capacity to adapt to fluctuating energy demands. Emerging evidence suggests that dietary interventions, particularly time-restricted feeding (TRF), may help restore this flexibility. In this study, we demonstrate that feeding upregulates PRMT3 and asymmetric dimethylarginine (ADMA)-containing proteins via insulin–pAKT signaling, while fasting reduces their expression. Pharmacological inhibition of PRMT3 attenuates diet-induced obesity (DIO) and enhances adipocyte glycolysis in male mice. Mechanistically, PRMT3 drives the expression of citrate transporter SLC25A1 during feeding through direct arginine methylation. A 16:8 TRF regimen normalizes PRMT3 and ADMA levels while suppressing SLC25A1 expression. Notably, PRMT3 inhibition recapitulates the metabolic benefits of 16:8 TRF and improves metabolic flexibility. Furthermore, adipocyte-specific deletion of Slc25a1 in male mice protects against DIO and enhances insulin sensitivity. Collectively, these findings identify PRMT3-mediated arginine methylation in vWAT as a nutrient-responsive regulatory axis that impairs metabolic flexibility in obesity, which is a potential therapeutic target.

Introduction
Recent research has highlighted the health benefits of dietary restriction, including fasting, calorie and nutrient restrictions1,2. These practices induce adaptive metabolic responses across various organs, improving weight management, promoting ageing delay, and contributing to health optimization3. Among these metabolic benefits, dietary restrictions have shown promising effects in restoring fuel switching, known as metabolic flexibility, which is impaired by overnutrition4. Although dietary-restricted rodent studies have consistently demonstrated positive outcomes5–7, human trials have shown mixed results, and challenges in compliance further complicate the reliability of these studies8–11. This controversy has sparked interest in investigating the molecular mechanisms behind the metabolic benefits of dietary restriction. Researchers are now seeking to develop methods that can replicate these beneficial effects, specifically to aid in combating obesity and associated cardiometabolic complications.

Several key signaling pathways and molecules—such as insulin-AKT, FOXO, mammalian target of rapamycin (mTOR), nicotinamide adenine dinucleotide (NAD+), AMP-activated protein kinase (AMPK), fibroblast growth factor 21 (FGF21), and circadian rhythms—have been identified as relevant in mediating the anti-obesity effects of dietary restriction7,12. Reducing the intake of the essential amino acid methionine has been shown to be particularly effective in preventing weight gain and enhancing metabolism in both rodent and human studies13–16. Furthermore, hepatic levels of S-adenosylmethionine (SAMe), a metabolite of methionine, positively correlate with body mass index (BMI) and contribute to fasting-induced metabolic adaptations17. Moreover, inhibition of methionine adenosyltransferase (Mat1a) in the liver also demonstrates potential in mitigating diet-induced obesity and metabolic syndrome18. However, it remains unclear how downstream SAMe-mediated substrate methylation might influence adiposity and fasting responses, particularly in adipose tissue.

Results
Diurnal Expression Patterns of Arginine Methylation in eWAT
Protein arginine methylation—a post-translational modification catalyzed by protein arginine methyltransferases (PRMTs)—utilizes S-adenosylmethionine (SAMe), derived from methionine, as a methyl donor19. To explore the relationship between arginine methylation and nutritional state, we assessed total arginine methylation levels across metabolically active tissues, including liver, brown adipose tissue (BAT), and epididymal white adipose tissue (eWAT), by collecting samples at 4 h intervals over a 24 h period.

We found that proteins bearing asymmetric dimethylarginine (ADMA)—but not mono-methylarginine (MMA) and symmetric dimethylarginine (SDMA)—displayed distinct diurnal expression patterns in eWAT. Specifically, MMA and ADMA levels decreased during the daytime fasting period and increased during the nocturnal feeding period, paralleling the natural feeding behavior of mice (Fig. 1a and Supplementary Fig. S1b, S1a). In contrast, no significant diurnal changes in MMA, ADMA, or SDMA levels were observed in the liver or BAT (Supplementary Fig. S1c, d).

To further characterize this regulation, we subjected mice to defined fasting and refeeding cycles. MMA and ADMA levels in eWAT declined during fasting and rapidly rebounded within 3–6 h of refeeding (Fig. 1b and Supplementary Fig. S1e). Notably, serum SAMe concentrations—though not tissue levels in eWAT—also dropped during fasting and recovered upon refeeding (Fig. 1c, d).

Next, we assessed the impact of diet-induced obesity (DIO) on arginine methylation. After 12 weeks of high-fat diet (HFD), eWAT from obese mice showed marked increases in MMA- and ADMA-modified proteins (Fig. 1e), and the normal circadian oscillation of ADMA-containing proteins was abolished (Supplementary Fig. S1f). Consistent with these findings, in vitro differentiation of inguinal WAT (iWAT) preadipocytes revealed a progressive increase in MMA and ADMA levels during adipocyte maturation (Supplementary Fig. S1g, h).

To investigate whether these findings translate to humans, we analyzed visceral adipose tissue from individuals with body mass indexes (BMIs) ranging from 22.5 to 31.4. Western blot analysis revealed significantly higher levels of MMA-modified proteins in individuals with BMI > 25 compared to those with BMI < 25 (Fig. 1f, g and Supplementary Fig. S2a), consistent with patterns seen in rodent DIO models. ADMA-containing proteins also increased with BMI in human adipose tissue (Fig. 1h–j). Notably, ADMA levels in visceral WAT did not differ significantly between normoglycemic and hyperglycemic individuals (Supplementary Fig. S2b, c). However, serum levels of free ADMA positively correlated with BMI in both males and females (Fig. 1k and Supplementary Fig. S2d, e). Together, these results demonstrate that elevated levels of MMA and ADMA in adipose tissue are associated with obesity in both mice and humans, suggesting that they may have potential as metabolic biomarkers.

PRMT3 Regulates adipose ADMA during fasting in an insulin–pAKT-dependent manner
The diet-dependent changes in MMA and ADMA levels suggest that Type I PRMTs—enzymes responsible for catalyzing mono- and asymmetric dimethylarginine modifications on target proteins—likely mediate the fasting- and obesity-associated methylation patterns observed in rodent and human visceral adipose tissue. To identify the specific PRMT involved in regulating arginine methylation during fasting and refeeding in white adipose tissue, we first examined the expression of Prmt genes before and after 8 days of preadipocyte differentiation in vitro. This allowed us to compare Prmt expression profiles between preadipocytes and mature adipocytes. Notably, mRNA levels of Prmt1–6 (except Prmt2) were upregulated by day 8, with Prmt3 and Prmt5 showing the most pronounced increases (Supplementary Fig. S3a). Complementary RNA sequencing analysis of epididymal white adipose tissue (eWAT) from wild-type mice at Zeitgeber Time (ZT) 4 and ZT16 confirmed the expression of all Prmt genes except Prmt8, with Prmt1, Prmt4, and Prmt5 being the most abundantly expressed (Supplementary Fig. S3b).

PRMT1 has previously been identified as the predominant enzyme responsible for catalyzing ADMA formation in various tissues20. To evaluate its role in white adipose tissue, we assessed PRMT1 abundance in eWAT following fasting. No significant change in PRMT1 protein levels was observed in eWAT after fasting (Supplementary Fig. S3c, d). To further investigate, we administered the established PRMT1 inhibitor AMI-1 to mice subjected to a 12 h fast. While AMI-1 treatment did lead to a moderate reduction in ADMA levels in eWAT, inhibition of PRMT3 using SGC707 produced a markedly greater effect (Supplementary Fig. S3e, f).

Given that adipocyte-specific deletion of Prmt1 has been shown to promote insulin resistance in diet-induced obesity (DIO) models21, we shifted focus to Prmt3, Prmt4, and Prmt5 as alternative candidates. In line with mRNA expression data, protein levels of PRMT3, PRMT4, and PRMT5 were all upregulated during adipogenic differentiation (Fig. 2a), suggesting potential functional roles in mature adipocytes. Notably, only PRMT3 and PRMT4—both Type I PRMTs—exhibited dynamic changes in expression that mirrored the patterns of MMA and ADMA during fasting and refeeding (Fig. 2b, c).

Further temporal analysis in mouse eWAT revealed that PRMT3 protein levels followed a diurnal rhythm, decreasing during the daytime fasting phase and increasing during nocturnal feeding (Fig. 2d, e). No comparable diurnal pattern was detected in PRMT3 levels from brown adipose tissue (BAT) or liver (Supplementary Fig. S3g), aligning with the tissue-specific ADMA and MMA changes observed in eWAT. These findings support a model in which PRMT3 acts as a key regulator of diurnal arginine methylation in eWAT proteins.

To validate the specificity of the anti-PRMT3 antibody used in our study, we expressed and purified recombinant mouse PRMT3 protein (rPRMT3) in a prokaryotic system. Immunoblotting confirmed that the antibody specifically recognized rPRMT3 (Supplementary Fig. S3h). We also examined PRMT4 protein levels in eWAT, which did not display diurnal variation (Supplementary Fig. S3i), distinguishing it from PRMT3. Further analysis revealed that PRMT3 protein levels in eWAT progressively declined with increasing fasting duration—showing modest reductions after 12 h and reaching an nadir at 36 h (Fig. 2f, g). In contrast, PRMT3 levels remained unchanged in BAT and liver across time points (Supplementary Fig. S3j), reinforcing its role as the primary adipose-specific PRMT responsive to nutritional cues.

To investigate the metabolic role of PRMT3-mediated arginine methylation in eWAT during fasting, we administered SGC707—a selective chemical inhibitor of PRMT3—to mice. Following a 12-hour fast, eWAT from untreated mice showed no significant change in MMA or ADMA levels. However, the combination of SGC707 treatment and 12 h fasting markedly accelerated the decline in MMA and ADMA levels, producing a reduction comparable to that observed after 36 h of fasting (Fig. 2h, i). Notably, PRMT3 inhibition had no effect on ADMA levels in ad libitum–fed mice (Supplementary Fig. S4a, b). These findings indicate that PRMT3 specifically mediates the fasting-induced, diurnal regulation of arginine methylation in visceral adipose tissue.

To identify the upstream signals regulating PRMT3, we investigated the pathways activated by feeding that promote arginine methylation in white adipose tissue. Glucose-induced insulin secretion facilitates energy storage in WAT via activation of AKT signaling. Specifically, PRMT3 protein levels—markedly reduced in mouse eWAT after 36 h of fasting—were rapidly restored within 3 h of either insulin injection or refeeding (Fig. 3a, b). This restoration coincided with increased phosphorylation of AKT at serine 473 and an elevated pAKT/AKT ratio following both insulin administration and refeeding (Fig. 3a, b). Furthermore, insulin treatment after fasting elevated ADMA levels in eWAT, an effect that was abrogated by PRMT3 inhibition with SGC707 (Supplementary Fig. S4c, d). These findings indicate that insulin–pAKT signaling acts upstream of PRMT3, mediating feeding-induced arginine methylation in visceral adipose tissue.

To determine whether PRMT3 is regulated by the insulin–pAKT signaling pathway, we treated mice with MK-2206, a well-characterized AKT phosphorylation inhibitor22. MK-2206 effectively blocked the insulin-induced upregulation of PRMT3 in eWAT, while not affecting PRMT3 levels in brown adipose tissue (BAT) or liver (Fig. 3c, d and Supplementary Fig. S4e, f). In vitro, MK-2206 treatment of differentiated adipocytes similarly reduced PRMT3 protein levels, whereas PRMT5 levels remained unchanged (Supplementary Fig. S4g). Interestingly, PRMT4 levels increased following MK-2206 treatment (Supplementary Fig. S4g), suggesting distinct regulatory mechanisms among PRMT isoforms. Notably, PRMT3 exhibited a band shift upon both insulin and MK-2206 treatment, similar to that observed for AKT, implying a possible direct post-translational modification of PRMT3 in response to insulin signaling and pAKT inhibition (Supplementary Fig. S4h).

To determine whether PRMT3 is a direct phosphorylation target of pAKT, we overexpressed a MYC-tagged PRMT3 plasmid and performed immunoprecipitation assays. These assays demonstrated that PRMT3 could co-immunoprecipitate with pAKT, indicating a physical interaction (Fig. 3e). Using a phospho-threonine/serine (pT/S) antibody, we confirmed that PRMT3 was phosphorylated at threonine/serine residues (Fig. 3f). An in vitro phosphorylation assay further showed that pAKT enhances PRMT3 phosphorylation (Supplementary Fig. S4i).

To validate this finding in adipocytes, we used an adenoviral construct expressing FLAG-tagged PRMT3 to infect differentiated adipocytes in vitro. Immunoprecipitation with a FLAG antibody confirmed phosphorylation of PRMT3 at T/S residues (Fig. 3g). Importantly, MK-2206 treatment reduced PRMT3 phosphorylation following insulin stimulation, further supporting the role of pAKT in this process (Fig. 3h). Moreover, co-treatment with MK-2206 and 12-hour fasting decreased both PRMT3 and ADMA levels in mouse eWAT (Fig. 2i, j) and similarly suppressed MMA and ADMA levels in cultured adipocytes (Supplementary Fig. S4j, k). Together, these results establish that the insulin–pAKT–PRMT3 signaling axis governs fasting-regulated arginine methylation in visceral adipose tissue.

Inhibition of PRMT3 ameliorates diet-induced obesity by enhancing adipocyte glucose metabolism
Given the role of PRMT3 in mediating arginine methylation in response to nutritional state in eWAT, we investigated its physiological relevance in metabolic regulation. Mice were treated with the selective PRMT3 inhibitor SGC70723, at 30 mg/kg. Tissue distribution analysis revealed that SGC707 was primarily localized in the liver and feces, with detectable concentrations in adipose depots, including eWAT (Supplementary Fig. S5a, b).

After 8 weeks on a high-fat diet (HFD), mice receiving SGC707 for 10 weeks showed significantly reduced body weight compared to vehicle-treated controls (Fig. 4a, b). Body composition analysis confirmed a marked reduction in fat mass (Fig. 4c, d). Metabolic testing demonstrated improved glucose tolerance and insulin sensitivity, as evidenced by glucose and insulin tolerance tests (GTT and ITT) (Fig. 4e, f). SGC707 treatment also led to significant reductions in the weights of brown adipose tissue (BAT), inguinal WAT (iWAT), and eWAT (Fig. 4g, h). Liver weight was also decreased, accompanied by a reduction in hepatic steatosis associated with diet-induced obesity (DIO) (Fig. 4h–j).

Histological analysis revealed smaller adipocytes in eWAT following treatment (Fig. 4j). Early intervention with SGC707 prevented HFD-induced weight gain and improved metabolic outcomes without affecting lean muscle mass, indicating selective fat loss (Supplementary Fig. S6a–g). Liver and eWAT histology confirmed reduced steatosis and smaller adipocytes (Supplementary Fig. S6h). GTT and ITT results were similarly improved with early SGC707 administration (Supplementary Fig. S6i, j). Despite the reductions in adiposity and body weight, SGC707-treated mice showed no significant differences in oxygen consumption, carbon dioxide production, or food intake compared to controls (Fig. 4k–n and Supplementary Fig. S7a–d). However, nighttime energy expenditure was specifically elevated (Supplementary Fig. S7e, f), and the delta respiratory exchange ratio (ΔRER) increased significantly, indicating improved metabolic flexibility and a shift toward greater carbohydrate utilization (Fig. 4o, p and Supplementary Fig. S7g, h). In addition, there were no difference in food intake (Supplementary Fig. S7i, j).

The specific inhibition of MMA and ADMA formation in eWAT by SGC707—alongside improvements in glucose tolerance (GTT), insulin sensitivity (ITT), and increased ΔRER—prompted further investigation into the effects of PRMT3 inhibition on adipocyte glucose metabolism. Stromal vascular fraction (SVF) preadipocytes were isolated from mouse eWAT and differentiated in vitro. Following 12 h of SGC707 treatment, the expression of key glucose metabolism genes—including Pkm, Gapdh, Hk2, and Glut4—was significantly upregulated compared to vehicle-treated controls (Fig. 4q). Seahorse metabolic flux analysis further revealed that SGC707 enhanced the glycolytic capacity of these differentiated adipocytes (Fig. 4r, s).

Similarly, in cultured porcine white adipocytes, SGC707 treatment increased the expression of glucose metabolism-related genes, including Pkm, Gapdh, Tpi1, and Pgam1 (Supplementary Fig. S7k). Targeted metabolomic profiling confirmed significant elevations in glycolytic intermediates and end-products, including fructose-6-phosphate, fructose-1,6-bisphosphate, glucose-6-phosphate, and lactic acid (Fig. 4t). In addition, metabolomic analysis of eWAT following SGC707 administration combined with 12 h fasting identified glucose and citrate as the top two significantly altered metabolites (Supplementary Fig. S7l). These findings indicate that the increased ΔRER observed in DIO mice treated with SGC707 reflects enhanced adipocyte glucose metabolism, linking PRMT3 inhibition to improved metabolic flexibility and energy utilization in white adipose tissue.

To further investigate the in vivo function of PRMT3 in epididymal fat, we employed an AAV-mediated adipocyte-specific knockdown (KD) of Prmt3 (Supplementary Fig. S8a). This approach effectively reduced PRMT3 protein levels in eWAT, confirming successful gene silencing (Supplementary Fig. S8b). Eight weeks after local AAV injection into eWAT of DIO mice, no significant differences in body weight or food intake were observed between KD and control groups (Supplementary Fig. S8c, d).

Metabolic chamber analysis revealed that Prmt3 KD led to increased VO₂, heat production, and RER, indicating enhanced energy expenditure and a shift toward carbohydrate utilization (Supplementary Fig. S8e–g). Furthermore, Prmt3 KD improved glucose clearance, as demonstrated by reduced blood glucose levels and area under the curve (AUC) during glucose tolerance testing (Supplementary Fig. S8h). These findings suggest that adipocyte-specific inhibition of PRMT3 in eWAT enhances systemic glucose metabolism and promotes metabolic flexibility in obese mice.

SLC25A1 Is a Direct Target of PRMT3 in a Feeding-Dependent Manner
Metabolic flux analysis using 13C-labeled glucose revealed an increased incorporation of isotopic carbon into citrate in eWAT (Supplementary Fig. S7m). Given the observed reduction in citrate levels following PRMT3 inhibition (Supplementary Fig. S7l, Supplementary Data 1), we hypothesized that PRMT3-mediated arginine methylation may regulate citrate metabolism. Citrate exports from mitochondria to the cytosol—crucial for de novo lipogenesis—is mediated by the citrate transporter SLC25A124,25. To assess whether SLC25A1 was a feeding-dependent and tissue-specific substrate of PRMT3, we examined its expression across metabolic tissues. SLC25A1 protein levels were specifically reduced in eWAT, but not in liver or brown adipose tissue (BAT), following 12 hours of fasting combined with SGC707 treatment (Fig. 5a, b). Although previous bioinformatic analyses had predicted potential interactions between SLC25A1 and either PRMT3 or PRMT526, our co-immunoprecipitation (Co-IP) experiments in 293T cells overexpressing PRMT3 and SLC25A1 confirmed a specific interaction: SLC25A1 co-precipitated with PRMT3, but not with PRMT5 (Fig. 5c, S9a). These findings identify SLC25A1 as a direct and specific target of PRMT3 in a nutritionally regulated, tissue-selective manner. Further evidence of arginine methylation on SLC25A1 was obtained using anti-ADMA and anti-MMA antibodies, which successfully pulled down SLC25A1, indicating the presence of both mono-methylated and asymmetrically di-methylated arginine residues on the protein (Fig. 5d and Supplementary Fig. S9b). Treatment with the PRMT3 inhibitor SGC707 led to a reduction in methylated SLC25A1 levels, as detected by ADMA-specific immunoblotting (Fig. 5e and Supplementary Fig. S9c).

Sequence alignment revealed that arginine residues R282 and R285—located within the citrate-binding domain of SLC25A1—are conserved across species and represent potential methylation sites (Fig. 5f, g). Structural modeling indicated that mutation of these residues (SLC25A1Mut) reduced the protein’s interfacial contact area, potentially altering its conformation (Fig. 5g). Co-IP experiment in 293T cells expressing PRMT3 with either SLC25A1WT/SLC25A1Mut confirmed that methylation was significantly reduced in the mutant form (Fig. 5h). Consistently, anti-ADMA antibodies pulled down SLC25A1WT but not SLC25A1Mut, indicating that R282 and R285 are the primary sites of asymmetric dimethylation (Fig. 5i). Moreover, the interaction between PRMT3 and SLC25A1 was weakened by mutation of these residues (Fig. 5j).

To assess the functional consequence of arginine methylation on protein stability, we performed a cycloheximide (CHX) chase assay. The results demonstrated that PRMT3 stabilized SLC25A1 protein in a methylation-dependent manner (Fig. 5k, l), supporting a model in which arginine methylation enhanced SLC25A1 protein stability and function.

The expression of SLC25A1 in eWAT displayed a diurnal pattern that closely mirrored that of PRMT3 (Fig. 6a, b). In line with the fasting-induced reduction in PRMT3, SLC25A1 protein levels were slightly decreased after 12 hours of fasting and markedly reduced after 36 hours (Fig. 6c, d). Refeeding for just 3 hours after fasting restored SLC25A1 levels in eWAT (Fig. 6e, f), further supporting its feeding responsiveness.

To evaluate the dependence of SLC25A1 expression on feeding time, we subjected mice to night-restricted feeding (NRF) or daytime-restricted feeding (DRF) for one month (Supplementary Fig. S9d). These experiments revealed a strong dietary association: SLC25A1 protein levels increased with feeding and decreased with fasting (Fig. 6g, h). Under DRF and NRF conditions, SLC25A1 expression at Zeitgeber Time 0 (ZT0) and ZT12 showed an anti-phasic pattern (Fig. 6i, j), consistent with feeding status. Notably, Slc25a1 mRNA levels in eWAT remained unchanged across ad libitum (AL), NRF, and DRF conditions (Supplementary Fig. S9e, f), indicating that regulation occurs primarily at the post-translational level.

Insulin injection restored SLC25A1 protein levels in fasted mice, mimicking the effects seen with PRMT3 (Fig. 6k, l). Conversely, treatment with MK-2206, an inhibitor of AKT phosphorylation, blocked the insulin-induced increase in SLC25A1 expression (Fig. 6k, l). These effects were specific to eWAT and were not observed in BAT or liver (Supplementary Fig. S9g). Similarly, MK-2206 treatment reduced SLC25A1 protein levels in cultured adipocytes (Fig. 6m, n). Furthermore, insulin increased SLC25A1 levels under fasting conditions, but this effect was abolished when PRMT3 was inhibited with SGC707 (Fig. 6o, p), demonstrating that insulin–pAKT signaling regulates SLC25A1 expression via PRMT3. Together, these findings establish that SLC25A1 is a direct methylation target of PRMT3 and is upregulated in response to feeding through the insulin–pAKT–PRMT3 signaling axis.

Time-restricted feeding reduces PRMT3/SLC25A1 levels and ameliorates diet-induced obesity
Recent studies have underscored the metabolic benefits of time-restricted feeding (TRF) and intermittent fasting, particularly their anti-obesity and anti-diabetic effects6,27. Given the sensitivity of PRMT3-mediated protein methylation and SLC25A1 expression to fasting and insulin–pAKT signaling, we examined whether TRF modulates the PRMT3–SLC25A1 axis in epididymal white adipose tissue (eWAT).

Using a 16:8 TRF regimen (feeding from 9 p.m. to 5 a.m.) under a regular chow diet, we observed a marked reduction in MMA, ADMA, PRMT3, and SLC25A1 protein levels in eWAT, with no corresponding changes in the liver (Fig. 7a, b). When the same TRF protocol was applied to mice on a high-fat diet (HFD) (Fig. 7c), it led to a significant reduction in body weight without affecting total food intake (Fig. 7d and Supplementary Fig. S10a–c). Metabolic chamber analysis revealed that TRF did not significantly alter their overall oxygen consumption or carbon dioxide production (Supplementary Fig. S10d–i). However, nighttime energy expenditure and the ΔRER were significantly increased (Fig. 7e, f and Supplementary Fig. S10j–l), suggesting enhanced metabolic flexibility. In addition, mice under the 16:8 TRF protocol exhibited increased physical activity during the nighttime feeding window (Supplementary Fig. S10m, n). Further analysis revealed that TRF significantly improved glucose homeostasis. Mice subjected to TRF exhibited lower blood glucose levels during glucose tolerance tests (GTT) and insulin tolerance tests (ITT), along with reduced AUC and increased area above the curve (AAC), indicating enhanced glucose clearance and insulin sensitivity (Supplementary Fig. S10o–r).

After both 5 and 12 weeks of TRF, significant reductions in liver and adipose tissue weights were observed compared to ad libitum-fed controls, while skeletal muscle mass remained unchanged, highlighting the tissue specificity of TRF-induced fat loss (Supplementary Fig. S10s, t). TRF also effectively prevented high-fat diet (HFD)-induced hepatic steatosis, as shown by histological analysis (Supplementary Fig. S10u, v). Notably, elevated serum S-adenosylmethionine (SAMe) levels in diet-induced obese (DIO) mice were significantly reduced following 16:8 TRF (Supplementary Fig. S10w), further supporting the role of TRF in modulating methylation-related metabolic pathways.

To explore the time-dependent metabolic effects of PRMT3 inhibition, we assessed the impact of a single SGC707 injection in combination with 12 hours of fasting. This treatment effectively reduced total ADMA levels in eWAT at ZT12 but not at ZT0 (Supplementary Fig. S11a, b), indicating a temporal dependency in PRMT3 activity. To determine whether the timing of PRMT3 inhibition influenced metabolic outcomes, SGC707 was administered to HFD-fed mice at either ZT0 or ZT12. In both cases, SGC707 significantly reduced body weight, weight gain, and fat mass without affecting lean mass (Fig. 7g, h and Supplementary Fig. S11c–f). Glucose tolerance was also improved in treated mice (Supplementary Fig. S11g, h). Consistent with findings from the 16:8 TRF protocol, SGC707 treatment did not alter overall oxygen consumption or carbon dioxide production (Supplementary Fig. S11i, j). However, injections at either ZT0 or ZT12 led to a significant increase in the ΔRER, suggesting enhanced metabolic flexibility. These effects occurred without changes in food intake and closely mirrored those induced by TRF (Fig. 7i, j and Supplementary Fig. S11k).

Significant volume reductions in white adipose tissues and SLC25A1 protein levels were observed in both ZT0 and ZT12 SGC707-treated groups (Supplementary Fig. S11l–n). Following 12 weeks of 16:8 TRF, marked reductions in MMA, ADMA, PRMT3, and SLC25A1 protein levels were detected specifically in eWAT at ZT13 (fasted state) (Fig. 7k–m and Supplementary Fig. S12) but not at ZT1 (fed state) or in the liver (Supplementary Fig. S13). Interestingly, while SLC25A1 levels decreased at both ZT13 and ZT1 in eWAT, the reduction was more pronounced in the fasted state (Fig. 7l, m and Supplementary Fig. S12).

Although both ZT0 and ZT12 SGC707 treatment groups exhibited comparable anti-diet-induced obesity (anti-DIO) effects, fasting glucose levels were significantly lower in mice treated at ZT12 compared to those treated at ZT0 (Fig. 7n). In addition, key genes involved in lipid and glucose metabolism—including Cpt2, Mcad, Fasn, Acaca, Gapdh, Hk2, and Glut4—were more strongly upregulated in eWAT of ZT12-treated mice (Fig. 7o). These findings suggest that pharmacological inhibition of PRMT3 by SGC707 mimics the beneficial metabolic effects of 16:8 TRF by promoting glucose metabolism and suppressing SLC25A1 expression in a time-of-day–dependent manner.

Adipocyte-specific Slc25a1 deletion enhances glucose metabolism in a time-dependent manner
Given the association between reduced SLC25A1 levels and weight loss, we investigated the functional role of SLC25A1 in adipocytes in vivo. Protein levels of SLC25A1 were significantly upregulated during adipogenesis in white adipocytes and further increased in eWAT following diet-induced obesity (DIO) induced by a high-fat diet (HFD) (Fig. 8a–c). To assess the adipocyte-specific function of SLC25A1, we generated Slc25a1 adipocyte-specific knockout (Slc25a1-AKO) mice by crossing Adiponectin-Cre mice with Slc25a1-floxed mice. In Slc25a1-AKO mice, SLC25A1 protein levels were markedly reduced in eWAT, while liver expression remained unaffected, confirming tissue specificity (Fig. 8d). At 13 weeks of age, there were no significant differences in body weight, fat mass, or lean mass between Slc25a1-AKO and wild-type (WT) littermates under baseline conditions (Supplementary Fig. S14a–c). However, Slc25a1-AKO mice exhibited improved glucose tolerance, as evidenced by lower blood glucose levels during glucose tolerance testing (GTT) (Supplementary Fig. S14d). In addition, there were no differences in the weights of ATs and liver (Supplementary Fig. S14e).

Upon HFD feeding, Slc25a1-AKO mice exhibited reduced body weight beginning at 5 weeks post-treatment, primarily attributable to a decrease in lean mass, while fat mass remained unchanged (Fig. 8e, f). Deletion of Slc25a1 significantly enhanced glucose metabolism, as shown by improved glucose clearance and insulin sensitivity in GTT and ITT) (Fig. 8g, h). Fasting blood glucose levels were also significantly lower in Slc25a1-AKO mice compared to WT controls (Fig. 8l). Despite these metabolic benefits, no differences were observed in adipose tissue mass or adipocyte size between the two groups (Fig. 8i, j), suggesting that changes in adiposity did not drive the improvements.

Interestingly, liver weight was reduced, and histological analysis revealed amelioration of HFD-induced hepatic steatosis in Slc25a1-AKO mice (Fig. 8j). In addition, serum biochemical analysis showed significant reductions in total cholesterol and HDL cholesterol levels (Fig. 8k). Treatment of cultured mouse eWAT-derived adipocytes with CTPI-2, a specific inhibitor of SLC25A128, recapitulated the enhanced glycolytic capacity observed with PRMT3 inhibition (Fig. 8m, n). Seahorse metabolic flux analysis further confirmed that both genetic deletion and pharmacological inhibition of SLC25A1 promoted compensatory glycolysis in adipocytes (Fig. 8o).

RNA sequencing of eWAT from Slc25a1-AKO mice revealed that loss of Slc25a1 predominantly impacted genes involved in lipid and carbohydrate metabolism (Fig. 8p). Notably, glycolysis-related genes such as Gapdh, Pdk1, Hk2, Gpam, Pkm, and Acss2 were significantly upregulated (Fig. 8q and Supplementary Data 2). These transcriptomic changes were accompanied by increased protein levels of pyruvate carboxylase (PC) and GAPDH in eWAT (Fig. 8r, s), further supporting enhanced glycolytic flux.

Metabolic chamber analyses showed that Slc25a1-AKO mice exhibited elevated oxygen consumption, carbon dioxide production, and energy expenditure (EE) during the early dark phase (ZT12–ZT18), coinciding with the mice’s active/feeding period (Fig. 8t and Supplementary Fig. S14f–i). Together, these findings suggest that lowering SLC25A1 levels in eWAT—whether through Slc25a1 deletion, ZT12-targeted SGC707 injection, or 16:8 time-restricted feeding—enhances adipocyte glucose metabolism and energy expenditure, thereby preventing diet-induced obesity and improving insulin sensitivity.

We then locally injected AAV9-Fabp4-Slc25a1 into the eWAT of high-fat diet–induced obese mice and subsequently treated them with the PRMT3 inhibitor SGC707 for four weeks. Two weeks post-injection, Slc25a1 overexpression markedly impaired glucose tolerance following intraperitoneal glucose administration (Supplementary Fig. S15a, b). Consistently, SGC707 treatment failed to improve glucose tolerance in mice overexpressing Slc25a1 (Supplementary Fig. S15a, b). Furthermore, metabolic chamber analyses revealed that Slc25a1 overexpression significantly decreased ΔRER (Supplementary Fig. S15c, d) and heat production (Supplementary Fig. S15e, f), and SGC707 treatment did not reverse these effects. Similarly, SGC707 treatment failed to increase PC protein levels or Hk2 mRNA expression in eWAT from Slc25a1-overexpressing mice (Supplementary Fig. S15g, h). Together, these results demonstrate that Slc25a1 overexpression counteracts the beneficial metabolic effects of PRMT3 inhibition, supporting the notion that reduced SLC25A1 mediates, at least in part, the phenotype observed with PRMT3 loss of function.

Methods
Animal care and mouse strains
Animal studies were conducted in the accredited SPF animal facility in the CAM-SU Genomic Resource Center and approved by the Animal Care and Use Committee of CAM-SU under a protocol of ZJ-2021-1 approved on 12/24/2021. Mice were maintained at room temperature (23–25°C) when indicated with a 12 h light/dark cycle (illuminated from 8:00) and free access to acidified water and radiated and autoclaved food unless specified. All mice were maintained on a C57BL/6N background. C57BL/6N-Slc25a1tm1c(EUCOMM)Wtsi/Cmsu were obtained by injecting mutant mouse ES cells into mouse blastocysts CAM-SU Genomic Resource Center. The Adipocyte-specific deletion of Slc25a1 was generated by breeding Adiponectin-Cre (stock #010803) and Slc25a1-flox/flox mice. The pups were identified by PCR followed by sequence analysis using primers (Cre-F: ACGGACAGAAGCATTTTCCA; Cre-R: GGATGTGCCATGTGAGTCTG; flox-F: GGAAGGCAATCCTAGCAGGTGGGTC; flox-R: CAGCTGCGTCTTCACGTATTCGGTC).

Wild type male C57BL/6N were obtained from CAM-SU Genomic Resource Center. For fasting experiments, male C57BL/6N (10–12 weeks of age) were divided into three groups: ad libitum and fasted (12 h or 36 h). The fasted group fasted for 12 h or 36 h, began at ZT12, and were sacrificed at ZT0 with the ad libitum group. For fasting and refeeding experiments, male C57BL/6N (10–12 weeks of age) were divided into two groups: fasted and refed. The fasted group fasted for 36 h, and the refed group fasted for 36 h and then refed the chow diet for 3–6 h before the study.

Human samples
Visceral adipose tissues from individuals with different BMIs were surgically collected from the Second Affiliated Hospital of Soochow University under a protocol of JD-LK-2020-038-01 approved by the Ethics Community of the Second Affiliated Hospital of Soochow University. Human serum was isolated from venous blood collection under standard procedures from the Second Affiliated Hospital of Soochow University. The signed informed consent has been collected from all participants.

Quantitation of ADMA in human plasma samples
Human plasma samples were extracted with an extraction solvent containing internal standards (40 ng/mL d7-ADMA). The whole extraction process was the same as for drug analysis. The ADMA standard curve was made by dissolving the standard to a series concentration using 5% BSA as a surrogate matrix. Standards d7-ADMA (purity: 98%) were purchased from Cambridge Isotope Laboratories (Tewksbury, MA, USA), and ADMA (purity: 98%) from targetMOl (Boston, MA, USA).

UPLC separation was performed using a Shimadzu Nexera X2 liquid chromatography system with a binary gradient pump, autosampler (4°C), and column oven (40°C) for chromatographic separation. A hydrophilic interaction liquid chromatographic column (HILIC, 150 × 2.1 mm, 1.7 µm, Atlantis, Waters) was applied for separation. The injection volume was 2 µL. A binary eluent system was used at a flow rate of 0.3 mL/min. Mobile phase A was an aqueous buffer containing 10 mM ammonium formate, and 0.1% formic acid, and mobile phase B was acetonitrile with 0.1% formic acid. The programmed gradient was 0-0.5 min: 90% B, 0.5–1 min: 90% to 70% B, 1–6 min: 70% to 65% B, 6–6.5 min: 65%-40% B, 6.5-7.5 min: 40% B, 7.5–8 min: 40%-90% B, 8-9 min: 90% B. Multiple reaction monitoring (MRM) data acquisition mode (for ADMA: 203.1–46.3) were performed on an AB Sciex 6500 QTrap® hybrid triple quadrupole/linear ion trap mass spectrometer with IonDrive™ Turbo V ion source and ESI probe. Spectra were obtained in ESI+ under the following conditions: curtain gas 35 psi, ion spray voltage (IS) 5500 V, the pressure of ion source gas 1 55 psi, ion source gas 2 55 psi, declustering potential 60 V, entrance potential 10 V, and collision cell exit potential 10 V. Data collection and quantification were carried out by SCIEX Analyst and SCIEX OS, respectively.

Feeding schedule and diets
For all high-fat diets (HFD, XTHF60, Xietong Pharmaceutical Bioengineering) feeding experiments, mice began dietary intervention at 6–8 weeks old with a body weight of ~25 g. Under 16:8 TRF, mice were allowed access to food between ZT13 (9 p.m.) and ZT21 (5 a.m.). Food access was regulated by transferring mice daily between cages with food and water and cages with water only. As a control, ad-lib-fed mice were also transferred between feeding cages at the same time.

For NRF and DRF, mice were allowed access to food between nighttime (8 p.m. to 8 a.m.) or daytime (8 a.m. to 8 p.m.) for one month, separately.

Drug treatments
SGC707 (S7832, PRMT3 inhibitor), CTPI-2 (S2968, SLC25A1 inhibitor) and MK-2206 2HCL (S1078, AKT1/2/3 kinase inhibitor) were purchased from MaoKang Biotechnology. For prevention or reversion phenotypes of mice fed an HFD, SGC707 was administered at 10 mg/kg or 30 mg/kg via intraperitoneal injection on alternated days, respectively.

SGC707 was diluted in DMSO (at 5% final concentration), and DMSO served as the vehicle control.

For MK-2206 treated experiments, MK-2206 was diluted in DMSO (at 5% final concentration) and administered at 50 mg/kg via intraperitoneal injection, with DMSO serving as a vehicle control.

Drug analysis by using HPLC-MS/MS
The drug was fully dissolved in methanol at 1 µg/mL for HPLC-MSMS analysis. The drug compounds in mice tissue samples were first extracted with methanol and acetonitrile (1:9, v/v). After thorough vortex and ultrasonication, the samples were centrifuged (×18,000 g, 4°C for 15 min) to extract the supernatants. Following vacuum dryness (Savant SPD2010, Thermo scientific), the residues were redissolved in acetonitrile and H2O (1:1, v/v) for further analysis. All analytical chemicals, including formic acid (Honeywell), methanol (Macklin), acetonitrile (Fisher) and H2O (ThermoFisher) used in the analysis were MS-grade.

The extracts were analyzed using HPLC-QTOF MS/MS (1290II-6530C, Agilent) equipped with a Thermo Hypersil GOLD aQ C18 column (2.1 × 100 mm, 1.9 μm) with a 24 min gradient. Flow rate 0.4 ml/min, Column temperature 40°C. MS-grade water with 0.1% formic acid (v/v) was used as mobile phase A, and MS-grade ACN with 0.1% formic acid (v/v) was used as mobile phase B. The gradient started at 5% B, keep 1 min, gradually increased to 55% B at 12 min, and to 100% B at 17 min and maintained until 20 min, then reverted to the initial condition of 5% at 22 min and then held at 5% to 24 min. Sample analysis was conducted in positive ionization mode (ESI+) and collected in full-scan and targeted MS/MS modes, respectively. The nitrogen temperature of the ion source was set to 250°C, the dry gas 10 L/min, the nebulizer 45 psi, the sheath gas temperature 350°C, the sheath gas flow rate 11 L/min, the nozzle voltage 1000 V, the Vcap 4000 V, the fragmentor 100 V, the skimmer 45 V, and the detection window 20 ppm. For targeted MS/MS analysis, the collision energy was set to 20 eV.

Data was collected by Agilent data acquisition software in centroid mode. The Masshunter quantitative analysis software from the vendor was further used to extract the targeted ion chromatogram and MSMS spectra and to calculate the retention time, peak areas, signal-to-noise ratio, etc.

HEK293T, primary adipocyte and porcine adipocyte culture
HEK293T cells (CRL-3216, ATCC) were grown in Dulbecco’s modified eagle medium (DMEM)/high glucose with 10% fetal bovine serum (FBS) as well as 100 μ/mL penicillin and 100 μg/mL streptomycin (15140-122, Gibco). All cell lines were maintained under standard conditions (37°C, 5% CO2).

For primary adipocyte culture, subcutaneous and epididymal adipose tissues from 6- to 8-week-old mice were minced into small pieces, followed by incubation with 1.25 mg/mL Collagenase from Clostridium histolyticum (C0130-5G, Sigma-Aldrich) in DMEM containing Penicillin/Streptomycin for 40–50 min in a 37°C shaker water bath. Cell suspension buffer was filtered through a 100 μm cell strainer and centrifugated for 10 min at 700×g to get the stromal vascular fraction pellet. For white adipocyte differentiation, the stromal vascular fraction was cultured in DMEM complete medium to 90–95% confluence and then differentiated in DMEM complete medium containing (0.64 μM dexamethasone, 0.625 mM IBMX, 1.6 μg/ml insulin and 1 μM rosiglitazone) for 4 days, change medium every 2 days. Cells were then changed to DMEM complete medium containing 1.6 μg/ml insulin every 2 days until more than 90% of cells were fully differentiated to mature adipocytes before the following experiment. Insulin (PB180432) was obtained from Pricella Biotechnology; Dexamethasone (D4902), 3-isobutyl-methylxanthine (410957, IBMX), Rosiglitazone (557366-M) and 3,3’,5-Triiodo-L-thyronine (T2877, T3) were purchased from Sigma-Aldrich.

Immortalized porcine white pre-adipocytes were cultured in growth medium containing Dulbecco’s modification of Eagle’s medium (DMEM), 15% FBS (Gibco), 1% penicillin/streptomycin (Invitrogen) at 37°C with 5% CO2. For porcine white adipocytes, adipogenic differentiation, the cells were induced with induction medium (IM) containing DMEM, 10% FBS (Gibco), 500 μM 3-isobutylmethylxanthine (Yeasen Biotechnology Co., Ltd.), 5 μg/mL insulin (WISENT Inc.), 1 μg/mL rosiglitazone (Yeasen Biotechnology Co., Ltd.), and 1 μM dexamethasone (Yeasen Biotechnology Co., Ltd.) for 3 days and then differentiated in differentiation medium (DM) contains DMEM, 10% FBS and 5 μg/mL insulin for 2 days.

Histological analysis
Mice were anesthetised with isoflurane and then euthanasia via cervical dislocation, and adipose and liver tissues were harvested and fixed with 4% paraformaldehyde. Paraffin embedding, tissue sectioning, and hematoxylin and eosin staining were performed.

Total RNA extraction and real-time PCR
Mice were anesthetized with isoflurane, and then euthanasia via cervical dislocation and tissue samples were collected and frozen with liquid nitrogen. Total RNA was extracted from cells or tissues by using Trizol Reagent (15596026, Invitrogen) according to the manufacturer’s instructions. Synthesis of cDNA was performed using 1 μg total RNA from each sample using HiScript IV Reverse Transcriptase (R423-01, vazyme). Quantitative PCR (qPCR) was carried out using the ChamQ Universal SYBR qPCR Master Mix (Q711, vazyme) and analyzed on a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific). Primer sequences were obtained from PrimerBank.

Transcriptome sequencing
Total RNA was extracted according to the instructions manual of the TRlzol Reagent (Life Technologies, California, USA). RNA concentration and purity were measured using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the NEBNext Ultra TM RNA Library Prep Kit for Illumina (NEB, USA), following the manufacturer’s recommendations. Index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X. First-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase. Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adapter with hairpin loop structure was ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 240 bp in length, the library fragments were purified with the AMPure XP system (Beckman Colter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. Then, PCR was performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. Finally, PCR products were purified (AMPure XP system), and library quality was assessed using the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated.

Immunoprecipitation
The cells were collected and suspended in 1000 μL lysis buffer (1% Nonidet P-40, 1 mM EDTA, 150 mM NaCl in 50 mM Tris-HCL, pH 7.5) containing protease inhibitors cocktail (B14001, Bimake) and phosphatase inhibitors cocktail (B15001, Bimake), then passed 12 times through a 22G needle followed by centrifugation at 12,000×g for 10 min at 4°C. Eighty microlitres of supernatants mixed with membrane solubilization buffer plus the 5× loading buffer as the input sample. 800 μL of supernatants were immunoprecipitated with beads conjugated with the indicated antibodies at 4°C for 6–8 h. After incubation, the beads were washed four times with a lysis buffer, and the precipitates were eluted with 5× loading buffer. Elutes and whole-cell extracts were resolved on SDS-PAGE, followed by a western blot with the indicated antibodies. The following plasmids were constructed by standard molecular cloning technics: pcDNA3.1-PRMT3-MYC encodes full-length human PRMT3 followed by MYC epitope tag, pCMV-PRMT3-HA encodes full-length mouse PRMT3 followed by HA epitope tag, pcDNA3.1-PRMT5-MYC encodes full-length human PRMT5 followed by MYC epitope tag, pCGN-SLC25A1-Flag encodes full-length mouse SLC25A1 followed by Flag epitope tag.

Immunoblotting
Mice were anesthetized with isoflurane, and then euthanasia via cervical dislocation and tissue samples were collected and frozen with liquid nitrogen. Cells or tissues were collected and homogenized in the RIPA buffer (1% Nonidet P-40, 0.1% SDS, 1 mM EDTA, 150 mM NaCl in 50 mM Tris-HCL, pH 7.5) supplemented with protease inhibitor cocktail and phosphatase inhibitors cocktail. The protein concentrations of lysates were determined using the BCA method (A55864, Thermo Fisher Scientific). The equal amounts of total proteins were resolved by SDS–PAGE and transferred to PVDF membranes (IPVH00010, Merck Millipore). Immunoblots were blocked with 5% non-fat milk (1172GR500, BioFroxx) in Tris-buffered saline (TBS) containing 0.01% Tween 20 (TBST), probed with indicated primary antibodies overnight at 4°C. After washing in TBST three times, blots were incubated with horseradish peroxidase-conjugated secondary antibodies diluted in TBST supplemented with 5% skim milk for 1 h at room temperature and followed by at least 3 washes with TBST. Immunoreactivity was developed with enzymatic detection using SuperPico ECL Chemiluminescence Kit (E422, E423, Vazyme).

Primary antibodies used for immunoblots were as follows: anti-Mono-Methyl Arginine (8015, MMA, diluted 1:1000), Asymmetric Di-Methyl Arginine Motif (13522, ADMA, diluted 1:1000), Symmetric Di-Methyl Arginine Motif (13222, SDMA, diluted 1:1000), Normal anti-rabbit lgG (2729), Normal anti-mouse lgG (68860), Phospho-Akt (Ser473) (4060, diluted 1:1000), AKT (9272, diluted 1:1000), PRMT4 (3379, diluted 1:1000), PRMT5 (79998, diluted 1:1000), PKM1/2 (3106, diluted 1:1000), Pyruvate Carboxylase (49381, PC, diluted 1:1,000), LDHA (2012, diluted 1:1000), CPT2 (52552, diluted 1:1000), PGK1 (63536, diluted 1:1000), PCK1 (12940, diluted 1:1000), Phospho-HSL (Ser660) (45804, diluted 1:1000) antibodies were purchased from Cell Signaling technology. Anti-ATGL (55190-1-AP, diluted 1:1000), HSL (17333-1-AP, diluted 1:1000), HSP90 (13171-1-AP, diluted 1:1000), PRMT3 (17628-1-AP, diluted 1:1000), Beta Actin (66009-1-Ig, diluted 1:1000), GAPDH (60004-1-Ig, diluted 1:1000), Alpha Tubulin (66031-1-Ig, diluted 1:5000), SLC25A1 (15235-1-AP, diluted 1:1000) antibodies were obtained from Proteintech. Anti-FLAG-M2 (F1804, diluted 1:5000), FLAG (SAB4301135, diluted 1:5000), HA (H6908, diluted 1:5000), FLAG (H3663, diluted 1:5000), MYC (C3956, diluted 1:5000), MYC (M4439, diluted 1:5000) antibodies were purchased from Sigma. Horseradish peroxidase-conjugated goat anti-mouse (115-035-003, diluted 1:5000) and anti-rabbit (711-035-152, diluted 1:5000) secondary antibodies were from Jackson ImmunoResearch Laboratories.

Glucose and insulin tolerance test
Glucose and insulin tolerance tests (GTT, ITT) were performed by an intraperitoneal injection of glucose (2 g/kg body weight), insulin (0.03 μg/kg body weight) in mice following 14 hours fasting (for GTT) or 6 hours fasting (for ITT). Blood was collected from a small incision from the tip of the tail before injection (Time 0), then 15, 30, 60, 90 and 120 min after injection of glucose or insulin. Blood glucose levels were measured by a blood glucose meter (Accu-Chek® Active, Roche).

Indirect calorimetry and body composition measurement
The oxygen consumption (VO2) and carbon dioxide production (VCO2) of the mice were measured using an indirect calorimetry system (Oxymax, Columbus Instruments). The system was kept at a stable environmental temperature (24°C) that also had a 12 h light (8 AM-8 PM) and a 12 h dark cycle (8 PM-8 AM). Mice were individually placed in each chamber with free access to food and water. Mice were adapted to the chamber for 24 h before the measurements. The data were presented as uncorrected energy expenditure levels. Average energy expenditure of day (8 AM-8 PM) and night (8 PM-8 AM) values were the average mean value of all points measured during the 12 h. Total body fat and lean mass in live animals without anesthesia were measured by using a Minispec LF50 body composition analyzer located in the Small Animal Facility of CAM-SU. Animals were placed in a specially sized, clear plastic holder without sedation or anesthesia. The holder was then inserted into a tubular space on the side of the Minispec LF50 system. The animals were forced to not move in the holder to guarantee the accuracy of the results. Each scan took about 2 minutes.

Blood biochemistry
Blood biochemistry was performed by using a clinical chemistry analyzer (Hitachi 7100). An appropriate volume of blood that was required (160–200 μl of plasma) for the test was collected from each mouse (mice were anesthetized with isoflurane) and transferred to a gel tube containing lithium Heparin and centrifuge for 15 minutes at 5000×g in a refrigerated centrifuge set at 4°C. If plasma samples cannot be analyzed immediately, keep them at −80°C before analysis. Use plasma samples undiluted or diluted to a ratio of 1:2 with deionised water if the volume was insufficient.

Targeted metabolomics
Collected samples use a liquid mass platform (UPLC-TQ-MS) to detect energy metabolism-related metabolites in biological (Metabo-Profile, Shanghai, P. R. China). The solvents used were chromatographic grade methanol, acetonitrile, and formic acid, which were purchased from Merck Chemicals, Germany. The experimental ultrapure water was prepared by the Millipore Reference ultrapure water system (Billerica, MA, USA) equipped with a 0.22 μm filter head for LC/MS. Before processing the sample, the sample needs to be thawed in an ice bath to reduce sample degradation. Weigh 10 mg tissue into a 1.5 mL EP tube, add 20 μL ultrapure water and 10 magnetic beads, homogenize for 3 min (BB24, Next Advance, Inc., Averill Park, NY, USA), and then add 120 μL pre-cooling. The methanol solution containing internal standard was homogenized again for 3 min. Centrifuge at 18000×g for 20 min at 4°C (Microfuge 20R, Beckman Colter, Inc., Indianapolis, IN, USA), take 100 μL the supernatant in a 1.5 mL EP tube, and concentrate at 4°C (Labconco, Kansas City, MO, USA), add 100 μL 80% methanol solution for reconstitution, centrifuge and transfer to 96-well plate, waiting for sample injection analysis. An ultra-high pressure liquid chromatography-triple quadrupole mass spectrometer (ACQUITY-IUPLC/Xevo TQ-S) from Waters Corporation was used to detect energy metabolism-related metabolites. The raw data generated by UPLC-TQ-MS will use Waters MassLynx software (v4.1, Waters, Milford, MA, USA) for peak extraction, integration, identification and quantitative analysis of each metabolite. Self-developed iMAP software (V1.0, Metabo-Profile, Shanghai, China) is used for subsequent statistical analysis, such as PCA, OPLS-DA and one-dimensional statistical testing.

Measurement of ECAR
ECAR of CTPI-2 (S2968, SLC25A1 inhibitor) and SGC707 treated adipocytes were analyzed using an XF96 Analyzer (Seahorse Bioscience). In brief, 2×104 cells per well were plated in Seahorse XF96 plates in 200 μl of DMEM supplemented with 2 mM glutamine. The plates were incubated for 1 h at 37°C in a humidified incubator without CO2. After assessing the ECAR at baseline, the following compounds were sequentially added to the analyzer, and the change in ECAR was measured: (1) after the addition of 10 mM glucose; (2) after the sequential addition of 1 μM oligomycin and (3) after the sequential addition of 50 mM 2-DG. In this assay, maximum ECAR occurs after the addition of oligomycin. All ECAR measures were performed three times in a mix–wait–measure cycle of 3, 2, and 3 min, respectively. Rotenone (S2348), 2-Deoxy-D-glucose (S4701, 2-DG), Oligomycin A (S1478) and Indomethacin (S1723) from Selleck Chemicals; Antimycin A (MS0070) were obtained from Shanghai MaoKang Biotechnology, China.

Statistical analysis
Data analysis was conducted mainly in Prism 8.0.1. The rhythmicity of gene expression was evaluated using JTK analysis, with a p-value of <0.05 indicative of rhythmicity. All analyses were conducted using the Student’s t test (two-tail). All experimental data were presented as mean ± SEM. Comparisons with P-values <0.05, <0.01 or <0.001 were considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Huang et al. investigated how dietary conditions, such as time-restricted fasting (TRF) and diet-induced obesity (DIO), affect protein arginine methylation and how mitigation of PRMT activity impacts the metabolic flexibility of the fat tissues. Specifically, they focus on the role of PRMT3 in the visceral adipose tissue (vWAT) of mice. First, they observed that the global arginine methylation level fluctuates in correlation with the feeding cycle and higher methylation levels are observed in mice fed with high-fat diet and in obese individuals. Second, they selectively focus on PRMT3, whose expression pattern mirrors the methylation changes, and showed that inhibiting PRMT3 activity, using a small molecule inhibitor SGC807, reduces DIO by enhancing adipocyte glycolysis. Along this line, they also revealed that the insulin-AKT signaling pathway acts upstream to regulate the levels of PRMT3 protein expression and arginine methylation. Third, they focus on SLC25A1, a citrate transporter located in the mitochondria, and showed that SLC25A1 interacts with PRMT3 and likely is methylated by PRMT3 and that its expression also follows the pattern of the feeding cycle. Fourth, they demonstrated that time-restricted fasting together with PRMT3 inhibition greatly reduces diet-induced obesity. Lastly, they showed that adipocyte-specific deletion of SLC25A1 could reduce high fat-diet induced obesity by enhancing glucose metabolism, indicating that modulating SLC25A1 expression, maybe through PRMT3-mediated pathway, could promote adipocyte glucose metabolism and prevent diet-induced obesity.\nOverall, this manuscript made a few interesting observations, such as 1) global changes of arginine methylation in response to the feeding cycle; 2) mitigating of HFD-induced obesity by small molecule inhibitor SGC707; 3) correlation of SLC25A1 expression with the feeding cycle; and 4) reduction of HFD-induced obesity by adipocyte-specific knockout of SLC25A1. Although these individual conclusions seem to be well supported by data, they are not logically connected due to the lack of mechanisms. At this stage, the manuscript is deemed too descriptive and preliminary.\nMajor critiques:\n1. Regarding the global changes of arginine methylation in response to feeding. The authors suggest that changes in PRMT3 protein level are the cause. However, this conclusion is not well supported: 1) gene expression changes under the feeding cycle, fasting, and high-fat diet could contribute to the changes of methylation signal on western blot gel. It might not be related to altered methylation on a given protein. 2) PRMT1 is the major PRMT that catalyzes more than 90% of ADMA, however, it was excluded at the very beginning of this study. The authors mentioned a similar study (it was not cited), which reported a strong induction of PRMT1 upon HFD. They should determine the extent to which the alternation of global arginine methylation is contributed by PRMT1 vs PRMT3. 3) PRMT3 has only a few reported substrates. It is unlikely that PRMT3 is responsible for global methylation changes. In addition to using SGC707, they should also knockdown or knockout PRMT3 in eWAT to complement the results with the inhibitor. 4) The alternation of certain metabolites involved in the methylation pathway could lead to tissue-specific and global changes of methylation. Because the feeding cycle, fasting, and high-fat diet could have significant impacts on metabolism, this possibility should be tested.\n2. How PRMT3 expression is regulated by the insulin-AKT pathway is not clear. The authors implicate that PRMT3 could be phosphorylated, although the data supporting this claim is weak. This needs to be thoroughly characterized, such as by which kinase, at which site, what is the impact of methylation. Additionally, if insulin elevates PRMT3 expression, what is its impact on methylation? Does it restore methylation under fasting or in the presence of SGC707?\n3. The impact of PRMT3 inhibition, specifically by the small molecule inhibitor SGC707, on lipid metabolism has been reported. This study was not cited or discussed (https://www.nature.com/articles/s41598-021-04524-w). Importantly, although the specificity of SGC707 was initially characterized against other methyltransferases in vitro, its specificity in vivo is not clear. The in vivo study using SGC707 (Fig. 3) should be complemented with adipocyte-specific PRMT3 knockout.\n4. Regarding the regulation of SLC25A1 by PRMT3. The expression pattern of SLC25A1 in response to the feeding cycle is interesting. Although this change mirrors the changes of PRMT3, whether there is a direct causative relationship is not addressed. Also, the rationale for focusing on SLC25A1 as the downstream target of PRMT3 is not strong. They mentioned a previous publication regarding SLC25A1 interaction with PRMT3 and PRMT5. This paper was not cited. The authors suggest that PRMT3 might methylate SLC25A1 or at least regulate SCL25A1 methylation. This needs to be thoroughly characterized, including the sites of methylation on SLC25A1, and how methylation may directly affect SLC25A1 protein level. Additionally, the authors suggest that SLC25A1 expression is also regulated by the insulin-AKT pathway (Fig. 4p). Is this through PRMT3? Lastly, what contributes to the tissue-specific regulation of PRMT3 and SLC25A1 in eWAT, but not in other adipose tissues?\n5. Although PRMT3 inhibition by SGC707 and adipocyte-specific knockout of SLC25A1 both reduce diet-induced obesity, the extent to which PRMT3-mediated SLC25A1 methylation contributes to this process is not clear. For example, will supplement the expression of SLC25A1 in PRMT3 KO restores some of the metabolic features? Similarly, what happens if applying SGC707 on SCL25A1 conditional knockout mouse model? It is more convincing that SLC25A1 expression changes in response to feeding, whereas changes in PRMT3 are rather minor.\n6. Page 9, section title “TRF reduces PRMT3-SLC25A1 interaction…” was not supported by the data shown in Fig. 5.\nOther critiques\n1. The authors use recombinant proteins to test the specificity of PRMT3 antibody. This method is not appropriate. They should use PRMT3 knockout cell lines to confirm that the signal they see with the antibody is indeed PRMT3 (PRMT3 knockout cells are viable). In many cases, the changes of PRMT3 protein levels are very marginal (eg. Fig. 2b, 2f, 2j, 2l, Fig. 3r, Fig. 5a).\n2. The correlation between methylation levels and BMI is weak.", "reviewer_2": "In this manuscript the authors study how different dietary approaches change levels of PRMT3 and MMA/ADMA in white adipose tissue (WAT). In WAT, the authors firstly show a circadian regulation of MMA/ADMA levels, which seems to be abrogated upon HFD. Furthermore, inhibition of PRMT3 by SGC707 reverts weight gain on a HFD as well as it accelerates the decrease of MMA/ADMA levels during a 12h fast to levels similar to a 36h fast, on an insulin dependant way. The authors also claim that SLC25a1 is implicated. Besides the role of PRMT3 in the levels of MMA/ADMA, the authors study the changes in these proteins using a Time Restricted Feeding (TRF) paradigm and draw a parallel between TRF and SGC707 action upon long term HFD consumption.\nFormer studies have implicated PRMT3/SGC707 in weight loss and ameliorated phenotype from liver steatosis, also showing an improvement on overall WAT morphology. Overall, literature on PRMT3 or its inhibitor is lacking in models of HFD, and its effects on body weight, although known, are still quite understudied. This adds to the novelty of this present study.\nRegarding the study itself, in my opinion, some major considerations are as follows:\n1. In figure 1c the authors claim that animals on HFD showed a “marked increase” in proteins containing MMA and ADMA. HFD feeding is known to alter several parameters that have a circadian component to it as well as feeding patterns. As such, one can wonder whether the time of collecting the tissue can have an impact on MMA and ADMA levels, and whether repeating the experiment done on figure 1a would help to get a better understanding on whether this changes in protein level are continuous during a 24h period or shifted in comparison to NCD. In my opinion, this experiment has relevance towards the claim of the paper, since understanding the dynamics of protein levels both in NCD as in HFD could influence the timing of fasting for improved outcomes.\n2. In figure 2, the authors show that a 12h fast in addition to the administration of SGC mimics the results of a 36h fasting, regarding MMA and ADMA levels. This experiment is central to the claim of the paper, since one of the main premisses is that by inhibiting PRMT3 during a 12h the observed decrease in MMA and ADMA levels is similar to a 36h fast. It is of my opinion that is highly important to also show that, in this particular experiment (figure 2h and 2i) the levels of ADMA and MMA in ad libitum animals administered the drug remain unchanged. In this way its possible to ensure that proper controls are in place.\n3. The authors present data on the effects of PRMT3 inhibition in overall metabolism and body mass. The authors claim that no changes in food intake nor energy expenditure were observed on a 24h period. 24h readings can be misleading as small incremental changes can be observed when measures are taking for extended periods of time. A prolonged measurement entailing 5-7 days would allow for a better overview and a more convincing claim that the weight loss is not due to small cumulative changes in feeding amounts/energy expenditure. A longer assessment of the RER could also provide better proof that this delta RER changes are repeated daily in prolonged periods and not on a particular single isolated event.\n4. It has been published that TRF changes body weight without affecting total caloric intake, and the authors are able to replicate this finding. Furthermore, in figure S9m, we can observe an increase in locomotion during the night cycle, which also goes with current literature. In this present study, we observe changes in body weight in HFD animals under a TRF paradigm, without alteration of food intake or energy expenditure (Figure S9). It would be important for the authors to adress this particular data set, where increased locomotion but unchanged energy expenditure and feeding behaviour are observed. An important suggestion would be to look at cumulative energy expenditure during a longer period of time, or even calculating it using the two days presented in figure S9.\n5. Overall, and related to the previous points, the authors claim in different parts of the manuscript that weight loss is observed with no changes in food intake or energy expenditure. It would be of importance that the authors could address better this question, with (as suggested above) prolonged measurements and if the phenotype holds, maybe look into absorption/intestinal issues that SGC707, for example, or any other model used might be inducing.\n6. In relation to the titles of the first section of the paper (as well as the text), the authors claim that “Mono and Asymemetric (…) in correlation with feeding cycles” and in the second section the authors claim “PRMT3 Regulates Adipose ADMA during Feeding in an Insulin-pAKT-Dependent Manner”. Both in figure 1 and in figure 2 there is no particular experiment that shows that changes in ADMA or MMA levels are conneted to feeding behavior itself. Especially in figure 1, the experiments done show that there seems to exist in fact a circadian variantion in the level of these proteins, that might in fact mirror or be dependent on feeding/nutritional levels, but no experiments were done at this point to make such claims only with correlational data. I would suggest changing the claim according to the data presented where in figure 1 its possible to observe diurnal/nocturnal variations that might be circadian in nature and in figure 2 the changes with fasting might be correlated with fasting/lack of nutrition and not “feeding” as the title implies.\n7. Also regarding the text on these sections, I would strongly recommend changing the first narrative, where the authors claim that the patterns observed mirror the feeding patterns of mice. Although it is true, that these changes to mirror the feeding patterns of animals on HFD, they also might mirror the sleeping patterns, locomotion patters, among other behaviors that take place during night time when the animals are active. I totally understand that it is part of constructing the narrative, but it is of my personal opinion that data should be the driver in these circumstances, and the data presented in the first sections does not allow for any conclusion on what is driving the observations.\nAnd some additional comments that, although not major, are of some importance to the quality of the present work:\n8. Time restricted feeding lowers Slc25a1 levels in WAT while the levels in the liver remain unchanged. To tackle how the changes in Slc25a1 affect the metabolic status of the animal, the authors created a mouse model that expresses the Cre recombinase under the Adiponectin promoter, assuring that the expression of Cre is restricted to adipocytes. To ensure that this deletion is specific to WAT (following the rationale of the paper where most changes in PRMT3 are seen in WAT), BAT data on figure 6d should be added.\n9. Regarding figure 2, as it has been repeatidely described in the literature, that prolonged fasting decreases phosphorylation levels of AKT, as shown for example in figure 2r. Unfortunately, in figure 2j and 2l the levels of pAKT presented seem to be quite elevated for a 36h fast. On top, a ratio of pAKT/AKT is not present and AKT levels are not shown in figure 2j. In figure 3 the legend claims that WB were performed in 12week old animals upon 36h of fasting. With all conditions being equal, it would be important to address the differences in pAKT levels and also include a pAKT/AKT ratio.\n10. The overexpression model does not seem to answer any particular question and does’t seem to add any particular insight into the question tackled in this current manuscript. If the authors want to use the overexpression model, it would be ideal at least to show that the over expression worked, since it seems to have an effect on glucose levels homeostasis.\n11. A slightly minor comment (and surely irrelevant for the revision process) but, regarding some of the wording used, the expression “time-restricted feeding” is more commonly used than the expression “time-restricted fasting”.\n12. I would strongly recommend further proof reading since some words seem to be missplaced, as for example in line 330 the authors write “primarily due to a decrease in liver mass”. Looking at the figure, I would imagine that the authors probably were referring to lean mass."}, "author_response": "In this manuscript, Huang et al. investigated how dietary conditions, such as time-restricted fasting (TRF) and diet-induced obesity (DIO), affect protein arginine methylation and how mitigation of PRMT activity impacts the metabolic flexibility of the fat tissues. Specifically, they focus on the role of PRMT3 in the visceral adipose tissue (vWAT) of mice. First, they observed that the global arginine methylation level fluctuates in correlation with the feeding cycle, and higher methylation levels are observed in mice fed with high-fat diet and in obese individuals. Second, they selectively focus on PRMT3, whose expression pattern mirrors the methylation changes, and showed that inhibiting PRMT3 activity, using a small molecule inhibitor SGC807, reduces DIO by enhancing adipocyte glycolysis. Along this line, they also revealed that the insulin-AKT signaling pathway acts upstream to regulate the levels of PRMT3 protein expression and arginine methylation. Third, they focus on SLC25A1, a citrate transporter located in the mitochondria, and showed that SLC25A1 interacts with PRMT3 and likely is methylated by PRMT3 and that its expression also follows the pattern of the feeding cycle. Fourth, they demonstrated that time-restricted fasting together with PRMT3 inhibition greatly reduces diet-induced obesity. Lastly, they showed that adipocyte-specific deletion of SLC25A1 could reduce high fat-diet induced obesity by enhancing glucose metabolism, indicating that modulating SLC25A1 expression, maybe through PRMT3-mediated pathway, could promote adipocyte glucose metabolism and prevent diet-induced obesity.\nOverall, this manuscript made a few interesting observations, such as 1) global changes of arginine methylation in response to the feeding cycle; 2) mitigating of HFD-induced obesity by small molecule inhibitor SGC707; 3) correlation of SLC25A1 expression with the feeding cycle; and 4) reduction of HFD-induced obesity by adipocyte-specific knockout of SLC25A1. Although these individual conclusions seem to be well supported by data, they are not logically connected due to the lack of mechanisms. At this stage, the manuscript is deemed too descriptive and preliminary.\nMajor critiques:\n1. Regarding the global changes of arginine methylation in response to feeding. The authors suggest that changes in PRMT3 protein level are the cause. However, this conclusion is not well supported: 1) gene expression changes under the feeding cycle, fasting, and high-fat diet could contribute to the changes of methylation signal on western blot gel. It might not be related to altered methylation on a given protein.\nAuthors’ response: Thanks for raising this concern. We have now checked the temporal expression of Prmt3 during the feeding cycle, and the results show that the Prmt3 mRNA level in eWAT was not synchronised with the feeding cycle (Fig. R1A). In addition, the mRNA expression level of Prmt3 was increased (Fig. R1A), but not decreased after 36h of fasting, which was opposite to that of the protein expression pattern.\nFigure. R1 Expression levels of Prmt3 in eWAT during feeding cycle (A) and 36h of fasting (B).\n2) PRMT1 is the major PRMT that catalyzes more than 90% of ADMA, however, it was excluded at the very beginning of this study. The authors mentioned a similar study (it was not cited), which reported a strong induction of PRMT1 upon HFD. They should determine the extent to which the alternation of global arginine methylation is contributed by PRMT1 vs PRMT3.\nAuthors’ response: We appreciated the comments about PRMT1. We checked the expression level of PRMT1 in eWAT after fasting and found that there was no significant change (Figure R2A, B). We also applied a well-established PRMT1 inhibitor, AMI-1, to treat the mice, along with a 12-hour fast. The results showed that though inhibition of PRMT1 by AMI-1 caused a significant change in AMDA levels of eWAT, inhibition of PRMT3 by SGC707 introduced a more profound change, indicating that PRMT3 would be the more dominant PRMT during fasting in eWAT. We have added these data in Fig. S3c-f of the revised manuscript. We have also cited the above-mentioned PRMT1 paper (1).\nFigure. R2 Expression levels of PRMT1 in eWAT after fasting (A, B) and relative AMDA levels after inhibition of PRMT3 and PRMT1 by SGC707 and AMI-1 (C, D), respectively.\n3) PRMT3 has only a few reported substrates. It is unlikely that PRMT3 is responsible for global methylation changes. In addition to using SGC707, they should also knockdown or knockout PRMT3 in eWAT to complement the results with the inhibitor.\nAuthors’ response: Thanks for the comments. According to the suggestion, we have constructed an adipocyte-specific AAV9 carrying Prmt3 siRNA and injected it locally into the eWAT of DIO mice. Consistent with the reduction in ADMA and SLC25A1 levels after SGC707 treatment, the knockdown of Prmt3 reduced the levels of ADMA and SLC25A1 in eWAT (Figure R3A, B).\nFigure. R3 Protein expression levels of PRMT3 and total ADMA in eWAT from DIO mice after injection of control or siPrmt3 adeno-associated virus (AAV).\n4) The alternation of certain metabolites involved in the methylation pathway could lead to tissue-specific and global changes of methylation. Because the feeding cycle, fasting, and high-fat diet could have significant impacts on metabolism, this possibility should be tested.\nAuthors’ response: We totally agreed that metabolites involved in the methylation pathway could lead to tissue-specific and global changes of methylation, as the previous study suggests that hepatic SAMe levels are correlated with fasting (2). We have now measured the SAMe level in serum and eWAT during fasting and refed. The results showed that the serum level of SAMe decreased after fasting and increased after refed. In contrast, there was no difference in the concentration of SAMe in eWAT (Figure R4A, B). These data have been added to Fig. 1c, d in the revised manuscript.\nAdditionally, we have measured serum SAMe levels after a diet of induced obesity (DIO) and a 16:8 time-restricted feeding (TRF) regimen. The results indicated that the increased levels of serum SAMe levels after DIO were reduced by 16:8 TRF (Figure R4C). This data has been added to the Fig. S10w.\nFigure. R4 (A, B) Concentrations of SAMe in serum (A) and eWAT (B) at ad libitum (AL), fasted for 36 h (F-26), or fasted for 36 h and then refed a standard chow diet for 3-6 h (Refed). (C) Concentrations of SAMe in serum at regular chow diet (CD), FA (High-fat diet, ad libitum) or FT (High-fat diet, TRF) groups.\n2. How PRMT3 expression is regulated by the insulin-AKT pathway is not clear. The authors implicate that PRMT3 could be phosphorylated, although the data supporting this claim is weak. This needs to be thoroughly characterized, such as by which kinase, at which site, what is the impact of methylation. Additionally, if insulin elevates PRMT3 expression, what is its impact on methylation? Does it restore methylation under fasting or in the presence of SGC707?\nAuthors’ response: Thanks for the comments. We have now added new data showing that PRMT3 is phosphorylated by pAKT (Figure R5A), which could enhance its methyltransferase activity in eWAT. This data has been added in Fig. S4i of the revised manuscript. In contrast, the blockade of pAKT by MK-2206 reduced the total levels of ADMA in eWAT (Fig. 2r, s) and cultured adipocytes (Fig. S4j, k). We did not investigate the phosphorylation site of PRMT3 by pAKT, which was proposed for further study in the following study.\nIn addition, we have now shown that insulin increased the total levels of ADMA under fasting, but not in the presence of SGC707 (Figure R5B, C), indicating that Insulin-pAKT was the upstream regulator of PRMT3. These data have been added in Fig. S4c, d of the revised manuscript.\nFigure. R5 (A) PRMT3 and pAKTS473 were immunoprecipitated, and an in vitro phosphorylation assay was performed. (B, C) Western blot analysis of ADMA in eWAT of 12 weeks male B6N mice after fasting for 12 h (Ctrl), fasted for 12 h and then treated with insulin via intraperitoneal injection for 3 h (Insulin) with or without SGC707 (Insulin+SGC707).\n3. The impact of PRMT3 inhibition, specifically by the small molecule inhibitor SGC707, on lipid metabolism has been reported. This study was not cited or discussed (https://www.nature.com/articles/s41598-021-04524-w). Importantly, although the specificity of SGC707 was initially characterized against other methyltransferases in vitro, its specificity in vivo is not clear. The in vivo study using SGC707 (Fig. 3) should be complemented with adipocyte-specific PRMT3 knockout.\nAuthors’ response: Thanks for the suggestions. We have now cited the above reference (3) in the revised manuscript and added some discussion regarding this study. We also applied an AAV-mediated adipocyte-specific Prmt3 knockdown (KD) to further characterise the function of PRMT3 in epididymal fat in vivo (Figure R6A). The results showed that the PRMT3 protein level was significantly downregulated in eWAT, indicating the successful knockdown of Prmt3 (Figure R6B). After 8 weeks of local injection into eWAT from DIO mice, there were no differences in the body weight and food intake of the mice (Figure R6C, D). Metabolic chamber results demonstrated that Prmt3 KD increased the VO , heat production and RER of the DIO mice (Figure R6E-G). In addition, Prmt3 KD enhanced the glycose clearance of the mice, as indicated by lower glucose levels and AUC in GTT (Figure R6H). These results suggest that KD of Prmt3 in eWAT of the DIO mice promotes global glucose metabolism. The above data have been added to Fig.S8 of the revised manuscript.\nFigure. R6 (A) A diagram showing the injection of AAV8 to knockdown Prmt3 in epididymal fat of DIO mice in vivo. (B) Knockdown efficiency as detected by PRMT3 WB. (C, D) Body weight (C) and food intake (D) of the mice. (E-G) VO2 (E), heat production (F) and RER (G) of the mice as measured by a metabolic chamber. (H) Glucose concentrations during GTT and AUC.\n4. Regarding the regulation of SLC25A1 by PRMT3. The expression pattern of SLC25A1 in response to the feeding cycle is interesting. Although this change mirrors the changes of PRMT3, whether there is a direct causative relationship is not addressed. Also, the rationale for focusing on SLC25A1 as the downstream target of PRMT3 is not strong. They mentioned a previous publication regarding SLC25A1 interaction with PRMT3 and PRMT5. This paper was not cited.\nAuthors’ response: Thanks for the comments. We have now cited the reference that indicates the interaction between PRMT3 and SLC25A1 (4).\nThe authors suggested that PRMT3 might methylate SLC25A1 or at least regulate SCL25A1 methylation. This needs to be thoroughly characterized, including the sites of methylation on SLC25A1, and how methylation may directly affect SLC25A1 protein level.\nAuthors’ response: We have now identified that R282 and R285 of SLC25A1, which were located in the citrate binding region of SLC25A1, were the methylation sites (Figure R7A, B). Mutation of R282G and R285C (SLC25A1Mut) reduced the interface area of the SLC25A1 protein (Figure R7B). Co-IP experiments in 293T cells transfected with PRMT3 and SLC25A1WT/SLC25A1Mut confirmed that SLC25A1 mutation reduced its methylation (Figure R7C). Further, the anti-ADMA antibody successfully pulled down SLC25A1WT but not SLC25A1Mut, indicating that R282 and R285 were the asymmetrically di-methylated arginine residues of SLC25A1 (Figure R7D). In addition, Co-IP experiments demonstrated that the interaction between PRMT3 and SLC25A1 was weakened after mutation (Figure R7E). We then performed a CHX assay to assess the effects of SLC25A1 arginine methylation on its protein stability. The results showed that PRMT3 could stabilise the SLC25A1 protein in a methylation-dependent manner (Figure R7F, G). These data have been added to Fig.4f-l of the revised manuscript.\nFigure. R7 (A) Protein sequence alignment of SLC25A1 from Drosophila melanogaster to Homo sapiens indicating the conserved R282 and R285 sites. (B) Structural prediction of WT SLC25A1 (SLC25A1WT) and R282G/R285C (SLC25A1Mut) by AlphaFold 3. (C) IP of FLAG in 293T cells transfected with PRMT3 and SLC25A1WT/SLC25A1Mut and WB of ADMA confirmed that SLC25A1 mutation reduced its methylation. (D) IP of ADMA in 293T cells transfected with PRMT3 and SLC25A1WT/SLC25A1Mut and WB of FLAG confirmed that SLC25A1 mutation reduced its methylation. (E) IP of FLAG in 293T cells transfected with PRMT3 and SLC25A1WT/SLC25A1Mut and WB of MYC confirmed that SLC25A1 mutation reduced its interaction with PRMT3. (F, G) Protein stability of SLC25A1WT and SLC25A1Mut with or without PRMT3 by CHX assay.\nAdditionally, the authors suggest that SLC25A1 expression is also regulated by the insulin-AKT pathway (Fig. 4p). Is this through PRMT3?\nAuthors’ response: We have now shown that insulin increased the levels of SLC25A1 under fasting but not in the presence of SGC707 (Figure R8A, B), indicating the Insulin-pAKT was the upstream regulator of SLC25A1 through PRMT3. This data has been added in Fig. 5o, p of the revised manuscript.\nFigure. R8 (A, B) Western blot analysis (A) and quantification (B) of SLC25A1 in eWAT at fasted for 36 h (Ctrl), fasted for 36 h, then treated with insulin via intraperitoneal injection for 3 h (Insulin) or fasted for 36 h then treated with insulin plus SGC707 (Insulin+SGC707).\nLastly, what contributes to the tissue-specific regulation of PRMT3 and SLC25A1 in eWAT, but not in other adipose tissues?\nAuthors’ response: We found that injection of CL316,243, which is a β3-adrenoceptor agonist, reduced the levels of PRMT3 and SLC25A1 in eWAT, but not BAT and liver. Moreover, the effect was similar to that of fasting (Figure R9A, B). CL316,243 is an effective stimulant of adipocyte lipolysis and increments of pHSL660 were observed in eWAT after fasting or CL316,243 treatment.\nFigure. R9 (A, B) Western blot analysis (A) and quantification (B) of SLC25A1 and PRMT3 in eWAT, BAT and Liver of 12 weeks old male B6N mice at ad libitum (Fed), fasted for 36 h, ad libitum with PBS (Fed+ PBS) or CL316,243 (Fed+ CL316,243) via intraperitoneal injection.\nIn addition, in cultured adipocytes derived from mouse eWAT (SVF), enhancement of adipocyte lipolysis by CL316,243 or Norepinephrine treatment also decreased the levels of PRMT3 and SLC25A1 (Figure R10A, B), raising the possibility that adipocyte lipolysis may contribute to the tissue-specific regulation of PRMT3 and SLC25A1. This hypothesis will be further tested in our following study.\nFigure. R10 (A) Western blot analysis of PRMT3 and SLC25A1 in cultured adipocyte differentiated from eWAT-derived SVF cells with or without CL316,243 treatment. (B) Western blot analysis of PRMT3 a in cultured adipocyte differentiated from eWAT-derived SVF cells with or without Norepinephrine treatment.\n5. Although PRMT3 inhibition by SGC707 and adipocyte-specific knockout of SLC25A1 both reduce diet-induced obesity, the extent to which PRMT3-mediated SLC25A1 methylation contributes to this process is not clear. For example, will supplement the expression of SLC25A1 in PRMT3 KO restores some of the metabolic features? Similarly, what happens if applying SGC707 on SCL25A1 conditional knockout mouse model? It is more convincing that SLC25A1 expression changes in response to feeding, whereas changes in PRMT3 are rather minor.\nAuthors’ response: We have now treated WT and Slc25a1AKO mice with SGC707. The results indicated that there were no additional effects in the glucose sensitivities with/without either SLC25A1 or SGC707. Thus, the PRMT3-mediated SLC25A1 methylation contributes to improved glucose metabolism.\nFigure. R11 (A, B) Blood glucose levels (A) and area under the curve (AUC) (B) of male WT and Slc25a1AKO mice during glucose tolerance test after 10 weeks of HFD with or without SGC707 treatment.\n6. Page 9, section title “TRF reduces PRMT3-SLC25A1 interaction…” was not supported by the data shown in Fig. 5.\nAuthors’ response: Thanks for the suggestion. We have corrected it into ‘Time-Restricted Fasting Reduces PRMT3/SLC25A1 levels and Ameliorates Diet-Induced Obesity’ in the revised manuscript.\nOther critiques\n1. The authors use recombinant proteins to test the specificity of PRMT3 antibody. This method is not appropriate. They should use PRMT3 knockout cell lines to confirm that the signal they see with the antibody is indeed PRMT3 (PRMT3 knockout cells are viable). In many cases, the changes of PRMT3 protein levels are very marginal (eg. Fig. 2b, 2f, 2j, 2l, Fig. 3r, Fig. 5a).\nAuthors’ response: Thanks for the comment. As shown above in Figure. R6B, we have now included an AAV-mediated adipocyte-specific Prmt3 knockdown (KD) to validate the specificity of the antibody further.\n2. The correlation between methylation levels and BMI is weak.\nAuthors’ response: Thanks for the suggestion. Although the current study found a significant correlation between methylation levels and BMI, we would like to include more human samples in the future to enhance the statistical power.\nReference #1\n1. Choi, S., Choi, D., Lee, Y. K., Ahn, S. H., Seong, J. K., Chi, S. W., ... & Koo, S. H. (2021). Depletion of Prmt1 in adipocytes impairs glucose homeostasis in diet-induced obesity. Diabetes, 70(8), 1664-1678.\n2. Capelo-Diz, A., Lachiondo-Ortega, S., Fernández-Ramos, D., Cañas-Martín, J., Goikoetxea-Usandizaga, N., Serrano-Maciá, M., ... & Varela-Rey, M. (2023). Hepatic levels of S-adenosylmethionine regulate the adaptive response to fasting. Cell Metabolism, 35(8), 1373-1389.\n3. de Jong, L. M., Zhang, Z., den Hartog, Y., Sijsenaar, T. J., Martins Cardoso, R., Manson, M. L., ... & Hoekstra, M. (2022). PRMT3 inhibitor SGC707 reduces triglyceride levels and induces pruritus in Western-type diet-fed LDL receptor knockout mice. Scientific Reports, 12(1), 483.\n4. Wei, H. H., Fan, X. J., Hu, Y., Tian, X. X., Guo, M., Mao, M. W., ... & Wang, Z. (2021). A systematic survey of PRMT interactomes reveals the key roles of arginine methylation in the global control of RNA splicing and translation. Science Bulletin, 66(13), 1342-1357.\nReviewer #2 (Remarks to the Author):\nIn this manuscript the authors study how different dietary approaches change levels of PRMT3 and MMA/ADMA in white adipose tissue (WAT). In WAT, the authors firstly show a circadian regulation of MMA/ADMA levels, which seems to be abrogated upon HFD. Furthermore, inhibition of PRMT3 by SGC707 reverts weight gain on a HFD as well as it accelerates the decrease of MMA/ADMA levels during a 12h fast to levels similar to a 36h fast, on an insulin dependant way. The authors also claim that SLC25a1 is implicated. Besides the role of PRMT3 in the levels of MMA/ADMA, the authors study the changes in these proteins using a Time Restricted Feeding (TRF) paradigm and draw a parallel between TRF and SGC707 action upon long term HFD consumption.\nFormer studies have implicated PRMT3/SGC707 in weight loss and ameliorated phenotype from liver steatosis, also showing an improvement on overall WAT morphology. Overall, literature on PRMT3 or its inhibitor is lacking in models of HFD, and its effects on body weight, although known, are still quite understudied. This adds to the novelty of this present study.\nRegarding the study itself, in my opinion, some major considerations are as follows:\n1. In figure 1c the authors claim that animals on HFD showed a “marked increase” in proteins containing MMA and ADMA. HFD feeding is known to alter several parameters that have a circadian component to it as well as feeding patterns. As such, one can wonder whether the time of collecting the tissue can have an impact on MMA and ADMA levels, and whether repeating the experiment done on figure 1a would help to get a better understanding on whether this changes in protein level are continuous during a 24h period or shifted in comparison to NCD. In my opinion, this experiment has relevance towards the claim of the paper, since understanding the dynamics of protein levels both in NCD as in HFD could influence the timing of fasting for improved outcomes.\nAuthors’ response: Thanks for the suggestion. We have now performed time-course sampling for HFD, and added data showing that the circadian patterns of ADMA, PRMT3 and SLC25A1 during 24 hours disappeared after HFD (Figure R12A, B). This data has been added to Fig. S1f of the revised manuscript.\nFigure. R12 (A) Western blot analysis (A) and quantification (B) of proteins carrying ADMA, SLC25A1 and PRMT3 in eWAT of DIO mice sacrificed at the indicated time points in a 12-hour light/dark cycle (n = 4).\n2. In figure 2, the authors show that a 12h fast in addition to the administration of SGC mimics the results of a 36h fasting, regarding MMA and ADMA levels. This experiment is central to the claim of the paper, since one of the main premisses is that by inhibiting PRMT3 during a 12h the observed decrease in MMA and ADMA levels is similar to a 36h fast. It is of my opinion that is highly important to also show that, in this particular experiment (figure 2h and 2i) the levels of ADMA and MMA in ad libitum animals administered the drug remain unchanged. In this way its possible to ensure that proper controls are in place.\nAuthors’ response: Thank you for the suggestion. We have supplemented the data from ad libitum animals administered SGC707. The result showed that the inhibition of PRMT3 by SGC707 did not change the ADMA level in ad libitum animals (Figure R13A, B). This data has been added to Fig. S4a, b of the revised manuscript.\nFigure. R13 (A) Western blot analysis (A) and quantification (B) of proteins carrying ADMA in eWAT of mice at ad libitum (Fed) and ad libitum with SGC707 via intraperitoneal injection.\n3. The authors present data on the effects of PRMT3 inhibition in overall metabolism and body mass. The authors claim that no changes in food intake nor energy expenditure were observed on a 24h period. 24h readings can be misleading as small incremental changes can be observed when measures are taking for extended periods of time. A prolonged measurement entailing 5-7 days would allow for a better overview and a more convincing claim that the weight loss is not due to small cumulative changes in feeding amounts/energy expenditure. A longer assessment of the RER could also provide better proof that this delta RER changes are repeated daily in prolonged periods and not on a particular single isolated event.\nAuthors’ response: Thank you for your comment. Based on these suggestions, we have placed the SGC707-treated mice in a metabolic chamber for continuous observation over 7 days. The results showed that there was a slight increase in energy expenditure, especially at night time (Figure R14A), which was also aligned with the increased RER at night time. There was also no difference in the average food intake of the mice (Figure R14B). The daily changes in delta RER were repeatable in the prolonged periods (Figure R14C). These data have been added to Fig. S7e-h of the revised manuscript.\nFigure. R14 (A) energy expenditure as indicated by heat production (kcal/hr) was measured in a metabolic chamber for 7 continuous days, and the average night heat production was quantified. (B) Average food intake of the mice. (C) RER was measured in a metabolic chamber for 7 continuous days, and the average delta RER level was measured.\n4. It has been published that TRF changes body weight without affecting total caloric intake, and the authors are able to replicate this finding. Furthermore, in figure S9m, we can observe an increase in locomotion during the night cycle, which also goes with current literature. In this present study, we observe changes in body weight in HFD animals under a TRF paradigm, without alteration of food intake or energy expenditure (Figure S9). It would be important for the authors to adress this particular data set, where increased locomotion but unchanged energy expenditure and feeding behaviour are observed. An important suggestion would be to look at cumulative energy expenditure during a longer period of time, or even calculating it using the two days presented in figure S9.\nAuthors’ response: Thank you for the suggestion. We have now recalculated the data in Fig. S9, and the result shows that TRF increased energy expenditure at nighttime (Figure R15A, B), which is consistent with the increased locomotor activity. We have corrected that data in Fig. S10k of the revised manuscript.\n` Figure. R15 (A) energy expenditure as indicated by heat production (kcal/hr) was measured after 16:8 TRF and the average night heat production was quantified (B).\n5. Overall, and related to the previous points, the authors claim in different parts of the manuscript that weight loss is observed with no changes in food intake or energy expenditure. It would be of importance that the authors could address better this question, with (as suggested above) prolonged measurements and if the phenotype holds, maybe look into absorption/intestinal issues that SGC707, for example, or any other model used might be inducing.\nAuthors’ response: Thank you for the comments. Following your suggestions, we have re-analysed the data and added a metabolic chamber that has been tested for a prolonged period. The results indicated an increase in energy expenditure. We have corrected these points in the revised manuscript.\n6. In relation to the titles of the first section of the paper (as well as the text), the authors claim that “Mono and Asymemetric (…) in correlation with feeding cycles” and in the second section the authors claim “PRMT3 Regulates Adipose ADMA during Feeding in an Insulin-pAKT-Dependent Manner”. Both in figure 1 and in figure 2 there is no particular experiment that shows that changes in ADMA or MMA levels are conneted to feeding behavior itself. Especially in figure 1, the experiments done show that there seems to exist in fact a circadian variantion in the level of these proteins, that might in fact mirror or be dependent on feeding/nutritional levels, but no experiments were done at this point to make such claims only with correlational data. I would suggest changing the claim according to the data presented where in figure 1 its possible to observe diurnal/nocturnal variations that might be circadian in nature and in figure 2 the changes with fasting might be correlated with fasting/lack of nutrition and not “feeding” as the title implies.\nAuthors’ response: Thanks for the suggestions. We have changed Figure 1 into a diurnal expression pattern and Figure 2 into fasting, respectively. We have also changed the “feeding” in the title to “fasting”.\n7. Also regarding the text on these sections, I would strongly recommend changing the first narrative, where the authors claim that the patterns observed mirror the feeding patterns of mice. Although it is true, that these changes to mirror the feeding patterns of animals on HFD, they also might mirror the sleeping patterns, locomotion patters, among other behaviors that take place during night time when the animals are active. I totally understand that it is part of constructing the narrative, but it is of my personal opinion that data should be the driver in these circumstances, and the data presented in the first sections does not allow for any conclusion on what is driving the observations.\nAuthors’ response: Thanks for the suggestions. We have changed the description that the patterns observed mirror the feeding patterns of mice into a diurnal expression pattern in the first section.\nAnd some additional comments that, although not major, are of some importance to the quality of the present work:\n8. Time restricted feeding lowers Slc25a1 levels in WAT while the levels in the liver remain unchanged. To tackle how the changes in Slc25a1 affect the metabolic status of the animal, the authors created a mouse model that expresses the Cre recombinase under the Adiponectin promoter, assuring that the expression of Cre is restricted to adipocytes. To ensure that this deletion is specific to WAT (following the rationale of the paper where most changes in PRMT3 are seen in WAT), BAT data on figure 6d should be added.\nAuthors’ response: Thanks for the suggestions. The Adiponectin-cre is expressed in all mature adipocytes, including white and brown. Thus, SLC25A1 was also deleted in BAT (Figure R16). Though we only focused on the function of PRMT3-SLC25A1 in visceral adipose tissue, the role of SLC25A1 in brown adipocytes remains unknown and interesting. A recent work reported the function of ACLY in brown adipocytes, which is the downstream protein of SLC25A1. Future work generating a Ucp1-cre-driven Slc25a1 deletion model will better address this question.\nFigure. R16 Western blot analysis of SLC25A1 from BAT of WT and Slc25a1AKO mice.\n9. Regarding figure 2, as it has been repeatidely described in the literature, that prolonged fasting decreases phosphorylation levels of AKT, as shown for example in figure 2r. Unfortunately, in figure 2j and 2l the levels of pAKT presented seem to be quite elevated for a 36h fast. On top, a ratio of pAKT/AKT is not present and AKT levels are not shown in figure 2j. In figure 3 the legend claims that WB were performed in 12week old animals upon 36h of fasting. With all conditions being equal, it would be important to address the differences in pAKT levels and also include a pAKT/AKT ratio.\nAuthors’ response: Thanks for raising this concern. We have now included the tAKT band, as well as a calculation of the pAKT/AKT ratio (Figure R17), in Figures 2j and k of the revised manuscript. For Fig. 2l, since there was no AL control, the results make sense that the level of pAKT elevated after insulin treatment.\nFigure R17 Quantification of pAKT/AKT ratio of Fig. 2j.\n10. The overexpression model does not seem to answer any particular question and does’t seem to add any particular insight into the question tackled in this current manuscript. If the authors want to use the overexpression model, it would be ideal at least to show that the over expression worked, since it seems to have an effect on glucose levels homeostasis.\nAuthors’ response: We have deleted the part of Slc25a1 overexpression according to your suggestion.\n11. A slightly minor comment (and surely irrelevant for the revision process) but, regarding some of the wording used, the expression “time-restricted feeding” is more commonly used than the expression “time-restricted fasting”.\nAuthors’ response: Thanks for the suggestion. We have corrected time-restricted fasting into time-restricted feeding in the revised manuscript.\n12. I would strongly recommend further proof reading since some words seem to be missplaced, as for example in line 330 the authors write “primarily due to a decrease in liver mass”. Looking at the figure, I would imagine that the authors probably were referring to lean mass.\nAuthors’ response: Thank you for the suggestion. We have corrected the liver mass to lean mass. In addition, we have further examined the manuscript and corrected the typos and mistakes."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors’ efforts in addressing my comments and revise the manuscript. The study has been clearly improved. However, I still have some concerns about the characterization of SLC25A1 methylation by PRMT3 and extent to which reduced SLC25A1 protein level is responsible for the phenotypes associated with PRMT3 KO.\n1. The sites of PRMT3-catalyzed SLC25A1 methylation were determined by using a pan-ADMA antibody. More rigor experiments should be done to define the sites. They should perform in vitro methylation assay using recombinant proteins and detect the methylation and loss of methylation with the mutants using an antibody-independent approach, such as fluorography.\n2. The authors mutated the two candidate arginine methylation sites (R282 and R285) to Glycine (G) and Cystine (C) respectively. The underlying rationale for this mutation was not mentioned. Arginine is positively charged, and it is generally accepted that R-to-K (lysine) is the proper way to introduce arginine methylation deficiency while maintain the positive charge of arginine. The G or C mutation may cause structural changes that could lead to protein misfolding, thus indirectly affect methylation.\n3. As I mentioned in previous critiques, it is important to define the extent to which reduced SLC25A1 is responsible for PRMT3 KO phenotype. The authors should supplement/add back SLC25A1 in PRMT3 KO background and assess if it restores functional impact of PRMT3 KO.", "reviewer_2": "After carefully reviewing the updated manuscript, I find that the authors have satisfactorily addressed all the points raised during the first round of revisions.\nI would only suggest, since I am confident that the data is available from the metabolic studies, that the authors provide a cumulative curve of food intake over a period longer than 24 hours. This would help confirm that the drug does not have a small yet cumulative effect on food intake.\nIncluding this analysis would reinforce the central claim of the paper."}, "author_response": "Reviewer #1 (Remarks to the Author):\nI appreciate the authors’ efforts in addressing my comments and revise the manuscript.\nThe study has been clearly improved. However, I still have some concerns about the characterization of SLC25A1 methylation by PRMT3 and extent to which reduced SLC25A1 protein level is responsible for the phenotypes associated with PRMT3 KO.\n1. The sites of PRMT3-catalyzed SLC25A1 methylation were determined by using a pan-ADMA antibody. More rigor experiments should be done to define the sites. They should perform in vitro methylation assay using recombinant proteins and detect the methylation and loss of methylation with the mutants using an antibody-independent approach, such as fluorography.\nAuthors’ response:\nWe thank the reviewer for this valuable comment and appreciate the opportunity to clarify our approach. Due to the limited availability of isotope-labelled S-adenosyl methionine and the fact that SLC25A1 is a multi-pass membrane protein, it was technically challenging to perform a conventional in vitro methylation assay.\nAs an alternative, we employed LC–MS analysis to identify peptides containing methylated arginine residues. Specifically, we established three experimental conditions: (1) SLC25A1 alone, (2) PRMT3 + SLC25A1, and (3) PRMT3 + SLC25A1 + the PRMT3 inhibitor SGC707 (Figure R1A). Following immunoprecipitation using a FLAG antibody to enrich SLC25A1, the protein was digested into small peptides, and an ADMA-specific antibody was used to enrich peptides containing methyl-arginine.\nMass spectrometry analysis revealed that the peptide sequence AFYKGTVPRLGR, containing residues R282 and R285, was exclusively detected in the PRMT3 + SLC25A1 group, but not in the absence of PRMT3 or in the presence of SGC707 (Figure R1B). These findings confirm that R282 and R285 are the primary sites of PRMT3-mediated arginine methylation on SLC25A1.\nFigure. R1 (A) Experimental design depicting the identification of peptides carrying methylarginines. (B) Mapping of peptide hits of SLC25A1 from different groups.\n2. The authors mutated the two candidate arginine methylation sites (R282 and R285) to Glycine (G) and Cystine (C) respectively. The underlying rationale for this mutation was not mentioned. Arginine is positively charged, and it is generally accepted that R-to-K (lysine) is the proper way to introduce arginine methylation deficiency while maintaining the positive charge of arginine. The G or C mutation may cause structural changes that could lead to protein misfolding, thus indirectly affect methylation.\nAuthors’ response:\nWe thank the reviewer for this insightful comment. As noted, previous work (PMID: 29651165) identified Arg282 and Arg285 as critical residues for citrate binding in SLC25A1. In that study, the R282G/R285C double mutation did not affect cell proliferation but specifically disrupted the binding of CPTI-2 (SLC25A1 inhibitor) to SLC25A1. Based on this precedent, we initially adopted the same mutation strategy to investigate SLC25A1 methylation.\nWe fully agree with the reviewer that R-to-K substitution represents a more appropriate approach for generating methylation-deficient mutants, as it preserves the positive charge of arginine and minimizes structural perturbation. In response, we have now reconstructed the SLC25A1 R282 K/R285K mutant (hereafter referred to as SLC25A1R-to-K) and performed additional co-immunoprecipitation assays to assess its methylation status and interaction with PRMT3.\nOur experimental results demonstrated that the SLC25A1R-to-K mutant exhibited reduced PRMT3-mediated methylation and diminished binding to PRMT3 (Figure R2A, B). These findings further support the conclusion that R282 and R285 are bona fide sites for PRMT3-mediated arginine methylation.\nFigure. R2 (A) Structural prediction of WT SLC25A1 (SLC25A1WT) and R282K/R285K (SLC25A1R-to-K) by AlphaFold 3. (B) IP of ADMA in 293T cells transfected with PRMT3 and SLC25A1WT/SLC25A1R-to-K and WB of FLAG confirmed that SLC25A1 mutation reduced its methylation. (C) IP of FLAG in 293T cells transfected with PRMT3 and SLC25A1WT/SLC25A1R-to-K and WB of MYC confirmed that the SLC25A1 mutation reduced its interaction with PRMT3.\n3. As I mentioned in previous critiques, it is important to define the extent to which reduced SLC25A1 is responsible for PRMT3 KO phenotype. The authors should supplement/add back SLC25A1 in PRMT3 KO background and assess if it restores functional impact of PRMT3 KO.\nAuthors’ response:\nWe thank the reviewer for this valuable comment. To address the concern, we employed both in vivo and in vitro approaches to determine whether reduced SLC25A1 expression contributes to the phenotype observed in PRMT3 knockout or inhibition.\nIn vivo, we locally injected AAV9-Fabp4-Slc25a1 into the eWAT of high-fat diet–induced obese mice and subsequently treated them with the PRMT3 inhibitor SGC707 for four weeks. Two weeks post-injection, Slc25a1 overexpression markedly impaired glucose tolerance following intraperitoneal glucose administration (Figure R3A, B). Consistently, SGC707 treatment failed to improve glucose tolerance in mice overexpressing Slc25a1 (Figure R3A, B).\nFurthermore, metabolic chamber analyses revealed that Slc25a1 overexpression significantly decreased ΔRER (Figure R3C, D) and heat production (Figure R3E, F), and SGC707 treatment did not reverse these effects. Similarly, SGC707 treatment failed to increase PC protein levels or Hk2 mRNA expression in eWAT from Slc25a1-overexpressing mice (Figure R3G, H).\nIn vitro, consistent with the in vivo findings, SGC707 treatment failed to elevate the mRNA levels of Gapdh, Pkm, and Hk2 in adipocytes derived from eWAT overexpressing Slc25a1 (Figure R3I, J).\nTogether, these results demonstrate that Slc25a1 overexpression counteracts the beneficial metabolic effects of PRMT3 inhibition, supporting the notion that reduced SLC25A1 mediates, at least in part, the phenotype observed with PRMT3 loss of function.\nFigure. R3 (A) Blood glucose levels and area under the curve (AUC) (B) of mice during glucose tolerance test, (n = 4 per group). (C, D) 48h RER rhythms and delta RER of mice (n = 4 per group). (E, F) 48h heat production rhythms and average heat production of mice (n = 4 per group). (G, H) Relative expression levels of key proteins/genes involved in glucose metabolism from eWAT of mice. (I, J) Experimental design depicting the Adenoviral-mediated Slc25a1 overexpression in vitro and relative expression levels of key genes involved in glucose metabolism.\nReviewer #2 (Remarks to the Author):\nAfter carefully reviewing the updated manuscript, I find that the authors have satisfactorily addressed all the points raised during the first round of revisions.\nI would only suggest, since I am confident that the data is available from the metabolic studies, that the authors provide a cumulative curve of food intake over a period longer than 24 hours. This would help confirm that the drug does not have a small yet cumulative effect on food intake.\nIncluding this analysis would reinforce the central claim of the paper.\nAuthors’ response:\nWe sincerely thank the reviewer for their time and constructive feedback, which has helped us improve the quality of our manuscript. In the revised version, we have included the cumulative food intake curves for seven days following SGC707 treatment (Figure S7h, i). The results show that SGC707 administration did not significantly affect food intake (Figure R4).\nFigure. R4 Cumulative curve of (A) and average (B) food intake for 7 days after SGC707 treatment."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I appreciate the authors’ efforts in addressing my additional concerns. The manuscript is further strengthened by the new data demonstrating that increased SLC25A expression rescues the PRMT3 KO phenotype.\nI have one remaining comment regarding the efforts to address my request on defining SLC25A methylation sites using in vitro assay. The new results showing in Figure R1 do not provide independent confirmation that R282 and R285 are bona fide PRMT3 methylation sites. This approach is not quantitative and still relies on the same ADMA antibody used for western blot. Moreover, performing a second round of immunoprecipitation with the ADMA antibody introduces significant bias, making the results inherently circular and insufficient as independent validation.\nIf isotope-labelled SAM is not available to the authors, I will not insist on the experiment. However, the authors should explicitly acknowledge this limitation in the manuscript, as methylation of arginine residues critical for citrate binding could directly affect in citrate metabolism rather than indirectly influence protein stability.", "reviewer_2": "The authors have satisfactorily addressed all of the issues/concerns raised during this revision process. The manuscript is clear and presents solid and interesting data."}, "author_response": "Authors’ response:\nWe thank the reviewer for this valuable comment and appreciate the understanding of the difficulty. We have added the above-mentioned limitations regarding the lack of in vitro methylation assay in the discussion of revised manuscript as highlighted by yellow."}]

Decision letter