TOX3 in hypothalamic POMC-lineage cells regulates energy balance via the PTEN-AKT signaling axis

Domain: neuroscience
Published: 2026-04-13
Editorial outcome: major_revision

Abstract
Obesity and its associated metabolic disorders, including type 2 diabetes and fatty liver disease, represent a growing global health burden. Although the hypothalamus is well-established as the central regulator of energy homeostasis through specialized neuronal circuits, the molecular mechanisms governing these pathways remain incompletely elucidated. In this study, we identify thymocyte selection-associated high mobility group box 3 (TOX3) as a critical metabolic regulator in hypothalamic pro-opiomelanocortin (POMC)-lineage cells in mice. By employing cell-type-specific genetic manipulation in murine models, we establish that conditional TOX3 ablation in POMC-lineage cells exacerbates diet-induced obesity and metabolic dysfunction, while its overexpression in these neurons confers robust metabolic benefits. Mechanistically, TOX3 enhances the function of POMC-lineage cells through a post-translational regulatory mechanism involving PTEN proteasomal degradation, leading to potentiated AKT signaling. This central modulation drives sympathetic activation of brown adipose tissues, resulting in enhanced thermogenesis and energy expenditure. Notably, TOX3 exhibits striking neuronal specificity, as both loss- and gain-of-function manipulation in agouti-related peptide (AgRP) neurons produce negligible metabolic consequences. Our work identifies previously unrecognized physiological roles of TOX3 in POMC-lineage cells that are essential for maintaining energy homeostasis in mice, thereby revealing therapeutic opportunities for metabolic disorders.

Introduction
Obesity and its associated metabolic diseases, particularly type 2 diabetes and fatty liver disease, have emerged as a major global health challenge1. Elucidating the intricate mechanisms underlying the pathogenesis of obesity is critical for identifying potential preventive and therapeutic strategies. Fundamentally, obesity results from chronic energy homeostasis disruption due to sustained caloric intake exceeding expenditure2,3. Decades of research have established that energy balance is predominantly regulated by the central nervous system (CNS), with the hypothalamus serving as the central regulatory hub4-6.

The hypothalamic circuitry comprises several well-characterized nuclei, including the arcuate nucleus (ARC), which contains two interlinked but functionally antagonistic neuronal populations: anorexigenic pro-opiomelanocortin (POMC) neurons and orexigenic agouti-related peptide (AgRP)/neuropeptide (NPY) neurons6,7. POMC neurons are established as master regulators of negative energy balance, exerting dual control through central suppression of food intake and activation of peripheral sympathetic nervous system (SNS) output-mediated adipose tissue catabolism6,8. Anatomical tracing studies have demonstrated that POMC neurons innervate both white (WAT) and brown adipose tissue (BAT), where norepinephrine (NE) release activates β3-adrenoceptors to stimulate lipolysis in WAT and promote thermogenesis in BAT9-11.

As first-order metabolic sensors, POMC neurons are uniquely positioned to integrate peripheral signals due to their anatomical localization proximity to the median eminence, a circumventricular organ with a fenestrated blood-brain barrier8. This privileged location enables rapid detection of circulating metabolic cues, including nutrients (e.g., glucose, fatty acids) and hormones (e.g., leptin, insulin)6,7. Studies have demonstrated that elevated circulating leptin and insulin triggers a coordinated response in POMC neurons involving acute membrane depolarization and transcriptional upregulation of Pomc and subsequent processing into bioactive peptides5,6,12-14. These adaptations concurrently suppress feeding while enhancing energy expenditure through SNS-mediated adipose tissue remodeling. Importantly, disruption of this regulatory axis-whether through genetic variants, dietary stress, or inflammation-represents a critical node in the pathogenesis of obesity and its metabolic complications4-7.

Thymocyte selection-associated high mobility group box family member 3 (TOX3), a member of the high mobility group box superfamily, functions as an important transcriptional co-regulator15. Human studies and murine models consistently demonstrate predominant TOX3 expression in the brain16,17. In vitro evidence indicates TOX3 regulates calcium-dependent transcription in neurons15, exhibits neuroprotective effects16, and contribute to CNS development16,17, suggesting vital roles in neural physiology. Notably, genome-wide association studies (GWAS) have implicated TOX3 in polycystic ovary syndrome pathogenesis and insulin resistance18. Most recently, TOX3 was identified as a novel regulator of hepatic gluconeogenesis that exacerbates diabetic pathophysiology19, further underscoring its metabolic relevance. However, the precise physiological and pathophysiological roles of TOX3 in metabolic regulation, particularly in energy homeostasis, remain completely unexplored.

Here, we identify TOX3 as a POMC-lineage cell-specific guardian against obesity. Mechanistically, TOX3 expression in hypothalamic POMC-lineage cells promotes phosphatase and tense homolog (PTEN) degradation, enhancing protein kinase B (AKT) signaling to activate sympathetic-driven adipose catabolism. Genetic ablation of TOX3 in POMC-lineage cells exacerbates metabolic dysfunction, whereas its overexpression confers protection, with no significant effects observed in AgRP neurons. These findings position TOX3 as a critical molecular nexus linking hypothalamic signaling to peripheral energy metabolism.

Results
TOX3 is predominantly expressed in hypothalamic neurons
To characterize the expression patterns of TOX3, we first examined its distribution across major metabolic tissues in mice using a publicly available database. Our analysis revealed that Tox3 mRNA expression was most enriched in the brain compared to various peripheral tissues (Fig. 1a). Subsequent qPCR analysis confirmed that Tox3 levels were significantly higher in the hypothalamus than in other brain regions (Fig. 1b) or peripheral tissues (Figure S1a). To determine the cellular localization of TOX3 within hypothalamic cell populations, we performed RNAscope in situ hybridization for Tox3 combined with immunofluorescence labeling for neuronal nuclei antigen (NeuN, neuronal marker), vimentin (tanycyte marker), glial fibrillary acidic protein (GFAP, astrocyte marker), and ionized calcium binding adaptor molecule 1 (IBA1, microglia marker). The results revealed that Tox3 expression was predominantly neuronal, with most Tox3-positive cells co-expressing NeuN (90.54%), and almost all (>95%) NeuN-positive cells showing Tox3 expression (Figure S1b, c). In contrast, minimal colocalization was observed between Tox3 and markers of tanycytes, astrocytes, or microglia (Figure S1d-f). Consistent with these findings, analysis of a published single-cell RNA-sequencing dataset of the mouse hypothalamus confirmed that Tox3 was abundantly expressed in neurons, with negligible expression in tanycytes, astrocytes and microglia (Fig. 1c, d). These findings establish that TOX3 is specifically and highly expressed in hypothalamic neurons.

Knockdown of TOX3 in hypothalamic neurons exacerbates diet-induced obesity and metabolic dysfunctions
Given the prominent hypothalamic expression of TOX3, we examined its expression under different metabolic conditions. Hypothalamic Tox3 mRNA levels were significantly downregulated in high fat diet (HFD)-induced obese mice compared with chow diet (CD)-fed controls (Fig. 1e). To investigate the role of hypothalamic neuronal TOX3 in energy homeostasis, we bilaterally injected HFD-fed mice with AAV-shCtrl or AAV-shTox3 viruses (tracked with GFP) (Fig. 1f). Fluorescence imaging confirmed GFP expression in hypothalamic nuclei (ARC, VMH, DMH, LH, and PVN), with no detectable signal in extrahypothalamic regions such as the cortex (Fig. 1g). AAV-shTox3 injection significantly reduced hypothalamic Tox3 expression without affecting other family members (Tox, Tox2, Tox4) (Fig. 1h), while Tox3 levels remained unchanged in other regions including the thalamus, hippocampus and cortex (Figure S1g).

Hypothalamic Tox3 knockdown led to greater body weight gain (Fig. 1i; Figure S1h). Post-mortem analyses revealed increased weights of liver, epididymal WAT (eWAT), inguinal WAT (iWAT), and BAT (Fig. 1j), along with enlarged adipocytes in these depots (Fig. 1k-m). Livers of AAV-shTox3-injected mice exhibited more and larger lipid droplets (Fig. 1k) and elevated levels of hepatic triglycerides (TG), total cholesterol (TC), and non-esterified fatty acid (NEFA) (Fig. 1n-p). Compared with controls, mice with TOX3 knockdown in hypothalamic neurons displayed impaired glucose tolerance and reduced insulin sensitivity (Fig. 1q-t), accompanied by significantly higher serum insulin levels (Fig. 1u). Collectively, these results demonstrate that specific knockdown of TOX3 in hypothalamic neurons exacerbates HFD-induced obesity and metabolic dysfunction in mice.

Ablation of TOX3 in POMC-lineage cells exacerbates diet-induced obesity and metabolic disturbance
Hypothalamic POMC neurons is known as crucial nodes in energy homeostasis6,8,20. To determine TOX3 expression in these neurons, we generated POMC-lineage cell-specific tdTomato reporter mice (Pomc-cre;Ai9) and observed substantial co-localization of Tox3 mRNA with tdTomato-labeled POMC-lineage cells (Figure S2a). Interestingly, after 16 weeks of HFD feeding, Tox3 expression in POMC (tdTomato+)-lineage cells was significantly reduced compared to CD-fed controls (Figure S2a, b). A modest decrease in Tox3 expression was also observed in non-POMC (tdTomato-) cells (Figure S2c).

To investigate TOX3 function specifically in POMC-lineage cells, we generated POMC-lineage cell-specific TOX3 knockout mice (PTKO) by crossing Tox3flox/flox (LoxP) mice with Pomc-Cre mice (Figure S2d, e). These mice further crossed with tdTomato reporter mice to visualize POMC-lineage cells. RNAscope analysis confirmed efficient and specific deletion of Tox3 in POMC-lineage cells (Figure S2f-h), with no effect on their distribution or quantity (Figure S2i, j), indicating that Tox3 is not required for the development or survival of POMC-lineage cells.

We then performed a comprehensive metabolic phenotyping screen in mice fed a standard CD or HFD. Under CD, deletion of Tox3 in POMC-lineage cells impacted no detectable changes in body length, body weight or body weight gain, and body composition in male mice (Figure S3a-f). Serum TC, TG, NEFA, and glucose levels were also comparable (Figure S3g-j). Moreover, we did not observe any difference in glucose tolerance or insulin sensitivity between genotypes (Figure S3k, l). Similar results were observed in female mice fed a CD (Figure S3m-x).

In contrast, when challenged with HFD, male PTKO mice showed increased body weight gain without changes in body length (Fig. 2a-c; Figure S4a). Body composition analysis revealed elevated fat mass composition and reduced lean mass composition (Fig. 2d, e). PTKO males also exhibited increased weights of eWAT, iWAT, and BAT (Fig. 2f) and larger adipocytes in these depots (Fig. 2g-i). Consistent with increased adiposity, eWAT from PTKO mice showed upregulated expression of proinflammatory genes (Mcp1, Tnfα, F4/80, Cd68, Cd11b, Cd11c) (Figure S4b) and more F4/80-positive cells (Figure S4c, d), suggesting exacerbated adipose inflammation. In addition, liver weight was higher in PTKO mice (Fig. 2j), accompanied by larger liver size, greater lipid accumulation, and elevated hepatic TG, TC and NEFA (Fig. 2k-n). Serum TC was elevated, whereas TG and NEFA remained unchanged (Figure S4e-g). PTKO mice displayed impaired glucose tolerance and disrupted insulin sensitivity (Fig. 2o-r), together with higher serum insulin levels (Fig. 2s).

Similarly, when challenged with HFD, female PTKO mice exhibited increased body weight gain and fat mass composition, along with decreased lean mass composition (Figure S5a-e). Consistently, fat pads weights, including gonadal WAT (gWAT) and iWAT, were significantly increased in female PTKO mice (Figure S5f), with larger adipocyte sizes observed (Figure S5g-i). More lipid was accumulated in liver tissues of female PTKO mice (Figure S5j-n).

These data indicate that loss of Tox3 in POMC-lineage cells exacerbates diet-induced obesity and metabolic dysfunction in both sexes. Since knockout of TOX3 did not show gender differences, we selected male mice for subsequent experiments.

Overexpression of TOX3 in hypothalamic POMC-lineage cells mitigates HFD-induced metabolic dysfunction
Next, we evaluated whether gain-of-function TOX3 in hypothalamic POMC-lineage cells would be sufficient to ameliorate HFD-induced obesity and metabolic dysfunction. We generated POMC-lineage cell-specific TOX3-overexpressing mice (PTOE) via ARC injection of a Cre-dependent AAV (AAV-Tox3) into Pomc-Cre mice (Fig. 3a). Controls received AAV-GFP (Fig. 3a). Fluorescence imaging confirmed that GFP was specifically detected in the ARC, but not in other regions including the VMH, DMH, LH, cortex, and hippocampus (Figure S6a). We confirmed that hypothalamic Tox3 mRNA expression was upregulated in PTOE mice, while Tox, Tox2, and Tox4 were unaffected (Figure S6b). RNAscope analysis further confirmed that Tox3 was specifically overexpressed in POMC (tdTomato+)-lineage cells (Figure S6c).

On a chow diet, metabolic parameters were comparable between groups (Figure S6d-m). Under HFD challenge, mice with hypothalamic POMC-lineage cell-specific TOX3 overexpression exhibited significantly lower weight gain compared with their controls (Fig. 3b, c; Figure S6n). Body composition analysis revealed reduced fat weight, fat mass composition, and increased lean mass composition in PTOE mice (Fig. 3d, e). Post-mortem analyses also showed decreased weights of eWAT, iWAT, and BAT depots (Fig. 3f), along with smaller adipocyte sizes in eWAT and iWAT, as well as reduced lipid accumulation in BAT (Fig. 3g-i). Inflammatory infiltration in eWAT was ameliorated, as reflected by lower mRNA levels of Mcp1, F4/80, Cd68, and Cd11b (Figure S6o) and fewer F4/80-positive cells (Figure S6p). Additionally, PTOE mice exhibited significantly lower liver weight (Fig. 3j), accompanied by decreased lipid droplet accumulation (Fig. 3k) and reduced hepatic levels of TG, TC, and NEFA (Fig. 3l-n), indicating amelioration of hepatic steatosis. Serum TC levels were reduced in PTOE mice, while TG and NEFA remained unchanged (Figure S6q-s). Overexpression of TOX3 in POMC-lineage cells also improved glucose tolerance and insulin sensitivity (Fig. 3o-r), consistent with lower serum insulin levels (Fig. 3s).

Together, these findings demonstrate that specific overexpression of TOX3 in POMC-lineage cells is sufficient to ameliorate obesity and metabolic disturbances in mice fed an HFD.

TOX3 in POMC-lineage cells controls energy expenditure and adipose tissue catabolism
To understand the underlying mechanisms of how TOX3 in POMC-lineage cells affects energy homeostasis, we conducted indirect calorimetry to assess metabolic parameters in both TOX3 loss- and gain-of-function mice upon HFD feeding. In male LoxP and PTKO mice, food and water intake did not differ significantly (Figure S7a, b). Instead, TOX3 deletion in POMC-lineage cells significantly decreased O2 consumption, CO2 production, and energy expenditure (Fig. 4a, b; Figure S7c-f), without alterations in the respiratory exchange ratio (RER) or physical activity (Figure S7g, h). These results were accompanied by decreased rectal temperature in PTKO mice (Fig. 4c). Notably, thermal images in the interscapular regions indicated that the temperature around BAT regions was markedly reduced upon TOX3 ablation (Fig. 4d, e). Given that adipose tissue is a well-known organ in energy expenditure and obesity progression, we further conducted thorough studies to examine catabolic metabolism in both BAT and WAT. TOX3 deletion markedly downregulated BAT thermogenic genes (Ucp1, Pgc1α, Dio2, Cidea) (Fig. 4f), while browning-related transcripts in iWAT were unaltered (Figure S7i). Western blot analysis and immunohistochemistry further confirmed significantly lower UCP1 protein in BAT of PTKO mice (Fig. 4g-i). The thermogenesis in BAT utilizes fatty acids derived from white adipose lipolysis21,22. Coordinately, we found the expression or phosphorylation of lipolytic proteins were significantly lower in eWAT and iWAT (Figure S7j-l). In liver, genes promoting fatty acid uptake (Cd36) and lipogenic (Srebf1) were upregulated, whereas genes involved in fatty acid oxidation (Cpt1α) and lipoprotein secretion (Apob, Mttp) were downregulated; meanwhile, skeletal muscle (SKM) metabolic markers remained unchanged (Figure S7m, n).

We next investigated whether TOX3 overexpression in POMC-lineage cells could enhance energy expenditure. In metabolic cage studies, food and water intake were unaltered between Control and PTOE groups (Figure S8a, b). However, PTOE mice exhibited significantly higher O2 consumption, CO2 production, and energy expenditure (Fig. 4j, k; Figure S8c-f), with no change in RER or locomotor activity (Figure S8g, h). Rectal and interscapular temperatures were also elevated in PTOE mice (Fig. 4l-n). Expression of UCP1 and other thermogenic markers was increased in BAT (Fig. 4o-r), while iWAT browning markers were unaltered (Figure S8i). Correspondingly, the expression of phosphorylation of lipolytic proteins were greatly increased in WAT (Figure S8j-l). In liver, genes promoting fatty acid uptake (Cd36) and lipogenesis (Srebf1, Acaca) were downregulated, while fatty acid oxidation (Cpt2) was increased; metabolic genes in SKM remained unaffected (Figure S8m, n). These results indicate that TOX3 in POMC-lineage cells affects obesity and metabolic disturbances through its regulation of adipose tissue metabolism and energy expenditure.

TOX3 in POMC-lineage cells modulates adipose tissue metabolism through sympathetic nervous regulation
Adipose tissue receives direct sympathetic innervation, with SNS activity serving as a key regulator of adipose tissue metabolism23,24. To investigate whether TOX3 in POMC-lineage cells regulates energy homeostasis via the SNS pathway, we performed comprehensive analyses in both TOX3 loss- and gain-of-function models upon HFD feeding conditions.

In PTKO mice, tyrosine hydroxylase (TH) expression, a rate-limiting step enzyme required for catecholamine synthesis11,25, was significantly decreased in BAT (Fig. 5a-c; Figure S9a). Consistently, norepinephrine (NE) content was reduced in BAT, eWAT, and iWAT, but not in liver (Fig. 5d; Figure S9b). Adrenergic receptors (Adrβ1, Adrβ2, and Adrβ3) were downregulated in BAT but unchanged in WAT depots (Figure S9c-e). NE turnover in BAT, based on NE content before and after injection of α-methyl-para-tyrosine (AMPT), an inhibitor of TH26-29, was also lower in PTKO mice (Fig. 5e). In addition, the downstream effectors of the β3-adrenergic receptor signaling, phosphorylation of cAMP response element-binding protein (CREB) and p38 mitogen-activated protein kinase (MAPK), were both downregulated in BAT of PTKO mice (Figure S9f).

Cold exposure (4℃) further revealed impaired thermoregulation in PTKO mice, which showed a more rapid decline in rectal and interscapular BAT temperature (Figure S9g, h). While body weight and adipose depot weights were unchanged at room temperature, cold-exposed PTKO mice showed higher weights of BAT, eWAT and iWAT (Figure S9i-l), along with attenuated cold-induced upregulation of UCP1 and TH (Figure S9m-q). To confirm SNS involvement, we administered the β3-adrenergic agonist CL316,24330-32 or saline to HFD-fed LoxP and PTKO mice (Fig. 5f). We found that PTKO-induced higher BAT weight, lower thermogenic gene expression, and more lipid droplets were all abrogated by CL316,243 treatment (Fig. 5g-k).

Conversely, PTOE mice showed elevated TH expression in BAT (Fig. 5l-n; Figure S10a) and increased NE content in BAT, eWAT, and iWAT (Fig. 5o; Figure S10b). Adrenergic receptors (Adrβ2 and Adrβ3) were upregulated in BAT but not in WAT (Figure S10c-e). NE turnover and phosphorylation of CREB and p38 MAPK were also enhanced in BAT of PTOE mice (Fig. 5p; Figure S10f). To further confirm SNS involvement, we performed bilateral microinjections of 6-hydroxydopamine hydrobromide (6-OHDA), a selective catecholaminergic neurotoxin26,33, into the BAT pads of HFD-fed Control and PTOE mice (Fig. 5q; Figure S10g-i). Four weeks post-injection, the decreases in body weight and BAT weight observed in PTOE mice with an intact SNS were largely reversed (Fig. 5r; Figure S10j), with comparable expression of thermogenic parameters and BAT morphology between groups (Fig. 5s-v). We also performed bilateral injections of 6-OHDA into both the eWAT and iWAT depots of HFD-fed Control and PTOE mice (Figure S10k-m). Interestingly, this intervention did not alter the observed reduction in body weight and fat mass composition, or the increase in fat mass composition, in PTOE mice (Figure S10n, o). These findings demonstrate that TOX3 in POMC-lineage cells regulate energy expenditure through β-adrenergic signaling in BAT.

TOX3 controls the function of hypothalamic POMC-lineage cells
To provide insight into the central mechanisms through which TOX3 in POMC-lineage cells regulates system metabolism, we assessed whether TOX3 participates in the control of POMC-lineage cellular activity. HFD-fed Pomc-cre;Ai9 and PTKO;Ai9 mice were used for whole-cell current-clamp recordings (Fig. 6a). We observed that POMC-lineage cells in PTKO;Ai9 mice exhibited a significant decrease in spontaneous firing rate compared to those in Pomc-cre;Ai9 mice, along with a trend toward reduced membrane potential (Fig. 6b-d). Consistent with these electrophysiological findings, immunostaining revealed a significant reduction in c-FOS-positive POMC-lineage cells in PTKO;Ai9 mice (Fig. 6e, f). Furthermore, hypothalamic POMC expression was significantly reduced in PTKO mice at both mRNA (Fig. 6g) and protein levels (Fig. 6h, i) compared to LoxP controls, while other neuropeptides (Cart, Agrp, and Npy) remained unchanged (Fig. 6g). Conversely, TOX3 overexpression in POMC-lineage cells increased c-FOS immunoreactivity (Fig. 6j, k) and upregulated hypothalamic POMC expression (Fig. 6l-n), without altering Cart, Agrp, or Npy levels (Fig. 6l). These data establish TOX3 as a regulator of POMC-lineage cellular activity and peptide expression, revealing its essential role in maintaining the function of POMC-lineage cells within the hypothalamus.

TOX3 modulates AKT signaling by regulating PTEN degradation
To elucidate how TOX3 affects the function of POMC-lineage cells, we performed single-nucleus RNA sequencing (snRNA-seq) on hypothalamus of LoxP and PTKO mice (Figure S11a). Based on gene expression of specific markers34, we classified the cells into 9 distinct clusters (Fig. 7a; Figure S11b, c). The proportion of cells in each cluster did not differ significantly between groups (Figure S11d). Neurons were further subclustered into 26 subsets, including POMC neurons (Fig. 7b; Figure S11e). Transcriptome analysis identified differentially expressed genes in POMC neurons of PTKO versus LoxP mice (Fig. 7c). KEGG enrichment analysis showed enrichment of these genes in nervous system-related pathways, including insulin signaling (Fig. 7d). We also conducted RNA sequencing (RNA-seq) on fluorescence-activated cell sorting (FACS)-sorted POMC-lineage cells from HFD-fed Pomc-cre;Ai9 and PTKO;Ai9 mice (Figure S12a). This analysis identified 448 upregulated and 647 downregulated transcripts in TOX3-deficient POMC-lineage cells (Figure S12b). Notably, KEGG analysis also highlighted enrichment in the Phosphatidylinositol-3-kinase (PI3K)-AKT signaling pathway (Figure S12c).

We further performed insulin administration experiments to confirm the involvement of TOX3 in insulin responses. As expected, while insulin robustly induced p-AKT (S473) expression in Pomc-cre;Ai9 mice, this effect was markedly attenuated in PTKO;Ai9 mice (Fig. 7e). In vitro, Tox3 knockdown attenuated insulin-induced p-AKT (S473) expression (Fig. 7f), whereas TOX3 overexpression enhanced this signaling (Fig. 7g; Figure S12d).

To elucidate how TOX3 influences insulin signaling, we performed immunoprecipitation-mass spectrometry (IP-MS) on hypothalamic lysates from mice injected with AAV-Tox3-Flag. PTEN, a key phosphatase in the insulin pathway, was identified as a potential TOX3-interacting partner (Fig. 7h). We further confirmed this interaction by additional Co-IP and western blot analyses (Fig. 7i). Immunofluorescence staining also revealed the co-localization of PTEN and TOX3 in N2a cells (Figure S12e). TOX3 manipulation did not alter Pten mRNA levels (Figure S12f, g). However, TOX3 overexpression significantly decreased PTEN protein levels both in vivo (Fig. 7j; Figure S12h) and in vitro (Fig. 7i), suggesting post-translational regulation. Cycloheximide (CHX) chase assays in GT1-7 and N2a cells showed that TOX3 overexpression significantly accelerated PTEN degradation (Fig. 7k, l; Figure S12j, k). Moreover, the proteasome inhibitor MG132 blocked the TOX3-mediated decrease in PTEN protein (Fig. 7m), suggesting that TOX3 promotes proteasome-dependent PTEN turnover. Furthermore, restoring PTEN expression in TOX3-overexpressing cells suppressed insulin-induced AKT phosphorylation at Ser473 (Fig. 7n; Figure S12l). These results demonstrate that TOX3 potentiates AKT signaling through post-translational suppression of PTEN.

TOX3 in hypothalamic AgRP neurons is dispensable for energy homeostasis
AgRP neurons in the ARC represent another key population involved in metabolic regulation35,36. We therefore investigated whether TOX3 in AgRP neurons contributes to whole-body metabolism regulation. First, we characterized TOX3 expression in AgRP neurons under different conditions. In situ hybridization confirmed Tox3 expression in AgRP neurons (Figure S13a), though at markedly lower levels compared to POMC-lineage cells (Figure S2a). Tox3 expression was slightly decreased in AgRP neurons of diet-induced obese mice relative to chow-fed controls (Figure S13a, b).

To assess TOX3 function, we generated AgRP-specific knockout mice (ATKO) by crossing LoxP mice with Agrp-Cre mice (ATKO), which yielded efficient and specific deletion (Figure S13c, d). When fed a standard CD, no differences were observed in body length, body weight gain, body composition, adipose mass weights, glucose tolerance, or insulin sensitivity in male (Figure S13e-l) and female (data not shown) LoxP and ATKO mice. Similar results were observed in HFD-fed males (Figure S14a-n) and females (data not shown). Additionally, no changes in metabolic parameters were detected in mice challenged with chronic HFD feeding (Figure S14o-v).

We next overexpressed TOX3 in AgRP neurons by bilaterally injecting AAV-Tox3 or AAV-GFP into the ARC of HFD-fed male Agrp-Cre mice (Figure S15a). qPCR and in situ hybridization confirmed successful Tox3 overexpression (Figure S15b, c). Consistent with the knockout results, TOX3 overexpression in AgRP neurons did not affect body weight gain, body composition, adipose depot weights, adipocyte size, glucose tolerance, insulin sensitivity, or serum parameters (Figure S15d-m). Moreover, no significant differences in metabolic cage parameters were observed between ATOE and control mice (Figure S15n-u). Collectively, these data demonstrate that TOX3 in AgRP neurons is dispensable for whole-body energy homeostasis.

Methods
Mice
Pomc-Cre and Agrp-Cre strains were obtained from the Jackson Laboratory (BarHarbor, ME, USA). Tox3flox/flox mice were kindly provided by Prof. Tao Huang at Shandong University. To generate cell type-specific Tox3 knockout mice, Tox3flox/flox mice were crossed with Pomc-Cre or Agrp-Cre mice to selectively delete Tox3 from POMC-lineage cells or AgRP neurons, respectively. The resulting mice are hereafter referred to as PTKO (POMC-lineage cell-specific TOX3 knockout) or ATKO (AgRP neuron-specific TOX3 knockout) mice, with Tox3 flox/flox mice (designated as LoxP controls) serving as controls.

To assess TOX3 expression in hypothalamic POMC-lineage cells and AgRP neurons, we crossed Pomc-Cre or Agrp-Cre mice with Rosa26-lox-stop-lox-tdTomato (Ai9) reporter mice (The Jackson Laboratory) to generate Pomc-cre;Ai9 or Agrp-cre;Ai9 mice, respectively, in which POMC- or AgRP-expressing cells were labelled with tdTomato. To confirm TOX3 deletion specifically in POMC-lineage cells, Pomc-Cre and PTKO mice were crossed with Ai9 mice to generate conditional knockout mice or control mice with POMC-lineage cells were labelled by tdTomato, hereafter referred to as Pomc-cre;Ai9 or PTKO;Ai9 mice, respectively. Similarly, to confirm TOX3 deletion specifically in AgRP neurons, Agrp-Cre and ATKO mice were crossed with Ai9 mice to generate conditional knockout mice or control mice with AgRP neurons were labelled by tdTomato, hereafter referred to as Agrp-cre;Ai9 or ATKO;Ai9 mice, respectively.

The animals were housed under a 12 h light/12 h dark cycle (lights on at 7:00 am, lights off at 7:00 pm) in a temperature-controlled room (22-24℃) and had ad libitum access to food and water unless otherwise indicated. Standard environmental enrichment was provided. Mice were fed a standard chow diet (CD) (52 g/kg fat, cd-dossy, Chengdu, Sichuan, China) or a high-fat diet (HFD) (60% kcal from fat, D12492, Research Diets, NJ, USA). Weekly body weight measurements were taken between 6 to 20 weeks of age. All surgical and tissue collection were performed during the light phase. All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee of Sichuan University (No: 20230731003).

Stereotaxic surgery
For hypothalamic neuron-specific TOX3 knockdown, we constructed neuronal-targeted AAV-hSyn-eGFP-miR30shRNA vectors targeting Tox3 (AAV-shTox3) or control (AAV-shCtrl). The miR30 backbone enhances shRNA processing and stability, promoting more potent and durable gene silencing compared to conventional shRNA designs52. For TOX3 overexpression in hypothalamic neurons, we constructed neuronal-targeted AAV-hSyn-eGFP-Flag vectors targeting Tox3 (AAV-Tox3-Flag) or control (AAV-GFP-Flag). These viruses were packaged by the ObiO Technology (Shanghai, China). To induce the specific knockdown or overexpression of TOX3 in hypothalamic neurons, wild-type (WT) C57BL/6J mice were anesthetized with isoflurane and then subjected to bilateral stereotaxic injections of AAV-shTox3/AAV-shCtrl (for knockdown), or AAV-GFP-Flag/AAV-Tox3-Flag (for overexpression) into the hypothalamus (coordinates: bregma, anterior-posterior, -1.5 mm; dorsal-ventral, -5.6 mm; lateral, ± 0.35 mm; 1000 nl per site).

To generate mouse models of Tox3 overexpression specifically in POMC-lineage cells or AgRP neurons, AAV-DIO-eGFP (AAV-GFP) and AAV-DIO-Tox3-eGFP (AAV-Tox3) vectors were generated, which were also packaged by ObiO Technology. To evaluate the therapeutic potential of TOX3 in the context of established obesity, Pomc-Cre or Agrp-Cre mice were first fed HFD for 8 weeks to induce obesity, and then anesthetized with isoflurane and received bilateral stereotaxic injections of either AAV-GFP or AAV-Tox3 vectors into the ARC (coordinates: bregma, anterior-posterior, -1.5 mm; dorsal-ventral, -5.85 mm; lateral, ± 0.3 mm; 500 nl per site).

Body length and composition analysis
The body length (nose-to-anus length) of mice was determined by measuring the linear distance from the rostral tip of the nose to the caudal base of the tail53. Body composition (lean and fat mass) was quantitatively assessed using the Small Animal Body Composition Analysis and Imaging System (MesoQMR23-060H-I; Nuimag Corp., Shanghai, China) at specified time points.

Metabolic cage measurements
Metabolic parameters, including food intake, water intake, oxygen consumption (VO2), carbon dioxide production (VCO2), energy expenditure, RER, and locomotor activity, were measured using the Promethion Metabolic Monitoring System (Sale Systems International, LV, USA). Following a 2-day acclimation period, continuous measurements were recorded over 72 h. Data are presented as both hourly values over the 72 h-period and as averaged values across the light (07:00-19:00) and dark phases (19:00-07:00).

Body temperature measurements in mice
Core body temperature was assessed via rectal measurement using a digital thermometer equipped with a specialized rectal probe (KEW, Nanjing, China), as previously described27. Interscapular BAT surface temperature was determined by standardized infrared imaging technique using a calibrated infrared thermal imaging camera (FORTRIC, Shanghai, China), as previously described27.

Glucose tolerance test (GTT) and insulin tolerance test (ITT)
For the GTT, mice were fasted for 6 h before receiving an intraperitoneal (i.p) injection of glucose dissolved in saline (2 g/kg body weight for CD-fed mice; 1 g/kg for HFD-fed mice). For the ITT, mice were fasted for 4 h prior to i.p. administration of insulin (0.75 U/kg for CD-fed mice; 1.5 U/kg for HFD-fed mice). Blood glucose levels were measured from the tail vein using an automatic glucose monitor (Accu-Check; Roche, Switzerland) at baseline (0 min) and at 15, 30, 60, 90, 120 min post-injection.

Biochemical and hormone analysis in the serum and liver
Blood samples were collected from mice, and serum was isolated by centrifugation at 6,000 g for 10 min. For hepatic biochemical analysis, liver samples were homogenized in phosphate buffer solution (PBS) and extracted using chloroform: methanol (2:1, v/v), followed by centrifugation at 3,000 g for 10 min. The following commercial assay kits were used for biochemical and hormonal measurements: glucose, TC, and TG (Biosino, Beijing, China); NEFA (LabAssay NEFA, Wako Chemicals, VA, USA); insulin (ELISA kit #10-1247-01, Mercodia, Sweden).

Norepinephrine turnover measurement
NE turnover was assessed using the AMPT method, as previously described26,27. Briefly, one cohort of mice was left untreated and sacrificed to obtain baseline tissue NE content. The remaining paired mice were injected with AMPT (300 mg/kg) at baseline, followed by a supplemental dose (150 mg/kg) 2 h later to sustain TH inhibition. Four hours after the initial AMPT injection, these mice were sacrificed, and adipose tissues were rapidly dissected, weighed, snap frozen in liquid nitrogen, and stored at -80℃ until analysis. NE content in adipose tissues was quantified according to an established protocol54. Briefly, tissues were homogenized in ice-cold extraction buffer (0.05 M hydrochloric acid in methanol) using a tissue dismembrator, followed by centrifugation at 12,000 g for 10 min at 4℃. The supernatant was collected, and 30 μL of tissue extract was mixed with 270 μL of 50% acetonitrile (in water) containing 0.05% ascorbic acid. After vortexing, the mixture was centrifuged again. Finally, 200 μL of the supernatant was analyzed by liquid chromatography-mass spectrometry (LC-MS) (AB6500, AB SCIEX, USA). NE turnover was calculated according to the following formulas: k = (lg[NE]0 -lg[NE]4)/(0.434 × 4) and K = k[NE]0, where k represents the rate constant of NE efflux, [NE]0 is the initial NE concentration, [NE]4 is the NE concentration after 4 hours, and K corresponds to NE turnover.

Activation and denervation of sympathetic nervous system activity
For acute cold exposure experiments, 16-week-old chow-fed mice were exposed to 4℃ for 6 h under fasting conditions. Rectal and interscapular BAT temperatures were monitored at specified time points, and tissues were collected after 6 hours of cold exposure for further analysis.

For the activation of sympathetic nervous system activity, 14-week-old HFD-fed mice were fasted for 2 h before and after an i.p. bolus injection of CL316,243 (1 mg/kg body weight) administered at 10:00 am to minimize confounding effects of feeding. Tissues were collected 4 h post-injection for further analysis.

For the sympathetic denervation in BAT or WAT, HFD-fed Pomc-Cre mice first received bilateral stereotaxic injections of either AAV-GFP or AAV-Tox3. Subsequently, local denervation was performed by injecting 6-OHDA (10 mg/mL in saline containing ascorbic acid) into the adipose depots. As previously described33,55, 6-OHDA was administered at five distinct sites per BAT pat, or at 15 distinct sites per eWAT and iWAT pad (1 μL per site). Adipose tissues were collected four weeks after chemical denervation for subsequent analysis.

Histological analysis
Mice were euthanized by CO2 inhalation prior to tissue collection. Tissues including liver, eWAT, iWAT, and BAT were collected and fixed in 10% PBS neutral buffered formalin (NBF) for 48-72. Following fixation, the samples were dehydrated and embedded in paraffin according to a standard procedure and sectioned into 4-μm sections for further use. For hematoxylin and eosin (H&E) staining, tissue sections were processed according to the manufacturer’s protocol. For immunohistochemistry (IHC) analysis, antigen retrieval was performed using citrate acid buffer, followed by blocking with 10% normal donkey or goat serum for 1 h at room temperature. Primary antibodies were applied and incubated overnight at 4℃, followed by appropriate secondary antibody incubation for 1 h at room temperature. Signal detection was achieved using chromogenic substrates. Lipid accumulation was assessed in liver cryosections (8 μm) using Oil Red O staining according to established protocols. The sections were visualized under optics microscope (Olympus, Japan) or laser scanning confocal microscopy (Nikon, Japan). Quantitative analysis of IHC staining was performed using ImageJ software (NIH, Bethesda, MD, USA).

Immunofluorescence and RNA fluorescence in situ hybridization (FISH)
Mice were deeply anesthetized with isoflurane and transcardially perfused with saline followed by ice-cold 4% paraformaldehyde in PBS prior to brain collection. For brain immunofluorescence staining, mice were transcardially perfused with saline followed by ice-cold 4% paraformaldehyde (PFA) in PBS. Brains were dissected and post-fixed in 4% PFA for 24 h at 4℃, dehydrated in a graded sucrose series (10%, 20%, and 30% in PBS), and sectioned at 14 μm thickness. Brain sections were dried at 60℃ for 30 min, fixed in cold NBF for 15 min, and dehydrated through an increasing gradient ethanol. The slices were treated with hydrogen peroxide, and boiled in co-detection retrieval reagent, followed by primary incubation at 4℃ overnight. Subsequently, they were incubated with appropriate secondary antibodies and counterstained with DAPI.

For combined RNA fluorescence in situ hybridization (FISH) and immunofluorescence staining, mouse brain sections were processed using the RNAscope Multiplex Fluorescent Reagent Kit v2 and RNA-Protein Co-detection Ancillary Kit (323181 and 323100-USM, ACD, CA, USA) according to the manufacturer’s protocol. Briefly, the brain slices were incubated with primary antibodies at 4℃ overnight. The slices were then incubated with Protease Plus (40℃, 30 min) followed by hybridization with the target probe (40℃, 2 h) in a HybEZTM oven (ACD). Then, signal amplification was achieved using amplifiers AMP1-3 and visualized by TSA Vivid Fluorophore 650. After HRP blocker treatment, the slices were incubated with corresponding secondary antibodies. Sections were then counterstained with DAPI for 2 min at room temperature and mounted.

Images were obtained using laser scanning confocal microscopy (Nikon, Japan; Zeiss, Germany). Quantitative analysis was performed using ImageJ software (NIH). For quantitative assessment of overall signal intensity within a defined anatomical region, we first delineated the entire ARC as the region of interest (ROI) based on a reference atlas and DAPI staining. The fluorescent intensity within the ROI was measured for the specific channel using ImageJ. For co-localization analyses, we manually outlined the ARC as the ROI in each section, guided by DAPI staining and anatomical landmarks. Within this ROI, the tdTomato-positive or negative cells were identified using the corresponding fluorescence channel, and the mean fluorescence intensity of another signal was measured in each cell. The percentage of co-localized cells was calculated per animal and used for statistical analysis.

Electrophysiology
Whole-cell patch-clamp recordings of neurons from Pomc-cre;Ai9 and PTKO;Ai9 mice fed with HFD were performed. Briefly, mice were deeply anesthetized with isoflurane and then transcardially perfused with 95% O2 and 5% CO2 oxygenated ice-cold NMDG cutting solution comprising 93 mM NMDG, 93 mM HCl, 2.5 mM KCl, 1.2 mM NaH2PO4, 30 mM NaHCO3, 25 mM D-glucose, 20 mM HEPES, 5 mM Na-ascorbate, 2 mM thiourea, 3 mM Na-pyruvate, 10 mM MgSO4, and 0.5 mM CaCl2. The brain was quickly removed and immersed in ice-cold NMDG cutting solution. Coronal brain slices containing ARC (250 μm) were cut with a Leica VT1200s vibratome, recovered at 34 ℃ for 10-13 min in NMDG cutting solution, and then maintained at 25 ℃ in oxygenated artificial cerebrospinal fluid (ACSF) (126 mM NaCl, 2.5 mM KCl, 2 mM CaCl2, 2 mM MgCl2, 26 mM NaHCO3, 1.25 mM NaH2PO4, and 10 mM glucose) for 1 h until recordings. Slices were transferred to the recording chamber and perfused with oxygenated ACSF (~ 3 ml/min). Slices were visualized with infrared optics using an upright microscope equipped with a 40× water-immersion lens (Examiner. A1). Neurons from ARC were identified by their location and fluorescence. While-cell patch-clamp recording of action potential (AP) was obtained with a MultiClamp 700B amplifier, a Digidata 1440A analog-to-digital converter, and pClamp 10.2 software (Molecular Devices). Voltage traces were sampled at 10 kHz and filtered at 2 kHz. Recording pipettes were filled with a solution containing: 135 mM K-gluconate, 5 mM KCl, 0.5 mM CaCl2, 10 mM HEPES, 2 mM Mg-ATP, 0.1 mM GTP, 5 mM EGTA, 300 mOsm). Resting membrane potentials (RMP) were measured within 1 min of break-in to the whole-cell configuration.

Single-nucleus RNA sequencing and data processing
For single-nucleus RNA sequencing (snRNA-seq), hypothalamic tissues were rapidly dissected from HFD-fed LoxP and PTKO mice immediately after euthanasia by CO2 inhalation, flash-frozen in liquid nitrogen, and stored at -80℃ until processing. All subsequent snRNA-seq procedures were performed by Shanghai Applied Protein Technology Co., Ltd. Briefly, frozen tissues were thawed in lysis buffer and nuclei were isolated via gentle mechanic homogenization, followed by filtration and purification through a density gradient. Nuclei were resuspended in appropriate buffer, and their concentration was adjusted to approximately 1000 nuclei/μL. The suspension was then loaded onto the 10× Genomics Chromium Single Nuclei Gene Expression platform for library preparation and sequencing. Post-sequencing, FASTQ files were processed using CellRanger (10× Genomics) to generate raw count matrices by mapping RNAs to the transcriptome, assigning transcripts to individual cells, and removing duplicate reads. The resulting count matrices were processed using the Seurat package in R. Cell clusters were annotated based on canonical marker genes, which were cross-validated using CellMarkers 4.0 to ensure accurate cell-type identification.

Neuron isolation, fluorescence-activated cell sorting (FACS), and RNA sequencing
Hypothalamus from three Pomc-cre;Ai9 or PTKO;Ai9 male mice fed an HFD for 16 weeks were pooled per experimental sample. Single-cell suspensions were prepared using the Adult Brain Dissociation Kit (Miltenyi Biotec, #130-107-677) according to the manufacturer’s instructions. Briefly, freshly dissected hypothalamus was rinsed in ice-cold Dulbecco’s PBS (D-PBS) and subsequently transferred into gentleMACS C Tubes (Miltenyi Biotec, #130-093-237) containing the enzymatic digestion mixture. Tissue dissociation was performed using the gentleMACS Octo Dissociator with Heaters (Miltenyi Biotec, #130-096-427). The resulting cell suspension was filtered through a 70 μm nylon mesh and centrifuged at 300 × g for 10 minutes at 4°C, followed by removal of cellular debris and erythrocytes. The pelleted cells were resuspended in D-PBS, and cell viability was assessed using 7-aminoactinomycin D (7-AAD; Beyotime Biotechnology, #C1053S, Shanghai, China). Fluorescence-activated cell sorting was conducted using a BD FACSAria SORP cell sorter (BD Biosciences) at the Flow Cytometry Core Facility. tdTomato-positive cells were selectively collected for subsequent RNA sequencing analysis, which was performed by Shi-Bio Technology (Wuhan, China).

Immunoprecipitation-mass spectrometry (IP-MS) and Co-immunoprecipitation (Co-IP)
To identify TOX3-interacting partners in hypothalamic neurons, we generated a mouse model enabling neuro-specific overexpression of TOX3 within the hypothalamus. Four weeks post-injection, mice were euthanized by CO2 inhalation, and hypothalamic tissues were immediately isolated and homogenized in IP lysis buffer. Lysates were incubated with anti-Flag antibody overnight at 4°C, followed by incubation with Protein A/G magnetic beads (Selleck, #B23202) for 4 h at 4°C. After extensive washing with lysis buffer, immunoprecipitates were resolved by SDS-PAGE. Gel sections were excised for in-gel tryptic digestion, and resulting peptides were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Potential TOX3 interactors were defined as proteins exclusively identified in AAV-Tox3-Flag samples but absent in AAV-Flag controls.

For Co-IP validation, hypothalamic lysates from AAV-Tox3-Flag-injected mice were immunoprecipitated with either control IgG or anti-Flag antibody overnight at 4°C, followed by Protein A/G bead capture (4 h, 4°C). After washing, complexes were eluted and analyzed by SDS-PAGE and immunoblotting.

Cell culture, transfections, and treatments
GT1-7 cells and N2a cells were kindly provided by Prof. Lu Gan at Sichuan University. These cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37°C in a 5% CO₂ humidified atmosphere. To overexpress PTEN, the full-length coding sequence of mouse Pten was cloned into the pcDNA3.1-Myc vector, and cells were transfected with either Pten-Myc construct or empty vector control using LEPI transfection reagent. For TOX3 overexpression experiments, cells were transfected with vectors encoding either mouse Tox3-Flag or vehicle for 48 h. For TOX3 knockdown, cells were transfected with Tox3-specific small interfering RNA (siRNA) using Hiperfect transfection reagent (Qiagen, #301707) according to the manufacturer's protocol. For insulin signaling analysis, cells were serum-starved for 4 hours prior to stimulation with insulin (100 nM) for 0, 5, or 10 minutes at 37°C.

RNA extraction, reverse transcription, and quantitative PCR
Total RNA was extracted from tissues harvested from mice euthanized by CO2 inhalation using TRIzol reagent (Invitrogen) according to the manufacturer’s protocol. RNA quality was assessed by measuring the A260/A280 ratio using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). cDNA was generated by using a reverse transcription kit (Takara Bio, China) according to the manufacturer’s instructions. Quantitative real-time (qPCR) was performed using a Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) with SYBR Green qPCR Master Mix (Vazyme, Nanjing, China). Target gene expression was normalized to 18s rRNA, and the results were presented as fold change by comparison with the control groups. All primer sequences are provided in Table S1.

Protein extraction and Western blot analysis
Tissue samples harvested from mice euthanized by CO2 inhalation or cell samples were homogenized in lysis buffer. The lysates were centrifuged at 12,000 g for 10 min at 4 °C to obtain clarified supernatants. Protein concentrations were determined using a bicinchoninic acid (BCA) protein assay kit (Sangon Biotech, Shanghai, China) following the manufacturer’s protocol. Equal amounts of proteins were loaded on SDS-PAGE gels, transferred to a nitrocellulose membrane, and probed with primary antibodies at 4℃ overnight. The following day, blots were rinsed and incubated with corresponding secondary antibodies for 1 h at room temperature, then visualized using the LI-COR Odyssey System (Lincoln, NE, USA). β-Actin served as the loading control for normalization. All primary antibodies used are detailed in Table S2. The uncropped blots were provided in the Source Data file.

Statistics
All statistical analyses were conducted using GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA). Data are expressed as mean ± SEM. Significance was determined by two-way analysis of variance (ANOVA) with Sidak’s multiple comparisons test, unpaired two-tailed Student’s t test, and one-way ANOVA with post-hoc Tukey’s test as specified in the figure legends. Differences in the relationship between energy expenditure and lean body mass were analyzed by two-sided analysis of covariance (ANCOVA) with lean body mass included as a covariate, using the online tool CalR2 (available at CalR2 (calrapp.org)). A p-value of less than 0.05 was considered statistically significant. The specific sample sizes (n values) for each experimental group are provided in the corresponding figure legends, representing biological replicates (individual mice). At least three independent experiments were repeated with similar results.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript the authors have investigated the role of the transcriptional co-regulator TOX3 in the regulation of energy balance. The authors claim a critical role for TOX3 in this context by action through the hypothalamic POMC neurons. The work as it stands is fundamentally incremental and characterized by several pitfalls and limitations that reduce the enthusiasm of this reviewer for the manuscript.\n1. the authors have used a genetic approach (POMC-Cre mice) where recombination and deletion of TOX3 will not only happen during development, but it will also not exclusive of POMC neurons in the ARC. POMC precursors in the ARC give rise to several other neuronal types, including AgRP and kisspeptin neurons (PMID: 35044906; PMID: 20348924). POMC is also expressed during development in extrahypothalamic brain structures affecting feeding behavior, such as the hippocampus and amygdala, and in adulthood expression is maintained in the brainstem and pituitary. Authors do not provide any evidence that the modifications they see in their model applies specifically to hypothalamic POMC neurons in adulthood. This lack of specificity for POMC neurons in the ARC is obvious by the images provided in supplementary Figure 2. Authors have GFP and tdtomato expression in basically all cells in the ARC. Authors need to be aware that POMC-Cre-ERT2 genetic model is available in the field.\n2. Similarly for the overexpression of TOX3 in POMC neurons, the authors do not provide any evidence that this is specific to POMC neurons. The expression of GFP fluorescence in supplementary figure 6 (panels A and C, not POMC as wrongly reported in panel C) does not prove specificity for POMC neurons and again by looking at the distribution of the signal, this does not seem the typical distribution for POMC neurons.\n3. Conceptually speaking, although it is interesting that TOX3 regulates PTEN and AKT, there is already ample evidence of the role of the insulin-signlaing pathways in the hypothalamic control of energy balance, as the authors also mention in the discussion.\n4. In general, this reviewer finds the work an excellent phenotypic characterization of the animal models studied, but again, the effects observed concerning lipid and glucose metabolism are often the results of animals gaining more (in the KO) or less (in the overexpression model), and modulation of the sympathetic output (again not surprising).\n5. This reviewer wonder if the POMC KO phenotype described is the result of altered cell proliferation/neurodevelopment (figure 6H, less POMC protein or less POMC neurons?) considering that TOX3 is removed from POMC cell precursors and that TOX3 has a role in neurodevelopment, as mentioned by the authors in their introduction.\n6. Analysis of downregulation/overexpression of TOX3 by RNAscope is rather qualitative. Other quantitative methods should be included.\n7. data in figure 1, what about expression of TOX 3 in other brain areas in addition to the hypothalamus? And in which part of the hypothalamus were the images shown in Fig. 1B taken from? In data shown in Fig. 1 G, authors should verify changes in TOX3 expressions in structure just above those receiving the injection, including thalamus and hippocampus. Are “WT” mice, C57Bl6 mice? Please provide correct information.\n8. for all data with BW change (for example Fig. 1H, 2B, 3B…), authors need to provide actual BW curve and changes before/after AAV administration.\n9. Concerning the snRNAseq data: in Suppl Figure 11C, by looking at the UMAP plots, it seems that there are differences in astrocytes, tanycytes and olygodendrocytes? Is it the case?\n10. Data in cell lines: did authors check for expression of POMC in the cell lines used? GT1-7 cells do not express POMC, but are known model for GnRH neurons. Same for N2acells, they are known to not express POMC endogenously.\n11. Statistical analyses: authors state they used one way ANOVA, but for several analysis they need to carry out either repeated ANOVAs (time is a factor, together with genotype) or 2 way ANOVAs (genotypes and diets or treatments as factors).\n12. Are Tox3 flox mice characterized? Are there data showing the model works properly?\n13. In general, for the representation of the data, authors should include SEM represented in both directions.\n14. For indirect calorimetry data, authors should refrain from correcting the representation of the data by lean mass (Fig. 4A, 4J….) for the energy expenditure, as this representation would be misleading given diffreences in body weight of the animals studies.\n15. For all western blot analyses throughout the manuscript, 3 samples/condition is too low and it does not reflect the biological variability that it is associated to these types of measurements.\n16. In general, description of methods lacks details/information for several experiments shown in the figures. For instance, how is NE turnover carried out exactly ? How quantification of the different labelling in the hypothalamic images shown are carried out? What ROI was used? Details about the different surgeries carried out is missing. Details about the housing of the animals (single/grouped housing), when light is on/off, when experiments are done in relation to the light cycle, cages enrichment, info about chow diet used are missing. Line 537, why is a mir30 shRNA used? And later in the text, how were tissues collected/prepared/processed for snRNAseq?\n17. Wording is awkward in several parts of the manuscript, for instance: line 53 “peripheral” activation- this is rather central activation of peripheral SNS output; line 55 “directly” -POMC neurons do not “directly” innervate the adipose tissue, that happens through multi-synaptic connections onto SNS neurons. Line 86, “POMC neuron-derived TOX3” odd phrasing, TOX3 is not produced/secreted by POMC neurons.\n18. Clarify in all figure legends if data are from males (Figure 1? Figure 3?...) or females.", "reviewer_2": "While obesity and related metabolic disorders such as type 2 diabetes and fatty liver disease have become a growing public health concern, the molecular mechanisms controlling central energy balance remain incompletely understood. Previous studies established that the hypothalamus, particularly pro-opiomelanocortin (POMC) neurons in the arcuate nucleus, a first-order metabolic sensor, serves as a central regulator of energy homeostasis. In this study, the authors identified thymocyte selection-associated high mobility group box 3 (TOX3) as a key transcriptional co-regulator in hypothalamic POMC neurons. By utilizing cell-type specific genetic mouse models, the authors demonstrated that conditional deletion of TOX3 in POMC neurons exacerbates high-fat diet (HFD)–induced obesity, adipocyte hypertrophy, hepatic steatosis, and insulin resistance, whereas TOX3 overexpression protects mice against these metabolic abnormalities. Indirect calorimetry and thermography results revealed that TOX3 enhanced adipose tissue metabolism and energy expenditure through stimulating sympathetic outflow to adipose tissue, promoting lipolysis and brown adipose thermogenesis. Mechanistically, they identified a novel regulatory effect of TOX3 in proteasomal degradation of PTEN, leading to enhanced AKT signaling and increased POMC neuronal activity. Meanwhile, similar manipulations in agouti-related peptide (AgRP) neurons showed no significant metabolic effects. Although TOX3 has been previously implicated in metabolic disorders, this study newly identifies hypothalamic TOX3, specifically within POMC neurons, as a key regulator of energy homeostasis. And this effect is mediated through the TOX3–PTEN–AKT signaling axis. The data are rather comprehensive. There are several issues the authors should address to improve the manuscript.\nSpecific comments:\n1. The title of the manuscript is somewhat vague. It would be better if the authors would revise it and improve the clarity. A suggested title is “TOX3 in hypothalamic POMC neurons improves adipose tissue energy homeostasis by targeting PTEN–AKT signaling axis.”\n2. The authors examined the TOX3 mRNA expression across various mouse tissues and cell types using qPCR and RNAscope. It would strengthen the study if they also verified TOX3 distribution using available single-cell RNA sequencing databases to confirm TOX3 distribution across tissues and neuronal cell types.\n3. In addition to comparing TOX3 levels between chow diet–fed and high-fat diet–fed mice, the authors can incorporate analysis of relevant human datasets to enhance the clinical relevance. If the human datasets are not available, the limitation should be clearly discussed.\n4. In Supplementary Figure 2A, most TOX3 signals appear to be located outside POMC neurons, and high-fat diet treatment does not seem to significantly reduce TOX3 expression, which seems to be inconsistent with the results shown in Figure 1D. The rationale of focusing on POMC neuron–specific TOX3 should be better elaborated.\n5. The degree of obesity differs between the loss-of-function and gain-of-function experiments. The control group in the gain-of-function study (Control) appears more obese than the LoxP control group in the loss-of-function study, with higher body weight, liver weight, and more fat tissue. Please explain this inconsistency.\n6. The study proposes that TOX3 in POMC neurons regulates adipose tissue metabolism through sympathetic nervous system modulation. In addition to assessing NE content and turnover, the authors should also examine the expression levels of β-adrenergic receptors in adipose tissues to support this mechanistic claim.\n7. The study only confirms the protective role of the sympathetic nervous system in TOX3-mediated effects in brown adipose tissue (BAT) using 6-hydroxydopamine hydrobromide (6-OHDA). The authors should also evaluate the effects of sympathetic inhibition in white adipose tissue (WAT) before concluding that the protective effect is specifically mediated via BAT.\n8. The current study focuses on the TOX3/POMC effect on the adipose tissue, but some of the adipose data were associations, meaning the author cannot exclude the effect of TOX3/POMC on extra-adipose tissues. The systemic use of β3-adrenergic agonist also has its limitation in interpreting the adipose phenotype. The authors should profile the expression of key metabolic genes in the liver and skeletal muscles to see if there are collateral benefits in these metabolically important extra-adipose tissues.", "reviewer_3": "The manuscript by Qin Tang, Juan Pang and Jinhang Zhang et al. presents a compelling and well-designed study that significantly advances the understanding of central regulation of energy homeostasis. The authors provide robust evidence identifying TOX3 as a critical and previously overlooked metabolic regulator specifically within POMC neurons. Using genetic models, the authors show that TOX3 loss exacerbates diet-induced obesity, while its overexpression improves metabolism via a post-translational mechanism involving PTEN degradation and enhanced AKT signaling. The findings reveal a neuron-specific role for TOX3 in energy homeostasis and suggest its potential as a therapeutic target for metabolic disorders. However, there are some experiments and descriptions need to be improved.\nMajor concerns:\n1. The authors should discuss the apparent discrepancy wherein POMC neuron-specific TOX3 ablation shows no phenotype on a chow diet (CD), despite the significant metabolic effect observed upon broader hypothalamic TOX3 knockdown under the same conditions. Please clarify whether this suggests that TOX3 in non-POMC hypothalamic neurons is responsible for the metabolic regulation observed under CD, and how this aligns with the proposed POMC-specific role for TOX3.\n2. It would also be important to know the phenotypic effects of overexpressing TOX3 in POMC neurons under chow diet conditions, to fully elucidate its physiological role without the confounder of dietary stress.\n3. The authors should address the discrepancy between the increased energy expenditure and the unaffected RER in PTKO mice.\n4. Given the reduced thermogenesis in BAT, were there also corresponding changes in the beiging markers of WAT?\n5. Evidence demonstrating the efficacy of 6-OHDA-mediated sympathetic denervation (e.g., measurements of tissue norepinephrine levels or tyrosine hydroxylase staining) should be provided.\nMinor concerns：\n1. Regarding Figure 6J, it appears that the green fluorescence channel may be missing from the merged image. This should be clarified or corrected.\n2. For the Tox3 in situ hybridization data validating the knockdown efficiency, it is recommended that representative images be provided that more accurately reflect the corresponding quantitative results."}, "author_response": "Dear Reviewers:\nThank you for your letter and for the reviewers’ comments concerning our manuscript\nentitled “TOX3 controls energy homeostasis in hypothalamic POMC neurons” (ID:\nNCOMMS-25-70758-T). These comments are all valuable and very helpful for\nrevising and improving our paper, as well as guiding our research. We have studied\nthe comments carefully and have made corrections, hoping they will meet with your\napproval. The major modifications in the revised manuscript are tracked in red, and\nour point-by-point responses to the reviewer’s comments are provided below:\nReviewer #1\nIn this manuscript the authors have investigated the role of the transcriptional\nco-regulator TOX3 in the regulation of energy balance. The authors claim a critical\nrole for TOX3 in this context by action through the hypothalamic POMC neurons.\nThe work as it stands is fundamentally incremental and characterized by several\npitfalls and limitations that reduce the enthusiasm of this reviewer for the manuscript.\nResponse: Thank you for your thorough, constructive review and for the time\ninvested in evaluating our manuscript. We appreciate your central concern regarding\nthe specificity of our models and the conceptual novelty of the findings.\nIn response, we have conducted additional experiments, analyses, and revisions to\naddress each point directly. Below, we provide point-by-point responses and outline\nthe changes made in the revised manuscript.\n1. The authors have used a genetic approach (POMC-Cre mice) where recombination\nand deletion of TOX3 will not only happen during development, but it will also not\nexclusive of POMC neurons in the ARC. POMC precursors in the ARC give rise to\nseveral other neuronal types, including AgRP and kisspeptin neurons (PMID:\n35044906; PMID: 20348924). POMC is also expressed during development in\nextrahypothalamic brain structures affecting feeding behavior, such as the\nhippocampus and amygdala, and in adulthood expression is maintained in the\nbrainstem and pituitary. Authors do not provide any evidence that the modifications\nthey see in their model applies specifically to hypothalamic POMC neurons in\nadulthood. This lack of specificity for POMC neurons in the ARC is obvious by the\nimages provided in supplementary Figure 2. Authors have GFP and tdTomato\nexpression in basically all cells in the ARC. Authors need to be aware that\nPOMC-Cre-ERT2 genetic model is available in the field.\nResponse: Thank you for raising this critical point regarding the specificity of the\ngenetic model used in our study. We acknowledge that the constitutive POMC-Cre\ndriver can mediate recombination in POMC-derived lineages beyond mature ARC\nPOMC neurons, as well as in extrahypothalamic regions, which is an inherent\nlimitation of this widely used tool.\nWhile the inducible POMC-Cre-ERT2 model would offer superior temporal control,\ngenerating a new TOX3 conditional knockout on this background is not feasible\nwithin the revision timeline. Instead, we respectfully highlight key experimental\nevidence that collectively supports a primary role for TOX3 in adult hypothalamic\nPOMC neurons in regulating energy balance.\nMost critically, we performed adult-onset, ARC POMC neurons-restricted\noverexpression of TOX3 using stereotactic delivery of a Cre-dependent AAV\n(AAV-DIO-Tox3) into POMC-Cre mice. This intervention produced a phenotype\nfunctionally opposite to the constitutive knockout: while genetic Tox3 deletion\nincreased body weight and adiposity with reduced energy expenditure, adult-specific\nTOX3 overexpression reduced body weight and adiposity while increasing energy\nexpenditure (see Figure 2-4 in our revised manuscript). Although contributions from\nother Cre-expressing cells cannot be entirely excluded in our knockout model, the\nconcordance between the knockout phenotype and the adult ARC-specific\noverexpression phenotype strongly prioritizes the hypothalamic POMC neuron\npopulation as the primary site of action for the metabolic effects of TOX3.\nRegarding the images in Supplementary Figure 2, the tdTomato signal is from the Ai9\nreporter allele crossed into our models, which is a widely used tool to label POMC\nneurons in the ARC in previous studies1, 2. The widespread labeling in the ARC is\nconsistent with the known behavior of this driver line. Importantly, these data confirm\na significant reduction of Tox3 mRNA specifically within the tdTomato+ cell\npopulation in the ARC upon Cre-mediated recombination, validating effective\nknockdown within our targeted lineage.\nWe have explicitly addressed this model limitation in the revised Discussion as\nfollows: Third, the constitutive Pomc-Cre driver used here may mediate\nrecombination beyond mature POMC neurons, including in extrahypothalamic\nlineages and developmental precursors3, 4, complicating phenotypic attribution. While\nour AAV-based approach achieved adult-onset overexpression, the loss-of-function\nmodel still carries the possibility of contributions from Tox3 deletion in off-target cell\npopulations. The development and application of temporally controlled genetic tools\nwill be necessary to precisely define the adult-specific functions of Tox3 in POMC\nneurons.\n2. Similarly for the overexpression of TOX3 in POMC neurons, the authors do not\nprovide any evidence that this is specific to POMC neurons. The expression of GFP\nfluorescence in supplementary figure 6 (panels A and C, not POMC as wrongly\nreported in panel C) does not prove specificity for POMC neurons and again by\nlooking at the distribution of the signal, this does not seem the typical distribution for\nPOMC neurons.\nResponse: Thank you for raising this important point and for bringing the labeling\nerror in panel C to our attention. We regret any confusion it may have caused and\nappreciate the opportunity to clarify our viral strategy and the controls for specificity.\nFirst, we would like to emphasize that our approach was designed to achieve both\ngenetic and anatomical specificity. We used a Cre-dependent AAV (AAV-DIO-Tox3)\nthat expresses the transgene exclusively in the presence of Cre recombinase. This\nvirus was stereotactically delivered into the ARC region of adult POMC-Cre mice.\nTherefore, TOX3 overexpression is genetically restricted to Cre-expressing cells\nwithin the POMC lineage and spatially confined to the viral injection site in the ARC,\nas supported by the localized GFP expression shown in our original Supplemental\nFigure 6a (Figure R1.1a).\nRegarding panel C, we apologize for the erroneous labeling in the original figure\nlegend. The green signal in this panel is not GFP but corresponds to tdTomato\nimmunofluorescence. In these experiments, we used Pomc-cre;Ai9 mice, in which all\nCre-expressing cells are genetically labelled with tdTomato. Because the RNAscope\nprocedure completely quenches endogenous tdTomato fluorescence, we performed\nimmunofluorescence for tdTomato along with RNAscope to visualize the targeted cell\npopulation, as described in our Methods section. This panel thus shows the\nco-localization of tdTomato-labeled POMC lineage cells (green) with virally\noverexpressed Tox3 mRNA (red, detected by RNAscope), confirming successful\ndelivery and expression within the intended Cre-expressing population.\nTo further strengthen our evidence, we have replaced the original panel C with a\nhigher-magnification image that more clearly shows Tox3 mRNA localized within\ntdTomato+ cells in the ARC region (Figure R1.1b). We have also carefully corrected\nthe figure legend to accurately describe the methodology and signals represented.\nFigure R1.1 (a) Representative images of GFP expression in the hypothalamus (left)\nand in specific brain regions (right) 8 weeks after the injecting AAV-GFP into the\nARC of Pomc-Cre mice. Scale bars, 100 μm (left) and 50 μm (right). (b) \nRepresentative confocal images showing POMC (tdTomato+, green), Tox3 mRNA\n(red), and DAPI (blue) fluorescence in the ARC of Pomc-cre;Ai9 mice injected with\nAAV-GFP or AAV-Tox3. 3V, third ventricle. Scale bar, 25 μm.\n3. Conceptually speaking, although it is interesting that TOX3 regulates PTEN and\nAKT, there is already ample evidence of the role of the insulin-signaling pathways in\nthe hypothalamic control of energy balance, as the authors also mention in the\ndiscussion.\nResponse: We thank the reviewer for this comment and appreciate the reviewer's\nacknowledgment of the established role of insulin signaling in the hypothalamus for\nregulating energy homeostasis. As noted in our discussion, our goal was not simply to\nassociate TOX3 with this canonical pathway, but to identify it as a novel upstream\nregulator that specifically modulates a key node within this pathway in POMC\nneurons.\nWhile insulin is a primary activator of PI3K-AKT, the intrinsic mechanisms that set\nthe basal tone or sensitivity of this pathway in POMC neurons remain less understood.\nOur study identifies TOX3 as a novel, neuron-enriched factor that critically regulates\nthis pathway through its effect on PTEN, a major negative regulator. Importantly, our\nfindings reveal a previously unreported mechanism whereby TOX3 influences PTEN\nprotein stability, adding a novel regulatory layer to this essential metabolic signaling\nmodule. And this advances the field from knowing that the pathway is important to\nunderstanding how its baseline activity is wired into the identity of these crucial\nmetabolic cells.\nWe have incorporated the following statements into the revised Discussion to more\nclearly articulate this conceptual contribution: “Our findings significantly extend this\nunderstanding by identifying TOX3 as a key upstream regulator of insulin signaling\ncascades in POMC neurons.” “our study establishes a direct functional link between\nTOX3-previously uncharacterized in hypothalamic function-and a core metabolic\nsignaling axis within the specific POMC neuronal population central to energy\nhomeostasis”. “Moreover, we have identified a novel molecular interaction between\nTOX3 and PTEN that reveals a previously unrecognized mechanism of metabolic\nregulation”.\n4. In general, this reviewer finds the work an excellent phenotypic characterization of\nthe animal models studied, but again, the effects observed concerning lipid and\nglucose metabolism are often the results of animals gaining more (in the KO) or\nless (in the overexpression model), and modulation of the sympathetic output (again not\nsurprising).\nResponse: We sincerely thank the reviewer for the positive assessment of our\nphenotypic work and for raising these critical points.\n(1) Regarding the possibility that glucose/lipid phenotypes are secondary body\nweight/composition change.\nWe agree with the reviewer’s insightful perspective. The glucose intolerance in PTKO\nmice and the improved tolerance in PTOE mice under high-fat diet (HFD) appear\ntightly coupled to changes in adiposity. Notably, under chow diet (CD)-where body\nweight remained comparable-we observed no significant differences in fasting\nglucose or glucose/insulin tolerance (see our Figure S3j-l and Figure S6k-m). These\nsuggest that systemic glucose alterations are likely secondary to changes in body\nweight and adiposity resulting from TOX3 manipulation in POMC neurons.\nNevertheless, even under CD without significant weight differences, we detected\nchanges in the expression of key hepatic lipid metabolism genes (Figure R1.2a,b).\nThis indicates that TOX3 in POMC neurons can influence hepatic lipid metabolic\ngene programs through mechanisms at least partly independent of overall body weight,\npointing to a more direct effect on liver metabolism that warrants future investigation.\nMost importantly, this careful phenotypic dissection underscores the central finding of\nour study: TOX3 in POMC neurons plays a primary and potent role in determining\nbody weight and adiposity set point. The discovery of a novel key regulator that\nrobustly governs energy expenditure through the canonical\nPOMC→sympathetic→BAT axis represents a significant conceptual advance.\nWe have revised the Discussion as follows: However, several important questions\nremain unanswered, including whether hypothalamic POMC neuron TOX3 directly\nmodulates other peripheral metabolic processes - such as hepatic gluconeogenesis,\nhepatic lipid metabolism, pancreatic endocrine function, and skeletal muscle\nmetabolism - as well as the molecular mechanisms underlying these potential effects.\nFigure R1.2 (a) qPCR analysis of key metabolic genes in the liver of CD-fed LoxP\nand PTKO mice (n=5:6). (b) qPCR analysis of key metabolic genes in the liver of CD\nfed Control and PTOE mice (n=4).\n(2) Concerning the novelty of modulating sympathetic output.\nWe acknowledge that POMC-neuron-mediated sympathetic output is a\nwell-established pathway. The novelty of our study, therefore, does not lie in the\nvalidation of this circuit. Instead, our work provides two novel conceptual advances\nthat build upon this established physiology. We identified a new molecular regulator\nthat governs this well-known physiological circuit. Specifically, we identify TOX3 as\na previously unrecognized molecular governor that fine-tunes the activity of the\nPOMC neuron-BAT axis from within POMC neurons themselves. While the existence\nof this efferent pathway is not new, the molecular mechanisms determining its\nsensitivity and set-point remain incompletely understood. Furthermore, we elucidated\na novel intracellular mechanism. We demonstrate that TOX3 regulates the core AKT\npathway through post-translationally stabilizing PTEN protein. This represents a\nnovel mode of regulation that adds a previously unknown layer of control to the\ninsulin signaling cascade within these metabolically critical neurons.\nWe have incorporated the following statements into the revised Discussion section:\n“Our findings significantly extend this understanding by identifying TOX3 as a key\nupstream regulator of insulin signaling cascades in POMC neurons.” “our study\nestablishes a direct functional link between TOX3-previously uncharacterized in\nhypothalamic function-and a core metabolic signaling axis within the specific POMC\nneuronal population central to energy homeostasis”. “Moreover, we have identified a\nnovel molecular interaction between TOX3 and PTEN that reveals a previously\nunrecognized mechanism of metabolic regulation”.\n5. This reviewer wonder if the POMC KO phenotype described is the result of altered\ncell proliferation/neurodevelopment (figure 6H, less POMC protein or less POMC\nneurons?) considering that TOX3 is removed from POMC cell precursors and that\nTOX3 has a role in neurodevelopment, as mentioned by the authors in their\nintroduction.\nResponse: We thank you for this important question regarding potential\nneurodevelopmental effects, which is a valid consideration given the constitutive\nknockout model and reported roles of TOX3.\nTo directly address whether TOX3 deletion reduces POMC neuron number, we\nperformed lineage-tracing experiments. By crossing our models with tdTomato\nreporter mice, we permanently labeled all cells derived from the POMC-expressing\nlineage. Quantification revealed no difference in the total number, distribution, or\nmorphology of tdTomato+ cells in the ARC between Pomc-cre;Ai9 and PTKO;Ai9\nmice (Figure R1.3a,b). This result strongly indicates that Tox3 deletion does not\nimpair the survival or final specification of POMC-lineage neurons. Therefore, the\nreduction in POMC protein levels (see Figure 6h of our revised manuscript) most\nlikely reflects a cell-autonomous functional deficit in mature POMC neurons rather\nthan a loss of neurons.\nWhile subtle developmental effects on circuit maturation cannot be entirely excluded,\nthe phenotypic mirroring between the constitutive knockout and our adult-specific\ngain-of-function model further supports that TOX3 plays a critical functional role in\nmature POMC neurons. We have incorporated the following statements in the revised\nDiscussion section: Interestingly, previous studies have implicated TOX3 in the\ndevelopment of CNS5, 6. In our work, however, we observed no difference in POMC\nneuronal number or distribution between LoxP and PTKO mice, indicating that TOX3\nis not required for the development or survival of POMC neurons. Therefore, the\nobserved reduction in POMC protein levels and the downregulation of POMC\nneuronal functions likely reflect cell-autonomous functional impairment in mature\nPOMC neurons rather than a loss of neuronal populations.\nFigure R1.3 (a) Representative images of tdTomato-positive cells in the ARC of\nPomc-cre;Ai9 and PTKO;Ai9 mice. Scale bar, 50 μm. (b) Distribution and number of\nPOMC neurons (tdTomato+) throughout the ARC of Pomc-cre;Ai9 and PTKO;Ai9\nmice (n=3-4).\n6. Analysis of downregulation/overexpression of TOX3 by RNAscope is rather\nqualitative. Other quantitative methods should be included.\nResponse：Thank you for the suggestion to include additional quantitative methods. In\nresponse, we have performed the following supplemental analyses:\nFor the AAV-mediated Tox3 overexpression model, in addition to the RNAscope\nanalysis confirming specificity and successful overexpression (Figure R1.1b), we had\nalready provided qPCR data from hypothalamic tissues in our original submission\n(see Figure S6b in the revised manuscript) (Figure R1.4a). This qPCR result confirms\na significant increase in Tox3 mRNA levels in mice injected with AAV-Tox3\ncompared with controls, providing direct quantitative evidence of transcript-level\noverexpression.\nRegarding the sparse, POMC-neuron-specific knockout model, bulk-tissue qPCR is\ntechnically challenging due to profound signal dilution, as POMC neurons constitute\nonly a minute fraction of the total hypothalamic population7. Therefore, our validation\nrelied on RNAscope combined with a tdTomato reporter to visualize gene expression\nspecifically within the targeted neurons—an approach commonly employed in the\nfield for similar studies on sparse neuronal populations including POMC neurons1, 8.\nWe have now performed a quantitative re-analysis of the RNAscope images. Using\nImageJ, we quantified Tox3 mRNA fluorescence intensity specifically within\ntdTomato+ POMC neurons and tdTomato- cells. This analysis confirms a significant\nreduction in Tox3 mRNA signal in knockout neurons compared with controls, but not\nin other cells (Figure R1.4b-d), as well as in HFD-fed Pomc-cre;Ai9 mice relative to\nCD-fed controls (Figure R1.4e-g). We have updated these quantitative results in our\nrevised manuscript.\nFigure R1.4 (a) qPCR analysis of Tox, Tox2, Tox3, and Tox4 mRNA expression of\nPomc-Cre mice injected with AAV-GFP or AAV-Tox3. (b) Representative confocal\nimages of POMC (tdTomato+, green), Tox3 (red), and DAPI (blue) in the ARC of\nPomc-cre;Ai9 and PTKO;Ai9 male mice. Scale bar, 50 μm. Integrated density\nquantification of Tox3 signal in tdTomato+ (c) and tdTomato- (d) neurons from (b). (e)\nRepresentative confocal images of POMC (tdTomato+, green), Tox3 (red), and DAPI\n(blue) in the ARC of Pomc-cre;Ai9 male mice fed a standard CD or HFD for 16\nweeks. Scale bar, 50 μm. Integrated density quantification of Tox3 in tdTomato+ (f)\nand tdTomato- (g) neurons from (e).\n7. Data in figure 1, what about expression of TOX3 in other brain areas in addition to\nthe hypothalamus? And in which part of the hypothalamus were the images shown in\nFig. 1B taken from? In data shown in Fig. 1G, authors should verify changes in TOX3\nexpressions in structure just above those receiving the injection, including thalamus\nand hippocampus. Are “WT” mice, C57Bl6 mice? Please provide correct information.\nResponse: We appreciate the reviewer’s insightful questions.\n(1) TOX3 expression in other brain regions: We have performed additional qPCR\nanalysis to determine Tox3 expression across several brain regions. The results\nconfirm that Tox3 is expressed in the thalamus, hippocampus, and cortex, with its\nhighest level detected in the hypothalamus (Figure R1.5a). This new data has been\nincorporated into the revised manuscript.\n(2) Location of images in Figure 1B: The images in Figure 1B (now Figure S1b in the\nrevised manuscript) were taken from the ARC region of the hypothalamus. We have\nclarified this in the revised figure legend.\n(3) Specificity of AAV-mediated overexpression (Figure 1G): Thank you for this\ncritical suggestion. To confirm the anatomical specificity of our stereotactic injections,\nwe have performed additional qPCR analysis to examine whether our AAV-mediated\nknockdown of Tox3 in the hypothalamus affected Tox3 expression in adjacent\nstructures. No significant difference in Tox3 mRNA levels was observed in the\nthalamus, hippocampus, or cortex of mice injected with AAV-shTox3 compared to\ncontrols (Figure R1.5b). These new data have been incorporated into the revised\nmanuscript.\n(4) Strain of “WT” mice: The “WT” mice used throughout the study are indeed\nC57BL/6J mice. We have specified this in the revised Methods section and\ncorresponding figure legends to avoid any ambiguity.\nFigure R1.5 (a) qPCR analysis of Tox3 mRNA levels in different brain regions of\nwild-type C57BL/6J mice (n=3). (b) qPCR analysis of Tox3 mRNA expression in the\nthalamus, hippocampus, and cortex following hypothalamic stereotaxic injection of\nAAV-shCtrl or AAV-shTox3 (n=5).\n8. for all data with BW change (for example Fig. 1H, 2B, 3B…), authors need to\nprovide actual BW curve and changes before/after AAV administration.\nResponse: We thank the reviewer for this suggestion to provide more detailed body\nweight (BW) data. As suggested, we have now added the corresponding complete BW\ncurves for all relevant experiments (Figure R1.6a-i) to the Supplementary\nInformation of our revised manuscript. We have also updated the Results section and\nfigure legends accordingly. The corresponding numerical data are also provided in the\naccompanying Source Data file.\nFigure R1.6 (a) Body weight curve of mice following bilateral hypothalamic\ninjection of AAV-shCtrl or AAV-shTox3 under HFD feeding (n=7). (b) Body weight\ncurve of CD-fed LoxP and PTKO male mice (n=16:9). (c) Body weight curve of\nHFD-fed LoxP and PTKO male mice (n=18:12). (d) Body weight curve of CD-fed\nLoxP and PTKO female mice (n=7:6). (e) Body weight curve of HFD-fed LoxP and\nPTKO female mice (n=6). (f) Body weight curve of Pomc-Cre mice following\nbilateral ARC injection of AAV-GFP or AAV-Tox3 under HFD feeding (n=5:6). (g)\nBody weight curve of CD-fed LoxP and ATKO male mice (n=6:5). (h) Body weight\ncurve of HFD-fed LoxP and ATKO male mice (n=9:10). (i) Body weight curve of\nAgrp-Cre mice following bilateral ARC injection of AAV-GFP or AAV-Tox3 under\nHFD feeding (n=8:7).\n9. Concerning the snRNAseq data: in Suppl Figure 11C, by looking at the UMAP\nplots, it seems that there are differences in astrocytes, tanycytes and olygodendrocytes?\nIs it the case?\nResponse: We thank the reviewer for this critical and insightful observation. This is\nindeed an important point we had previously overlooked, and we sincerely appreciate\nthe reviewer bringing it to our attention. To directly address this question and\ndistinguish between a genuine biological difference and potential individual sample\nvariability, we expanded the study by increasing the number of snRNA-seq biological\nreplicates.\nUpon re-analysis with the larger dataset, quantification of the proportional abundance\nof all major cell clusters—including astrocytes, tanycytes, and\noligodendrocytes—revealed no statistically significant differences between LoxP and\nPTKO mice (Figure R1.7). Importantly, the expanded and refined dataset continues\nto robustly support our original conclusions regarding the transcriptomic alterations\nspecifically within the POMC neuron cluster. We have updated relevant analyses in\nour revised manuscript.\nFigure R1.7 Proportions of hypothalamic cell populations, as determined by\nsingle-cell nucleus RNA sequencing, in LoxP and PTKO mice under HFD feeding\n(n=3).\n10. Data in cell lines: did authors check for expression of POMC in the cell lines used?\nGT1-7 cells do not express POMC, but are known model for GnRH neurons. Same\nfor N2a cells, they are known to not express POMC endogenously.\nResponse: We sincerely thank the reviewer for this insightful and technically precise\ncomment regarding the cell lines used in our study.\nThe reviewer is correct that GT1-7 cells are primarily a model for GnRH neurons,\nwhile N2a cells are a neuroblastoma line; neither is a canonical, high-fidelity model\nfor mature POMC neurons. Our own qPCR analysis did detect low or modest levels\nof endogenous Pomc expression in both lines (Ct ~29 in GT1-7; Ct ~24 in N2a).\nThis reflects a broader technical limitation in the field of hypothalamic neurobiology:\nwell-characterized, faithful in vitro models of specific neuronal populations, such as\nPOMC neurons, remain scarce. In the absence of such ideal systems, established\nneuronal cell lines have been employed in prior studies1, 9 to dissect molecular\nmechanisms relevant to hypothalamic signaling pathways-including those central to\nour investigation-providing a precedent for their use in this context.\nWhile not a perfect surrogate, these models allowed us to perform controlled\nmechanistic studies of the TOX3-PTEN-AKT axis in a neuronal setting. We have\nadded the following statements in the revised manuscript to explicitly acknowledge\nthis limitation: Furthermore, the lack of ideal cellular models that faithfully\nrecapitulate POMC neurons remains a limitation in the field. Although GT1-7 and\nN2a cells have been used in prior studies1, 9 and in the present study as in vitro models\nfor POMC research, neither fully recapitulates the molecular profile of mature POMC\nneurons. Future studies would benefit from developing more physiologically relevant\nsystems—such as POMC-enriched cell lines or hypothalamic organoids—to better\nmodel cell-autonomous mechanisms in these neurons.\n11. Statistical analyses: authors state they used one way ANOVA, but for several\nanalysis they need to carry out either repeated ANOVAs (time is a factor, together\nwith genotype) or 2 way ANOVAs (genotypes and diets or treatments as factors).\nResponse: We thank the reviewer for this critical and accurate comment regarding our\nstatistical analysis. In response, we have systematically reanalyzed all relevant\ndatasets (including body weight curves, GTTs, ITTs, rectal/interscapular temperature,\nindirect calorimetry data, and other experiments with multiple factors) using the\ncorrect statistical models, followed by appropriate post hoc tests where applicable.\nWe have comprehensively updated the manuscript to reflect these corrections. The\nStatistical Analysis section in the Methods has been revised to accurately describe the\napplication of these models: Significance was determined by two-way analysis of\nvariance (ANOVA) with Sidak’s multiple comparisons test, unpaired two-tailed\nStudent’s t test, and one-way ANOVA with post-hoc Tukey’s test as specified in the\nfigure legends.\n12. Are Tox3 flox mice characterized? Are there data showing the model works\nproperly?\nResponse: Thank you for raising this important point regarding the characterization of\nthe Tox3-flox mouse model.\nThe Tox3-flox mouse line used in our study was kindly provided by Prof. Tao Huang\nat Shandong University. In our colony, all mice were rigorously genotyped via\nstandard PCR on tail DNA to confirm the presence of the correctly inserted loxP sites\nflanking the critical exons of the Tox3 gene (representative results are shown in\nFigure R1.8a), ensuring the integrity of the floxed allele. Corresponding\nrepresentative genotyping results for the Pomc-Cre mice are also provided (Figure\nR1.8b). These new data have been incorporated into the revised manuscript.\nCrucially, the functionality of this specific floxed allele has been previously validated.\nThe originating laboratory has demonstrated that this same mouse line, when crossed\nwith an Albumin-Cre driver, results in a hepatocyte-specific knockout with a clear and\nexpected metabolic phenotype (e.g., altered hepatic gluconeogenesis)10. This prior\nindependent application confirms that the Tox3-flox allele is functional and that\nCre-mediated recombination effectively generates a null allele.\nTherefore, the Tox3-flox model used here is both genotypically verified in our\nlaboratory and has been functionally validated in prior work, robustly supporting its\nsuitability for our conditional knockout approach.\nFigure R1.8 (a) Agarose-gel electrophoretic assay of Tox3+/+, Tox3flox/+, and\nTox3flox/flox mice. (b) Agarose-gel electrophoretic assay of Pomc-Cre+ and Pomc-Cre-\nmice.\n13. In general, for the representation of the data, authors should include SEM\nrepresented in both directions.\nResponse: We thank the reviewer for this guidance on data presentation. As suggested,\nwe have systematically reviewed all relevant figures in the manuscript and have\nupdated them to ensure that error bars now explicitly represent the ±SEM in both\npositive and negative directions from the mean. This revision has been applied\nconsistently throughout the manuscript to enhance the clarity and statistical\ntransparency of our data presentation.\n14. For indirect calorimetry data, authors should refrain from correcting the\nrepresentation of the data by lean mass (Fig. 4A, 4J….) for the energy expenditure, as\nthis representation would be misleading given differences in body weight of the\nanimals studies.\nResponse: We thank the reviewer for raising this critical methodological point\nregarding the presentation of indirect calorimetry data. We fully agree that\nnormalizing metabolic parameters to lean mass can be misleading when comparing\ngroups with significant differences in body composition, as the normalization itself\nmay confound the interpretation of true physiological effects.\nIn light of this comment, we have conducted a comprehensive re-evaluation of all our\nindirect calorimetry data. All relevant figures (including Fig. 4A, 4J, and other panels)\nnow present the absolute, uncorrected values as the primary data (Figure R1.9a-j).\nImportantly, the overall direction and statistical significance of the genotype effects\nremain unchanged following this revision, as there were no significant differences in\nabsolute lean body mass between the compared genotypes (LoxP vs. PTKO, and\nControl vs. PTOE) in our cohorts (see Figure 2d and 3d). All relevant figures and their\ncorresponding legends have been updated accordingly to reflect the presentation of\nabsolute values.\nFigure R1.9 Energy expenditure, oxygen consumption, and carbon dioxide\nproduction of HFD-fed LoxP and PTKO male mice (a-e; n=5); of HFD-fed Control\nand PTOE male mice (f-j; n=5); of HFD-fed LoxP and ATKO mice (k-n; n=5:6); of\nHFD-fed Control and ATOE mice (o-r; n=6).\n15. For all western blot analyses throughout the manuscript, 3 samples/condition is\ntoo low and it does not reflect the biological variability that it is associated to these\ntypes of measurements.\nResponse: We thank the reviewer for this important comment.\nWe would like to clarify that all Western blot experiments presented in the manuscript\nwere initially performed with biological replicates of n ≥ 5 per condition. The blots\ndisplayed in the figures were selected as representative images to illustrate the\nconsistent pattern observed across all replicates.\nTo directly address the reviewer’s concern and strengthen statistical rigor, we have\nnow expanded the quantitative analysis to include all independent biological\nreplicates (n ≥ 5 per group) for each Western blot. All individual, uncropped blot\nimages used in this expanded quantification have been compiled and uploaded as\nSource Data files accompanying the revised manuscript. The bar graphs in the figures\nnow reflect the statistical analysis based on this full dataset, which continues to\ndemonstrate significant group differences.\nThus, while representative images are shown for clarity, the statistical conclusions are\nbased on a sample size that adequately accounts for biological variability. We believe\nthis approach ensures both analytical rigor and visual accessibility.\n16. In general, description of methods lacks details/information for several\nexperiments shown in the figures. For instance, how is NE turnover carried out\nexactly? How quantification of the different labelling in the hypothalamic images\nshown are carried out? What ROI was used? Details about the different surgeries\ncarried out is missing. Details about the housing of the animals (single/grouped\nhousing), when light is on/off, when experiments are done in relation to the light cycle,\ncages enrichment, info about chow diet used are missing. Line 537, why is a mir30\nshRNA used? And later in the text, how were tissues collected/prepared/processed for\nsnRNAseq?\nResponse: Thank you for this comprehensive feedback regarding the level of detail in\nour Methods section. We have now thoroughly revised and incorporated the following\ndetails into the Methods section of our revised manuscript.\n(1) Measurement of Norepinephrine (NE) turnover: “Briefly, one cohort of mice was\nleft untreated and sacrificed to obtain baseline tissue NE content. The remaining\npaired mice were injected with AMPT (300 mg/kg) at baseline, followed by a\nsupplemental dose (150 mg/kg) 2 h later to sustain TH inhibition. Four hours after the\ninitial AMPT injection, these mice were sacrificed, and adipose tissues were rapidly\ndissected, weighed, snap frozen in liquid nitrogen, and stored at -80℃ until analysis”.\n“NE turnover was calculated according to the following formulas: k =\n(lg[NE] -lg[NE] )/(0.434 × 4) and K = k[NE] , where k represents the rate constant of\n0 4 0\nNE efflux, [NE] is the initial NE concentration, [NE] is the NE concentration after 4\n0 4\nhours, and K corresponds to NE turnover.\n(2) Image quantification: For quantitative assessment of overall signal intensity within\na defined anatomical region, we first delineated the entire ARC as the region of\ninterest (ROI) based on a reference atlas and DAPI staining. The fluorescent intensity\nwithin the ROI was measured for the specific channel using ImageJ. For\nco-localization analyses, we manually outlined the ARC as the ROI in each section,\nguided by DAPI staining and anatomical landmarks. Within this ROI, the\ntdTomato-positive or negative cells were identified using the corresponding\nfluorescence channel, and the mean fluorescence intensity of another signal was\nmeasured in each cell. The percentage of co-localized cells was calculated per animal\nand used for statistical analysis.\n(3) Surgical procedures: Details for stereotaxic injections have been consolidated and\nelaborated: To induce the specific knockdown or “overexpression of” TOX3 in\nhypothalamic neurons, wild-type (WT) C57BL/6J mice were “anesthetized with\nisoflurane and then subjected to” bilateral stereotaxic injections of\nAAV-shTox3/AAV-shCtrl (for knockdown), or AAV-GFP-Flag/AAV-Tox3-Flag (for\noverexpression) into the hypothalamus (coordinates: bregma, anterior-posterior, -1.5\nmm; dorsal-ventral, -5.6 mm; lateral, ± 0.35 mm; 1000 nl per site). To induce specific\nTOX3 overexpression in POMC or AgRP neurons, “Pomc-Cre or Agrp-Cre mice were\nfed with HFD for 8 weeks to induce obesity, and then anesthetized with isoflurane and\nreceived bilateral stereotaxic injections of either” AAV-GFP or AAV-Tox3 vectors\ninto the ARC (coordinates: bregma, anterior-posterior, -1.5 mm; dorsal-ventral, -5.85\nmm; lateral, ± 0.3 mm; 500 nl per site).\n(4) Animal husbandry and experimental timing: “The animals were housed under a 12\nh light/12 h dark cycle (lights on at 7:00 am, lights off at 7:00 pm) in a\ntemperature-controlled room (22-24℃)” and had ad libitum access to food and water\nunless otherwise indicated. “Standard environmental enrichment was provided”. Mice\nwere fed a standard “chow diet (52 g/kg fat, cd-dossy, Chengdu, Sichuan, China)” or a\nhigh-fat diet (60% kcal from fat, D12492, Research Diets, NJ, USA). Weekly body\nweight measurements were taken between 6 to 20 weeks of age. “All surgical and\ntissue collection were performed during the light phase.”\n(5) Use of the microRNA-30 (mir30) scaffold: The miR30 backbone enhances shRNA\nprocessing and stability, promoting more potent and durable gene silencing compared\nto conventional shRNA designs11.\n(6) snRNA-seq sample preparation and processing: For single-nucleus RNA\nsequencing (snRNA-seq), hypothalamic tissues were rapidly dissected from HFD-fed\nLoxP and PTKO mice immediately after euthanasia, flash-frozen in liquid nitrogen,\nand stored at -80℃ until processing. All subsequent snRNA-seq procedures were”\nperformed by Shanghai Applied Protein Technology Co., Ltd. “Briefly, frozen tissues\nwere thawed in lysis buffer and nuclei were isolated via gentle mechanic\nhomogenization, followed by filtration and purification through a density gradient.\nNuclei were resuspended in appropriate buffer, and their concentration was adjusted\nto approximately 1000 nuclei/μL. The suspension was then loaded onto” the 10×\nGenomics Chromium Single Nuclei Gene Expression platform “for library\npreparation and sequencing.\n17. Wording is awkward in several parts of the manuscript, for instance: line 53\n“peripheral” activation- this is rather central activation of peripheral SNS output; line\n55 “directly” -POMC neurons do not “directly” innervate the adipose tissue, that\nhappens through multi-synaptic connections onto SNS neurons. Line 86, “POMC\nneuron-derived TOX3” odd phrasing, TOX3 is not produced/secreted by POMC\nneurons.\nResponse: Thank you for your careful reading and for highlighting the instances of\ninaccurate and awkward wording in our manuscript. We sincerely appreciate your\ninsights, which have helped improve the clarity and scientific precision of our work.\nWe have specifically revised the text as follows: 1) Line 53: changed “peripheral\nactivation” to “activation of peripheral sympathetic nervous system (SNS) output”; 2)\nLine 55: Removed the term “directly” to correctly reflect the multi-synaptic nature of\nthe pathway from POMC neurons to adipose tissue; 3) Line 86: Revised “POMC\nneuron-derived TOX3” to “TOX3 expression in POMC neurons” to clarify that TOX3\nis an intracellular protein expressed within these cells.\nIn addition, we have performed a thorough review of the entire manuscript to identify\nand correct any similar instances of imprecise phrasing, ensuring consistency and\naccuracy throughout.\n18. Clarify in all figure legends if data are from males (Figure 1? Figure 3?...) or\nfemales.\nResponse: Thank you for this important comment. We have now revised all relevant\nfigure legends to explicitly state the sex of the animals used in the corresponding\nexperiments, as requested.\nReviewer #2\nWhile obesity and related metabolic disorders such as type 2 diabetes and fatty liver\ndisease have become a growing public health concern, the molecular mechanisms\ncontrolling central energy balance remain incompletely understood. Previous studies\nestablished that the hypothalamus, particularly pro-opiomelanocortin (POMC)\nneurons in the arcuate nucleus, a first-order metabolic sensor, serves as a central\nregulator of energy homeostasis. In this study, the authors identified thymocyte\nselection-associated high mobility group box 3 (TOX3) as a key transcriptional\nco-regulator in hypothalamic POMC neurons. By utilizing cell-type specific genetic\nmouse models, the authors demonstrated that conditional deletion of TOX3 in POMC\nneurons exacerbates high-fat diet (HFD)–induced obesity, adipocyte hypertrophy,\nhepatic steatosis, and insulin resistance, whereas TOX3 overexpression protects mice\nagainst these metabolic abnormalities. Indirect calorimetry and thermography results\nrevealed that TOX3 enhanced adipose tissue metabolism and energy expenditure\nthrough stimulating sympathetic outflow to adipose tissue, promoting lipolysis and\nbrown adipose thermogenesis. Mechanistically, they identified a novel regulatory\neffect of TOX3 in proteasomal degradation of PTEN, leading to enhanced AKT\nsignaling and increased POMC neuronal activity. Meanwhile, similar manipulations\nin agouti-related peptide (AgRP) neurons showed no significant metabolic effects.\nAlthough TOX3 has been previously implicated in metabolic disorders, this study\nnewly identifies hypothalamic TOX3, specifically within POMC neurons, as a key\nregulator of energy homeostasis. And this effect is mediated through the TOX3–\nPTEN–AKT signaling axis. The data are rather comprehensive. There are several\nissues the authors should address to improve the manuscript.\nResponse: We thank you for the positive assessment of our study on hypothalamic\nTOX3 in energy homeostasis via POMC neurons. Your constructive feedback is\nhighly valuable. In response, we have conducted additional experiments, analyses,\nand revisions to address each point directly. Below, we provide point-by-point\nresponses and outline the changes made in the revised manuscript.\nSpecific comments:\n1. The title of the manuscript is somewhat vague. It would be better if the authors\nwould revise it and improve the clarity. A suggested title is “TOX3 in hypothalamic\nPOMC neurons improves adipose tissue energy homeostasis by targeting PTEN–AKT\nsignaling axis.”\nResponse: Thank you for the helpful suggestion to improve the manuscript title. In\nresponse, we have revised the title to: “TOX3 in hypothalamic POMC neurons\nregulates energy balance via the PTEN-AKT signaling axis” to accurately and\nconcisely captures a central mechanistic finding of our study. We believe this change\nsignificantly enhances the clarity and focus of our manuscript, and we sincerely\nappreciate this constructive recommendation.\n2. The authors examined the TOX3 mRNA expression across various mouse tissues\nand cell types using qPCR and RNAscope. It would strengthen the study if they also\nverified TOX3 distribution using available single-cell RNA sequencing databases to\nconfirm TOX3 distribution across tissues and neuronal cell types.\nResponse: Thank you for this excellent suggestion. As suggested, we have analyzed\npublicly available datasets.\nFirst, examination of bulk RNA-seq data across tissues (PRJCA019393) confirmed\nthat Tox3 mRNA expression is significantly higher in the brain compared to peripheral\ntissues (Figure R2.1a), consistent with our initial qPCR results (see Supplementary\nFigure 1a). To achieve higher resolution, we then examined Tox3 expression across\ndifferent brain regions. This analysis revealed that Tox3 expression levels are highest\nin the hypothalamus, being significantly higher than those in the thalamus,\nhippocampus, and cortex (Figure R2.1b).\nMost importantly, as suggested, we interrogated existing mouse hypothalamic\nscRNA-seq datasets (GSE188646). These data robustly corroborated our RNAscope\nfindings, demonstrating that Tox3 expression is predominantly enriched in neuronal\npopulations, with minimal expressions in astrocytes, tanycytes, and microglia (Figure\nR2.1c,d). An interesting additional observation was unexpectedly high Tox3\nexpression in oligodendrocyte precursor cells (OPCs) (Figure R2.1d), which hints at\na potential novel role for TOX3 in OPC biology, a point we have discussed in the\nrevised Discussion section.\nWe have integrated these new analyses into the revised manuscript, presenting them in\nthe Results section and as new Fig. 1a-d.\nFigure R2.1 (a) Tox3 expression profile across different tissues by analyzing bulk\nRNAseq dataset (PRJCA019393). (b) qPCR analysis of Tox3 mRNA expression\nacross different brain regions of wild-type C57BL/6J mice (n=3). (c) UMAP plot\nshowing the clustering of cell types in the hypothalamus by analyzing public\nscRNA-seq dataset (GSE188646). (d) Dot plots displaying Tox3 expression in\ndifferent cell types of mouse hypothalamus from snRNA-seq dataset (GSE188646).\n3. In addition to comparing TOX3 levels between chow diet–fed and high-fat diet–fed\nmice, the authors can incorporate analysis of relevant human datasets to enhance the\nclinical relevance. If the human datasets are not available, the limitation should be\nclearly discussed.\nResponse: We thank the reviewer for the valuable suggestion to enhance the clinical\nrelevance of our findings. We have thoroughly searched relevant public datasets but\nwere unable to identify human data that directly compare TOX3 expression in the\nbrain under different dietary conditions. We have therefore explicitly acknowledged\nthis as a current limitation in the Discussion section of the revised manuscript as\nfollows: Finally, while our murine models have provided mechanistic insights into\nTOX3's role in POMC neurons, we note that direct evidence linking TOX3 expression\nto obesity in humans is currently unavailable. Future studies that incorporate human\ndatasets or relevant physiological models, such as hypothalamic organoids derived\nfrom human pluripotent stem cells, would help establish the translational relevance of\nthis pathway in human energy homeostasis.\n4. In Supplementary Figure 2A, most TOX3 signals appear to be located outside\nPOMC neurons, and high-fat diet treatment does not seem to significantly reduce\nTOX3 expression, which seems to be inconsistent with the results shown in Figure 1D.\nThe rationale of focusing on POMC neuron–specific TOX3 should be better\nelaborated.\nResponse: Thank you for your insightful comment.\nTo address the concern directly, we have replaced the original image in\nSupplementary Figure 2A with a more representative one and performed new\nquantitative analysis using ImageJ. This re-analysis specifically measured Tox3\nmRNA fluorescence intensity within POMC (tdTomato-positive) neurons. The results\nconfirm a significant reduction in Tox3 signal in POMC neurons under high-fat diet\n(HFD) compared to chow diet (CD) (Figure R2.2a,b).\nWe fully agree with the reviewer’s observation that Tox3 is also expressed in other\ncell types within the ARC, as evidenced by signal in tdTomato-negative cells.\nQuantitative analysis of these non-POMC cells indeed shows a modest, though less\npronounced, decrease under HFD (Figure R2.2c). This finding, together with the\nwhole-hypothalamus data in Figure 1D, supports that Tox3 expression is not exclusive\nto POMC neurons but is more widespread in the hypothalamus. We acknowledge that\nwe cannot exclude a functional role for TOX3 in other neuronal populations or\nregions.\nFigure R2.2 (a) Representative confocal images of POMC (tdTomato+, green), Tox3\n(red), and DAPI (blue) in the ARC of Pomc-cre;Ai9 male mice fed a standard CD or\nHFD for 16 weeks. Scale bar, 50 μm. Integrated density quantification of Tox3 in\ntdTomato+ (b) and tdTomato- (c) neurons from (a).\nOur decision to focus on POMC (and AgRP) neurons stems from several established\nconsiderations: (1) our preliminary data showed that hypothalamic Tox3 knockdown\ndisrupts whole-body energy homeostasis; (2) the ARC is a key hypothalamic nucleus\nfor metabolic regulation; and (3) POMC and AgRP neurons represent the two primary,\nantagonistic populations within the ARC that centrally coordinate energy balance12, 13,\n14. Studying TOX3 in these well-defined, metabolically critical neurons provides a\nclear and physiologically relevant model to dissect its cell-type-specific role.\nWe have incorporated the following statements in the Discussion section of our\nrevised manuscript: First, although we have comprehensively characterized TOX3\nexpression and function in POMC and AgRP neurons, it is also expressed in other\nhypothalamic neuronal populations. Moreover, while minimal Tox3 expression was\ndetected in tanycytes, astrocytes, and microglia, public scRNA-seq datasets indicate\nthat Tox3 is enriched in oligodendrocyte precursor cells (OPCs), suggesting a\npotential role in glial biology. The physiological relevance of TOX3 in these other\nneuronal and non-neuronal cell types, particularly under metabolic challenge,\nrepresents an important avenue for future research.\n5. The degree of obesity differs between the loss-of-function and gain-of-function\nexperiments. The control group in the gain-of-function study (Control) appears more\nobese than the LoxP control group in the loss-of-function study, with higher body\nweight, liver weight, and more fat tissue. Please explain this inconsistency.\nResponse: Thank you for raising this important observation regarding the baseline\nadiposity differences between the control groups in our loss-of-function and\ngain-of-function studies. We appreciate the opportunity to clarify this point.\nThe apparent discrepancy arises primarily from the distinct experimental designs,\nespecially the timing of high-fat diet (HFD) initiation relative to the intervention.\nIn the loss-of-function study, both LoxP and PTKO mice began HFD at 6 weeks of age\nand remained on the diet for 12 weeks. In contrast, in the gain-of-function study,\nPomc-Cre mice underwent an 8-week HFD pre-feeding period to establish\nobesity before AAV-mediated TOX3 overexpression (or control). After intervention,\nthey continued HFD for another 12 weeks. As a result, the control group in the\ngain-of-function study had already accumulated 8 weeks of HFD-induced weight gain\nprior to the experimental comparison, naturally leading to higher baseline adiposity,\nliver weight, and body weight than the controls in the loss-of-function study.\nAdditionally, the two studies used independent cohorts of mice with different genetic\nbackgrounds (Tox3flox/flox controls vs. Pomc-Cre controls), bred and maintained at\ndifferent times. While diet and housing conditions were standardized, some\ninter-cohort variability in the severity of diet-induced obesity is common and can\ninfluence absolute metabolic parameters.\nThus, the difference in control phenotypes is not an inconsistency but a logical\noutcome of the distinct experimental timelines and cohorts. The central and consistent\nfinding across both studies remains clear: reducing TOX3 exacerbates obesity, while\nincreasing it confers protection—each relative to its internally matched control group.\nWe have clarified these methodological details in the revised manuscript to ensure\ntransparency.\n6. The study proposes that TOX3 in POMC neurons regulates adipose tissue\nmetabolism through sympathetic nervous system modulation. In addition to assessing\nNE content and turnover, the authors should also examine the expression levels of\nβ-adrenergic receptors in adipose tissues to support this mechanistic claim.\nResponse: Thank you for raising this insightful suggestion, which prompted us to\nstrengthen our mechanistic analysis. As suggested, we have conducted qPCR analysis\nto examine the mRNA expression levels of β-adrenergic receptors (Adrβ1, Adrβ2, and\nAdrβ3) in eWAT, iWAT, and BAT.\nOur new data reveal that TOX3 in POMC neurons orchestrates a coordinated\nregulation of both sympathetic tone and target tissue sensitivity. In PTKO mice, the\nreduction in BAT NE levels and turnover was accompanied by a downregulation of\nAdrβ1, Adrβ2, and Adrβ3 mRNA (Figure R2.3c). Conversely, TOX3 overexpression\n(PTOE) increased Adrβ2 and Adrβ3 mRNA expression (Figure R2.3f). These\nfindings demonstrate that the TOX3-POMC axis likely modulates not only\nneurotransmitter availability but also the adrenergic responsiveness of the target tissue.\nNotably, β-adrenergic receptor expression remained unchanged in eWAT and iWAT\n(Figure R2.3a,b; 3d,e), highlighting the specificity of this regulation to BAT.\nTogether with our functional rescue experiment using a β3-AR selective agonist\n(Figure 5f-k), these results establish a dual-level mechanism: TOX3 in POMC\nneurons maintains both the sympathetic drive to BAT and the intrinsic adrenergic\nsignaling capacity of the tissue itself. These data have been incorporated into the\nrevised manuscript.\nFigure R2.3 qPCR analysis of the mRNA expression levels of β-adrenergic receptors\nin eWAT (n=5:5) (a), iWAT (n=5:5) (b), and BAT (n=9:5) (c) of HFD-fed LoxP and\nPTKO mice. qPCR analysis of the mRNA expression levels of β-adrenergic receptors\nin eWAT (n=5:5) (d), iWAT (n=5:5) (e), and BAT (n=5:6) (f) of HFD-fed Control and\nPTOE mice.\n7. The study only confirms the protective role of the sympathetic nervous system in\nTOX3-mediated effects in brown adipose tissue (BAT) using 6-hydroxydopamine\nhydrobromide (6-OHDA). The authors should also evaluate the effects of sympathetic\ninhibition in white adipose tissue (WAT) before concluding that the protective effect is\nspecifically mediated via BAT.\nResponse: Thank you for raising this important point, which prompted us to perform\nthe critical experiment you suggested. To specifically evaluate the role of sympathetic\ninnervation in WAT, we performed bilateral, local microinjections of 6-OHDA into\nboth the eWAT and iWAT depots of control and PTOE mice.\nThe results showed that, following chemical denervation of eWAT and iWAT (Figure\nR2.4a-c), PTOE mice continued to exhibit a lower body weight than control mice\n(Figure R2.4d), along with reduced fat mass and increased lean mass composition\n(Figure R2.4e). Combined with our BAT denervation results, these data provide\nstrong functional evidence that sympathetic innervation of BAT is the primary and\nnecessary efferent pathway through which elevated TOX3 in POMC neurons exerts\nits protective effect against weight gain.\nFigure R2.4 NE content in eWAT (a), iWAT (b), and BAT (c) of Pomc-Cre mice\ninjected with AAV-GFP or AAV-Tox3, followed by bilateral injection of either saline\nor 6-OHDA into both eWAT and iWAT. (d) Body weight of Control and PTOE mice 4\nweeks after 6-OHDA injection. (e) Mass composition of Control and PTOE mice 4\nweeks after 6-OHDA injection.\nIt is noteworthy that, although TOX3 overexpression increased NE content in WAT\ndepots (See Supplementary Figure 10d), the denervation experiment indicates that this\nmodulation alone is not sufficient to drive the core body weight phenotype under our\nexperimental conditions. Sympathetic tone to WAT may play a modulatory role but\ndoes not appear to be the dominant mediator of the TOX3-dependent effect on\nwhole-body energy balance.\nThus, our data supports a model in which TOX3 in POMC neurons preferentially\nenhances sympathetic output to BAT. The recruited BAT then acts as the primary\ndriver of increased energy expenditure, outweighing the relative contribution of\nsympathetic-mediated effects in WAT to the overall metabolic phenotype.\nWe have updated the Methods, Results and Discussion sections to include these new\nWAT denervation data and to refine our conclusion accordingly: Notably, although\nTOX3 overexpression also increased NE content in WAT depots, sympathetic\ndenervation experiments indicate that this modulation alone is not sufficient to drive\nthe core body weight phenotype under our experimental conditions. Thus, while\nsympathetic tone to WAT may exert a modulatory role, it does not appear to be the\ndominant mediator of TOX3-dpendent regulation of whole-body energy balance.\nInstead, our data support a model in which TOX3 in POMC neurons preferentially\nenhances sympathetic output to BAT. The BAT then acts as the primary driver of\nincreased energy expenditure, outweighing the relative contribution of\nsympathetic-mediated effects in WAT to the overall metabolic phenotype.\n8. The current study focuses on the TOX3/POMC effect on the adipose tissue, but\nsome of the adipose data were associations, meaning the author cannot exclude the\neffect of TOX3/POMC on extra-adipose tissues. The systemic use of β3-adrenergic\nagonist also has its limitation in interpreting the adipose phenotype. The authors\nshould profile the expression of key metabolic genes in the liver and skeletal muscles\nto see if there are collateral benefits in these metabolically important extra-adipose\ntissues.\nResponse: Thank you for this insightful suggestion. As recommended, we have\nperformed qPCR analysis to profile the expression of key metabolic genes in the liver\n(Cd36, Srebf1, Fasn, Acaca, Cpt1α, Cpt2, Apob, Mttp) and skeletal muscle (Cd36,\nFabp3, Fatp1, Ucp3, Cpt1α, Acox1, Atp2a1, Atp2a2).\nWe found that PTKO mice exhibited significantly higher mRNA levels\nof Cd36 and Srebf1, together with lower levels of Cpt1α, Apob, and Mttp in the liver\ncompared to LoxP controls, while no significant changes were observed in skeletal\nmuscle (Figure R2.5a,b). Conversely, PTOE mice showed downregulated expression\nof Cd36, Srebf1, Acaca, and Apob, alongside upregulated Cpt2 in the liver, compared\nto controls (Figure R2.5c). Again, no significant alterations were detected in skeletal\nmuscle (Figure R2.5d). These data have been incorporated in our revised manuscript.\nWe revised our Discussion section as follows: However, several important questions\nremain unanswered, including whether hypothalamic POMC neuron TOX3 directly\nmodulates other peripheral metabolic processes - such as hepatic gluconeogenesis,\nhepatic lipid metabolism, pancreatic endocrine function, and skeletal muscle\nmetabolism - as well as the molecular mechanisms underlying these potential effects.\nFigure R2.5 qPCR analysis of key metabolic genes in the liver (n=9:5) (a) and SKM\n(n=10:5) of HFD-fed LoxP and PTKO mice. qPCR analysis of key metabolic genes in\nthe liver (n=5:6) (c) and SKM (n=6:6) of HFD-fed Control and PTOE mice.\nReviewer #3:\nThe manuscript by Qin Tang, Juan Pang and Jinhang Zhang et al. presents a\ncompelling and well-designed study that significantly advances the understanding of\ncentral regulation of energy homeostasis. The authors provide robust evidence\nidentifying TOX3 as a critical and previously overlooked metabolic regulator\nspecifically within POMC neurons. Using genetic models, the authors show that\nTOX3 loss exacerbates diet-induced obesity, while its overexpression improves\nmetabolism via a post-translational mechanism involving PTEN degradation and\nenhanced AKT signaling. The findings reveal a neuron-specific role for TOX3 in\nenergy homeostasis and suggest its potential as a therapeutic target for metabolic\ndisorders. However, there are some experiments and descriptions need to be\nimproved.\nResponse: Thank you for your constructive review and positive assessment of our\nstudy on TOX3 in POMC neurons and energy homeostasis. Your constructive\nfeedback is highly valuable. In response, we have conducted additional experiments,\nanalyses, and revisions to address each point directly. Below, we provide\npoint-by-point responses and outline the changes made in the revised manuscript.\nMajor concerns:\n1. The authors should discuss the apparent discrepancy wherein POMC\nneuron-specific TOX3 ablation shows no phenotype on a chow diet (CD), despite the\nsignificant metabolic effect observed upon broader hypothalamic TOX3 knockdown\nunder the same conditions. Please clarify whether this suggests that TOX3 in\nnon-POMC hypothalamic neurons is responsible for the metabolic regulation\nobserved under CD, and how this aligns with the proposed POMC-specific role for\nTOX3.\nResponse: We appreciate the opportunity to clarify this apparent discrepancy, which\nstems from a key difference in the experimental conditions that we may not have\nemphasized sufficiently.\nThe significant metabolic effect following broader hypothalamic Tox3 knockdown\nwas specifically observed under high-fat diet (HFD) feeding (see Figure 1). In\ncontrast, the POMC neuron-specific ablation was tested on a CD, where we did not\nobserve a metabolic phenotype.\nHowever, a critical and consistent finding that further strengthens our conclusion is\nthat under HFD conditions, the POMC neuron-specific Tox3 knockout (see Figure 2)\nrecapitulates the key metabolic phenotypes observed with the broader hypothalamic\nknockdown. This demonstrates that Tox3 in POMC neurons is both necessary and\nsufficient to mediate the metabolic effects observed under dietary challenge.\nTherefore, the absence of a phenotype in the POMC-specific KO mice on CD cannot\nbe directly compared to the effects of global hypothalamic knockdown, as the latter\nwas assessed under metabolically challenged (HFD) conditions. The cell-type-specific\neffects are diet-context dependent. We fully agree that non-POMC hypothalamic\nneurons might also be involved in Tox3-dependent metabolic regulation, especially\nunder dietary stress. This view is consistent with our expression data showing Tox3 in\nother neuronal populations (See Figure S2a) and with the known complexity of\nhypothalamic energy balance circuits.\nWe have revised the Discussion to explicitly acknowledge that Tox3 likely functions\nin a cell-type- and context-specific manner, and that its role in non-POMC\nneurons—particularly under metabolic challenge—deserves future investigation:\nalthough we have comprehensively characterized TOX3 expression and function in\nPOMC and AgRP neurons, it is also expressed in other hypothalamic neuronal\npopulations. Moreover, while minimal Tox3 expression was detected in tanycytes,\nastrocytes, and microglia, public scRNA-seq datasets indicate that Tox3 is enriched in\noligodendrocyte precursor cells (OPCs), suggesting a potential role in glial biology.\nThe physiological relevance of TOX3 in these other neuronal and non-neuronal cell\ntypes, particularly under metabolic challenge, represents an important avenue for\nfuture research.\n2. It would also be important to know the phenotypic effects of overexpressing TOX3\nin POMC neurons under chow diet conditions, to fully elucidate its physiological role\nwithout the confounder of dietary stress.\nResponse: Thank you for this important suggestion. As suggested, we overexpressed\nTOX3 specifically in ARC POMC neurons of Pomc-Cre mice maintained on a\nstandard chow diet (CD).\nOur results showed that, under CD conditions, TOX3 overexpression in POMC\nneurons did not significantly affect body weight, body composition (fat and lean\nmass), organ weights (liver and adipose depots), serum lipid profiles, glucose\ntolerance, or insulin sensitivity (Figure R3.1a-j). This indicates that in the absence of\na metabolic challenge such as a HFD, elevated TOX3 expression in POMC neurons\ndoes not exert a pronounced effect on systemic energy homeostasis. These data have\nbeen included as a Supplementary Figure in the revised manuscript, and the\ncorresponding Results and figure legends have been updated accordingly.\nFigure R3.1 Pomc-Cre mice were bilaterally injected into the ARC with either\nAAV-GFP or AAV-Tox3 and maintained on a chow diet (n=5). (a) Body weight curve.\n(b) Body weight change curve. (c) Fat mass and lean mass weight. (d) Fat and lean\nmass composition. (e) Liver and adipose tissues weight. Serum TC (f), TG (g), and\nglucose (h) levels. (i) Glucose tolerance test. (j) Insulin tolerance test.\n3. The authors should address the discrepancy between the increased energy\nexpenditure and the unaffected RER in PTKO mice.\nResponse: Thank you for your valuable comment. We appreciate the reviewer’s\ncareful attention of the data. For clarity, the results are as follows: in PTKO mice, we\nobserved a decrease in energy expenditure alongside unchanged RER. Conversely, in\nmice with POMC neuron-specific Tox3 overexpression, we observed an increase in\nenergy expenditure, also with an unchanged RER.\nThus, the key and consistent finding across both models is that a change in energy\nexpenditure is not necessarily accompanied by a shift in whole-body fuel preference\n(RER). This pattern is physiologically coherent. An unchanged RER indicates stable\nproportional oxidation of carbohydrates and fats. The observed alterations in energy\nexpenditure, therefore, likely reflect changes in basal metabolic rate and/or adaptive\nthermogenesis-processes modulated by sympathetic outflow to tissues such as\nBAT-rather than a fundamental switch in primary energy substrate or in locomotor\nactivity. This interpretation is well-supported by our results including BAT\nthermogenesis, NE turnover and related data in both models.\nWe have added the following statement in the Discussion section of our revised\nmanuscript: Specifically, we observed that TOX3 deletion in POMC neurons\nsignificantly reduced energy expenditure, while its overexpression in hypothalamic\nPOMC neurons increased it-consistent with the established roles of POMC neurons in\npromoting energy dissipation12, 15, 16, 17. Importantly, the RER remained unchanged in\nboth loss- and gain-of-function models, indicating that the altered energy expenditure\ndid not result from a shift in whole-body fuel preference. This pattern of metabolic\nchanges points to alterations in basal metabolic rate and/or adaptive thermogenesis as\nthe primary driver.\n4. Given the reduced thermogenesis in BAT, were there also corresponding changes in\nthe beiging markers of WAT?\nResponse: Thank you for raising this important suggestion. As suggested, we\nperformed qPCR analysis to measure key beiging marker genes in the iWAT of\nHFD-fed LoxP and PTKO mice, as well as of HFD-fed Control and PTOE mice.\nOur data indicates that neither POMC neuron-specific knockout nor overexpression of\nTOX3 significantly altered the expression of these beiging markers in iWAT\n(Figure R3.2a,b). This suggests that, under our experimental conditions, the thermogenic\neffects mediated by TOX3 in POMC neurons are primarily reflected in BAT function\nand do not extend to the beiging process in iWAT. These results have been included in\nthe revised manuscript.\nFigure R3.2 (a) qPCR analysis of genes related to browning in iWAT of HFD-fed\nLoxP and PTKO male mice (n=9:5). (b) qPCR analysis of genes related to browning\nin iWAT of HFD-fed Control and PTOE mice (n=5).\n5. Evidence demonstrating the efficacy of 6-OHDA-mediated sympathetic denervation\n(e.g., measurements of tissue norepinephrine levels or tyrosine hydroxylase staining)\nshould be provided.\nResponse: Thank you for raising this essential methodological point. As suggested,\nwe directly measured norepinephrine (NE) content in eWAT, iWAT, and BAT\nfollowing targeted 6-OHDA injection into the BAT.\nOur results show that NE levels in eWAT and iWAT were not significantly altered by\nBAT-localized 6-OHDA injection compared to controls (Figure R3.3a,b). However,\nin the targeted BAT depot itself, NE content was significantly reduced in the\n6-OHDA-treated group (Figure R3.3c).\nThese findings confirm both the efficacy of sympathetic denervation within the\ninjected BAT and its anatomical specificity, as the intervention did not produce\noff-target effects in distal white adipose depots. We have incorporated these validation\ndata into the revised manuscript (Results section) to substantiate the effectiveness and\nprecision of our denervation approach.\nFigure R3.3 (a) NE content eWAT (a), iWAT (b), and BAT (c) of Pomc-Cre mice\ninjected with AAV-GFP or AAV-Tox3, followed by injection of saline or 6-OHDA\nspecifically into the BAT.\nMinor concerns:\n1. Regarding Figure 6J, it appears that the green fluorescence channel may be missing\nfrom the merged image. This should be clarified or corrected.\nResponse: Thank you for your careful observation regarding Figure 6J and for\nprompting this important clarification.\nThe reason the green (GFP) channel is not visible in the merged image is intentional,\nreflecting the molecular design of our AAV construct. The vector expresses TOX3 and\nGFP as separate proteins linked by a P2A self-cleaving sequence. Consequently, while\nGFP fluorescence robustly indicates successful viral transduction, it does not report\nthe subcellular localization of the POMC neurons or TOX3 protein.\nThe GFP image was originally included only to document transduction efficiency. We\nrecognize that presenting it alongside the c-FOS signal could inadvertently suggest\nco-localization, which is biologically incorrect. To prevent any misinterpretation and\nto present the neuronal activation (c-FOS) data with maximum clarity, we have\nrevised the figure as suggested. The updated Figure 6j now shows only the individual\nand merged images of c-FOS immunofluorescence (red) and DAPI (blue), directly\nvisualizing the activation state of neurons in the relevant region.\n2. For the Tox3 in situ hybridization data validating the knockdown efficiency, it is\nrecommended that representative images be provided that more accurately reflect the\ncorresponding quantitative results.\nResponse: Thank you for this suggestion. We have performed additional quantitative\nimage analysis on the in situ hybridization data to ensure the visual representation\naccurately reflects the knockdown efficiency. The results confirm a significant\nreduction in Tox3 mRNA signal intensity, consistent with the efficacy of the knockout\n(Figure R3.4a,b).\nFurthermore, to address any potential concern regarding specificity, we also\nquantified Tox3 signal in tdTomato-negative (non-POMC) cells of Pomc-cre;Ai9 and\nPTKO;Ai9 mice. The results showed that the knockdown was significant only in\nPOMC neurons, with no significant difference observed in non-POMC neurons\n(Figure R3.4c). These results confirm the cell-type specificity of our approach. These\nquantitative results have been included in the revised manuscript.\nFigure R3.4 (a) Representative confocal images of POMC (tdTomato+, green), Tox3\n(red), and DAPI (blue) in the ARC of Pomc-cre;Ai9 and PTKO;Ai9 male mice. Scale\nbar, 50 μm. Integrated density quantification of Tox3 signal in tdTomato+ (b) and\ntdTomato- (c) neurons from (a).\nReferences\n1. Chen Z, et al. Histone lactylation mediated by Fam172a in POMC neurons regulates energy\nbalance. Nat Commun 15, 10111 (2024).\n2. Ye H, et al. 27-Hydroxycholesterol acts on estrogen receptor α expressed by POMC neurons\nin the arcuate nucleus to modulate feeding behavior. Sci Adv 10, eadi4746 (2024).\n3. Padilla SL, Carmody JS, Zeltser LM. Pomc-expressing progenitors give rise to antagonistic\nneuronal populations in hypothalamic feeding circuits. Nat Med 16, 403-405 (2010).\n4. Yu H, Rubinstein M, Low MJ. Developmental single-cell transcriptomics of hypothalamic\nPOMC neurons reveal the genetic trajectories of multiple neuropeptidergic phenotypes. Elife\n11, (2022).\n5. Dittmer S, et al. TOX3 is a neuronal survival factor that induces transcription depending on\nthe presence of CITED1 or phosphorylated CREB in the transcriptionally active complex.\nJournal of cell science 124, 252-260 (2011).\n6. Sahu SK, et al. TOX3 regulates neural progenitor identity. Biochimica et biophysica acta 1859,\n833-840 (2016).\n7. Mercer AJ, Hentges ST, Meshul CK, Low MJ. Unraveling the central proopiomelanocortin\nneural circuits. Front Neurosci 7, 19 (2013).\n8. Zhang M, et al. GHS-R1a signaling drives anxiety-related behavior by shaping excitability of\nventromedial hypothalamic neurons. Nat Commun 16, 3858 (2025).\n9. Qiu S, et al. AZGP1 in POMC neurons modulates energy homeostasis and metabolism\nthrough leptin-mediated STAT3 phosphorylation. Nat Commun 15, 3377 (2024).\n10. Liu C, et al. TOX3 deficiency mitigates hyperglycemia by suppressing hepatic\ngluconeogenesis through FoxO1. Metabolism 152, 155766 (2024).\n11. Stegmeier F, Hu G, Rickles RJ, Hannon GJ, Elledge SJ. A lentiviral microRNA-based system\nfor single-copy polymerase II-regulated RNA interference in mammalian cells. Proceedings of\nthe National Academy of Sciences of the United States of America 102, 13212-13217 (2005).\n12. Argente J, et al. Hypothalamic obesity: from basic mechanisms to clinical perspectives.\nLancet Diabetes Endocrinol 13, 57-68 (2025).\n13. Brüning JC, Fenselau H. Integrative neurocircuits that control metabolism and food intake.\nScience 381, eabl7398 (2023).\n14. Saeed S, Bonnefond A, Froguel P. Obesity: exploring its connection to brain function through\ngenetic and genomic perspectives. Mol Psychiatry 30, 651-658 (2025).\n15. Toda C, Santoro A, Kim JD, Diano S. POMC Neurons: From Birth to Death. Annu Rev\nPhysiol 79, 209-236 (2017).\n16. Quarta C, et al. POMC neuronal heterogeneity in energy balance and beyond: an integrated\nview. Nat Metab 3, 299-308 (2021).\n17. Jais A, Brüning JC. Arcuate Nucleus-Dependent Regulation of Metabolism-Pathways to\nObesity and Diabetes Mellitus. Endocr Rev 43, 314-328 (2022)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have only partly addressed the concerns of this reviewer. The main limitations about the genetic model used and the approaches to demonstrate deletion/overexpression in mature POMC neurons still stand.\nConcerning the demonstration that the manipulation impact also adult POMC neurons, for figure S2A, S2F and S6C, instead of using fluorescence of POMC-Cre Ai9 mice, please perform staining for POMC protein or mRNA in combination with Tox3 mRNA staining. As already mentioned, POMC-Cre Ai9 label all POMC-lineage cells. The argument used by the authors that other studies have used this genetic approach to reveal deletion (or overexpression) in POMC cells, does not make the approach correct.\nIn the manuscript, it needs to be clear for the reader that the genetic model used does not allow specific deletion only in POMC neurons. The authors need to consider using terms like POMC-lineage cells throughout the manuscript.\nFor the energy expenditure data throughout the manuscript: as there are differences in body weight, please also use ANCOVA for EE analysis (for details please see PMID: 22205519 and PMID: 34489606).", "reviewer_2": "In the revision, the authors have addressed most of my comments and as a result, the manuscript has improved from its previous version.\nA remaining issue is that the authors explained that the difference in the degree of obesity between the loss-of-function and gain-of-function models is due to the different timing of high-fat diet exposure relative to the gene manipulation. The authors should clarify the rationale for initiating HFD feeding before TOX3 overexpression in the gain-of-function model, rather than overexpressing TOX3 prior to HFD challenge, which would be more comparable with the loss-of-function studies.", "reviewer_3": "The authors have fully addressed all my previous concerns. I have no further questions and recommend acceptance."}, "author_response": "Dear Reviewers:\nThank you for your letter and for the reviewers’ comments concerning our manuscript entitled “TOX3\nin hypothalamic POMC neurons regulates energy balance via the PTEN-AKT signaling axis” (ID:\nNCOMMS-25-70758A). These comments are all valuable and very helpful for revising and improving\nour paper, as well as guiding our research. We have studied the comments carefully and have made\ncorrections, hoping they will meet with your approval. The major modifications in the revised\nmanuscript are tracked in red, and our point-by-point responses to the reviewer’s comments are\nprovided below:\nReviewer #1 (Remarks to the Author):\nThe authors have only partly addressed the concerns of this reviewer. The main limitations about the\ngenetic model used and the approaches to demonstrate deletion/overexpression in mature POMC\nneurons still stand.\nConcerning the demonstration that the manipulation impact also adult POMC neurons, for figure S2A,\nS2F and S6C, instead of using fluorescence of POMC-Cre Ai9 mice, please perform staining for\nPOMC protein or mRNA in combination with Tox3 mRNA staining. As already mentioned, POMC-\nCre Ai9 label all POMC-lineage cells. The argument used by the authors that other studies have used\nthis genetic approach to reveal deletion (or overexpression) in POMC cells, does not make the\napproach correct.\nIn the manuscript, it needs to be clear for the reader that the genetic model used does not allow specific\ndeletion only in POMC neurons. The authors need to consider using terms like POMC-lineage cells\nthroughout the manuscript.\nFor the energy expenditure data throughout the manuscript: as there are differences in body weight,\nplease also use ANCOVA for EE analysis (for details please see PMID: 22205519 and PMID:\n34489606).\nResponse: Thank you for your further insights and for pushing us to address the remaining concerns\nregarding our genetic model and data analysis. We appreciate your continued effort to improve the\nrigor and clarity of our work. We have carefully revised our manuscript to address all the issues you\nhave raised.\n(1) Regarding the genetic model we used.\nWe fully acknowledge and agree with your assessment that the POMC-Cre mouse line labels POMC-\nlineage cells rather than exclusively mature POMC neurons. While this genetic approach is widely\nused in this field, we understand that scientific precision requires accurate terminology that reflects\nthe true nature of the model. Therefore, as you suggested, we have systematically revised our\nmanuscript by replacing all instances of “POMC neurons” with “POMC-lineage cells” throughout the\ntext where we describe our own experimental findings and genetic model. In the Introduction and\nDiscussion sections, where we discuss general background knowledge and established functions of\nPOMC neurons based on prior literature, we have retained the term \"POMC neurons\" as this reflects\nthe conventional terminology in the field. Additionally, in our snRNA sequencing analysis, we refer to\ncells that express Pomc mRNA as \"POMC neurons\" because this identification is based on actual gene\nexpression rather than Cre-mediated lineage tracing, which aligns with standard practice in\ntranscriptomic studies.\nWe have also substantially expanded the Discussion to explicitly address the POMC-lineage\nconsideration as follows: “Third, the constitutive Pomc-Cre driver used here targets POMC-lineage\ncells rather than exclusively mature POMC neurons, including in extrahypothalamic lineages and\ndevelopmental precursors1,2, complicating phenotypic attribution. The observed metabolic phenotypes,\nincluding effects on BAT thermogenesis and obesity development, are consistent with established\nfunctions of hypothalamic POMC neurons in energy balance regulation. However, we cannot\ndefinitively distinguish between contributions from mature POMC neurons and other POMC-lineage\ncells within the hypothalamus. While our AAV-based approach achieved adult-onset overexpression\nwithin the ARC, the loss-of-function model still carries the possibility of contributions from Tox3\ndeletion in off-target cell populations. The development and application of temporally controlled\ngenetic tools will be necessary to precisely define the adult-specific functions of TOX3 in POMC\nneurons.”\nWe believe these revisions appropriately address your concerns by making clear to readers that our\ngenetic models target POMC-lineage cells rather than exclusively mature POMC neurons, while\nappropriately contextualizing our findings within the established physiological functions of POMC-\nexpressing neurons.\n(2) Regarding energy expenditure data analysis.\nThank you for your insightful suggestion regarding the analysis of energy expenditure data. We\ncompletely agree that ANCOVA is the appropriate method to account for differences in body weight\nwhen evaluating energy expenditure. In fact, we had already performed ANCOVA in our manuscript,\nusing lean body mass as the covariate, as this is the metabolically more relevant compartment and\naligns with the recommendations in the literature (including PMID: 22205519 and PMID:\n34489606)3,4. The results of these analyses are presented in Figure 4b and Figure 4k, and the Methods\nsection describes the ANCOVA approach as follows: “Differences in the relationship between energy\nexpenditure and lean body mass were analyzed by analysis of covariance (ANCOVA) with lean body\nmass included as a covariate, using the online tool CalR2 (available at CalR2 (calrapp.org))”. We\nconfirm that the main conclusions regarding energy expenditure remain robust after this adjustment.\nWe appreciate your attention to rigorous statistical analysis and hope that our revised manuscript meets\nyour expectations.\nReviewer #2 (Remarks to the Author):\nIn the revision, the authors have addressed most of my comments and as a result, the manuscript has\nimproved from its previous version.\nA remaining issue is that the authors explained that the difference in the degree of obesity between the\nloss-of-function and gain-of-function models is due to the different timing of high-fat diet exposure\nrelative to the gene manipulation. The authors should clarify the rationale for initiating HFD feeding\nbefore TOX3 overexpression in the gain-of-function model, rather than overexpressing TOX3 prior to\nHFD challenge, which would be more comparable with the loss-of-function studies.\nResponse: Thank you for your positive assessment of our revised manuscript and for your insightful\ncomment regarding the timing of HFD exposure in our gain-of-function study.\nOur gain-of-function model was specifically designed to evaluate the therapeutic potential of TOX3\nin the context of established obesity. We therefore first induced obesity by feeding mice HFD for 8\nweeks, and then overexpressed TOX3 in the ARC via AAV injection. This approach mimics a clinically\nrelevant scenario when intervention occurs after obesity has already developed. Importantly, we\nobserved that endogenous TOX3 expression in POMC-lineage cells is significantly reduced after\nprolonged HFD feeding, providing a strong rationale for restoring TOX3 levels specifically in the\nobese state.\nWe acknowledge that this experimental design differs from the loss-of-function studies, where HFD\nchallenge began at 6 weeks of age. However, the two models address complementary questions: the\nloss-of-function model assesses whether endogenous TOX3 is required for normal resistance to diet-\ninduced obesity, while the gain-of-function model tests whether overexpression of TOX3 can reverse\npre-existing obesity (a therapeutic approach). Despite this difference in timing, the consistent between\nloss-of-function and gain-of-function ameliorates provides convergent evidence supporting a critical\nrole for TOX3 in energy balance regulation.\nTo clarify this rational for readers, we have revised the Methods section as follows: “To evaluate the\ntherapeutic potential of TOX3 in the context of established obesity, Pomc-Cre or Agrp-Cre mice were\nfirst fed HFD for 8 weeks to induce obesity, and then anesthetized with isoflurane and received bilateral\nstereotaxic injections of either AAV-GFP or AAV-Tox3 vectors into the ARC”.\nWe agree with the reviewer that overexpressing TOX3 prior to HFD challenge would allow direct\ncomparison with the loss-of-function model. However, we believe that our current design is\nscientifically valid and appropriately tailored to the distinct questions each model was intended to\naddress. Thank you again for your insightful feedback.\nReviewer #3 (Remarks to the Author):\nThe authors have fully addressed all my previous concerns. I have no further questions and recommend\nacceptance.\nResponse: Thank you very much for your positive assessment and for your valuable contributions\nthroughout the review process. We are pleased that you find our revisions satisfactory and appreciate\nyour recommendation for acceptance.\nReferences\nrise to antagonistic neuronal populations in hypothalamic feeding circuits. Nat Med\n16, 403-405, doi:10.1038/nm.2126 (2010).\nhypothalamic POMC neurons reveal the genetic trajectories of multiple\nneuropeptidergic phenotypes. Elife 11, doi:10.7554/eLife.72883 (2022).\ncorrect mouse metabolic rate for body mass. Nat Metab 3, 1134-1136,\ndoi:10.1038/s42255-021-00451-2 (2021).\n57-63, doi:10.1038/nmeth.1806 (2011)."}]

Decision letter