Energy-sensing molecule RORγ regulates cholesterol metabolism and immune signaling in diabetic kidney disease and aging

Zhen Liang; Jiaqing Xiang; Guangyan Yang; Xiaomai Liu; Lixing Li; Yanchun Li; Yan Lu; Lin Kang; Yuanli Chen; Chuanrui Ma; Shu Yang

doi:10.1038/s41467-026-69724-2

. 2026 Feb 18;17:2906. doi: 10.1038/s41467-026-69724-2

Energy-sensing molecule RORγ regulates cholesterol metabolism and immune signaling in diabetic kidney disease and aging

Zhen Liang ^1,^2,^#, Jiaqing Xiang ^1,^2,^#, Guangyan Yang ^1,^2,^#, Xiaomai Liu ^1,², Lixing Li ^1,², Yanchun Li ^1,², Yan Lu ³, Lin Kang ^1,^2,^4,^✉, Yuanli Chen ^5,^✉, Chuanrui Ma ^6,^7,^✉, Shu Yang ^1,^2,^✉

PMCID: PMC13031971 PMID: 41708627

Abstract

Aging is a major risk factor for diabetic kidney disease (DKD), with both conditions exhibiting similar renal pathology. We identify the energy-sensing molecule Retinoic acid-related orphan receptor γ (RORγ) as significantly downregulated in diabetic and aged kidneys. Tubule-specific RORγ deficiency exacerbates kidney injury, whereas its overexpression protects. Mechanistically, RORγ stabilizes insulin-induced gene 1 (INSIG1) by upregulating the deubiquitinase YOD1 and enhancing AMPK activity via CAB39, which together promote INSIG1 phosphorylation and subsequent stabilization. Stabilized INSIG1 potently blocks the ER-to-Golgi transport and activation of SREBP2 (cholesterol synthesis) and STING (inflammatory signaling). In diabetes, RORγ itself is suppressed transcriptionally by CTCF and functionally by impaired AMPK/SIRT1 signaling, which hinders its activation. Importantly, administration of a RORγ agonist or RORγ-enriched exosomes effectively alleviates diabetic kidney injury. Thus, RORγ emerges as a key regulatory node that mitigates DKD and renal aging by co-regulating AMPK-mediated metabolic and STING-driven innate immune pathways through INSIG1 stabilization.

Subject terms: Mechanisms of disease, Chronic kidney disease, Inflammation

Aging boosts diabetic kidney disease risk via shared renal pathology. Renal RORγ is downregulated in diabetes/aging; its tubule-specific loss worsens injury, and overexpression protects. RORγ stabilizes INSIG1 via YOD1/CAB39, blocking SREBP2/STING.

Introduction

Lipid metabolism disorders, including dyslipidemia, are prevalent across all stages of diabetic kidney disease (DKD)¹. Research has demonstrated a positive correlation between renal lipid accumulation and the extent of tubulointerstitial damage in DKD^2–4. Proposed mechanisms leading to lipid accumulation in proximal tubules include increased lipid uptake and synthesis and diminished β-oxidation and cholesterol efflux^5–7. Previous studies suggest that disulfide-bond A oxidoreductase-like protein protects against lipid-induced renal damage in DKD through enhanced triglyceride breakdown and decreased cholesterol synthesis³. Intracellular cholesterol sensing is mainly regulated via sterol regulatory element-binding protein (SREBP and its known isoforms SREBP-1a, SREBP-1c, and SREBP-2), an endoplasmic reticulum (ER) residents. Newly synthesized SREBPs are held in an inactive state on the ER membrane by associating with SREBP-cleavage activating protein (SCAP)⁸. When cellular sterol levels are sufficient, SCAP restrains SREBP on the ER membrane by interacting with insulin-induced genes (INSIGs)^9,10. Elevated SREBP expression contributes to kidney damage in obesity-related diabetes and in mice fed on a high-fat diet (HFD)^6,11–13. Correspondingly, SREBP isoforms inhibition attenuates renal phenotypes such as albuminuria or mesangial expansion in age-related renal disease and DKD^11,14–16. Although the role of SREBP in exacerbating renal pathology is increasingly recognized, the precise molecular mechanisms underlying its abnormal activation in renal tubules remain poorly defined.

In DKD progression, immunopathology is initiated by the abnormal release of self-DNA derived from the nuclei or mitochondria of podocytes. This release activates cyclic GMP-AMP synthase (cGAS) to produce cyclic GMP-AMP (cGAMP)¹⁷. cGAMP then directly activates the stimulator of interferon genes (STING) signaling, which is a critical player in metabolic inflammation¹⁸. Activation of STING in a cGAS- and cGAMP-independent manner is also possible and has been described previously in studies of STING gain-of-function mutants^19–22. Both ligand-dependent and -independent STING activation require STING translocation from the ER to the ER-Golgi intermediate compartment and Golgi²³. Mitochondrial dysfunction and the subsequent activation of the mtDNA-cGAS-STING pathway in tubular cells are critical regulators of kidney injury²⁴. Cisplatin induced mtDNA leakage into the cytosol in tubules, with subsequent activation of the cGAS-STING pathway, thereby triggering inflammation and acute kidney injury progression, which is improved in STING-deficient mice²⁴. A recent study showed that the enrichment of m6A in the cGAS-STING pathway led to increased mRNA stability for inflammatory genes cGAS and STING1, which in turn, induced a sterile inflammatory response and kidney fibrosis²⁵. The activation of the cGAS-STING pathway may exacerbate renal aging, particularly in renal tubular epithelial cells, as evidenced by the diminished expression of senescence-associated secretory phenotypes with the use of the STING inhibitor, suggesting its potential role in cellular aging²⁶. Despite the importance of STING in modulating inflammation, little is known about the master regulator of STING in renal tubules that operates in a cGAS- and cGAMP-independent manner and how this regulator controls STING signaling.

Aging is a significant risk factor for DKD²⁷. High glucose stimulation has been shown to accelerate cellular senescence in several types of kidney cells²⁸. The aging kidney also shares many features of DKD, including glomerulosclerosis, tubulointerstitial fibrosis, and tubular atrophy²⁷. The pathophysiology of age-associated renal injury is multifactorial and includes the progressive loss of nephrons, cell senescence, inflammation, lipid accumulation, and dysfunctional mitochondria with ROS production²⁹. These findings suggest that integrative research on aging and DKD is important for the development of novel diagnostic and therapeutic strategies. To explore the shared pathogenic mechanisms of DKD and aging, we performed comparative transcriptomic analyses of renal tissues from diabetic mice, aged mice, and human patients with diabetes. Through the intersection analysis of these datasets, we observed a significant downregulation of the transcription factor Retinoic acid-related orphan receptor gamma (RORγ) in all three datasets. RORγ and the related RORα and RORβ constitute a subfamily of the nuclear receptor superfamily of transcription factors that are attractive therapeutic targets for metabolic and autoimmune diseases³⁰. RORγ exists in two subtypes, namely RORγ1 (RORγ) and RORγ2 (commonly referred to as RORγt), the latter of which is the primary transcription factor for the cytokine IL-17 and is mainly expressed in Th17 cells, γδT cells, and type 3 innate lymphocytes. Research on RORγ typically concentrates on immune cells^31–33, but its role in kidney function is relatively unknown. By interrogating the role of RORγ in the development of DKD, here we identify RORγ as a positive feedback regulator of AMP-activated protein kinase (AMPK), which senses the cellular energy status and thereby limits the excessive intracellular cholesterol synthesis mediated by SREBP2 and inhibits the STING signaling pathway induced by the diabetic milieu. Therefore, our findings define RORγ as a previously unsuspected master regulator of cholesterol biosynthesis and inflammation in DKD development and renal aging, and identify it as an attractive therapeutic target.

Results

RORγ deficiency in kidneys of DKD mouse models and patients

To explore the shared pathogenic mechanisms of DKD and aging, we performed comparative transcriptomic analyses of renal tissues from the following three comparisons: diabetic mice versus wild-type (WT) mice (Supplementary Data 1), aged mice versus young mice³⁴ (Supplementary Data 2), and human patients with diabetes versus healthy living donors (Supplementary Data 3; GSE30122). Through intersectional analysis of these datasets, we observed a significant downregulation of the transcription factor RORγ (also known as RORC) in all three datasets, with 16 genes downregulated and 4 genes upregulated (Supplementary Fig. 1A). Single-cell transcriptomics data from Kidney Interactive Transcriptomics (https://humphreyslab.com/SingleCell/) showed that RORγ expression was highest in the renal tubules of human kidneys (Fig. 1A). Agarose gel-based RT-PCR analysis further determined RORγ expression in renal parenchymal cells, including mouse podocytes (MPCs), mouse glomerular endothelial cells (GECs), mouse tubule epithelial cells (TECs), and mouse fibroblasts (MFs) (Fig. 1B). Compared with its compression in TECs, the basal levels of RORγ in MPCs, GECs and MFs were relatively low (Fig. 1B). Therefore, we primarily focused on the role of RORγ in the TECs. Our study revealed a downregulation of RORγ in TECs subjected to high glucose and palmitic acid (HGPA) treatment, an in vitro condition designed to simulate the diabetic environment³⁵, which is also known to induce cell senescence³⁶ (Fig. 1C). In both the Ju CKD and Woroniecka databases (https://www.nephroseq.org/), RORγ transcript levels were lower in the tubulointerstitium of patients with DKD compared with those in healthy living donors (HLDs) and were positively correlated with the estimated glomerular filtration rate (eGFR) (Fig. 1D, E). To further validate RORγ expression in DKD, we analyzed renal biopsies from human patients pathologically diagnosed with DKD, using samples from patients diagnosed with minimal change disease as control (Supplementary Table 1). Significant fibrosis was observed in the DKD group versus the control, with a marked reduction in RORγ expression in the renal tubules of DKD kidneys (Fig. 1F, G). We then assessed renal RORγ levels in DKD mice kidneys and revealed that RORγ expression declined with diabetes progression (Fig. 1H–J). Notably, RORγ expression decreased at 12 weeks post-streptozotocin (STZ) treatment, whereas TNF-α and kidney injury molecule-1 (KIM-1 or Havcr1; primarily expressed in the proximal tubules) expression began to increase (Fig. 1I). Moreover, RORγ expression was reduced in the kidney of aging mice (24 months of age), accompanied by an increase in aging markers (p21 and p16) (Supplementary Fig. 1, C). In both the Rodwell Aging Kidney and the Ju CKD and Woroniecka databases (https://www.nephroseq.org/), RORγ transcript levels were negatively correlated with age and the expression of p21 and p53 (Supplementary Fig. 1-H). Overall, RORγ downregulation in both DKD and aging mouse kidneys, along with its positive correlation with eGFR, suggests a protective role for RORγ in the pathogenesis of DKD and in the aging process.

Selective deletion of Rorγ in TECs exacerbates renal injury in diabetic and aging mice

Rorγ was predominantly expressed in TECs (Fig. 1A, B). We crossed Rorγ^flox/flox and Ksp-cre mice to generate Rorγ TEC-specific knockout (RTKO) mice (Fig. 2A and Supplementary 1D, E). In chow-fed 8-month-old RTKO mice, histopathological and functional analyses (H&E staining, KW/BW, UACR, BUN) showed no significant differences from WT littermates, including KIM-1 expression, renal cholesterol content, collagen deposition, and genes related to cholesterol synthesis, inflammation, or fibrosis (Fig. 2B–G). By contrast, in a STZ/HFD-induced DKD model, RTKO mice exhibited exacerbated tubular injury, higher KW/BW, UACR, BUN, and KIM-1 expression versus WT controls (Fig. 2C, D). RTKO DKD mice also showed increased renal cholesterol content, CSGs expression, macrophage infiltration, collagen deposition, and upregulated inflammatory/profibrotic cytokines (Fig. 2E–G). At 2 years old, RTKO mice fed a chow diet also exhibited increased levels of tubular injury score, UACR, and KIM-1 expression (Fig. 2H–J). Aged RTKO mice accumulated more cholesterol, consistent with the upregulation of CSGs expression (Fig. 2K). Aged RTKO mice showed increased macrophage infiltration and collagen deposition in the kidney, as well as increased inflammatory cytokines, profibrotic genes, and biomarkers of aging (p21 and p16) expression (Fig. 2L–N). Aging-induced elevations in serum TNFα and MCP1 levels, accumulation of SA-β-gal and p21-positive cells, and upregulation of p21, p16, and p53 expression were all exacerbated in RTKO mice (Supplementary Fig. 1–K). Overall, our findings indicate that RORγ in TECs is vital for DKD progression, with its absence exacerbating diabetes and aging-induced kidney injury.

Deletion of RORγ activates the STING signaling pathway

To elucidate the mechanism by which RORγ influences kidney injury induced by diabetes and aging, we induced RORγ overexpression in human TECs and conducted RNA sequencing (RNA-seq) analysis. Gene set enrichment analysis (GSEA) indicated that the cytosolic DNA-sensing pathway (i.e., cGAS-STING signaling pathway)-related genes were highly enriched after RORγ overexpression (Supplementary Data 4; Fig. 3A). Indeed, TECs isolated from Rorγ KO mice showed remarkably higher STING, phosphorylated (p)TBK1 and pIRF3 proteins and mRNA levels of several interferon (IFN)-stimulated genes (ISGs), IFN regulatory factor 7 (Irf7), C-X-C motif chemokine ligand 10 (Cxcl10), ISG15 ubiquitin-like modifier (Isg15), and IFN beta 1 (Ifnb1), in the resting state or under HGPA stimulation, compared with those in WT mouse TECs (Fig. 3Band Supplementary Fig. 2A). Consistently, following injury in TECs, there was a notable increase in the protein levels of STING and pTBK1, and the expression of ISGs²⁴. HGPA stimulation induced the proportion of β-galactosidase and p21-positive cells, expression of senescence markers (p16 and p21), and IL-6 secretion in cells, and these changes were further amplified after RORγ KO (Supplementary Fig. 2A–D). Stimulation with the STING agonist 5,6-dimethylxanthenone-4-acetic acid (DMXAA) induced significantly higher Ifnb1 mRNA expression in Rorγ^KO TECs compared with that in WT TECs (Supplementary Fig. 2E). Consistently, Rorγ overexpression inhibited HGPA-mediated STING signaling activation and subsequent senescence-associated phenotypes in WT TECs (Supplementary Fig. 2F–J). Moreover, the RORγ agonist zymostenol (ZTL) repressed senescence markers and ISGs expression in WT TECs upon HGPA treatment, whereas the RORγ antagonist GSK805 increased senescence markers and ISGs expression (Supplementary Fig. 2K–O). Reconstitution with WT Rorγ, but not with the transcriptionally inactive mutants (Rorγ E502Q³⁷), in Rorγ^KO mouse TECs reduced ISGs expression to levels similar to those of WT mice TECs under HGPA stimulation (Fig. 3C), implying that RORγ-mediated ISGs regulation is contingent upon its transcriptional activity. Further knockout of Sting1 (which encodes STING) significantly reduced pTBK1 and pIRF3 protein levels as well as ISGs mRNA levels in Rorγ^KO TECs without HGPA stimulation, whereas Cgas additional knockout (Rorγ^KOCgas^KO) did not have this effect (Fig. 3D, E and Supplementary Fig. 2B, C). Next, we examined the role of cGAMP. To this end, we reconstituted Rorγ^KOSting1^KO TECs using WT STING, mutant STING Y239S³⁸ (which disrupts cGAMP binding), or mutant STING S365A³⁹ (which disrupts IFN signaling) (Fig. 3F). Both WT STING and STING Y239S, but not STING S365A, increased ISGs expression in Rorγ^KOSting1^KO TECs (Fig. 3G), suggesting that cGAMP binding is not required, unlike TBK1-IRF3 recruitment, for STING-mediated immune activation in Rorγ^KO TECs. These data suggest that loss of RORγ activates STING independently of cGAS and cGAMP.

Fig. 3 — A GSEA showing the enrichment of Cytosolic DNA-sensing pathway. B WT or *Rorγ*^KO TECs were incubated with or without HGPA (30 mmol/L glucose and 200 μM PA) for 24 h. Left panel: immunoblot of whole-cell lysates with indicated antibodies. Right panel: ISGs were determined by qPCR (n = 5 biological replicates). C Left panel: *Rorγ*^KO TECs were transfected with Ad-null, Ad-*Rorγ*, or Ad-*Rorγ* E502Q (E502Q) for 12 h, replaced with fresh medium culture for 48 h, and then cultured with HGPA for an additional 24 h. Middle panel: immunoblot analysis. Right panel: ISGs were determined by qPCR (n = 5 biological replicates). D, E TECs were isolated from WT, *Rorγ*^KO, *Rorγ*^KO *Sting1*^KO, or *Rorγ*^KO *cGas*^KO mice. Left panel: Immunoblot analysis. Right panel: ISGs were determined by qPCR (n = 5 biological replicates). F, G F: *Rorγ*^KO *Sting*^KO TECs were transfected with Ad-null, Ad-*Sting* WT (*Sting*^WT), Ad-*Sting* Y239S (Y239S), or Ad-*Sting* S365A (S365A) for 12 h, replaced with fresh medium culture for 48 h (WT or *Rorγ*^KO TECs were transfected with Ad-null as a control), and then incubated with HGPA for 24 h. G Left panel: Immunoblot analysis. Right panel: heat map showing the expression of ISGs. H Immunoblot analysis of kidney lysates. I Representative Images of H&E and Sirius red staining in kidney sections from mice are shown. J KW/BW, UACR, BUN, *KIM-1* mRNA levels, and tubular injury score of mice (n = 6 mice). K Kidney cholesterol content (left panel) and mRNA levels of CSGs and ISGs (right panel) of kidneys from mice (n = 6 mice). Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by one-way ANOVA with Bonferroni’s correction. Panels C and F were created in BioRender. Shu Yang. (2025) https://biorender.com.

We hypothesized that the elevated STING-dependent kidney injury phenotype plays a key role in Rorγ deficiency-induced kidney injury. To investigate the functional role of STING, we crossed RTKO mice with Sting1 knockout (RSKO) mice. Compared with their WT littermates with DKD, Sting1^KO mice exhibited less kidney injury (assessed by tubular injury scores, KW/BW, UACR, BUN, and KIM-1 expression) and collagen deposition, cholesterol accumulation, and lower ISGs expression (Fig. 3H–K). Sting1^KO and RSKO DKD mice showed equivalent ISGs expression as well as pTBK1, pIRF3, and STING protein levels (Fig. 3H, K and Supplementary Fig. 2D), implying that the Sting1 KO is sufficient to block the activation of STING and the consequent upregulation of ISGs expression that is triggered by the Rorγ KO. However, relative to those of Sting1^KO DKD mice, RSKO DKD mice exhibited higher renal cholesterol along with increased nuclear accumulation of SREBP2 and target gene expression (Fig. 3H–K). Our findings indicate that the regulation of the STING signaling pathway by RORγ is dependent on STING itself.

RORγ represses STING signaling in an SREBP2-dependent manner

Initially, recognizing RORγ as a transcription factor, we sought to determine its direct transcriptional regulation of STING1. Regrettably, our findings showed that the Sting1 promoter was not amplified from the precipitates when using an anti-RORγ antibody in TECs (Supplementary Fig. 2P). Moreover, we found that Rorγ KO promoted nSREBP2 accumulation in the kidneys of DKD mice independently of STING (Fig. 3H). SREBP2 primes STING signaling by ‘tethering’ the trafficking of STING from the ER to the Golgi, which is sufficient for activating STING²³. Therefore, we hypothesized that Rorγ^KO primes STING signaling by regulating the trafficking of STING-SREBP2. Consequently, we observed that STING is predominantly localized to the ER membrane in WT cells, shifting its localization to the ERGIC membranes in Rorγ^KO TECs (Fig. 4A). Cell fractionation analyses showed that Rorγ^KO induced the translocation of SCAP from the ER to the Golgi apparatus (Fig. 4B). Rorγ^KO in TECs resulted in the redistribution of STING from the ER to the ERGIC and SCAP from ER to Golgi apparatus, indicating a direct impact on STING-SREBP2 trafficking. Colocalization analyses revealed that RORγ knockout significantly accelerated the kinetics of STING-ERGIC co-localization (Fig. 4C, D), as evidenced by increased colocalization intensity (e.g., at 0.5, 2, and 4 hours post-stimulation in knockout cells versus the corresponding time points in wild-type cells). These results support the role of RORγ in regulating STING trafficking to the Golgi apparatus. Next, we compared the activation status of SREBP2 and STING in WT and Rorγ^KO TECs. The mRNA expression of CSGs (Sqle, Dhcr7, and Hmgcr) and ISGs (Oasl, Cxcl10, and Ifnb1), nuclear-cleaved SREBP2 accumulation, and STING, pTBK1, and pIRF3 protein levels were higher in Rorγ^KO TECs compared with those in WT TECs, indicating that both SREBP2 and STING are activated (Fig. 4E, F and Supplementary Fig. 2E). Moreover, the knockdown of Srebf2 (which encodes SREBP2) reduced both SREBP2 and STING activation in Rorγ^KO TECs (Fig. 4E, F), implying that SREBP2 trafficking inhibition restrained the activation of STING signaling in Rorγ^KO TECs. To directly assess SREBP2 trafficking versus transcriptional activities, we knocked down Srebf2 and reconstituted cells with WT SREBP2, transcriptionally inactive mutants [SREBP2 (L511A/S512A), which cannot be cleaved at the Golgi⁴⁰], or SREBP2 (ΔbHLH) in which the basic leucine helix-loop-helix zipper transcriptional domain is deleted⁴¹. Srebf2 knockdown in Rorγ^KO TECs significantly reduced ISGs expression (Fig. 4G, H), which was restored by reconstitution with either WT Srebf2 or transcriptionally inactive mutants (Fig. 4G, H). The expression of CSGs was not restored by SREBP2 transcriptionally inactive mutations (Fig. 4H). These data suggest that the activation of STING signaling by Rorγ^KO depends on SREBP2 trafficking.

Fig. 4 — A WT or *Rorγ*^KO TECs were subjected to an Opti-Prep gradient ultracentrifugation. Ten fractions were collected, diluted, and subjected to immunoprecipitation using an anti-STING. ERGIC-53, ERGIC marker; CALR, ER marker; GM130, Golgi marker. This experiment in this figure was repeated independently 3 times with similar results. B WT or *Rorγ*^KO TECs were subjected to homogenization and cell fractionation using gradient centrifugation. Immunoblotting analyses were performed with the indicated antibodies. Calnexin, ER marker; Golgin97, Golgi marker. This experiment in this figure was repeated independently 3 times with similar results. C, D WT or *Rorγ*^KO TECs were treated with HGPA as indicated in the figure. At different time points, the cells were subjected to immunofluorescence staining for STING1 and ERGIC-53, followed by colocalization analysis (for each group, three biological replicates were set up, and four random fields of view were selected for statistical analysis). E–H qPCR analysis of the expression of cholesterol-synthesis genes and ISGs in WT, *Rorγ*^KO, *Rorγ*^KO *Srebf2*^KD and *Rorγ*^KO *Srebf2*^KD TECs reconstituted with shRNA-resistant SREBP2 wild type (FL) or shRNA-resistant transcription-inactive mutants (L511A/S512A, ΔbHLH) (n = 5). (**I, J**) WT or *Rorγ*^KO TECs were incubated with Trip (14 μM) or vehicle, or transfected with Ad-null or AdInsig1 as indicated. The total cell lysates and nuclear fractions were prepared and subjected to western blotting using the indicated antibodies (left). (Right) qPCR analysis of the expression of CSGs and ISGs in cells (n = 5). K HK2 cells were transfected with INSIG1 shRNA (shINSIG1), Ad-*RORγ*, Ad-*INSIG1* WT (shRNA-resistant), or Ad-*INSIG1* D205A (shRNA-resistant) as indicated (upper panel). The total cell lysates and nuclear fractions were prepared and subjected to western blotting using the indicated antibodies (bottom panel). This experiment was repeated independently 3 times with similar results. Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by one-way ANOVA with Bonferroni’s correction. G and K were created in BioRender. Shu Yang. (2025) https://biorender.com.

To further corroborate our findings, we sought other pharmacological and non-pharmacological means to induce SREBP2 trafficking and then measured their effect on Rorγ KO-induced STING signaling activation. Triparanol (Trip) inhibits the conversion of desmosterol into cholesterol, thereby promoting SREBP2 trafficking. We found that treating WT TECs with Trip increased STING, pTBK1, pIRF3 protein levels, and ISGs expression (Fig. 4I, Supplementary Fig. 4A, B, and Fig. 2F). Notably, this increase was not further regulated by Rorγ knockout, ZTL (a RORγ agonist), or GSK805 (a RORγ inhibitor) treatments (Fig. 4I and Supplementary Fig. 4A, B). Trafficking of the SCAP/SREBP complex is suppressed by the ER-retention protein INSIG1⁴². Small interfering RNA (siRNA)-mediated knockdown of INSIG1 induced STING activation, and these changes were not further regulated by ZTL treatment (Supplementary Fig. 4C). In WT TECs, STING activation is suppressed by the overexpression of INSIG1, and this suppression is not increased by Rorγ KO or GSK805 treatment (Fig. 4J, Supplementary 4D, and 2G). To directly assess the necessity of INSIG1’s capacity to restrict the trafficking of SREBP2 for RORγ-mediated regulation of SREBP2 trafficking and STING activation, we knocked down INSIG1 and reconstituted cells with WT or D205A INSIG1 mutants (mutants with loss of the ability to suppress SREBP2 trafficking⁴³) (Fig. 4K upper panel). INSIG1 knockdown in RORγ-overexpressing HK2 cells (human TECs) significantly increased nuclear SREBP2 accumulation and STING, pTBK1, and pIRF3 protein levels, which were restored by reconstitution with WT INSIG1, but not with INSIG1 D205A (Fig. 4K and Supplementary Fig. 2H). Collectively, these data suggest that SREBP2 trafficking is necessary for the STING activation induced by Rorγ KO.

RORγ-driven transcriptional activation of YOD1 promotes INSIG1 deubiquitination and stabilization

Sterols are crucial for the binding of INSIG1 proteins to SCAP, and for the retention of the SREBP–SCAP complex in the ER⁴². Our results demonstrate that the inactivation of INSIG1, either through mutation (Fig. 4K) or deficiency (Supplementary Fig. 4C), as well as the significant reduction of cholesterol content caused by Trip treatment (Supplementary Fig. 4A, B, E), collectively blocks the role of RORγ in SREBP2 trafficking and STING activation. Furthermore, Rorγ KO or inhibition leads to a reduction in INSIG1 protein levels (Fig. 4J and Supplementary Fig. 4C), while a RORγ agonist results in an upregulation of INSIG1 protein levels under HGPA stimulation (Supplementary Fig. 4D). Based on these observations, we further investigated whether RORγ modulated the transport of SREBP2 and STING through its regulatory effects on INSIG1. Indeed, the protein levels of INSIG1 in Rorγ^KO TECs were lower than those in the control group, whereas Rorγ overexpression had the opposite effect (Supplementary Fig. 4F). Therefore, we explored whether RORγ affects INSIG1 protein stability. Under cycloheximide (CHX, a protein synthesis inhibitor) treatment, Rorγ KO shortened the half-life of INSIG1, whereas its overexpression or ZTL treatment prolonged the half-life of INSIG1 (Fig. 5A, Supplementary Fig. 4H, and 5A). Moreover, the half-life of INSIG2, one of the two INSIG protein isoforms, did not exhibit significant changes between Rorγ KO TECs and their WT counterparts (Supplementary Fig. 4G). Next, we found that only MG132 (a proteasome inhibitor targeting the 26S proteasome to inhibit protein degradation) treatment inhibited INSIG1 reduction in Rorγ^KO TECs compared with that in WT TECs (Fig. 5B and Supplementary Fig. 4I), but not lysosome inhibitor NH₄Cl, and autophagosome inhibitor 3-MA (Supplementary Fig. 4J). Consistently, Rorγ KO increased, whereas its overexpression or ZTL treatment decreased INSIG1 ubiquitination (Fig. 5C and Supplementary Fig. 5B). Taken together, we suggest that RORγ inhibits INSIG1 ubiquitination and degradation.

Fig. 5 — A, B WT or *Rorγ*KO TECs were treated with CHX (100 ng/ml) or MG132 (10 µM) as indicated, followed by immunoblot analysis. C Co-IP of INSIG1 was performed from lysates of WT or *Rorγ*KO TECs with immunoblotting. D WT TECs were transfected with the indicated siRNAs and analyzed by immunoblotting. E Co-IP of INSIG1 was performed from lysates of WT or *Yod1*KO TECs with immunoblotting. F HK2 cells were transfected with Ad-Flag-YOD1 WT or C160S mutant. Right: Co-IP of INSIG1 was performed. G Schematic of potential RORγ post-translational modification sites from database. H Left: Diagram of INSIG1 cytosolic lysines. Right: HK2 cells were transfected with shYOD1 and Ad-Flag-INSIG1 WT or K → R mutants (K33R, K156R, K158R, K273R) for immunoblot analysis. I Left: Conservation of INSIG1 K156/K158. Right: HK2 cells were transfected with shYOD1 and Ad-Flag-INSIG1 WT or DKR mutant (K156R/K158R) for Co-IP with anti-Flag. J Left: Co-IP of INSIG1 from mouse kidney lysates. Middle/Right: WT TECs were co-transfected with Ad-HA-Yod1 and/or Ad-Flag-Insig1 WT for Co-IP with anti-Flag. K Left: Predicted transmembrane topology of INSIG1. Right: IP of INSIG1 truncation mutants expressed in HK2 cells was performed. L Bottom: Immunoblot analysis of WT or *Rorγ*KO TECs. Top: *Yod1* mRNA was analyzed by qPCR (n = 5 biological replicates). M *YOD1* promoter sequence with predicted RORγ binding sites (RORE1, RORE2) and mutations (RORE1m, RORE2m). N Luciferase activity of truncated *YOD1* promoter fragments was measured in HEK293T cells (n = 3 biological replicates). O, P HK2 cells were transfected with Ad-RORγ. Left: ChIP was performed using anti-RORγ antibody at the *YOD1* promoter. Right: qPCR quantification (n = 5 biological replicates). Q Luciferase assay of *YOD1* promoter with mutated RORE sites was performed in HEK293T cells co-transfected with Ad-Rorγ (n = 3 biological replicates). R Co-IP of INSIG1 from mouse kidney lysates was performed. Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by two-tailed Student’s unpaired t-test (M). P values calculated by one-way ANOVA with Bonferroni’s correction (N, P, and Q). Panels F, H, and K were created in BioRender. Shu Yang. (2025) https://biorender.com.

To investigate the role of deubiquitinating enzymes (DUBs) on INSIG1, we selected 36 representative DUBs and conducted unbiased siRNA screening by monitoring INSIG1 levels. Among those tested, we focused on YOD1 (also known as OTUD2 or OTU1) because its siRNA-mediated depletion markedly reduced INSIG1 levels (Fig. 5D). First, our results showed that Yod1 expression was decreased at both the mRNA and protein levels in the kidneys of DKD mice (Supplementary Fig. 5C). Yod1 KO reduced INSIG1 protein levels, increased INSIG1 ubiquitination, and significantly upregulated the expression of CSGs, ISGs, and senescence markers in TECs isolated from the kidneys of mice (Fig. 5E and Supplementary Fig. 5D). In HK2 cells, INSIG1 ubiquitination and the expression of CSGs, ISGs, and senescence markers were reduced by transfection with WT YOD1, but not YOD1 C160S^44,45 (an enzyme activity-dead mutation) (Fig. 5F and Supplementary Fig. 5E). Based on the GPS database (http://cplm.biocuckoo.cn/), four INSIG1 ubiquitination sites are reported, including K33, K156, K158, and K273 (Fig. 5G). Hence, we mutated all four sites to arginine (R), a modification that mimicked protein deubiquitination. Both K156R and K158R mutants exhibited higher INSIG1 protein levels than those of INSIG1 WT, INSIG1 K33R, or INSIG1 K273R (Fig. 5H). These K156 and K158 residues were previously identified as critical ubiquitination sites in INSIG1⁴⁶, and our mutation analysis further validated their role in regulating INSIG1 stability. Furthermore, the double mutant K156R and K158R (DKR) reduced INSIG1 ubiquitination, which was not increased by YOD1 knockdown (Fig. 5I), suggesting they are the major INSIG1 deubiquitination sites mediated by YOD1. Next, endogenous and exogenous co-immunoprecipitation (Co-IP) demonstrated the interaction between INSIG1 and YOD1 (Fig. 5J). Expression of INSIG1 truncation mutants revealed that loop 1 of INSIG1 primarily bound to YOD1 (Fig. 5K). Next, we used a set of ubiquitin mutants in which each lysine was replaced by arginine (K6R, K11R, K27R, K48R, and K63R) to analyze the type of ubiquitin chain formed on INSIG1 by RORγ and YOD1 overexpression. Our results showed that K48-linked ubiquitination is the predominant type induced on INSIG1 by RORγ and YOD1 deficiency (Supplementary Fig. 5F). Our results strongly support the notion that YOD1 is an important deubiquitinating enzyme for INSIG1.

According to the RNA-seq data of RORγ overexpression in TECs, YOD1 transcription was significantly elevated after RORγ overexpression (Supplementary Data 4). Consistent with this, Rorγ KO treatment inhibited YOD1 mRNA and protein levels, whereas Rorγ overexpression or ZTL treatment enhanced YOD1 mRNA and protein levels (Fig. 5Land Supplementary Fig. 5G). Two putative RORγ binding motifs (RORE1 and RORE2) were predicted in the region of the YOD1 promoter using the promoter analysis tool JASPAR (Fig. 5M). We cloned a series of YOD1 promoter deletion mutants into the luciferase reporter system (Luc1–Luc4; Fig. 5N) and found that RORγ overexpression promoted YOD1 promoter activity in the Luc1 and Luc2 constructs. Moreover, the activity in the Luc1 construct was significantly higher than that in the Luc2 construct (Fig. 5N). This finding indicates that the minimal RORγ binding sites within the YOD1 promoter was between -1163 and -1913 bp and between -1913 and -3159 bp. ChIP assays further demonstrated that RORγ was recruited to the -3159 to -2969 bp and between -1294 to -1143 bp regions of the YOD1 promoter (Fig. 5O, P). Therefore, two mutant luciferase reporter plasmids containing two point mutations (RORE1: ATCAAGAggTCA→ATCAAGAccTCA and RORE2: AAAATTGagTCA→AAAATTGccTCA) at the respective RORγ binding sites were constructed and designated RORE1-mut and RORE2-mut, respectively (Fig. 5M, Q). RORγ overexpression increased luciferase activity when cells were transfected with the RORE1-wt or RORE2-wt reporter systems; however, this effect was abolished when the RORγ binding sites were mutated (Fig. 5Q). Next, the Yod1 promoter fragment containing a RORγ binding site was amplified from the precipitates in mouse kidneys using an anti-RORγ antibody (Supplementary Fig. 5H). Collectively, these data suggest that RORγ directly binds to the YOD1 promoter and activates its transcription.

To determine whether Rorγ promotes INSIG1 deubiquitination by upregulating YOD1 in the kidneys of mice, we injected WT and Yod1^KO mice with AAV-Ksp-Ctrl or AAV-Ksp-Rorγ (Supplementary Fig. 5I). Rorγ overexpression reduced, while Yod1 KO increased, INSIG1 ubiquitination in the kidneys of WT mice (Fig. 5R). In Yod1 KO mice, Rorγ overexpression reduced the level of INSIG1 ubiquitination, but the level of INSIG1 ubiquitination was still higher than that of INSIG1 ubiquitination with Rorγ overexpression in WT mice. In Yod1 KO mice, overexpression of Rorγ reduced the level of INSIG1 ubiquitination, but this level remained higher than that observed with Rorγ overexpression in WT mice (Fig. 5R). These results suggest that the RORγ-mediated regulation of INSIG1 ubiquitination is partly dependent on YOD1. In addition, these results suggest that YOD1 promotes INSIG1 deubiquitination and stabilizes INSIG1 protein levels.

RORγ enhances AMPK activation by transcriptional upregulation of CAB39

Moving forward, we aim to further explore the molecular mechanisms by which RORγ regulates the stability of the INSIG1 protein without reliance on YOD1. In addition to the cytosolic DNA-sensing pathway, RNA-seq GSEA also indicated that AMPK signaling pathway-related genes were highly enriched after Rorγ overexpression (Fig. 6A). AMPK-mediated phosphorylation of INSIG1 at Thr222 is known to disrupt the interaction between INSIG1 and the E3 ubiquitin ligase gp78 (an enzyme already confirmed to mediate INSIG1 ubiquitination⁴⁷), thereby preventing INSIG1 ubiquitination and subsequent degradation^47,48. Given this, we propose that RORγ might enhance the stability of the INSIG1 protein through AMPK-dependent phosphorylation at this specific residue. To test this hypothesis, we initially aim to ascertain whether RORγ could augment AMPK activation. Of the enriched genes in the AMPK signaling pathway, CAB39 (also known as MO25α) had the highest fold change. CAB39 functions as a scaffold protein that promotes the activity of LKB1 (a serine-threonine kinase that directly phosphorylates and activates AMPK) by stabilizing the LKB1/STRAD/CAB39 complex⁴⁹. CAB39 is a significant factor in diabetic cardiomyopathy, where its suppression by miR-451 impairs the LKB1/AMPK pathway and cardiac function⁵⁰. In the kidneys of DKD mice, we found that the protein level of CAB39 was decreased (Supplementary Fig. 5C).

Fig. 6 — A GSEA showing enrichment of the AMPK signaling pathway. B Co-IP of LKB1 from WT or *Rorγ*^KO TECs transfected with control (siCtrl) or *Cab39* siRNA, followed by immunoblotting. C LKB1 kinase activity (commercial assay) and RORγ protein levels in WT or *Rorγ*^KO TECs (n = 6 biological replicates). D Immunoblot analysis in WT or *Rorγ*^KO TECs transfected as indicated in figure. CAB39 protein and mRNA levels (n = 5 biological replicates) in (E) WT vs. *Rorγ*^KO TECs and F WT TECs transfected with Ad-null or Ad-RORγ. G Luciferase activity of truncated *CAB39* promoter fragments in HEK293T cells (n = 3 biological replicates). H ChIP-qPCR analysis of RORγ binding to the *CAB39* promoter in HK2 cells transfected with Ad-RORγ (n = 5 biological replicates). I *CAB39* promoter sequence (left) and transcriptional activity of the WT or mutant versions in HEK293T cells (right) (n = 5 biological replicates). J Immunoblot analysis of TECs from WT, *Cab39*^TKO, or *Rorγ*^TKOCab39^TKO (RCKO) mice. K Immunoblot analysis of WT or *Rorγ*^KO TECs co-transfected with Ad-INSIG1 and Ad-Flag-GP78. L Co-IP of Flag-GP78 in WT or *Rorγ*^KO TECs co-transfected with Ad-Flag-GP78 and Ad-INSIG1. M Co-IP of Flag-INSIG1 in WT or *Rorγ*^KO TECs transfected with Ad-Flag-INSIG1. N Co-IP of Flag-INSIG1 in HK2 cells transfected as indicated in the figure. O, P Co-IP of INSIG1 from kidney lysates of *Yod1*^KO mice treated with AAV-RORγ and/or the AMPK agonist CDC (10 mg/kg). Q Immunoblot analysis of mouse kidney lysates. R Representative H&E and Sirius red staining of mouse kidney sections. S KW/BW, UACR, BUN, renal cholesterol, *KIM-1* mRNA, and tubular injury score in mice (n = 6 mice). T Kidney mRNA levels of CSGs and ISGs in mice (n = 6 mice). These experiments were repeated independently 3 times with similar results (B, D, J, Q). Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by two-tailed Student’s unpaired t-test (C, E, and F). P values calculated by one-way ANOVA with Bonferroni’s correction (G, H, I, S, and T). Panel D was created in BioRender. Shu Yang. (2025) https://biorender.com.

To investigate the effect of RORγ on LKB1 complex formation, we immunoprecipitated LKB1 and detected coimmunoprecipitated STRAD and CAB39 by western blotting (Fig. 6B). In control cells, CAB39 and STRAD were easily detected in LKB1 immunoprecipitates. In Rorγ^KO TECs, the amounts of CAB39 and STRAD were substantially reduced, indicating disruption of the LKB1 complex (Fig. 6B). Comparable results were observed in WT TECs treated with Cab39 siRNA (Fig. 6B). Further analysis of downstream signaling pathways revealed that Rorγ KO reduces AMPK Thr172 phosphorylation and upregulates mTOR activity as expected due to reduced AMPK activity (Supplementary Fig. 5J). A considerable reduction in LKB1 activity was also detected using in vitro kinase assays of endogenous LKB1 immunoprecipitated from Rorγ^KO TECs (Fig. 6C). In contrast, RORγ overexpression or ZTL treatment enhanced LKB1 activity, the interaction of LKB1, CAB39, and STRAD, and AMPK Thr172 phosphorylation (Supplementary Fig. 5K, L). In HeLa cells, which lack LKB1, AMPK Thr172 phosphorylation was not changed after RORγ overexpression (Supplementary Fig. 5M). When CAB39 was absent, RORγ did not further regulate AMPK and substrate phosphorylation (Fig. 6D left panel). Mutation of both Arg (R) 240 and Phe (F) 243 did not affect the ability of CAB39 to interact with STRAD and LKB1, but the resulting complex was inactive⁵¹. We knocked down CAB39 and reconstituted the cells with vectors expressing either WT CAB39 or CAB39 mutants (R240A/F243A) under the control of the CAB39 promoter. RORγ^OE in WT CAB39-transfected cells significantly induced AMPK and substrate phosphorylation but not in mutant CAB39 TECs (Fig. 6D right panel). Immunoblotting showed no effects of Rorγ KO on LKB1 or CAB39L (MO25β) levels, supporting the role of CAB39 as the principal RORγ target (Supplementary Fig. 5N). In addition, RORγ did not affect Akt phosphorylation or the levels of CaMKK2, a calcium-sensitive AMPK activator (Supplementary Fig. 5N). These results suggest that RORγ regulates AMPK activation in a CAB39-dependent manner.

Next, we explored whether RORγ transcriptionally regulated CAB39. Rorγ KO inhibited, while its overexpression or ZTL treatment enhanced CAB39 mRNA and protein levels (Fig. 6E, F and Supplementary Fig. 5O). Furthermore, we cloned a series of CAB39 promoter deletion mutants into the luciferase reporter system (Luc1–Luc4; Fig. 6G) and found that RORγ overexpression promoted CAB39 promoter activity in the Luc1, Luc2, and Luc3 constructs, with all three showing comparable levels of activity. This finding indicates that the minimal RORγ binding sites within the CAB39 promoter were between -1455 and -567 bp. Putative RORγ-binding motif sequences (RORE) were predicted in the region of the CAB39 promoter using the promoter analysis tool JASPAR. ChIP-qPCR assays further demonstrated that RORγ indeed recruited to the −1148 to −984 bp region of the human CAB39 promoter (Fig. 6H). Therefore, a mutant luciferase reporter plasmid containing two point mutations (AAAGTagTTCA→AAAGTccTTCA) at the RORγ binding site was constructed and designated CAB39-Mut (Fig. 6I). The reporter system containing a WT CAB39 promoter was used as a control. RORγ overexpression increased luciferase activity when cells were transfected with the CAB39 WT reporter system; however, this effect was abolished when the RORγ binding site was mutated (Fig. 6I). Next, the Cab39 promoter fragment containing a RORγ binding site was amplified from the precipitates in mouse kidneys using an anti-RORγ antibody (Supplementary Fig. 5H). Next, we crossed Ksp-Cre/Cab39^flox/flox with Ksp-Cre/Rorγ^flox/flox mice to generate Ksp-Cre/Cab39^flox/flox·Rorγ^flox/flox (RCTKO) mice (Fig. 6J). In vivo, a lower level of AMPK Thr172 phosphorylation was observed in the kidneys of Cab39 TEC-specific KO mice, which was not further affected by Rorγ KO (Fig. 6J). Collectively, our results suggest that RORγ binds to the promoter of CAB39 and upregulates its expression transcriptionally, thereby promoting the activation of the AMPK signaling pathway.

RORγ inhibits INSIG1 protein degradation via AMPK-dependent INSIG1 phosphorylation

We have demonstrated that RORγ can enhance AMPK activity. Next, we investigated whether RORγ-induced AMPK activation is responsible for the increased stabilization of INSIG1 protein. Rorγ KO promoted the inhibitory effects of gp78 on INSIG1 (Fig. 6K). Real-time PCR analysis was performed to investigate whether RORγ-stimulated INSIG1 expression was mediated by regulating gp78 expression levels. Our results showed that gp78 mRNA levels were not changed by Rorγ KO or overexpression (Supplementary Fig. 5P). Next, a Co-IP analysis was performed to test whether RORγ upregulated INSIG1 protein levels by regulating the protein interaction between INSIG1 and gp78. Rorγ KO promoted the association between INSIG1 and gp78, with the reduction in AMPK phosphorylation and INSIG1 protein levels in cell lysates (Fig. 6L). Consistently, Rorγ overexpression partially abolished the inhibitory effects of gp78 on INSIG1 and reduced the interaction between INSIG1 and gp78 (Supplementary Fig. 5Q). Taken together, these data demonstrate that RORγ is sufficient to attenuate the gp78-mediated suppression of INSIG1.

Next, we determined whether RORγ regulated the interaction between INSIG1 and gp78 through the AMPK-mediated phosphorylation of INSIG1 at Thr222. The interaction between AMPKα1 and INSIG1 was greatly weakened by Rorγ KO (Supplementary Fig. 5R). Moreover, Rorγ KO profoundly reduced INSIG1 phosphorylation (Fig. 6M), whereas its overexpression had the opposite effect (Supplementary Fig. 5S). Importantly, the reduction in INSIG1 phosphorylation induced by RORγ knockdown was abolished by the non-phosphorylatable T222A mutant (Fig. 6N). These data indicate that the RORγ-mediated phosphorylation of INSIG1 at the Thr222 site is essential for stabilizing INSIG1 protein. Next, we found that Rorγ overexpression reduced INSIG1 ubiquitination in Yod1^KO TECs, and these changes were mostly blocked after treatment with compound C (CDC, AMPK inhibitor) (Fig. 6O, P).

Since INSIG1 degradation involves both ubiquitination and extraction from the ER membranes^52–54, we investigated whether RORγ regulates INSIG1 extraction directly or via its ubiquitination. Using the INSIG1 K156/158 R mutant (defective in ubiquitination), we analyzed ER retention and extraction under Rorγ knockout or Yod1/Cab39 knockdown. Rorγ knockout enhanced extraction of WT INSIG1 from the ER membranes, while K156/158 R remained ER-retained (Supplementary Fig. 5T). Similar results were observed with Yod1/Cab39 knockdown (Supplementary Fig. 5T). These data indicate that RORγ regulates INSIG1 extraction only by modulating its ubiquitination, with no direct effect on the extraction process itself.

Next, we investigated whether RORγ regulates YOD1 and CAB39 to affect the STING and SREBP2 signaling pathways and cellular senescence. Our results showed that deficiency in YOD1 and CAB39 promoted HGPA-induced activation of the STING and SREBP2 signaling pathways, as well as senescence-associated phenotypes, whereas these effects were not further modulated by RORγ knockout or RORγ overexpression (Supplementary Fig. 6A–F). To determine whether Rorγ decreases INSIG1 ubiquitination by upregulating Cab39 and Yod1 in vivo, we injected WT or Yod1 KO mice with AAV-Cdh16-shRNA targeting Cab39 and/or AAV-Cdh16-overexpressing Rorγ. Cab39 KO exacerbated kidney injury associated with diabetes, and decreased the protein levels of INSIG1 and its phosphorylation, and reduced AMPK activity while enhancing INSIG1 ubiquitination and upregulating the expression of CSGs and ISGs (Fig. 6Q–T). These changes induced by Cab39 KO were further exacerbated following Yod1 KO (Fig. 6Q–T). Moreover, the simultaneous KO of Cab39 and Yod1 completely nullified the renoprotective effects of RORγ overexpression against diabetes-induced kidney injury (Fig. 6Q–T). To definitively determine whether Yod1/Cab39 deficiency exerts these effects via SREBP2, we injected WT or Yod1 KO mice with AAV-shRNA targeting Cab39, AAV-shRNA targeting Srebp2, or AAV-Cdh16-overexpressing Rorγ (Supplementary Fig. 6, G–M). This design isolated pathway-specific contributions and resolved potential off-target effects of Yod1/Cab39 deletion. Srebf2 KO significantly ameliorated renal injury in DKD mice, accompanied by suppressed cholesterol synthesis genes (CSGs) expression and reduced levels of the inflammatory cytokines IL-6 and TNF-α (Supplementary Fig. 6G–M). Notably, Yod1/Cab39 deficiency still exacerbated renal injury in Srebf2 KO DKD mice—despite failing to further upregulate CSGs—with a marked reduction in AMPK phosphorylation (Supplementary Fig. 6G–M). AMPK is well established as a physiological suppressor of ER stress; one plausible mechanism is that AMPK promotes fatty acid oxidation (FAO) to alleviate lipid accumulation, thereby reducing ER stress⁵⁵. Consistent with this, these genetic manipulations induced significant lipid accumulation (assessed by oil red staining and renal triglyceride content), downregulated FAO-related genes (key AMPK targets), and upregulated ER stress-related genes (Supplementary Fig. 1G–M). Thus, loss of AMPK signaling likely underlies the persistent renal injury induced by Yod1/Cab39 deletion in Srebf2-deficient mice. Overall, these data suggest that YOD1 and CAB39 are important targets for RORγ against DKD.

Diabetic conditions elicit a decrease in RORγ transcriptional levels

Subsequently, we investigated the molecular mechanisms underlying the downregulation of RORγ expression in DKD. Using ChIP-Seq databases, we predicted that CCCTC-binding factor (CTCF), a ubiquitous and highly conserved zinc finger protein that acts as a transcriptional repressor, most likely binds to the RORγ promoter⁵⁶. We found that Ctcf mRNA and protein levels were increased in the kidneys of diabetic mice (Fig. 7A). Consistently, CTCF mRNA was upregulated in kidney samples from patients with DKD (Fig. 7B). RORγ expression was significantly increased in Ctcf-depleted cells while decreased in Ctcf-overexpressing cells (Fig. 7C and Supplementary Fig. 7A). Using the promoter analysis tool JASPAR, we predicted two CTCF binding sites (CBE1 and CBE2) in the RORγ promoter region. Furthermore, ChIP-qPCR analysis confirmed the direct binding of CTCF to the promoter of RORγ (Fig. 7D). HG reduced DNA methylation at CTCF sites and enhanced the binding of CTCF to target genes⁵⁷. Consistent with this, we observed increased CTCF binding to the RORγ promoter and reduced RORγ expression following HGPA stimulation or the pharmacological inhibition of DNA methylation by 5-aza-2′-deoxycytidine (5-aza) (Fig. 7C–E). Collectively, our results suggest that RORγ transcription levels were reduced in response to diabetic conditions.

AMPKα1 orchestrates nuclear translocation of RORγ

The transcriptional levels of RORγ started to reduce after 18 h of HGPA stimulation (Fig. 7F), at which point the protein levels of RORγ had not yet diminished (Fig. 1C). However, the mRNAs of YOD1 and CAB39 were reduced after less than 12 h under HGPA conditions (Supplementary Fig. 7B), suggesting other mechanisms by which HGPA regulates RORγ transcriptional activity. We used actinomycin D (Act-D; a transcription inhibitor that blocks RNA synthesis by binding to DNA) to determine whether HGPA regulates RORγ post-transcriptionally. HGPA inhibited nuclear RORγ transport and its binding to the YOD1 promoter under Act-D stimulation (Fig. 7G–I). Since phosphorylation of RORγt at Ser489 leads to its nuclear translocation⁵⁸, we explored whether phosphorylation of RORγ at Ser510, the homologous site to Ser489 in RORγt, affects its nuclear translocation in TECs. To do this, we transfected the WT RORγ or RORγ phosphorylation mutant (S510A, mimicking the dephosphorylated state of the protein) into HK2 cells. Our results showed that the S510A mutant decreased the nuclear accumulation of RORγ (Supplementary Fig. 7C). We then determined the potential kinase for phosphorylating Ser510 on RORγ using the Compendium of Protein Lysine Modifications 4.0 (CPLM 4.0; http://cplm.biocuckoo.cn/index.php). IKKα, MAPK9 (also known as JNK2), MAPK12 (ERK6), GSK3A, CDK6, and PRKAA1 (the gene that encodes AMPKα1) were predicted to phosphorylate Ser510 on RORγ (the kinases that scored in the top 6 of the predicted kinases). To identify the kinase that stimulates the phosphorylation of RORγ, we cotransfected IKKα, JNK2, ERK6, GSK3A, CDK6, or AMPKα1 with RORγ into HK2 cells, followed by Co-IP assays. AMPKα1 overexpression dramatically induced RORγ phosphorylation, whereas that of IKKα, MAPK9, MAPK12, GSK3A, or CDK6 did not (Fig. 7J). Subsequently, we investigated whether AMPKα1 stimulates RORγ phosphorylation, which then induces nuclear RORγ transport. The knockdown of AMPKα1 or the RORγ S510A mutant impeded HGPA-induced dephosphorylation and subsequent reduction in nuclear RORγ accumulation within HK2 cells (Fig. 7K). Endogenous and exogenous Co-IP demonstrated the interaction between AMPKα1 and RORγ (Fig. 7L). In HK2 cells, we added lentiviral particles containing shRNA targeting AMPKα1; stable cells were established after puromycin selection and designated “shAMPKα1” cells. HGPA treatment reduced RORγ phosphorylation in WT AMPKα1-transfected cells but not in AMPKα1-dominant negative (DN)-transfected cells (Fig. 7M), implying that HGPA reduced RORγ phosphorylation depending on the enzyme activity of AMPK. RORγ truncation mutants indicated that the N-terminal (1-118) was primarily involved in the binding to AMPKα1, while the C-terminal (241-518) was weakly involved (Fig. 7N). Furthermore, DNA damage stress (induced by cisplatin), ox-LDL, or TNF-α stimulation also reduced the phosphorylation of AMPKα and RORγ as well as nuclear RORγ transport (Supplementary Fig. 7D). These results collectively demonstrate that RORγ phosphorylation occurs universally in response to cellular stress. A previous report showed that HGPA treatment reduced the activity and phosphorylation of AMPKα1 in renal TECs⁵⁹. In vivo, a diminished expression of YOD1 and a decrease in RORγ phosphorylation were observed in the renal tissues of Cab39^KO DKD mice compared to those of the WT mice (Fig. 6Q and Supplementary Fig. 7E). Collectively, reduced AMPKα1 activity in injured renal TECs results in decreased RORγ phosphorylation at S510, subsequently impeding nuclear RORγ translocation.

SIRT1-mediated deacetylation of RORγ boosts its transcriptional activity

Unexpectedly, HGPA stimulation still reduced the binding of RORγ S510D (mimicking the phosphorylated protein state) to RORE under Act-D stimulation (Fig. 8A). Therefore, we speculated that other post-translational modifications (PTMs) besides phosphorylation are involved in regulating the effects of PA on RORγ activity. In nearly all cases, zinc finger C4 type (zf-C4) is the DNA-binding domain of nuclear hormone receptors⁶⁰. According to the PhosphoSitePlus® database, the zf-C4 domain of RORγ can undergo PTMs, including acetylation at lysine 37, SUMOylation at lysine 52, and ubiquitination at lysine 120 (Fig. 8B). An evident change in the acetylation of RORγ, but not in the interaction between RORγ and SUMO1 or the ubiquitination of RORγ, was observed under HGPA stimulation (Fig. 8C and Supplementary Fig. 7F). Both K37R (mimicking the deacetylated state of protein) and K37Q (lysine-to glutamine-mutant, mimicking protein hyperacetylation) mutants resulted in lower acetylation levels compared with those of WT RORγ (Fig. 8D). Conservation analysis of RORγ indicated that K37 is a highly conserved site from Sus scrofa to Homo sapiens (Fig. 8B). RORγ acetylation was also dramatically increased in cells treated with HG or TNF-α (Supplementary Fig. 7G), suggesting that the upregulation of RORγ acetylation is universal in response to diabetic environments. Importantly, the K37R mutation augmented, whereas the K37Q mutation diminished, the interaction between RORγ and RORE sequences in target genes (Fig. 8E and Supplementary Fig. 7H), suggesting that the acetylation state of RORγ indeed modulates its DNA binding affinity. To determine whether K37 on RORγ is a crucial site for the HGPA regulation of RORγ acetylation, we overexpressed RORγ WT or K37R using adenovirus in HK2 cells. HGPA treatment did not affect RORγ acetylation on the K37R or K37Q mutants (Fig. 8E and Supplementary Fig. 7H). Taken together, we demonstrated that RORγ K37 is acetylated, and its transcriptional activity is inhibited under diabetic environments.

Fig. 8 — A ChIP-qPCR of RORγ binding to the *YOD1* promoter in HK2 cells transfected with RORγ-S510D and treated with HGPA ± Act-D for 16 h (n = 5 biological replicates). B Left: Database schematic of RORγ post-translational modification sites. Right: Cross-species conservation of RORγ K37. C Co-IP of Flag-RORγ from HK2 cells treated with HGPA, followed by immunoblotting. D Co-IP of Flag-RORγ WT, K37R, or K37Q from HK2 cells treated with HGPA, followed by immunoblotting. E Left: Co-IP of Flag-RORγ WT or K37R from HK2 cells treated with or without HGPA. Right: ChIP-qPCR of Flag-RORγ (S510D or S510D + K37R) binding to the *YOD1* promoter (n = 5 biological replicates). F Co-IP of Flag-RORγ co-transfected with different SIRTs in HK2 cells treated with HGPA, followed by immunoblotting. G Co-IP of endogenous RORγ from WT TECs transfected with SIRT1 or siSIRT1 and treated with HGPA, followed by immunoblotting. H Co-IP of RORγ from mouse kidney lysates, followed by immunoblotting. I Co-IP of Flag-RORγ co-transfected with HA-SIRT1 in HK2 cells, followed by immunoblotting. J Cross-species conservation of the RORγ LEDLL motif. K HK2 cells co-transfected with HA-SIRT1 and Flag-RORγ WT or LEDAA mutant, then treated with HGPA. Left: Co-IP of Flag-RORγ. Right: ChIP-qPCR of RORγ binding to the *YOD1* promoter (n = 5 biological replicates). L Co-IP of Flag-RORγ from WT TECs transfected with siSIRT1 and treated with HGPA, followed by immunoblotting. M Co-IP of Flag-RORγ co-transfected with HA-SIRT1 WT or catalytically dead H363Y mutant in HK2 cells, followed by immunoblotting. N Co-IP of RORγ from WT or *Rorγ*^KO TECs treated with the AMPK agonist CDC, followed by immunoblotting. O SIRT1 mRNA levels and enzymatic activity in WT or *Rorγ*^KO TECs treated with CDC (n = 5 biological replicates). These experiments were repeated independently 3 times with similar results (C, D, F–I, L–M). Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by two-tailed Student’s unpaired t-test (A). P values calculated by one-way ANOVA with Bonferroni’s correction (E, K, and O). Panel M was created in BioRender. Shu Yang. (2025) https://biorender.com.

To identify the RORγ acetylase under diabetic environments, we treated TECs with trichostatin A (TSA; a histone deacetylase inhibitor that enhances histone acetylation to regulate gene transcription) or nicotinamide (NAM; a Sirtuin family inhibitor and NAD+ precursor involved in metabolic and epigenetic regulation). The NAM, but not TSA, increased RORγ acetylation (Supplementary Fig. 7I), indicating that sirtuins regulate RORγ deacetylation. NAM increased RORγ deacetylation in WT RORγ, but not in RORγ K37R (Supplementary Fig. 7J). Next, we co-transfected RORγ with different sirtuins—SIRT1, SIRT2, SIRT6, and SIRT7—and found that SIRT1 mainly inhibited RORγ acetylation (Fig. 8F). SIRT1 overexpression reduced, while SIRT1 knockdown enhanced, RORγ acetylation (Fig. 8G). Endogenous and exogenous Co-IP demonstrated the interaction between SIRT1 and RORγ (Fig. 8H, I). The LXXLL motif is essential for binding SIRT1 and its target protein^61,62. The LXXLL motif (LEDLL) in RORγ, together with conservation analysis of RORγ, indicated that LEDLL is a highly conserved site from Sus scrofa to Homo sapiens (Fig. 8J). We constructed an LEDAA mutant in which leucines 290 and 291 were substituted with alanine, and co-transfected cells with this mutant and SIRT1. Our findings revealed that SIRT1 diminished the acetylation of RORγ and enhanced its binding to RORE in cells transfected with WT RORγ, but these effects were not observed with the LEDAA RORγ mutant (Fig. 8K). In addition, the LEDAA mutant inhibited the interaction between SIRT1 and RORγ (Fig. 8K). Further experiments were performed to determine whether RORγ acetylation in response to HGPA was SIRT1-dependent. HGPA could not induce the acetylation of RORγ when SIRT1 was deficient (Fig. 8L). WT SIRT1 reduced RORγ acetylation, but the SIRT1 kinase-dead mutant did not (Fig. 8M).

AMPK controls the expression of genes involved in energy metabolism in mouse skeletal muscle by acting in coordination with another metabolic sensor, the NAD + -dependent type III deacetylase SIRT1⁶³. RORγ transcriptionally activated CAB39 to increase AMPK activity (Fig. 6). Therefore, we determined whether RORγ regulates SIRT1 activity in an AMPK-dependent manner. RORγ deficiency reduced SIRT1 activity and mRNA levels, and this reduction was abolished after CDC treatment (Fig. 8N, O). AMPKα1 knockdown abolished the reduction of SIRT1 activity induced by RORγ KO (Supplementary Fig. 7K). Furthermore, CDC abolished the RORγ-mediated upregulation of SIRT1 activity and mRNA level (Supplementary Fig. 7L). These results suggest that RORγ regulates SIRT1 activity in an AMPK-dependent manner.

K37R mutation potentiates, while S510A mutation diminishes, the Anti-DKD activity of RORγ

Targeted WT Rorγ overexpression in the renal TECs of RTKO mice reversed the decreases in YOD1, CAB39, pAMPK, and pINSIG1 levels, and attenuated kidney injury (evident from H&E and Sirius red staining, KW/BW, UACR, and BUN levels) induced by Rorγ KO (Supplementary Fig. 8A–F). Furthermore, Rorγ overexpression reduced renal cholesterol accumulation and INSIG1 ubiquitination, and downregulated the expression of ISGs and CSGs in RTKO mice (Supplementary Fig. 8A–F). Notably, compared with WT RORγ, the K37R mutant (could be acetylated, thus enhancing its DNA-binding ability and transcriptional activity) further amplified the restoration of YOD1/CAB39 expression, AMPK/INSIG1 phosphorylation, and kidney injury alleviation in RTKO mice (Supplementary Fig. 9A–F). Conversely, the S510A mutant (which inhibited its nuclear transport, thereby reducing its nuclear accumulation and consequently decreasing its transcriptional activity) showed attenuated effects in rescuing these parameters (Supplementary Fig. 9A–F). These results indicate that the acetylation status at lysine 37 (which modulates transcriptional activity) and the regulation of nuclear transport via serine 510 phosphorylation (which affects nuclear accumulation) are collectively critical for RORγ to mediate its anti-DKD function.

Rorγ overexpression specifically in renal TECs significantly attenuated kidney injury (evident from H&E and Sirius red staining, tubular injury score, UACR, and KIM-1 mRNA levels), inhibited renal cholesterol accumulation, and reduced the expression of ISGs and CSGs in the kidney of aging mice (Supplementary Fig. 10A–E). Additionally, RORγ overexpression suppressed the expression of senescence markers (p16 and p21) and the accumulation of senescent cells (evaluated by p21 immunofluorescence and β-galactosidase staining) in aging mice (Supplementary Fig. 10E, F). Overexpression of Rorγ led to an increase in YOD1, CAB39, pAMPK, and pINSIG1 levels, and a decrease in the levels of STING, nSREBP2, and INSIG1 ubiquitination in the kidneys of aging mice (Supplementary Fig. 10G). Conversely, Rorγ KO had the opposite effect on these changes (Supplementary Fig. 10H). Furthermore, we constructed exosomes containing RORγ protein (exoRORγ) and found that they upregulated the expression of RORγ, CAB39, and YOD1 in TECs (Fig. 9A–E). Parallel experiments with exoRORγ in DKD mice showed analogous results: exoRORγ treatment upregulated renal YOD1 and CAB39 expression (Fig. 9F, G) and mitigated kidney injury markers (KW/BW, UACR, BUN, tubular injury scores, KIM-1 levels). Additionally, exoRORγ suppressed renal cholesterol deposition and STING pathway activation, decreased ISGs/CSGs expression, and promoted pAMPK elevation (Fig. 9G–J). In summary, both renal TEC-specific Rorγ overexpression and exoRORγ treatment effectively alleviate kidney injury and exert protective effects against kidney diseases in aging and DKD models.

Fig. 9 — A Exosome construction schematic diagram. B Left panel: Representative transmission electron microscope (TEM) image of exosomes is shown. Right panel: Nanoparticle Tracking Analysis of exosomes is shown. This experiment was repeated independently 3 times with similar results. C Human or mouse TECs were treated with exoRORγ as indicated. The protein levels of *RORγ* determined by Western blot. D, E This experiment was repeated independently 3 times with similar results. HK2 cells incubated with exoRORγ as indicated. D: Left panel: the mRNA levels of *CAB39* and *YOD1* were detected by qPCR (n = 5 biological replicates). Right panel: luciferase reporter assays showing the activity of *YOD1* promoter fragments in HEK293T cells (n = 5 biological replicates). E: total cell lysates were prepared and subjected to western blotting using the indicated antibodies. F A schematic representation of the experimental design for kidney sampling in male WT DKD mice treated with exoRORγ or not (exoRORγ was administered via renal in situ injection). G Left panel: the total cell lysates from the kidneys of mice were prepared, and western blotting was performed using the indicated antibodies. Right panel: the mRNA levels of *Cab39* and *Yod1* in the kidneys of mice were detected by qPCR (n = 5). H Representative images of H&E and Sirius red staining in kidney sections from mice are shown. I The KW/BW, UACR, BUN, renal cholesterol, *KIM-1* mRNA levels, and tubular injury score of mice were measured (n = 6). J The expression of ISGs and cholesterol synthesis genes in the kidneys from mice were detected by qPCR (n = 6). Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by two-tailed Student’s unpaired t-test (D). P values calculated by one-way ANOVA with Bonferroni’s correction (G, I, and J). Panels A and F were created in BioRender. Shu Yang. (2025) https://biorender.com.

RORγ activation mitigates kidney injury in DKD mouse model

In our recent investigation, we found that Ganoderma lucidum spore powder has anti-atherosclerotic effects, primarily due to its main components: Ganoderic acids A, B, C6, G, and Ganodermanontriol (GMNT)⁶⁴. Utilizing SwissTargetPrediction, we predicted potential binding targets for these compounds and were surprised to find that RORγ is a predicted target for GMNT (Fig. 10A). The -CDOCKER Interaction Energy (CIE) of RORγ and GMNT was 52.4 kcal/mol (Fig. 10B). A publication reporting drug–protein interactions indicates that compounds with about 50 kcal/mol of a -CIE would work as effector molecules to their putative targets⁶⁵. GMNT did not regulate the expression of RORγ, but it did upregulate the transcriptional activity of RORγ and its target genes (CAB39 and YOD1) (Fig. 10C–G). GMNT enhanced AMPK phosphorylation while concurrently decreasing STING protein levels in TECs (Fig. 10G). These alterations were abrogated following RORγ KO (Fig. 10G). According to the results of molecular docking, GMNT binds to the ligand-binding domain (LBD) of nuclear hormone receptor (NR) region of RORγ (Fig. 10A). By using RORγ deletion mutants, we found that the interaction between RORγ and GMNT was mediated by the NR-LBD domain of RORγ (Fig. 10H). Thus, we assumed that GMNT could act as an activating ligand for RORγ.

Fig. 10 — A Predicted structural model of GMNT (Ganodermanontriol) (red dashed box) bound to the LBD and zf-C4 domain (black dashed box) of RORγ. B Molecular docking between the GMNT and RORγ. Green represents hydrogen bonding, pink represents PI-PI stacking, and green amino acids at the periphery for van der Waals forces. C RORγ mRNA (qPCR) and protein (immunoblot) levels in human or mouse TECs treated with GMNT for 24 h (n = 5 biological replicates). D Luciferase activity of the *YOD1* promoter in HEK293T cells (n = 5 biological replicates). E, F HK2 cells treated with GMNT (10 or 20 µM) for 24 h. E: *CAB39* and *YOD1* mRNA levels (qPCR, n = 5 biological replicates). F ChIP-qPCR of RORγ binding to the *CAB39* or *YOD1* promoter (n = 3 biological replicates). G Immunoblot analysis of WT or *Rorγ*KO TECs treated with HGPA and/or GMNT (10 µM) for 24 h. This experiment was repeated independently 3 times with similar results. H Left: Schematic of RORγ deletion mutants. Right: Pull-down of Flag-RORγ deletion mutants with GMNT-Biotin in 293 T cells. This experiment was repeated independently 3 times with similar results. I Schematic of kidney sampling in WT DKD mice treated with or without GMNT. J Left: the total cell lysates from kidney of mice were prepared and subjected to western blotting using the indicated antibodies. Right: the mRNA levels of *Cab39* and *Yod1* in the kidneys of mice were detected by qPCR (n = 5). K Representative images of H&E and Sirius red staining in kidney sections from mice are shown. L The KW/BW, UACR, BUN, renal cholesterol, *KIM-1* mRNA levels, and tubular injury score of mice were measured (n = 6 mice). M The expression of ISGs and cholesterol synthesis genes in the kidneys from mice (n = 6 mice). Source data are provided as a Source Data file. Data are presented as the mean ± SD. P values calculated by two-tailed Student’s unpaired t-test (F). P values calculated by one-way ANOVA with Bonferroni’s correction (C, D, E, J, L, and M). Panel I was created in BioRender. Shu Yang. (2025) https://biorender.com.

We then assessed the effects of GMNT in DKD mice and discovered that this treatment increased the expression of RORγ target genes YOD1 and CAB39 in renal tissues (Fig. 10J). GMNT also alleviated kidney injury, as evidenced by improvements in the KW/BW ratio, UACR, BUN levels, tubular injury scores, and KIM-1 expression. Mechanistically, GMNT inhibited renal cholesterol accumulation and STING signaling activation, reduced the expression of ISGs and CSGs, and enhanced pAMPK levels in the kidneys of DKD mice (Fig. 10J–M). Taken together, our findings indicate that small-molecule agonists can restore the activity of RORγ and the AMPK signaling pathway under pathological conditions, limiting STING and SREBP signaling pathways, terminating the excessive inflammatory response in tissues and cholesterol accumulation, and finally inhibiting DKD progression.

Discussion

Here, we unraveled the intricate regulatory network involving RORγ, AMPK, and SREBP2-STING pathways. We found that RORγ plays a pivotal role in modulating the activities of these pathways, which provides critical insights into the molecular underpinnings of DKD and renal aging. RORγ stabilizes INSIG1 to inhibit SREBP2 and STING activation in two ways. First, it activates the transcription of YOD1, which is a deubiquitinating enzyme for INSIG1, thereby preventing its ubiquitin-mediated degradation. Second, RORγ activates the transcription of CAB39, leading to increased AMPK activation. AMPK, in turn, phosphorylates INSIG1, preventing its degradation and thereby enhancing the stability of the INSIG1 protein. Our findings reveal that loss of RORγ aggravates SREBP2-STING activation, induces the inactivation of AMPK, promotes inflammation and lipid accumulation, and subsequently exacerbates kidney injury induced by diabetes and aging. In addition, we identified that RORγ induces the expression of CAB39, thereby activating AMPK; this AMPK activation, on the one hand, promotes the phosphorylation of RORγ (thereby facilitating its nuclear translocation), and on the other hand, enhances the activity of SIRT1 to inhibit the acetylation of RORγ—creating a positive feedback loop that enhances RORγ activation.

Our discovery is significant as it diverges from the established paradigm in other tissue contexts, such as triple-negative breast cancer, where RORγ is portrayed as a key activator of cholesterol biosynthesis, primarily via the dominant role of SREBP2⁶⁶. In our research, the function of RORγ—regulating INSIG1 to prevent its degradation and in turn inhibit the activation of SREBP2—presents a unique regulatory mechanism that is distinct from the role of RORγ in facilitating SREBP2 recruitment to cholesterol biosynthesis genes as observed in triple-negative breast cancer⁶⁶. Consistent with our findings, another study has reported that the absence of RORα/γ can lead to an overactivation of the SREBP-dependent lipogenic response, which exacerbates diet-induced hepatic steatosis⁶⁷. This discrepancy underscores the tissue/cell-specific manner in which RORγ modulates SREBP2 and its target genes, highlighting the complexity and context-dependency of RORγ‘s role in lipid metabolism^68,69. Unlike the findings in triple-negative breast cancer, our findings in renal tubular epithelial cells clarify RORγ’s role in metabolic regulation, highlighting its potential as a therapeutic target for DKD and renal aging. RORγ’s tissue-specific activity underscores the need for targeted therapies accounting for cell-type-specific metabolic crosstalk. In sum, our study advances understanding of RORγ’s regulation, emphasizes context-specificity in studying metabolic regulators, and lays the groundwork for novel therapies for DKD and renal aging.

Beyond its established role in cholesterol and fatty acid synthesis, INSIG1 has been identified as a critical inhibitor of SREBP1 and SREBP2 processing^48,70,71. While SREBP2 is predominantly associated with lipid homeostasis, SREBP1 has emerged as a multifaceted regulator with implications in renal fibrosis. Notably, SREBP1 has been shown to directly regulate the expression of TGF-β1, a key profibrotic cytokine in the kidney⁷². This regulation is not only glucose-dependent but also responsive to other stimuli such as Angiotensin II (AngII), as demonstrated in both in vitro and in vivo studies⁷³. The influence of SREBP1 extends beyond TGF-β synthesis. Plasminogen activator inhibitor-1 (PAI-1), an inhibitor of matrix breakdown, has been identified as a transcriptional target of SREBP1c in adipocytes⁷⁴. Collectively, these findings suggest a significant role for SREBP1 in the regulation of organ fibrosis. In our current study, we observed an increase in the transcription levels of TGF-β1, the nuclear accumulation of nSREBP1, and the transcription levels of other profibrotic genes in RTKO DKD mice (Figs. 2G and 3H). The interplay between RORγ, INSIG1, and SREBP1 provides a novel perspective on the regulatory mechanisms that link metabolic regulation to the fibrotic response in renal disease.

Nutrient and damage sensing are fundamental processes in living organisms, yet a direct molecular link between these two basic cellular behaviors has rarely been established. AMPK has emerged as a principal sensor of ATP deficiency and serves as the master regulator of various metabolic pathways, including glucose and lipid metabolism, protein synthesis, autophagy, mitochondrial biogenesis, and overall metabolism⁷⁵. The therapeutic potential of AMPK to treat metabolic diseases such as obesity, type 2 diabetes, and cancer is widely recognized. A recent study highlighted the unique function of TBK1 in adipocytes—this protein inhibits energy expenditure through direct phosphorylation and inhibition of AMPKα⁷⁶. Furthermore, once activated, AMPK directly phosphorylates TBK1 at S511, initiating the recruitment of IRF3 and the assembly of MAVS or STING signalosomes⁷⁷. AMPK also promotes autophagy by directly activating ULK1 through phosphorylation⁷⁸, with ULK1 subsequently phosphorylating STING to prevent prolonged innate immune signaling⁷⁹. Our study delineates a pivotal role for RORγ as an amplifier within the AMPK signaling pathway, offering a significant advancement in our understanding of AMPK’s regulatory network. We reveal that RORγ, through a positive feedback mechanism, enhances AMPK activity (Fig. 6), which in turn phosphorylates RORγ (Fig. 7), facilitating its nuclear transport and augmenting SIRT1 activity (Fig. 8). This activation cascade results in the activation of RORγ, thereby stabilizing INSIG1—a critical step in modulating the STING signaling pathway. Our research uncovers a mechanism: AMPK directly phosphorylates INSIG1, stabilizing it and inhibiting STING transport/activation, adding complexity to AMPK’s roles in metabolism and immunity. We identify RORγ as a key modulator of AMPK activity, advancing understanding of AMPK in renal function and revealing a tissue-specific regulatory mechanism with therapeutic potential. This clarifies RORγ-AMPK crosstalk, laying the groundwork for precision therapies in DKD and renal aging, and highlights a novel metabolic-immune target.

Here, we identified that RORγ regulates STING protein levels through a mechanism mediated by a RORγ-INSIG1 axis, which promotes STING degradation via the SEL1L-HRD1 ERAD pathway. Specifically, RORγ overexpression shortens STING half-life and enhances its K48-linked polyubiquitination, with this STING degradation blocked by proteasome inhibition (Supplementary Fig. 12A–E). This process is indirect: RORγ stabilizes INSIG1, which in turn retains STING in the ER, with INSIG1 depletion abrogating RORγ-induced STING ubiquitination; moreover, it depends on K48-linked ubiquitination at STING’s K150 site (Supplementary Fig. 12F–H). Consistent with ERAD involvement and prior studies demonstrating HRD1’s role in STING degradation⁸⁰, our data show that HRD1 knockdown increases basal STING levels and the expression of ISGs while blunting RORγ-mediated STING downregulation (Supplementary Fig. 12I). Together, these findings reveal that the RORγ-INSIG1 axis couples ER retention (via INSIG1) with SEL1L-HRD1-mediated ERAD to reduce STING protein levels, providing key insights into how RORγ modulates STING homeostasis in innate immunity.

The STING signaling pathway plays crucial roles in host defense against viral infections, genome stability maintenance, and inflammatory responses. For instance, gut microbiota-derived membrane vesicles activate the cGAS-STING axis to enhance antiviral immunity⁸¹, while cGAS itself inhibits homologous recombination-mediated DNA repair independently of STING⁸². Additionally, bacterial metabolites like cyclic di-AMP trigger STING-dependent inflammasome activation⁸³, and DNA damage from ATM deficiency drives type I IFN responses via the STING pathway⁸⁴. These findings underscore STING’s multifaceted roles in integrating microbial sensing, DNA damage responses, and inflammatory signaling. Notably, non-canonical STING activation has also been reported in host defense and inflammatory responses: prior work shows Golgi-derived sGAGs like heparin (HP) activate STING⁸⁵. Our study reveals that RORγ regulates STING signaling by stabilizing INSIG1 to inhibit its translocation from the ER to the Golgi (Figs. 4A and 5). Genetic ablation of RORγ enhances STING translocation from the ER to the Golgi (Fig. 4A). Since STING localizing to the Golgi is susceptible to activation by stimuli such as exogenous HP/HS, this enhanced trafficking in RORγ-deficient cells directly leads to increased TBK1/IRF3 phosphorylation and ISGs expression in response to HP/HS—effects that are suppressed by Slc35b2 knockout (which impairs sGAG sulfation and thus HP/HS-mediated STING activation) (Supplementary Fig. 13A–F). Moreover, SLC35B2 expression was increased in the kidneys of DKD patients compared with those in HLD (Supplementary Fig. 13G), and provided a tentative clinical clue that this pathway may be relevant to the pathological processes of DKD. This regulation is cGAS-independent, as RORγ-deficient cells show enhanced STING-Golgi localization and ISGs expression even without cGAS. These findings establish RORγ as a key regulator of STING subcellular trafficking, linking metabolic control to innate immunity via INSIG1.

While prior work demonstrated that INSIG2 phosphorylation at Ser106 by PKA (in response to polyunsaturated fatty acids) selectively inhibits SREBP1 processing⁸⁶, our study identifies a AMPK-mediated pathway specific to INSIG1. Phosphorylation of INSIG1 at Ser222 blocks ubiquitination by disrupting its interaction with gp78, stabilizing INSIG1. This enhances INSIG1 binding to SCAP, inhibiting ER export of SREBP2 (Supplementary Fig. 14A–J) and STING. Key distinctions include: (1) Kinase specificity (PKA vs. AMPK); (2) Upstream signals (cAMP/PUFA vs. energy stress/RORγ-AMPK); (3) Functional outcomes (SREBP-1 inhibition vs. SREBP2/STING inhibition); (4) Cellular context (metabolic tissues vs. renal stress). This positions INSIG2 as a lipid metabolism regulator and INSIG1 as an ER-Golgi transport controller in renal cytoprotection. Critically, RORγ integrates these by orchestrating INSIG1 stability via dual mechanisms (deubiquitination and phosphorylation), mitigating diabetic kidney injury and acting as a central energy-sensing node. In the prior work, INSIG1’s Ser149 (located NH₂-terminal to ubiquitination sites K156/K158) accelerates proteasomal degradation, whereas INSIG2’s Ser106 (COOH-terminally to K100/K102) retards degradation by modulating local conformation near ubiquitination sites⁸⁶. In our study, phosphorylation of Ser222 by AMPK promotes Insig1 dissociation from the E3 ligase gp78, thereby inhibiting ubiquitination and subsequent degradation. Notably, both Ser149 and Ser222 reside in the cytosolic domain of Insig1 but act through distinct pathways: Ser149 facilitates proteasomal access to ubiquitinated Insig1 by altering local folding, while Ser222 blocks ubiquitination initiation via AMPK-mediated disruption of the INSIG1-gp78 interaction. These findings together reveal a hierarchical regulatory network in which proximal (Ser149) and distal (Ser222) serine residues control Insig1 turnover through mechanistically distinct yet functionally complementary pathways.

In conclusion, this study highlights the pivotal role of RORγ as an intracellular energy sensor, adeptly regulating AMPK signaling pathway and SREBP/STING transport processes. Through this meticulous modulation, RORγ not only bolsters cellular energy resilience but also carves new therapeutic pathways against DKD and renal aging, showcasing a novel avenue for intervention in metabolic and kidney diseases.

Limitations of the study

There are several limitations of our study. The first limitation involves the role of kidney endothelial and mesangial cell RORγ in renal function. In the current study, we found that the Rorγ gene is expressed at very low levels in glomerular endothelial and mesangial cells. Functionally, kidney RORγ expression is positively associated with glomerular filtration rate according to the Ju CKD Glom database but not the Woroniecka Diabetes Glom database (Supplementary Fig. 10A, B). In addition, we found that RORγ mRNA levels were significantly reduced in the renal glomeruli of patients with DKD in the Ju DKD database but not in the Woroniecka tublnt database (Supplementary Fig. 10A). Hence, future studies are warranted to elucidate the regulatory role of endothelial and mesangial cell Rorγ in normal kidneys. In the context of DKD, it remains to be evaluated whether kidney Rorγ downregulation contributes to glomerular dysfunction and peritubular microvascular damage, two prominent pathophysiological features of DKD. The second limitation pertains to sexual dimorphism in renal physiology and diseases—including fluid balance, electrolyte homeostasis, and blood pressure regulation (for renal physiology), as well as DKD (for renal diseases). Our research exclusively utilized male mice to evaluate the renal response to Rorγ deficiency. Further investigation is required to determine whether female KO mice exhibit distinct phenotypes, which would enhance our understanding of sex-specific responses in renal pathophysiology related to Rorγ. Alterations in the circadian rhythm of renal functions are linked to the development of hypertension, CKD, DKD, renal fibrosis, and kidney stones^87,88. Given the crucial role of RORγ in maintaining the circadian rhythm⁸⁹, we investigated whether circadian rhythm dysfunction regulated RORγ expression in the present study. We found that sleep restriction suppressed RORγ expression in the kidneys of DKD mice (Supplementary Fig. 10C, D). The third limitation of our study concerns the mechanisms underlying the direct causal relationship between alterations in the circadian rhythm, specifically considering sleep restriction, and the observed downregulation of renal RORγ expression in DKD progression. Nonetheless, our findings suggest that long-term sleep restriction may increase the risk of developing DKD in humans through the downregulation of renal RORγ expression.

Methods

Studies in animals

All animal care and experimental protocols for in vivo studies conformed to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (NIH; NIH Publication No.:85–23, revised 1996). The sample size for the animal studies was calculated based on a survey of data from published research or preliminary studies. Rorγ^flox/flox (C57BL/6 JCya-Rorc^em1flox/Cya; Strain ID: CKOCMP-19885-Rorc-B6J-VA), Cab39^flox/flox (C57BL/6JCya-Cab39^em1flox/Cya; Strain ID: CKOCMP-12283-Cab39-B6J-VA), Sting1^-/- (C57BL/6NCya-Sting1^em1/Cya; Strain ID: KOCMP-72512-Sting1-B6N-VA), C57BL/6J mice, and Cdh16-Cre mice (Cat#: C001452) were obtained from Cyagen Biosciences (Guangzhou) Inc (Guangzhou, Guangdong, China). Rorγ conditional knockout mice specific to TECs (RTKO; Cdh16-cre×Rorγ^flox/flox; Cdh16-cre mice were used as controls) and Cab39 conditional knockout mice specific to TECs (Cab39TKO; Cdh16-cre×Cab39^flox/flox; Cdh16-cre mice were used as controls) were generated. These mice were maintained in SPF units of the Animal Center at Southern University of Science and Technology, First Affiliated Hospital, with a 12 h light cycle from 8 a.m. to 8 p.m., 23 ± 1 °C, and 60–70 % humidity. Mice were allowed to acclimatize to their housing environment for 7 days before the experiments. At the end of the experiments, all mice were anesthetized and euthanized in a CO₂ chamber, followed by the collection of kidney tissues. All animals were randomized before treatment. Mice were treated in a blinded fashion, as the drugs used for treating animals were prepared by researchers who did not carry out the treatments. No mice were excluded from the statistical analysis. Studies were performed in accordance with the German Animal Welfare Act, and reporting follows the ARRIVE guidelines.

Mouse kidneys were transfected with an adeno-associated virus vector (AAV)

8-week-old mice received in situ renal injection with AAV9- empty vector (AAV9-Ctrl; control group), AAV9- Ksp-Cadherin (also known as Cdh16) -Rorγ (Rorγ^OE group; Ksp, tubule specific promoter), AAV9-Ksp-Rorγ K37R (K37R), and AAV9-Ksp-Rorγ S510A (S510A) at three independent points (10 - 15 μl virus per point; virus injected dose: 2.5 E + 11 v.g.) in the kidneys of mice (n = 6 per group). AAV9 constructs, including GV501 empty vector, Rorγ, Rorγ K37R, and Rorγ S510A, were provided by GeneChem Company (Shanghai, China).

DKD mouse model and intervention

Unilateral nephrectomy was performed under isoflurane anesthesia. Baseline parameters were stipulated before establishing the DKD mouse model. In short, the 8-week-old male mice were fed a high-fat diet for 2 weeks and then underwent unilateral nephrectomy. The high-fat diet (HFD; 60% calories from fat) was continued for another 2 weeks (normal chow feed was used as control; 10% calories from fat), and STZ (50 mg/kg) was intraperitoneally injected into the mice for a continuous 5 days. Tail vein blood glucose was measured after 72 h. The establishment of the diabetes model was considered successful if fasting plasma glucose (FPG) ≥ 11.1 mmol/L or random plasma glucose (RPG) ≥ 16.7 mmol/L. Seven days after the STZ injection, blood glucose was measured again, and mice with stable blood glucose levels within 7 days were categorized as successfully established diabetic mice models. After successfully establishing the diabetic mice model, the mice were continuously fed an HFD for another 20 weeks. To specifically identify the role of exosomal RORγ in diabetes-induced kidney injury, RORγ-exos (1 × 10¹⁰ particles) were suspended in 200 μl PBS and injected through the renal in situ injection once a week for 12 weeks, starting 8 weeks after establishing the diabetic mice model. To specifically determine the role of Ganodermanontriol (GMNT) in diabetes-induced kidney injury, mice with established diabetes were treated using intragastric administration of GMNT (10 mg/kg body weight) every day for 12 weeks (starting 8 weeks after establishing the diabetic mice model).

Human renal specimens

Histopathological assessments were performed in a blinded manner by two experienced pathologists and classified according to the Renal Pathology Society’s Pathologic Classification of Diabetic Kidney Disease on the basis of glomerular pathology. Patients diagnosed with minimal change disease (MCD) served as the control group (n = 1). The clinical characteristics of DKD (n = 8) of the biopsy samples are detailed in Supplementary Table 1. Venous blood samples were collected from eight individuals with type 2 diabetes after an overnight (≥ 10 h) fast. Concentrations of fasting blood glucose (FBG) were measured using an automatic biochemical analyzer (AU5800; Beckman Coulter, USA). Reference ranges were obtained from the central laboratory of Shenzhen People’s Hospital (Shenzhen, China), and the measured variables were all within the reference ranges based on age, sex, and ethnicity. The study protocol was approved by the Institutional Review Board and the Ethics Committee of The First Affiliated Hospital of the Southern University of Science and Technology. Informed written consent was obtained from all participants prior to their inclusion in the study.

Cell culture and transfection

The HK2 cells were purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China). The cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Gibco, China) supplemented with 10% fetal bovine serum (FBS; CellMax, Australia) and 1% penicillin/streptomycin in a humidified atmosphere of 5% CO₂. Mouse kidney fibroblasts were also purchased from Procell Life Science & Technology Co. The fibroblasts were maintained in DMEM/F-12 medium (Hyclone, USA) containing 10% FBS (CellMax, Australia) and 1% penicillin/streptomycin and cultured in a humidified atmosphere of 5% CO₂. Regular mycoplasma testing was conducted using a qPCR test that was performed under ISO17025 accreditation to ensure the absence of mycoplasma contamination.

Cells were seeded in dishes and cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS). Upon reaching 80% confluence, cells were transduced with adenovirus vectors obtained from GeneChem Company (Shanghai, China). Cells were harvested at specified time points post-infection for subsequent analyses. For siRNA transfection, cells were transfected with commercially available siRNA using Lipofectamine 3000 reagent (Invitrogen, Carlsbad, California, USA) following the manufacturer’s protocol.

Small interfering RNAs (siRNAs) used in this study were purchased from Santa Cruz Biotechnology (Dallas, TX, USA), including mouse Sirt1 siRNA (catalog number: sc-40987), mouse Prkaa1 siRNA (catalog number: sc-29674), mouse Ctcf siRNA (catalog number: sc-35125), and mouse Srebf2 siRNA (catalog number: sc-36560); siRNAs targeting deubiquitinases (DUBs) were used as described in our previously published study⁹⁰.

DNA construction and mutagenesis

pcDNA3.1 Flag–short hairpin RNA (shRNA)-resistant INSIG1 constructs containing nonsense mutations of T693C, T696C, C699G, and G705A, shRNA-resistant SREBF2 constructs containing nonsense mutations of A438G, A441G, G444A, and G447C, shRNA-resistant CAB39 constructs containing nonsense mutations of A561G, C564T, T570C, and T576C, and shRNA-resistant PRKAA1 constructs containing nonsense mutations of G997C, G1000A, C1011A, and A1014C were constructed using a QuikChange site-directed mutagenesis kit (Stratagene). pGIPZ shRNA was constructed via ligation of an oligonucleotide targeting human INSIG1, SREBF2, CAB39, or PRKAA1 into an XhoI/MluI-digested pGIPZ vector. The following pGIPZ shRNA target sequences were used: control shRNA oligonucleotide, 5′- GCTTCTAACACCGGAGGTCTT-3′; INSIG1 shRNA oligonucleotide, 5′- TAATGGTGTCTATCAGTATAC-3′; SREBF2 shRNA oligonucleotides, 5′- CCTGAGTTTCTCTCTCCTGAA-3′; CAB39 shRNA oligonucleotides, 5′- GCCACATTCAAGGATTTACTT-3′; PRKAA1 shRNA oligonucleotides, 5′- GTTGCCTACCATCTCATAATA -3′.

Generation of SREBP2 ΔbHLH Construct

The SREBP2 ΔbHLH expression plasmid was generated by outsourcing to a commercial vendor (Shandong, China, WZ Biosciences Inc.). Briefly, the wild-type mouse Srebf2 cDNA was used as a template. The loop region within the bHLH domain (amino acids 340–349, encoding the peptide IELKDLVMGT) was deleted using overlapping PCR primers. The mutated fragment was subcloned into the pCMV7 expression vector (Invitrogen) and confirmed by Sanger sequencing to ensure the absence of unintended mutations. The resulting plasmid expresses an SREBP2 variant lacking the 10-amino acid loop in the bHLH domain, which abolishes dimerization while preserving the remainder of the protein sequence.

Isolation of primary renal TECs

Primary mouse renal tubular epithelial cells were isolated using an established protocol. Briefly, the cortex of the kidneys was carefully dissected and chopped into small pieces. Then, 1 mg/ml of collagenase solution was applied and incubated at 37 °C for 30 min with gentle agitation. The digestion was terminated by FBS and then filtered sequentially. Fragmented tubules were collected and maintained in a renal epithelial cell basal medium using a growth kit. The medium was changed for the first time after 72 h. The purity of the primary mouse renal tubular epithelial cells was confirmed by immunofluorescence staining of SGLT1 (Novus, NBP2-20338). Cells at passages 2–5 were used for the experiments.

Histological analysis

Kidney tissue was embedded in paraffin and sliced into 5 μm-thick serial sections using a paraffin slicer. For kidney histology, the paraffin sections were stained using Masson’s trichrome and Sirius red staining kit (Beyotime BioTechnology, Shanghai, China).

Immunohistochemical (IHC) staining

Paraffin-embedded mouse kidney samples were sliced into 4 μm-thick sections and subjected to IHC staining using a Rabbit two-step detection kit (Rabbit enhanced polymer detection system, ZSGB Bio, Beijing, China). Briefly, mouse kidney sections were deparaffinized, followed by antigen retrieval, treatment with peroxidase block, and incubation with rabbit anti-RORγ (1:100, Merck, Cat. No. ZRB1398) primary antibody overnight at 4 °C. Tissue sections were then washed and incubated with the reaction-enhancing solution at room temperature for 20 minutes. After washing three times with phosphate buffer saline (PBS), tissue sections were treated with enhanced enzyme-labeled goat anti-rabbit IgG polymerase for 20 minutes and then developed with 3,3’ Diaminobenzidine solution, counterstained with hematoxylin, and mounted with mounting medium. The kidney frozen slices from patients with minimal change disease and DKD were fixed with 4 % paraformaldehyde for 20 min, incubated with anti-RORγ primary antibody overnight at 4 °C, and subjected to immunohistochemistry staining as described above. The stained area was measured using ImageJ software.

Quantitative Real-time PCR (qPCR)

Total RNAs were extracted using Trizol (Invitrogen) and then dissolved in an appropriate amount of RNase-free water. cDNA was synthesized using a reverse transcription kit purchased from TransGen Biotech (Beijing, China). qPCR was performed using the ABI StepOnePlusTM Real-time PCR system (Applied Biosystem) with specific primers (Supplementary Table 2). The relative mRNA levels of target genes were analyzed using the 2^-ΔΔCT method. Tubulin was used as a housekeeping gene for normalization.

Protein extraction and Western blot analysis

Cytoplasmic or nuclear extracts were prepared from cells or kidneys using a cytoplasmic and nuclear protein isolation kit (Cat. No 78833, Thermo Fisher Scientific). Briefly, the tube was vortexed vigorously at the highest setting for 15 s to fully suspend the cell pellet (or tissues were homogenized in PBS). The tubes were incubated on ice for 10 min. Next, ice-cold cytoplasmic extraction reagent II solution was added to the tubes. The tube was vortexed and incubated on ice. The tube was centrifuged for 5 min at maximum speed in a microcentrifuge (~16,000 × g), and the supernatant (cytoplasmic extract) was transferred to a new tube. The insoluble (pellet) fraction, which contains nuclei, was suspended in ice-cold nuclear extraction reagent and then vortexed at the highest setting for 15 s. The sample was placed on ice and vortexed for 15 s every 10 min for 40 min. The tube was centrifuged at maximum speed (~16,000 × g) in a microcentrifuge for 10 min, and the supernatant (nuclear extract) fraction was transferred to a new tube. For western blot analysis, 30 µg of lysate was loaded onto sodium dodecyl sulfate-polyacrylamide gel electrophoresis gels and transferred onto polyvinylidene difluoride membranes (Millipore). Proteins were analyzed with their corresponding specific antibodies (1:1000). Detailed information of these antibodies is provided in Supplementary table 3. Densitometry analysis was performed using Quantity One® Software and quantified relative to the loading control, Tubulin. Antibodies used in Western blottings. Uncropped and unprocessed scans of key blots are provided in the Source Data file.

Immunofluorescence analysis

Immunofluorescence analysis was performed as previously reported⁹¹. Cultured cells were fixed by 4 % paraformaldehyde (PFA), treated with 0.1 % Triton X-100 for 10 min and blocked in 5 % BSA for 1 h. The cells were then incubated with primary antibodies at a dilution of 1:100. In antibody reaction buffer (PBS plus 1 % BSA, 0.3 % Triton X-100 at pH 7.4), samples were stained with primary antibodies against RORγ (1:50, Merck, Cat. No. ZRB1398) antibody overnight at 4 °C. After incubation with fluorescent-dye-conjugated secondary antibodies and DAPI, immunofluorescent microscopic images of the cells were obtained and viewed using an IX81 confocal microscope (Olympus America).

Deparaffinize and rehydrate the paraffin-embedded kidney tissue sections through a series of xylene and graded ethanol washes. Then, perform antigen retrieval using citrate buffer in a microwave or water bath. Next, block non-specific binding sites with a blocking buffer containing 5 % BSA. Incubate the sections with F4/80 antibody at 4 °C overnight, followed by incubation with fluorophore-conjugated secondary antibodies. Thoroughly wash the sections with PBS buffer between each incubation step. Finally, counterstain the nuclei with DAPI, mount the sections with an anti-fade mounting medium, and observe using a fluorescence microscope.

Coimmunoprecipitation (Co-IP)

After treatment, the cells were lysed in an ice-cold co-immunoprecipitation (co-IP) buffer containing 20 mM Tris-HCl (pH 8.0), 100 mM NaCl, 1 mM EDTA, and 0.5% NP-40, supplemented with a protease inhibitor cocktail (Roche, Cat. No. 04693132001), for 30 min. The cell homogenates were then centrifuged at 13,000 g for 15 min, and the resulting supernatant was incubated overnight at 4 °C on a shaker with primary antibodies [anti-Flag (Abcam, Cat. No. ab205606), anti- HA (Abcam, Cat. No. ab9110), anti-RORγ (Novus. Cat. No NBP2-24503), anti-INSIG1 (Santa Cruz, Cat. No. sc-390504), and anti-LKB1 (Santa Cruz, Cat. No. sc-32245) at a 1:200 dilution. To ensure complete saturation of the primary antibodies, sufficient cell lysates were prepared for immunoprecipitation (IP). The mixture of antibodies and proteins was subsequently incubated with protein A/G-agarose beads (Thermo Fisher Scientific, Cat. No. 78610) at 4 °C for 3 h. The beads were washed 5–6 times with cold IP buffer and resuspended in loading buffer. The cell lysates and immunoprecipitates were then denatured in loading buffer at 95 °C for 5 min, followed by western blotting analysis.

Measurement of lipid accumulation in kidneys

The quantification of intracellular free cholesterol in kidney samples and cells was performed by first exposing the samples to a chloroform/methanol solution (2:1 ratio) to isolate lipids, followed by homogenization with an ultrasonic homogenizer. The lipid phase was then collected, dried using a vacuum concentrator, and resuspended in 2-propanol containing 10% Triton X-100 for solubilization. Concurrently, the solid phase was treated with a 1 mol/L sodium hydroxide solution to dissolve proteins, allowing for protein quantification. The cholesterol concentrations were determined using a standard curve and normalized to the total cellular protein content, ensuring an accurate reflection of free cholesterol levels in relation to cellular protein.

Measurements of renal functions

Twenty-four-hour urine samples were collected. Urinary creatinine was measured using an ELISA kit for mouse albumin (Nanjing Jiancheng Chemical Industrial, Nanjing, China; Cat. No. C011-2-1). Proteinuria was measured using a commercial kit (Shanghai Enzyme-linked Biotechnology Co., Ltd). Diluted urea was added to each well and coincubated with the standard solution for 1 h at room temperature. After washing four times, HRP-conjugated anti-albumin antibody was pipetted into each well and shaken for 1 h. Then, chromogenic substrate reagent was loaded and shaken for 20 min. Finally, the reaction-stopping agent was added. The absorbance was measured at 450 nm, and the concentration of albumin was calculated according to a standard curve. Urea creatinine levels were examined by the alkaline picric acid method (Shanghai Jianglai Biotechnology Co., Ltd).

Determination of Triglyceride (TG) content

Renal cortex samples were homogenized on ice in 2 mM potassium phosphate buffer (pH 7.0, 10 mL buffer per mg tissue) using a glass homogenizer with 10–15 strokes. A 100 μL aliquot of the homogenate was mixed with hexane:isopropanol (3:2, v/v) at a 1:10 ratio (homogenate:solvent). The mixture was centrifuged at 12,000 × g for 5 min at 4 °C to separate the organic phase (supernatant). The organic phase was dried under nitrogen gas and reconstituted in 100 μL of isopropanol:NP-40 (9:1, v/v). Triglyceride content was quantified using a colorimetric assay kit (Cayman Chemical, USA) following the manufacturer’s protocol. A standard curve (0.1–2.0 mmol/L triglyceride standards) was included in each experiment. Preliminary tests were conducted to assess potential interfering substances, and appropriate pretreatment methods (e.g., filtration) were applied if necessary to eliminate interference.

Chromatin immunoprecipitation (ChIP) and sequential ChIP assay

ChIP assays were performed with a commercial kit from Sigma-Aldrich (St. Louis, Missouri, USA) following the manufacturer’s instructions with the primers listed in Supplementary Table 4. Briefly, kidney tissues were lysed in lysis buffer and sonicated (15 s on and 90 s off, repeated eight times). After precipitation with Agarose A for 30 min, the fragmented DNA was pulled down with RORγ (1:200; Novus. Cat. No NBP2-24503) and CTCF (1:200; Abcam, Cat. No. ab128873) antibodies or IgG and then subjected to amplification by qPCR.

Dual-luciferase reporter assay

The full-length human YOD1 and CAB39 from HEK293T cells (human embryonic kidney cell line) cDNA were amplified by PCR. Fragments of the target gene promoter were cloned by PCR and inserted into the pGL3 luciferase vector using the primers listed in Supplementary Table 5. All constructs were confirmed by DNA sequencing analysis. For dual luciferase reporter gene assays, HEK293T cells were transfected with the target gene promoter plasmids and Renilla luciferase. Firefly and Renilla luciferase activities were then measured by a dual luciferase reporter gene system (Promega, Madison, WI, USA).

Prediction of transcription factors

JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. The promoter region of the target gene was obtained from UCSC.

Analysis of the conservation of lysine 37 and LEDLL in RORγ

A gene panel from the NCBI database (https://www.ncbi.nlm.nih.gov/gene/) was used to download amino acid sequences of proteins from multiple species and subsequently compare sequence conservation between species using the UGENE software (version 39; the software can be downloaded from the following website: http://ugene.net/).

RORγ promoter methylation detection by Methylation-specific PCR (MSP)

The methylation status of the RORγ gene was detected using methylation-specific PCR (MSP). Genomic DNA was extracted from cells with the Master Pure™ Kit (Epicentre®), and its purity (ratio 260/280: ~1.8 y 2.0) and integrity were verified by Nanodrop and 1% agarose gel electrophoresis. Bisulfite modification of 4 μg DNA was performed using the EpiTect® Fast DNA Bisulfite Kit (QIAGEN, Venlo, Netherlands) to convert unmethylated cytosines to uracils while preserving methylated cytosines.

Primers for MSP were designed using MethPrimer to target a CpG island within 5 kb upstream of the RORγ promoter. The primer sequences were as follows:

Methylated primers (RORγ-M): Forward: 5’- ATTTTTATTTGAATGTTTTTGACGT -3’ Reverse: 5’- ACTTTTAAAATTTTATCAAATCCCG -3’
Unmethylated primers (RORγ-UM): Forward: 5’- ATTTTTATTTGAATGTTTTTGATGT-3’ Reverse: 5’- TTTTAAAATTTTATCAAATCCCACA-3’

Real-time qPCR was conducted with FastStart SYBR™ Green Master Mix under the following conditions: 94 °C for 5 s, 58 °C for 30 s, and 72 °C for 30 s (40 cycles), followed by melting curve analysis to ensure product specificity. The methylation status is calculated as follows: (the amount of methylated DNA ÷ the total amount of methylated and unmethylated DNA) × 100 (this yields the percentage result of the methylation status). Statistical analyses inc RORγ gene methylation (%)luded the Kruskal-Wallis test, Mann-Whitney U test, and Spearman’s correlation to evaluate group differences and associations with gene expression levels.

Loading of exosomes

The approaches for RORγ incorporation into exosomes were executed as mentioned before⁹². Naive exosomes released from HEK293T were diluted in PBS to a concentration of 0.15 mg/mL of total protein, then RORγ solution in PBS (0.5 mg/mL) was added to 250 μl of exosomes to a final concentration of 0.1 mg/mL total protein, and incubated at room temperature for 18 hours. In case of a saponin treatment, a mixture of RORγ and exosomes was supplemented with 0.2% saponin and placed on a shaker for 20 min at room temperature.

GSEA analysis

We used the clusterProfiler (version 3.8.1) for GSEA analysis. Annotated gene sets were used to distinguish subtypes by the identified differentially expressedgenes. We computed the consistency P-value for each gene set, and P-values less than 0.05 were considered significantly enriched. Subsequently, significantly enriched gene sets were ranked.

Molecular docking

Discovery Studio (DS) was used for the molecular docking of RORγ and compounds. DS 2019 version is a molecular modeling software for protein structure studies and drug discovery. The structures of small molecule compounds GMNT and RORγ were downloaded from TCSMP and the Protein Data Bank (PDB) database (https://www.rcsb.org), respectively. First, the GMNT was used for ligand preparation, a method to remove duplicates, enumerate isomers and tautomers, and generate 3D conformations. Next, a series of preparations was also applied to the protein receptor, including removing water molecules, adding hydrogen atoms, setting up active pockets, etc. Finally, CDocker was used for molecular docking, an algorithm that allows precise docking of any number of ligands to a single protein receptor. -CDOCKER Interaction Energy (CIE) reflects the ability of ligands and receptors to interact in molecular docking.

Membrane fractionation

Membrane fractionation was performed as described previously⁹³. Briefly, cells (30 10-cm dishes) were treated, then collected and homogenized. Homogenates were then subjected to sequential centrifugation at 1000 g (10 min), 5000 g (10 min), and 25,000 g (20 min) to collect the P1, P5, and P25 membranes, respectively. The P25 membranes were suspended in 0.75 ml 1.25 M sucrose buffer and overlayed with 0.5 ml 1.1 M and 0.5 ml 0.25 M sucrose buffer. Centrifugation was carried out at 120,000 g for 3 h. The P25L fraction at the interface between the 0.25 M and 1.1 M sucrose layers was selected and suspended in 1 ml 19 % Opti-Prep for the following Opti-Prep step gradient from bottom to top: 0.33 ml 22.5 %, 0.66 ml 19 % (sample), 0.6 ml 16 %, 0.6 ml 12 %, 0.66 ml 8 %, 0.33 ml 5 %, and 0.14 ml 0 %. Each density of Opti-Prep was prepared by diluting 50 % Opti-Prep (20 mM Tricine-KOH, pH 7.4, 42 mM sucrose, and 1 mM EDTA) with a buffer of 20 mM Tricine-KOH, pH 7.4, 250 mM sucrose, and 1 mM EDTA. The Opti-Prep gradient was centrifuged at 150,000 g for 3 h and 10 fractions, 0.5 ml each, were collected from the top. Fractions were diluted and subjected to immunoprecipitation.

Cell fractionation

The cell fractionation procedure followed a previously described protocol⁹⁴. Post-treatment, cells were chilled on ice and subjected to two washes with both PBS and homogenization buffer [comprising 10 mM triethanolamine-acetic acid at pH 7.4, 0.25 M sucrose, 1 mM sodium EDTA, and a protease inhibitor cocktail from Roche]. The cells were then pelleted and resuspended in 0.8 ml of the same buffer and disrupted by repeated passage (13 times) through a 25-gauge needle attached to a 1-ml syringe. Following centrifugation at 2000 g for 15 minutes at 4 °C, the supernatant was extracted and applied to pre-equilibrated iodixanol gradients. These discontinuous gradients, consisting of 2.65 ml layers of 24%, 19.33%, 14.66%, and 10% iodixanol, were prepared by mixing a 60% iodixanol stock with cell suspension medium (containing 0.85% (w/v) NaCl, 10 mM Tricine-NaOH, pH 7.4). After allowing the gradients to equilibrate at room temperature for 2 hours, they were centrifuged at 171,000 × g in an SW40Ti rotor (Beckman Instruments) for 4 hours without braking during deceleration. The supernatant was carefully layered on top of the gradients and centrifuged again at 171,000 × g for an additional 2 hours. Fractions (15 in total, each 800 μl) were collected from the top down, with the lowest two fractions, rich in aggregated material, excluded from further analysis. Aliquots from each fraction were reserved for subsequent immunoblotting analysis. Isolation of ER fractions was achieved using an Endoplasmic Reticulum Isolation Kit (ER0100, Sigma-Aldrich), and the extracted ER proteins were then utilized for immunoblot analyses

Detection of mtDNA in cytosolic extracts

To detect mitochondrial DNA (mtDNA) in the cytosol, a digitonin-based extraction protocol was used⁹⁵. Cells were divided into two equal aliquots. One aliquot was resuspended in 500 μL of 50 mM NaOH, boiled for 30 min to solubilize DNA, and neutralized with 50 μL of 1 M Tris-HCl (pH 8) to serve as a normalization control for total mtDNA. The other aliquot was resuspended in 500 μL of buffer consisting of 150 mM NaCl, 50 mM HEPES (pH 7.4), and 25 μg/ml digitonin, mixed end-over-end for 10 min at room temperature to permeabilize the plasma membrane, then centrifuged at 1000 × g for 10 min to pellet intact cells. The cytosolic supernatant was transferred to a new tube and centrifuged at 17,000 × g for 10 min to remove remaining cellular debris. DNA was isolated from the cytosolic fraction using a NucleoSpin tissue kit. Quantitative PCR was performed on both whole-cell extracts and cytosolic fractions with mtDNA-specific primers, and CT values were used to reflect mtDNA abundance. Mitochondrial lysis was excluded by Western blot, as mitochondria (identified by TFAM expression) were not detected in the cytosolic fraction but were present in the mitochondrial fraction.

Study approval

All animal care and experimental protocols for in vivo studies conformed to the Guide for the Care and Use of Laboratory Animals, published by the National Institutes of Health (NIH; NIH publication no.: 85–23, revised 1996), was approved by the Animal Care Committees of the First Affiliated Hospital of Southern University of Science and Technology (No. AUP-240730-XJQ-478-01), and were performed in compliance with the ARRIVE guidelines.

Studies with human participants were conducted in line with the Declaration of Helsinki. The studies were approved by the Ethics Committee of the First Affiliated Hospital of Southern University of Science and Technology. Written informed consent for data publication was obtained from all patients. All data are de-identified, and sex/gender information has been retained in line with the journal’s policy.

Quantification and statistical analysis

All data were generated from at least three independent experiments. Each value was presented as the mean ± SD. All raw data were initially subjected to a normal distribution and analysis by a one-sample Kolmogorov-Smirnov (K-S) nonparametric test using SPSS 22.0 software. For animal and cellular experiments, a two-tailed unpaired Student’s t-test was performed to compare the two groups. One-way ANOVA followed by Bonferroni’s post-hoc test was used to compare more than two groups. To avoid bias, all statistical analyses were performed blindly. Statistical significance was indicated at *P < 0.05, **P < 0.01, and ***P < 0.001.

RNA seq was analyzed by R software

Raw sequencing reads were quality-controlled and filtered using fastp. Clean reads were aligned to the human (GRCh38/hg38) or mouse (GRCm39/mm39) reference genome using HISAT2, and gene expression was quantified as FPKM using StringTie. Differential expression analysis for defined comparisons was performed with DESeq2 in R, applying a two-sided Wald test with p-values adjusted using the Benjamini-Hochberg (FDR) procedure; specific FDR and log2 fold change thresholds for each comparison are stated in the results. Functional enrichment analysis (GO and KEGG) for differentially expressed genes was conducted using clusterProfiler.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

41467_2026_69724_MOESM1_ESM.pdf^{(5.2KB, pdf)}

Description of Additional Supplementary Files

Supplementary Information^{(3.9MB, pdf)}

Supplementary Data 1^{(304KB, xlsx)}

Supplementary Data 2^{(112.4KB, xlsx)}

Supplementary Data 3^{(372.9KB, xlsx)}

Supplementary Data 4^{(657.9KB, xlsx)}

Supplementary Data 5^{(231.2KB, xlsx)}

Reporting Summary^{(1.6MB, pdf)}

Transparent Peer Review file^{(4.4MB, pdf)}

Source data

Source Data^{(267.6MB, zip)}

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (82370876 to Shu Yang, 824B2020 to Jiaqing Xiang, 82170842 and 82371572 to Zhen Liang, 82171556 to Lin Kang), Natural Science Foundation of Shenzhen City, China (No. JCYJ20240813103902004 to Jiaqing Xiang), Anhui Provincial Natural Science Foundation (2308085MH240 to Yuanli Chen), the second batch of high-level talents in the health industry selection and training project (TJSQNYXXR-D2-064 to Chuanrui Ma);The Scientific Research Program of Tianjin Education Commission (2024ZD006 to Chuanrui Ma), Key Program Topics of Shenzhen Basic Research, China (No. JCYJ20220818102605013 to Lin Kang). Sequencing service was provided by Bioyi Biotechnology Co., Ltd. Wuhan, China.

Author contributions

J.X., G.Y., L.L., and Y.L. conducted experiments. S.Y., J.X., G.Y., and X.L. contributed to the acquisition of data, analysis, and interpretation of data. S.Y., Y.C., C.M., and Z.L. drafted the work or revised it critically for important intellectual content. S.Y., Y.L., Y.C., C.M., and L.K. analysed the data and revised the article critically for important intellectual content. S.Y. contributed to the conception and the study design. All authors gave their approval of the version to be published.

Peer review

Peer review information

Nature Communications thanks Reiko Inagi and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files. Source data are provided with this paper. The gene expression data generated in this study have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE317266 and GSE317491 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE317266 or GSE317491]. No raw data are restricted as they comply with relevant data sharing regulations. Source data are provided with this paper.

Code availability

No custom computer code or algorithms were developed or used in this study.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Zhen Liang, Jiaqing Xiang, Guangyan Yang.

Contributor Information

Lin Kang, Email: kang.lin@szhospital.com.

Yuanli Chen, Email: chenyuanli@hfut.edu.cn.

Chuanrui Ma, Email: chuanruima2013@mail.nankai.edu.cn.

Shu Yang, Email: yang.shu@szhospital.com.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-026-69724-2.

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Data Availability Statement

No custom computer code or algorithms were developed or used in this study.

[CR1] 1.Mitrofanova, A., Merscher, S. & Fornoni, A. Kidney lipid dysmetabolism and lipid droplet accumulation in chronic kidney disease. Nat. Rev. Nephrol.19, 629–645 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Energy-sensing molecule RORγ regulates cholesterol metabolism and immune signaling in diabetic kidney disease and aging

Zhen Liang

Jiaqing Xiang

Guangyan Yang

Xiaomai Liu

Lixing Li

Yanchun Li

Yan Lu

Lin Kang

Yuanli Chen

Chuanrui Ma

Shu Yang

Abstract

Introduction

Results

RORγ deficiency in kidneys of DKD mouse models and patients

Fig. 1. RORγ is reduced in diabetic kidney disease patients and mouse models.

Selective deletion of Rorγ in TECs exacerbates renal injury in diabetic and aging mice

Fig. 2. Specific knockout of RORγ in TECs aggravates diabetes and aging-induced kidney injury.

Deletion of RORγ activates the STING signaling pathway

Fig. 3. RORγ regulates cGas-STING signaling pathway dependent on STING itself.

RORγ represses STING signaling in an SREBP2-dependent manner

Fig. 4. RORγ represses STING activation by inhibiting SREBP2-STING trafficking.

RORγ-driven transcriptional activation of YOD1 promotes INSIG1 deubiquitination and stabilization

Fig. 5. RORγ transcriptionally upregulates YOD1 to deubiquitinate INSIG1.

RORγ enhances AMPK activation by transcriptional upregulation of CAB39

Fig. 6. RORγ-mediated activation of AMPK via CAB39 transcriptional upregulation.

RORγ inhibits INSIG1 protein degradation via AMPK-dependent INSIG1 phosphorylation

Diabetic conditions elicit a decrease in RORγ transcriptional levels

Fig. 7. RORγ expression is decreased and inactivated upon HGPA stimulation.

AMPKα1 orchestrates nuclear translocation of RORγ

SIRT1-mediated deacetylation of RORγ boosts its transcriptional activity

Fig. 8. SIRT1 deacetylates RORγ and enhances its transcriptional activity.

K37R mutation potentiates, while S510A mutation diminishes, the Anti-DKD activity of RORγ

Fig. 9. exoRORγ treatment alleviates diabetes-induced kidney injury.

RORγ activation mitigates kidney injury in DKD mouse model

Fig. 10. GMNT treatment alleviates diabetes-induced kidney injury.

Discussion

Limitations of the study

Methods

Studies in animals

Mouse kidneys were transfected with an adeno-associated virus vector (AAV)

DKD mouse model and intervention

Human renal specimens

Cell culture and transfection

DNA construction and mutagenesis

Generation of SREBP2 ΔbHLH Construct

Isolation of primary renal TECs

Histological analysis

Immunohistochemical (IHC) staining

Quantitative Real-time PCR (qPCR)

Protein extraction and Western blot analysis

Immunofluorescence analysis

Coimmunoprecipitation (Co-IP)

Measurement of lipid accumulation in kidneys

Measurements of renal functions

Determination of Triglyceride (TG) content

Chromatin immunoprecipitation (ChIP) and sequential ChIP assay

Dual-luciferase reporter assay

Prediction of transcription factors

Analysis of the conservation of lysine 37 and LEDLL in RORγ

RORγ promoter methylation detection by Methylation-specific PCR (MSP)

Loading of exosomes

GSEA analysis

Molecular docking

Membrane fractionation

Cell fractionation

Detection of mtDNA in cytosolic extracts

Study approval

Quantification and statistical analysis

RNA seq was analyzed by R software

Reporting summary

Supplementary information

Source data

Acknowledgements

Author contributions

Peer review

Peer review information

Data availability