Myeloid Mas drives pyruvate kinase M2-mediated Spi1 lactylation to fuel inflammatory senescence in MASLD

Zhao, Luying; Xu, Shujing; Qiao, Shikai; Wang, Zhe; Wang, Shenglan; Liu, Chun; Zhang, Shuo; Wang, Peng; Sun, Xianghua; Li, Shanshan; Chen, Li; Zhang, Xiaokun; Hu, Chengxi; Zhou, Yongping; Xia, Lu; Yang, Changqing; Li, Jing

doi:10.1038/s41392-026-02704-6

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Published: 19 May 2026

Myeloid Mas drives pyruvate kinase M2-mediated Spi1 lactylation to fuel inflammatory senescence in MASLD

Luying Zhao¹^na1,
Shujing Xu¹^na1,
Shikai Qiao¹^na1,
Zhe Wang¹^na1,
Shenglan Wang¹^na1,
Chun Liu¹,
Shuo Zhang¹,
Peng Wang²,
Xianghua Sun³,
Shanshan Li⁴,
Li Chen¹,
Xiaokun Zhang¹,
Chengxi Hu⁵,
Yongping Zhou⁵,
Lu Xia¹,
Changqing Yang¹ &
…
Jing Li¹

Signal Transduction and Targeted Therapy volume 11, Article number: 186 (2026) Cite this article

Subjects

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is driven by unresolved inflammation, yet precise mechanisms linking immune metabolism to disease progression remain elusive. Here, we identified myeloid-expressed Mas, a G protein-coupled receptor, as a critical metabolic checkpoint in MASLD. Mas expression is elevated in hepatic myeloid cells from patients and diet-induced mouse models. Myeloid-specific Mas1 deletion attenuated MASLD by restraining glycolytic reprogramming and inflammatory senescence. Single-cell RNA sequencing analyses revealed that this deletion specifically impaired the glycolytic flux and subsequent pathogenic differentiation of FN1⁺CCR2⁺ monocyte precursors. Mechanistically, Mas interacts with the glycolytic enzyme PKM2, enhancing lactate production that drives lactylation of the transcription factor Spi1 at lysine 208. Spi1-K208 lactylation promotes its nuclear localization and transcriptional activation of senescence-associated secretory phenotype (SASP) genes. Myeloid-specific Pkm2 ablation phenocopied the protective effect of Mas1 deletion, and PKM2 overexpression rescued the metabolic and transcriptional defects caused by Mas loss. Virtual screening identified theaflavin-3,3′-digallate (TFDG) as a Mas inhibitor that disrupts the Mas-PKM2 interaction. A macrophage membrane-coated nanoparticle (MM@NP-TFDG) delivered TFDG specifically to hepatic macrophages, suppressed the Mas-PKM2-Spi1 lactylation axis, and ameliorated MASLD pathology in vivo. Our findings define a novel Mas-PKM2-Spi1 lactylation axis that orchestrates glycolytic reprogramming, monocyte precursor differentiation, and macrophage-driven inflammation in MASLD, presenting a targeted nanotherapeutic strategy for its treatment.

Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) includes a spectrum of hepatic disorders, ranging from isolated hepatic steatosis to progressive steatohepatitis, with potential evolution toward fibrosis, cirrhosis, and hepatocellular carcinoma.¹ Affecting more than one-third of the adult population worldwide, MASLD has shown a steady rise in prevalence and is closely linked to insulin resistance, obesity, gut microbial dysbiosis, and genetic susceptibility.² Despite its substantial global burden, effective pharmacological interventions remain scarce. Accumulating evidence indicates that sustained myeloid cell–driven inflammation contributes materially to MASLD progression, suggesting that this axis represents a promising avenue for therapeutic development.^3,4,5

The proto-oncogene Mas1 encodes Mas, a G protein-coupled receptor (GPCR) with properties that support its suitability as a pharmacological target. Mas is broadly expressed across human and murine tissues, with relatively high abundance in myeloid monocytes.⁶ Substantial experimental evidence, including data generated in prior studies, indicates that Mas modulates hepatic pathophysiology through effects on metabolic and inflammatory signaling pathways.^7,8,9,10 Accordingly, Mas is likely to participate in the progression of MASLD. Nevertheless, investigations addressing Mas function in MASLD remain limited,¹¹ and the contribution of myeloid-specific Mas has not yet been systematically characterized.

A defining feature of MASLD is dysregulated glucose metabolism, typified by a Warburg-like shift in which glucose is preferentially converted to lactate despite adequate oxygen availability, resembling metabolic patterns observed in malignancy.¹² Glycolysis proceeds through a series of tightly regulated enzymatic steps, including glucose uptake mediated by GLUT1, phosphorylation by hexokinase 2 (HK2), conversion by phosphofructokinase (PFKFB3), and generation of pyruvate by pyruvate kinase (PK), followed by the reduction of pyruvate to lactate via lactate dehydrogenase A (LDHA). Among PK isoforms, PKM2 occupies a central position in metabolic reprogramming.^13,14 Emerging evidence identifies PKM2 as a key regulator of macrophage metabolic plasticity, thereby shaping functional phenotypes and influencing MASLD progression.^15,16 Hepatic lactate accumulation, a characteristic metabolic alteration in MASLD, shows a positive association with disease severity.¹⁷ Beyond its role as a metabolic end product, lactate promotes protein lactylation, a recently described posttranslational modification that modulates gene transcription, immune cell behavior, and lipid metabolic pathways.¹⁸ For instance, lactylation of fatty acid synthase (FASN) attenuates its enzymatic activity, contributing to lipid deposition and aggravation of MASLD.¹⁹ Taken together, myeloid Mas is implicated in the regulation of macrophage metabolic adaptation, including glycolytic flux and lactylation dynamics, thereby shaping the inflammatory milieu in MASLD and driving disease progression.

Cellular senescence, defined as an irreversible arrest of cell proliferation induced by stressors such as genomic injury or metabolic imbalance, contributes substantially to the pathogenesis of chronic liver disorders.²⁰ Senescent cells retain metabolic activity and are characterized by increased expression of p16, p53, p21, and DNA damage markers, including γ-H2AX. A defining feature of senescence is the senescence-associated secretory phenotype (SASP), marked by the release of proinflammatory cytokines (e.g., IL-6, IL-1β), chemokines (e.g., CCL2), matrix metalloproteinases (e.g., MMP-9), and growth factors that promote tissue remodeling and sustained inflammation.^21,22 SASP induction is largely mediated by stress-responsive transcription factors, including C/EBPβ and JUNB.^23,24 This phenomenon is closely associated with inflammatory senescence, a state of persistent low-grade inflammation implicated in multiple age-related pathologies. Emerging evidence has linked MASLD to inflammatory senescence, wherein PKM2-driven glycolytic reprogramming in hepatic Th17 cells enhances cytokine output and accelerates inflammatory aging.²⁵ Nonetheless, the contribution of macrophage inflammatory senescence to MASLD progression remains insufficiently defined. This study hypothesizes that macrophage senescence constitutes a key mechanism through which myeloid Mas influences MASLD progression.

In this investigation, a positive association between myeloid Mas expression and MASLD severity was observed in human specimens. Through the use of myeloid-specific Mas1 knockout mice in combination with single-cell sequencing and complementary in vivo and in vitro approaches, myeloid Mas was shown to interact with PKM2 during MASLD progression. This interaction promotes glycolytic flux in monocytes, leading to increased lactate generation and subsequent lactylation of the transcription factor Spi1, thereby enhancing SASP-related transcriptional activity. Disruption of this axis by myeloid-specific Mas1 deletion attenuated these metabolic and transcriptional alterations and ameliorated MASLD in murine models. Thus, myeloid Mas has been identified as a viable therapeutic target for MASLD.

Results

Mas is upregulated in myeloid cells during MASLD progression

Analysis of human liver sections and publicly available transcriptomic datasets demonstrated increased MAS1 expression in patients with MASLD relative to healthy controls (Fig. 1a, b, and Supplementary Fig. 1a). Immunohistochemical analysis verified elevated Mas protein abundance in MASLD liver tissue (Fig. 1c), while mIHC staining revealed preferential enrichment within myeloid populations, particularly CD68⁺CD14⁻ macrophages and CD16⁺CD68⁺CD14⁺ monocytes (Supplementary Fig. 1b). Within the GSE89632 cohort, MAS1 mRNA expression exhibited positive correlations with serum ALT, AST, TG, glucose, HOMA IR, and NAS score (Fig. 1d–g, and Supplementary Fig. 1c, d). Consistently, analysis of GSE135251 indicated significant associations between MAS1 expression and NAS score and hepatic TNFA, IL1B, SCD, and FABP1 levels (Fig. 1h–k, and Supplementary Fig. 1e), supporting a relationship between Mas expression and disease severity. Single-cell RNA sequencing (scRNA-seq) identified MAS1 expression across hepatocytes, cholangiocytes, endothelial cells, and myeloid cells, with the most marked upregulation observed in myeloid cells from MASLD livers (Fig. 1l, and Supplementary Fig. 1f, g). Concordantly, transcriptomic profiling of public datasets revealed substantial hepatic Mas1 upregulation in HFD-fed mice compared with controls (Supplementary Fig. 1h). In HFD-fed mice, hepatic Mas1 mRNA and protein levels were confirmed to be approximately twofold higher than those in chow-fed counterparts (Fig. 1m, n), with mIHC localizing this increase predominantly to myeloid monocytes (Fig. 1o, and Supplementary Fig. 1i). In agreement with the in vivo observations, LPS/PA-stimulated BMDMs displayed increased Mas mRNA and protein expression in vitro (Supplementary Fig. 1j, k). Overall, these data indicate that Mas is selectively upregulated in myeloid cells during MASLD and is closely associated with disease severity.

Myeloid-specific deletion of Mas1 protects against diet-induced MASLD in mice

To define the contribution of myeloid Mas to MASLD, myeloid-restricted Mas1 knockout mice (LysM^creMas1^f/f) were generated and validated (Fig. 2a). Under chow diet conditions, Mas1 deletion produced no significant changes in serum ALT, AST, TG, TC, or NEFA; however, high-fat diet (HFD)-induced elevations in these parameters were markedly attenuated (Fig. 2b, c, and Supplementary Fig. 2a–c). Consistent with these findings, hepatic TG, TC, and NEFA levels were substantially reduced in HFD-fed LysM^creMas1^f/f mice (Supplementary Fig. 2e–g). Compared with Mas1^f/f HFD controls, LysM^creMas1^f/f HFD-fed mice exhibited lower body weight, reduced liver-to-body weight ratio, diminished hepatic lipid accumulation, and alleviated histological steatosis (Fig. 2d, e, and Supplementary Fig. 2d, h, i), accompanied by decreased subcutaneous and visceral adipose mass (Supplementary Fig. 2j, k). At the molecular level, livers from LysM^creMas1^f/f–HFD-fed mice showed downregulation of lipogenic genes (Fasn, Scd1) and upregulation of fatty acid oxidation genes (Cpt1a, Ppara), along with reduced FABP1 (Supplementary Fig. 2l, and Fig. 2f), indicating a shift toward enhanced lipid catabolism. In parallel, myeloid Mas1 deficiency mitigated systemic and hepatic inflammatory responses. Circulating concentrations of proinflammatory cytokines were significantly decreased in LysM^creMas1^f/f HFD-fed mice compared with Mas1^f/f HFD controls, including reductions of approximately 45% for IL-6, 25% for IL-1β, and 50% for TNF-α (Fig. 2g), which were concordant with diminished hepatic mRNA expression of these mediators (Supplementary Fig. 2m).

As an unbiased approach for high-resolution cellular profiling in disease contexts, scRNA-seq was applied.²⁶ This analysis demonstrated a marked reduction in mononuclear phagocytes (MPs) within the livers of LysM^creMas1^f/f–HFD-fed mice (Fig. 2h, i). MPs isolated from LysM^creMas1^f/f–HFD-fed mice exhibited reduced expression of Il6, Il1b, Tnfa, Fasn, and Scd1, accompanied by increased Ppara expression (Fig. 2j). Gene Ontology (GO) enrichment analysis of MPs from HFD-fed mice identified overrepresentation of pathways associated with cytokine activity, immune responses, and monocyte differentiation (Fig. 2k). In agreement with these transcriptomic changes, flow cytometric analysis revealed a parallel decrease in hepatic Ly6C^high myeloid macrophages in LysM^creMas1^f/f–HFD-fed mice (Fig. 2l, and Supplementary Figs. 2n and 3a), which was further confirmed by immunofluorescence staining for F4/80 and CD11b (Fig. 2m, and Supplementary Fig. 3b).

To distinguish liver-intrinsic effects from secondary systemic metabolic influences, the MCD diet model, which induces steatosis independently of obesity, was employed. LysM^creMas1^f/f mice subjected to the MCD diet similarly showed reduced hepatic lipid accumulation and inflammation, along with favorable modulation of lipid metabolism–related gene expression (Supplementary Fig. 3c–e). In addition, conditioned medium derived from Mas1-deficient BMDMs diminished lipid deposition in hepatocytes and reproduced the transcriptional changes observed in vivo (Supplementary Fig. 3f, g). Analysis of myeloid cell subsets confirmed efficient Mas1 deletion across targeted populations, with the greatest reduction observed in monocyte–macrophage lineages (Supplementary Fig. 4a–c). Thus, myeloid Mas deficiency mitigates MASLD by limiting hepatic lipid accumulation, inflammatory signaling, and pathogenic macrophage expansion through both cell-autonomous and paracrine mechanisms.

Myeloid Mas1 deficiency limits glycolytic reprogramming and inflammatory senescence in monocyte-macrophages during MASLD

scRNA-seq analysis of MPs from HFD-fed mice revealed increased metabolic activity characterized by strong enrichment of glycolytic pathways (Fig. 3a, and Supplementary Fig. 5a, b). Gene set enrichment analysis (GSEA) demonstrated that MPs from LysM^creMas1^f/f–HFD-fed mice showed marked suppression of glycolysis, cellular senescence, and SASP-related gene signatures relative to controls (Fig. 3b, c, and Supplementary Fig. 5c). In line with these observations, the expression of key glycolytic regulators (HK2, PFKFB3, PKM2, GLUT1, LDHA) was reduced at both the transcriptional level in MPs (Supplementary Fig. 5d) and the protein level in liver tissue (Fig. 3d) from LysM^creMas1^f/f–HFD-fed mice. Immunofluorescence costaining further confirmed a decreased abundance of glycolytically active monocyte–macrophages in LysM^creMas1^f/f–HFD-fed mice (Fig. 3e), accompanied by substantial reductions in serum (approximately 60%) and hepatic (approximately 70%) lactate concentrations (Fig. 3f). Hepatic expression of the senescence markers p53, p21, and p16 was reduced by nearly 50% in knockout mice (Fig. 3g). Transmission electron microscopy (TEM) and SA-β-gal staining identified a lower frequency of senescent macrophages (Fig. 3h, and Supplementary Fig. 5e), while scRNA-seq analysis confirmed downregulation of p21, p16, and SASP-associated genes in MPs from LysM^creMas1^f/f–HFD-fed mice (Fig. 3i, and Supplementary Fig. 5f). In vitro, LPS/PA-stimulated BMDMs derived from LysM^creMas1^f/f mice exhibited a phenotypic shift from Ly6C^hi to Ly6C^lo populations (Fig. 3j), along with reduced numbers of PKM2⁺ and γ-H2AX⁺ senescent cells (Fig. 3k), diminished SA-β-gal staining (Supplementary Fig. 5g), and decreased lactate secretion (Supplementary Fig. 5h). Metabolic flux analysis further demonstrated attenuated glycolytic capacity in Mas1-deficient BMDMs, as indicated by a lower ECAR. However, the concomitant decrease in OCR was hindered (Fig. 3l, m, and Supplementary Fig. 5i–n). Overall, these data indicate that myeloid Mas deficiency alleviates MASLD by limiting glycolytic reprogramming and inflammatory senescence in monocyte–macrophages.

Myeloid Mas orchestrates spatiotemporal metabolic reprogramming of FN1⁺CCR2⁺ monocytes

scRNA-seq analysis revealed a selective reduction of the “Classical Mono” population, which was highly enriched in glycolytic pathways, within the livers of LysM^creMas1^f/f–HFD-fed mice (Fig. 4a, and Supplementary Fig. 6a–c). This population, designated Monocytes-Fn1 on the basis of elevated Fn1 expression, was distinct from the Ace-expressing nonclassical monocyte subset (Monocytes-Ace) (Fig. 4b). Monocyte-Fn1 cells displayed marked enrichment of glycolytic and additional metabolic programs (Supplementary Fig. 6d, e). Integration with a published human MASLD single-cell atlas (GSE212837) confirmed the presence of a conserved monocyte population coexpressing FN1 and glycolysis-associated genes, supporting the translational relevance of this cluster (Supplementary Fig. 6f, g). Mas1 mRNA was abundantly expressed in Monocytes-Fn1 from control mice and was efficiently deleted in the LysM^creMas1^f/f group (Supplementary Fig. 6h). Pseudotime trajectory analysis positioned Monocytes-Fn1 at an early differentiation stage closely linked to mature myeloid macrophages (Fig. 4c). Consistent results obtained using Slingshot and CytoTRACE assigned monocyte-Fn1 high differentiation potential scores and localized this subset to an upstream position along the monocyte-to-macrophage continuum (Supplementary Fig. 6i, j). Thus, these data indicate that Mas governs the differentiation trajectory, metabolic activation, and inflammatory programming of the pathogenic FN1⁺CCR2⁺ monocyte precursor compartment in MASLD.

Pathway enrichment analysis of the Monocyte-Fn1 cluster demonstrated robust activation of programs associated with monocyte differentiation, cytokine and chemokine signaling, TNF signaling, and cellular senescence (Fig. 4d, and Supplementary Fig. 7a). In LysM^creMas1^f/f–HFD-fed mice, macrophage subsets showed reduced expression of glycolysis- and SASP-associated genes (Supplementary Fig. 7b), accompanied by attenuation of inflammatory, senescent, and glycolytic pathways in GO analyzes (Supplementary Fig. 7c). CellChat analysis indicated strong interaction networks between monocytes-Fn1 and macrophages, which were substantially diminished following myeloid Mas1 deletion (Fig. 4e–g). Functionally, FN1-induced phosphorylation of β1 integrin and FAK was markedly reduced in Mas1-deficient BMDMs (Supplementary Fig. 7d), supporting a role for Mas in amplifying FN1–integrin signaling. Multiplex immunofluorescence of HFD-fed liver sections revealed a pronounced decrease in glycolytic (PKM2⁺) and senescent (γ-H2AX⁺) FN1⁺CCR2⁺ monocytes and macrophages in LysM^creMas1^f/f mice, with monocytes frequently observed in close proximity to macrophages (Fig. 4h, i). These alterations were restricted to the HFD condition, as chow-fed genotypes exhibited minimal and comparable staining patterns (Supplementary Fig. 7e). Interaction analyzes further suggested communication between myeloid subsets, particularly Monocytes-Fn1 and macrophages, and hepatocytes, with hepatocytes displaying a detectable but less pronounced response to FN1-mediated signaling (Supplementary Fig. 7g, h). Thus, these observations indicate that myeloid Mas drives metabolic reprogramming, inflammatory differentiation, and intercellular communication of FN1⁺ monocyte precursors through enhanced FN1–integrin signaling, thereby accelerating macrophage-associated inflammation and senescence in MASLD.

Myeloid Mas interacts with PKM2 to enhance inflammatory senescence

LC–MS/MS analysis of BMDMs identified PKM2 as a direct interacting partner of Mas (Fig. 5a, b). Structural modeling predicted the formation of nine hydrogen bonds between Mas (ASN314, HIS142, GLU312, ARG62, LYS145, HIS146, GLN147) and PKM2 (ARG278, ILE404, GLU282, GLU285, SER406), with a calculated binding energy of −64.7 ± 8.2 kJ/mol (Fig. 5c, e, and Supplementary Table 6). Coimmunoprecipitation assays verified a stimulus-dependent Mas–PKM2 interaction in LPS/PA-treated BMDMs as well as in hepatic myeloid cells isolated from HFD-fed mice (Fig. 5d, and Supplementary Fig. 8a–c). Immunofluorescence analysis demonstrated colocalization of Mas and PKM2 at the plasma membrane and within the cytoplasm (Fig. 5f). Nuclear–cytoplasmic fractionation further revealed that LPS/PA stimulation promoted PKM2 nuclear accumulation in control (Mas1^f/f) BMDMs (Supplementary Fig. 8d), whereas this response was markedly attenuated in hepatic myeloid cells from HFD-fed LysM^creMas1^f/f mice (Fig. 5k). Direct binding was confirmed by GST pull-down assays (Fig. 5g). Domain mapping using PKM2 truncation mutants identified the A1 and A2 domains as necessary for Mas interaction (Fig. 5h, i), and point mutations at these interfaces disrupted complex formation, eliminated Mas-dependent regulation of PK activity, and impaired PKM2 nuclear translocation (Fig. 5j, and Supplementary Fig. 8e).

To determine whether the Mas–PKM2 interaction contributes to inflammatory senescence, PKM2 was overexpressed (PKM2^OE) in Mas1-deficient BMDMs, which were then treated with LPS/PA. PKM2 overexpression restored the nuclear localization of PKM2 (Supplementary Fig. 8f), normalized glycolytic flux, ATP generation, and lactate release (Supplementary Fig. 9a–i), and reversed the reductions in DNA damage (γ-H2AX) and cellular senescence (SA-β-gal) observed in LysM^creMas1^f/f cells (Supplementary Fig. 9j–m, o, p). Cell cycle analysis demonstrated that PKM2 overexpression promoted G1-phase arrest (Supplementary Fig. 9n, q). In parallel, myeloid-specific Pkm2 knockout mice (LysM^crePkm2^f/f) reproduced the protective metabolic and inflammatory phenotype observed in Mas1-deficient mice under HFD challenge (Supplementary Fig. 10a–e). Thus, these results demonstrate that Mas directly associates with the A1/A2 domains of PKM2, enhances PKM2 nuclear translocation, and thereby augments glycolytic flux and inflammatory senescence in macrophages, a mechanism integral to MASLD progression.

The myeloid Mas-PKM2 complex drives Spi1 lactylation at K208 to activate SASP transcription

Given that metabolic byproducts are capable of directly modifying proteins to modulate cellular functions,^27,28 and in light of the established association between enhanced glycolytic flux, lactate overproduction, and protein lactylation in transcriptional regulation,^29,30 lactate-driven posttranslational modifications were posited to contribute substantially to MASLD. Untargeted metabolomic profiling of sera from patients with MASLD demonstrated marked enrichment of glycolysis-associated metabolites, including L-lactate (Fig. 6a, and Supplementary Fig. 11a). In murine models, the glycolytic monocyte-Fn1 subset exhibited elevated lactate dehydrogenase activity (Supplementary Fig. 11b), whereas myeloid Mas1 deficiency led to a visible decrease in global protein lactylation in BMDMs (Fig. 6b). Differential expression analysis of MPs isolated from HFD-fed mice revealed significant suppression of Spi1 expression in the LysM^creMas1^f/f group (Fig. 6c), with the most significant decrease observed within the Monocytes-Fn1 cluster (Supplementary Fig. 11c, d). In LPS/PA-stimulated BMDMs, Spi1 lactylation was enhanced by exogenous lactate exposure (Fig. 6d). Immunoprecipitation–mass spectrometry identified four lactylated lysine residues on Spi1 (K169, K208, K239, K244) (Fig. 6e). Among site-directed mutants, substitution of K208 with alanine resulted in a marked reduction in lactylation (Fig. 6f, g), indicating that K208, located within the evolutionarily conserved DNA-binding Ets domain (Fig. 6h), represents the principal functional site of Spi1 lactylation.

Given that the DNA-binding and transactivation domains of Spi1 govern promoter recognition,³¹ regulation of its nuclear localization was examined. Substitution of K208 with alanine impaired Spi1 nuclear accumulation (Supplementary Fig. 11e) and reduced the transcript levels of key SASP-associated genes, including Mmp9, Ccl2, Il6, and Tnfa (Supplementary Fig. 11f). Bioinformatic analyzes predicted conserved Spi1 binding motifs within the promoters of SASP genes (Fig. 6i, j, and Supplementary Fig. 11g). ChIP–qPCR confirmed Spi1 occupancy at these promoter regions, which was enhanced following LPS/PA stimulation and markedly reduced by the K208A mutation (Fig. 6k, l, and Supplementary Fig. 11h–l). Consistently, luciferase reporter assays demonstrated robust Spi1-dependent transactivation of the Ccl2 promoter, an effect abolished by the K208A substitution (Fig. 6m). Macrophages harboring CRISPR/Cas9-mediated endogenous Spi1 K208A knock-in exhibited diminished stimulus-induced Spi1 lactylation (Supplementary Fig. 12a), and siRNA-mediated Spi1 silencing in BMDMs recapitulated the functional consequences of the K208A mutation (Supplementary Fig. 12b). In vivo, AAV-mediated delivery of Spi1 K208A alleviated MASLD pathology and normalized SASP gene expression, including reversal of the aggravated phenotype induced by AAV-driven Mas overexpression (Supplementary Fig. 12c–g). Moreover, in primary human monocyte-derived macrophages, MAS1 knockdown attenuated LPS/PA-induced PKM2 nuclear translocation, Spi1 K208 lactylation, and SASP gene expression (Supplementary Fig. 12h–j), indicating conservation of this regulatory axis. Overall, these results demonstrate that lactate generated through the Mas–PKM2 axis promotes Spi1 lactylation at K208, enabling nuclear localization and transcriptional activation of SASP programmes, thereby linking myeloid metabolic reprogramming to inflammatory senescence in MASLD.

Identification of TFDG as a Mas-targeting compound that ameliorates MASLD

To identify pharmacological inhibitors of Mas, a virtual screening of 77,900 compounds was conducted (Fig. 7a). Candidate molecules predicted to occupy the functional binding pocket of Mas were prioritized, and 20 compounds with binding energies exceeding 12.7 kcal·mol⁻¹ were selected for subsequent evaluation (Supplementary Table 7). CCK-8 screening identified five compounds that improved BMDM viability under LPS/PA-induced stress (Fig. 7b), with their chemical structures and predicted binding conformations illustrated in Fig. 7c, d and Supplementary Fig. 13a–e. Among these candidates, only theaflavin-3,3′-digallate (TFDG) markedly reduced the protein abundance of Mas and nuclear PKM2 (Fig. 7e). The direct interaction between TFDG and Mas was validated by CETSA and SPR analyzes, with SPR revealing a KD of 44.8 µM (Fig. 7f–h). In LPS/PA-stimulated BMDMs, TFDG treatment decreased lactate production, partially disrupted the Mas–PKM2 complex (Fig. 7i, j), and attenuated DNA damage, as indicated by reduced γ-H2AX levels (Fig. 7k). In HFD-fed mice, high-dose TFDG administration lowered hepatic Mas expression and nuclear PKM2 abundance, accompanied by improvement in MASLD-associated parameters (Supplementary Fig. 13f, g). Pharmacokinetic profiling following intravenous administration (10 mg/kg) demonstrated rapid systemic clearance (T_1/2 = 2.21 ± 0.16 h; AUC_0–t = 3.71 ± 0.65 µg·h/mL) (Supplementary Fig. 14a). In human THP-1–derived macrophages, TFDG restored cell viability in a dose-dependent manner, with 10 µM producing the strongest effect and recovering viability to approximately 80–85% of baseline levels (Supplementary Fig. 14b). Overall, these results identify TFDG as a proof-of-concept Mas inhibitor capable of suppressing the Mas–PKM2–lactate axis and associated inflammatory senescence.

MM@NP-TFDG effectively targets and reduces the myeloid Mas-driven metabolic-inflammatory senescence axis to alleviate MASLD

To improve tissue targeting and therapeutic efficacy while limiting off-target exposure, TFDG was encapsulated within PLGA nanoparticles (NP-TFDG) and subsequently coated with macrophage-derived membranes (MM) to generate MM@NP-TFDG (Fig. 8a). The MM coating increased the hydrodynamic diameter by approximately 41 nm and decreased the surface charge, consistent with successful membrane fusion (Fig. 8b, and Supplementary Fig. 15a, b). MM@NP-TFDG exhibited sustained drug release kinetics, high colloidal stability in serum, and expression of macrophage-associated surface markers, including CD11b, F4/80, and CD68 (Supplementary Fig. 15c–f), indicating its suitability as a stable delivery platform for TFDG. For liver targeting, free Dil dye or MM@NP-TFDG-Dil was intravenously injected into mice. Free Dil dissipated rapidly, while MM@NP-TFDG-Dil showed sustained liver accumulation for up to 48 h, with minimal presence in other organs (Supplementary Fig. 15g). These results indicate enhanced liver targeting and reduced immune clearance conferred by macrophage membrane camouflage. In HFD-fed mice, MM@NP-TFDG treatment more effectively reduced bone marrow–derived Ly6C^hi macrophage populations than NP-TFDG or MM@NP alone (Fig. 8c, and Supplementary Fig. 15h). In HFD-fed mice, MM@NP-TFDG demonstrated superior therapeutic efficacy relative to NP-TFDG or MM@NP alone. Treatment resulted in a more pronounced reduction in hepatic Ly6C^hi macrophages (Fig. 8c, and Supplementary Fig. 15h), accompanied by decreased protein abundance of Mas, nuclear PKM2, nuclear Spi1, and senescence-associated markers (p53, p21, p16) (Fig. 8d, and Supplementary Fig. 15i), as well as diminished Mas⁺PKM2⁺Spi1⁺F4/80⁺ and γ-H2AX⁺F4/80⁺ cell populations (Fig. 8e–h). MM@NP-TFDG also achieved the greatest attenuation of hepatic steatosis, serum ALT/AST, lactate concentrations, and SASP gene expression (Fig. 8i, j, and Supplementary Fig. 15j–n). The therapeutic benefit was lost in myeloid-specific Pkm2 knockout (LysM^crePkm2^f/f) mice, confirming PKM2-dependent activity (Supplementary Fig. 16a–c). MM@NP alone did not induce systemic cytokine elevation, whereas MM@NP-TFDG significantly reduced circulating IL-1β, TNF-α, and IL-6 levels (Supplementary Fig. 16d–f). In parallel with SASP suppression, Spi1 mRNA expression was reduced following MM@NP-TFDG administration (Supplementary Fig. 16g). The macrophage membrane coating conferred precise hepatic targeting, as MM@NP-TFDG exhibited enhanced accumulation within liver macrophages compared with uncoated NP-TFDG (Supplementary Fig. 16h). Pharmacokinetic analyzes showed a prolonged half-life and increased plasma exposure AUC₍₀–_t) for MM@NP-TFDG relative to free TFDG (Supplementary Figs. 14a and 16i, and Table 8). Biodistribution assessment at 12 h post injection confirmed increased liver-specific retention and reduced systemic distribution of TFDG, consistent with a lower risk of off-target toxicity (Supplementary Fig. 16j). No evidence of organ toxicity or renal dysfunction was detected (Supplementary Fig. 17a–c).

Collectively, these results demonstrate that MM@NP-TFDG enables macrophage-targeted delivery of TFDG, disrupts the Mas–PKM2–Spi1 signaling axis, and safely reverses core metabolic, inflammatory, and senescence-associated features of MASLD.

Discussion

In MASLD, persistent inflammation promotes progression from simple steatosis to steatohepatitis and ultimately to liver fibrosis and hepatocellular carcinoma. Central pathogenic processes include lipid accumulation, oxidative stress, and immune cell activation.³² Limited mechanistic insight into these events has constrained the development of effective therapeutic strategies.^33,34 In the present study, myeloid-expressed Mas1 was identified as a key immunometabolic regulator in MASLD pathogenesis. A previously unrecognized Mas–PKM2–Spi1 lactylation axis is defined, through which myeloid glucose metabolism is reprogrammed, pathogenic FN1⁺ monocyte-to-macrophage differentiation is promoted, SASP transcription is induced, and chronic inflammatory signaling is sustained (Fig. 9). This signaling cascade was further validated as a druggable target through the application of a macrophage-targeted nanoformulation of the Mas inhibitor TFDG (MM@NP-TFDG).

**Fig. 9: Working model: The myeloid Mas-PKM2-Spi1 lactylation axis promotes MASLD progression and is targeted by MM@NP-TFDG.**

Recent advances in MASLD research describe a “domino effect” in which tissue-resident dendritic cells within the space of Disse sense hepatocellular stress and initiate inflammatory signaling.^35,36 This process activates Kupffer cells and promotes recruitment of myeloid immune populations; if unresolved, subsequent engagement of adaptive immunity contributes to the development of MASLD. GPCRs represent attractive pharmacological targets, yet their immunoregulatory functions in MASLD remain unclear.³⁷ The selection of Mas from among other upregulated GPCRs was based on its established involvement in the protective ACE2–Ang-(1–7)–Mas signaling axis, a pathway with recognized relevance to metabolic and inflammatory regulation, as well as prior work from our group demonstrating its roles in hepatic metabolism and immune resolution.^7,8,9,10 Mas was found to be selectively upregulated in hepatic macrophages and monocytes in both human and murine MASLD. Myeloid-specific Mas1 deletion markedly alleviated hepatic lipid accumulation and inflammation in HFD and MCD diet models, indicating liver-intrinsic, immune-mediated protection that was independent of systemic obesity. This protective effect was supported by extensive cell-autonomous transcriptomic reprogramming within pathogenic FN1⁺CCR2⁺ monocyte precursors, which display high basal Mas1 expression. Although the LysM-Cre system may also target neutrophils and dendritic cells, the most substantial transcriptional and functional alterations were confined to the monocyte–macrophage lineage. In addition, a paracrine regulatory mechanism was identified, whereby conditioned medium derived from Mas1-deficient macrophages directly improved hepatocyte lipid metabolism by shifting gene expression toward reduced lipogenesis and enhanced fatty acid oxidation. Collectively, these observations identify myeloid Mas as a central regulator of immunometabolic communication in MASLD. Nevertheless, the broad expression of Mas across multiple cell types introduces functional complexity, as previous studies^38,39 and recent work^8,9,10 indicate context-dependent roles for Mas across distinct liver disease settings.

PKM2 catalyzes the terminal step of glycolysis by converting phosphoenolpyruvate to pyruvate with concomitant ATP generation.⁴⁰ In oncogenesis, PKM2 functions through dynamic interconversion between tetrameric and dimeric conformations, whereas in autoimmune contexts, it supports Th17 cell differentiation via STAT3 signaling.^41,42 In the present study, a stimulus-dependent physical association between Mas and PKM2 was identified, which was strongly induced under pathological conditions (LPS/PA) and further enhanced in hepatic myeloid cells from HFD-fed mice. This interaction, mapped to the A1/A2 domains of PKM2, stabilizes the dimeric configuration of PKM2 and is required for its pathological nuclear translocation, as demonstrated by quantitative nuclear–cytoplasmic fractionation in both in vitro and in vivo settings. Within the nucleus, PKM2 functions as a transcriptional coregulator of HIF-1α and STAT3.⁴³ It was further demonstrated that PKM2-derived lactate drove a previously unrecognized epigenetic modification, namely, lactylation of the transcription factor Spi1 (PU.1) at lysine 208. Importantly, this modification was validated not only in ectopic expression systems but also in primary BMDMs subjected to LPS stimulation, supporting its physiological relevance. Lactylation at Spi1-K208 enhances nuclear retention and increases DNA-binding occupancy at SASP gene promoters, including Il6, Tnfa, Mmp9, and Ccl2, thereby mechanistically linking elevated glycolytic flux to inflammatory senescence. The in vivo requirement for this signaling cascade was further substantiated by myeloid-specific Pkm2 deletion, which phenocopied Mas1 deficiency, and by complete reversal of Mas-driven pathology through expression of a lactylation-defective Spi1-K208A mutant. Collectively, these data define the Mas–PKM2–Spi1 lactylation axis as a central determinant of macrophage identity and functional programming, directly regulating chemokine receptor expression and sustaining inflammatory signaling, in agreement with and extending prior knowledge of Spi1-mediated immune regulation.^44,45

Through virtual screening, TFDG was identified as a Mas inhibitor with reported efficacy in improving metabolic disorders via the Nrf2 pathway.⁴⁶ This compound functioned as a proof-of-concept agent that effectively disrupted the Mas–PKM2 interaction, suppressed PKM2 nuclear translocation, and recapitulated the protective effects observed with genetic Mas1 deletion. To improve targeting precision and delivery efficiency, a macrophage membrane–coated nanoparticle formulation (MM@NP-TFDG) was developed. Quantitative biodistribution and colocalization analyzes demonstrated enhanced accumulation of MM@NP-TFDG within hepatic F4/80⁺ macrophages, prolonged liver retention, and reduced systemic exposure. In HFD-fed mice, MM@NP-TFDG exhibited superior efficacy compared with free TFDG, resulting in comprehensive suppression of the Mas–PKM2–Spi1 signaling cascade, including reductions in nuclear PKM2 and Spi1 levels, attenuation of Spi1-K208 lactylation, and marked downregulation of SASP-associated gene expression. This therapeutic effect was strictly dependent on myeloid PKM2. Importantly, conservation of this mechanism was confirmed in human systems, as MAS1 silencing in primary human macrophages impaired PKM2 nuclear localization, Spi1 lactylation, and SASP production. In addition, TFDG displayed a defined protective concentration range (1–10 µM) in human THP-1 macrophages exposed to metabolic-inflammatory stress, further supporting its translational potential.

Several limitations of this study should be acknowledged. First, although the LysM-Cre system efficiently targets myeloid populations, the use of more lineage-restricted models, such as CCR2-Cre, would allow finer delineation of monocyte-specific functions. Second, the initial human association analyzes were derived from a relatively limited cohort; validation in larger, prospective studies, including paired liver biopsies obtained before and after therapeutic intervention, remains necessary. Third, TFDG, as a naturally occurring polyphenol, exhibits intrinsic polypharmacology. The observed reduction in total Mas protein abundance implies regulatory mechanisms extending beyond receptor antagonism, potentially involving enhanced protein degradation, which merits further investigation. Accordingly, focused medicinal chemistry efforts are required to generate next-generation Mas inhibitors with improved affinity, selectivity, and oral bioavailability. Fourth, therapeutic efficacy was evaluated exclusively in male mice; inclusion of female models in future studies will be essential to assess sex-specific responses. Finally, although the FN1–integrin signaling axis was functionally validated in vitro, the broader landscape of myeloid–parenchymal communication, particularly interactions with hepatic stellate cells, not captured in the current scRNA-seq dataset, should be addressed using spatial transcriptomics and advanced coculture systems.

In summary, this study identifies myeloid Mas1 as a key metabolic checkpoint that links glycolytic flux to Spi1-mediated prosenescence transcription through PKM2. The development and validation of a targeted nanotherapeutic approach directed at this pathway provide a mechanistically informed framework for the treatment of MASLD and related metabolic–inflammatory diseases.

Materials and methods

Human subjects

Liver specimens were collected from five patients with histologically confirmed MASLD and five healthy controls (HC) undergoing hepatic hemangioma resection at Shanghai Eastern Hepatobiliary Surgery Hospital (cohort 1). Peripheral blood samples were obtained from 61 patients with MASLD and 19 HCs during routine health examinations at Shanghai Tongji Hospital (cohort 2). MASLD diagnosis was established by histopathological evaluation or FibroScan assessment, defined by a controlled attenuation parameter exceeding 260 dB/m. Written informed consent was obtained from all participants. All procedures conformed to the principles of the Declaration of Helsinki and were approved by the Ethics Committees of Shanghai Tongji Hospital (K-2023-023) and Shanghai Eastern Hepatobiliary Surgery Hospital (EHBHK-Y2023-H033-P001). Detailed clinical characteristics of both cohorts are provided in Supplementary Table 1.

Animal studies

Male wild-type C57BL/6J mice were obtained from SLAC Laboratory Animal Co., Ltd. Mas1^f/f, Pkm2^f/f, and LysM-cre strains were purchased from Cyagen Biosciences. Myeloid-specific knockout lines (LysM^creMas1^f/f and LysM^crePkm2^f/f) were generated by crossing LysM-cre mice with Mas1^f/f or Pkm2^f/f mice, respectively. All experimental protocols were approved by the Animal Ethics Committee of Shanghai Tongji Hospital (20230101–20240630 DW 007). Animals were maintained under specific pathogen-free conditions with a 12-h light/dark cycle, controlled temperature (20–24 °C), and humidity (40–60%). Group allocation was randomized, and all outcome assessments were performed by investigators blinded to genotype and treatment. Mice received either a standard chow diet (Q031, Shilin Biotechnology), a high-fat diet (61.2% fat; TP23500, Trophic Animal Feed) for 16 weeks, or a methionine- and choline-deficient diet (TP3001, Trophic Animal Feed) for 8 weeks.

Analysis of public sequencing datasets

Publicly available transcriptomic datasets were retrieved from the Gene Expression Omnibus. Human MASLD datasets included GSE135251 (n = 216; comprising healthy controls [HC, n = 10], MASL [n = 51], MASH F0/F1 [n = 34], F2 [n = 53], F3 [n = 54], and F4 [n = 14]) and GSE89632 (n = 63; including simple steatosis [SS, n = 20], nonalcoholic steatohepatitis [NASH, n = 19], and HC [n = 24]). Data normalization was performed using the limma package (v3.50.0) with Relative Log Expression (RLE) transformation. Spearman correlation analyzes were applied to evaluate associations between MAS1 expression and clinical parameters (ALT, AST, NAS) or genes involved in inflammatory and lipid metabolic pathways, with statistical significance defined as an FDR-adjusted P < 0.05. For murine datasets (GSE165855: 6 chow-fed and 12 HFD-fed samples), Mas1 expression was visualized using z score–scaled heatmaps generated with pheatmap (v1.0.12). All computational analyzes were conducted in R version 4.3.1. The datasets analyzed are publicly accessible under accession numbers GSE135251, GSE89632, and GSE165855.

Metabolomics

Serum samples from human cohort 2 were processed for metabolomic analysis. Deproteinization was performed using precooled methanol/acetonitrile at a 1:1 volume ratio. Samples were centrifuged at 14,000 × g for 15 min at 4 °C. Supernatants were collected after centrifugation. The supernatants were dried under vacuum. Dried residues were reconstituted in acetonitrile/water at a 1:1 volume ratio. Untargeted metabolomic analysis was performed using a Vanquish UHPLC system. The UHPLC system was coupled to an Orbitrap Exploris™ 480 mass spectrometer (Thermo Fisher Scientific). Metabolite separation was carried out on an ACQUITY UPLC BEH Amide column with dimensions of 2.1 × 100 mm and a particle size of 1.7 µm. Mobile phase A consisted of 25 mM ammonium acetate with ammonium hydroxide. Mobile phase B consisted of acetonitrile. The gradient program started with 95% mobile phase B for 0.5 min. The proportion of mobile phase B was reduced from 95% to 65% over 6.5 min. The proportion was further reduced from 65% to 40% over 1 min. The proportion was increased from 40% to 95% over 0.1 min. Column re-equilibration was performed at 95% mobile phase B for 2.9 min. Mass spectrometric acquisition was performed in both positive and negative electrospray ionization modes with full-scan detection (m/z 70–1200, resolution 60,000) and data-dependent MS/MS acquisition. The ion source temperature was maintained at 350 °C, with spray voltages set to +3500 V and −2800 V for positive and negative modes, respectively. Quality control samples were injected at five-sample intervals.

Raw data files were converted using ProteoWizard and processed with XCMS, employing centWave peak detection (m/z tolerance 10 ppm; peak width 10–60 s) and feature grouping (bandwidth 5; m/z width 0.025). Metabolic features were annotated using CAMERA, and those with more than 50% nonzero values within each group were retained for downstream analysis. Data processing and analysis were performed by Gene De Novo Biotechnology (Guangzhou, China). Detailed cohort characteristics are summarized in Supplementary Table 2.

Single-cell RNA sequencing (scRNA-seq) and data analysis

Mouse liver specimens were harvested immediately following euthanasia, preserved in sCelLive™ Solution, and transported on ice to the Singleron facility. Single-cell suspensions were generated using the Singleron PythoN™ Automated Dissociator in conjunction with sCelLive™ Dissociation Mix. Cell viability was evaluated by trypan blue exclusion. Viable cells were resuspended in PBS at a concentration of 1 × 10⁵ cells/mL and subsequently processed using a microfluidics-based single-cell platform (GEXSCOPE®). Library construction was performed with the GEXSCOPE® RNA Library Kit on the Matrix® Processing System (Singleron Biotechnologies). Libraries were pooled at a final concentration of 4 ng/µL and sequenced on Illumina NovaSeq 6000 or NovaSeq X Plus platforms using a 150-bp paired-end configuration.

Raw sequencing data were processed using CeleScope v2.0.7. Cell barcodes and UMIs were extracted from R1 reads, whereas R2 reads were adapter- and poly(A)-trimmed and aligned to the GRCm38 (mm10) reference transcriptome using STAR (v2.7.11a) with the STARSolo module. Gene expression matrices were constructed by collapsing reads with identical cell barcodes, UMIs, and gene annotations.

Following quality control filtering, a total of 31,317 cells were retained for downstream analyzes. Dimensionality reduction was performed using PCA followed by UMAP embedding. Cell clustering was conducted using graph-based and K-means approaches. Differentially expressed genes were identified using the Wilcoxon rank-sum test, and GO enrichment analysis was performed with Fisher’s exact test followed by FDR correction.

Trajectory inference was carried out using Monocle 2 and Slingshot, and cellular differentiation potential was estimated with CytoTRACE. Intercellular communication networks were inferred using CellChat. Pathway activity scores were calculated using GSVA (GSEABase v1.44.0) and scMetabolism (v0.2.1) based on KEGG and REACTOME gene sets employing the VISION algorithm. Transcription factor regulatory networks were inferred using SCENIC.

Isolation of BMDMs

Femurs and tibias were harvested from 6 to 8-week-old male Mas1^f/f and LysM^creMas1^f/f mice under sterile laminar-flow conditions. Bone marrow cells were flushed from the marrow cavity using 10 mL of ice-cold DMEM delivered through a sterile syringe and needle. The resulting cell suspension was passed through a 70-µm nylon mesh filter and centrifuged at 1000 rpm for 10 min at 4 °C.

Isolation of human CD14⁺ Monocytes

Peripheral blood samples were collected into EDTA-containing tubes and diluted 1:1 with sterile PBS. PBMCs were isolated by Ficoll density gradient centrifugation (400 × g for 30 min without braking). The PBMC layer was collected, washed twice with PBS (300 × g for 10 min), and resuspended in MACS buffer consisting of PBS supplemented with 0.5% BSA and 2 mM EDTA. Cells were incubated with anti-human CD14 magnetic microbeads (Miltenyi Biotec) for 15 min at 4 °C, washed, and subjected to positive magnetic separation using pre-equilibrated MACS columns. The CD14⁺ fraction was eluted following removal of the column from the magnetic field, washed once, and resuspended in complete culture medium for subsequent experiments.

Cell culture and treatments

Mouse BMDMs were differentiated in DMEM supplemented with 10% FBS, 1% penicillin–streptomycin (Gibco), and 10 ng/mL recombinant mouse M-CSF (#315-02, PeproTech) for 7 days. To model diet-induced metabolic stress, fully differentiated BMDMs were exposed to 100 μM sodium palmitate (PA; KC004, Kunchuang Tech) for 24 h, followed by stimulation with 100 ng/mL LPS (Sigma) for an additional 24 h. Human macrophage THP-1 cells (FH0112, Fuheng) were maintained in RPMI 1640 containing 10% FBS. For metabolic-inflammatory challenge, THP-1 cells were treated with LPS/PA as described above and subsequently incubated with TFDG at the indicated concentrations (0, 1, 2.5, 5, 10, 15, 20, 25, 50, and 100 μM) for 24 h. Cell viability was evaluated using the CCK-8 assay (MedChemExpress). All cell cultures were maintained in a humidified atmosphere (37 °C, 5% CO₂).

Plasmid construction and transfection

Plasmids encoding full-length mouse PKM2 or domain-deleted variants (ΔA1, ΔB, ΔA2, ΔC) cloned and inserted into pcDNA3.1(+) were obtained from Tsingke Bio (Guangzhou). Spi1 wild-type and site-directed mutants (K169A, K208A, K239A, K244A) were similarly subcloned and inserted into pcDNA3.1(+) by the same provider. Empty vector (Vec) and PKM2 overexpression (PKM2^OE) plasmids were supplied by GeneChem (Shanghai). siRNAs targeting human MAS1 (si-MAS1) or mouse Spi1 (si-Spi1) were synthesized by UAGENEBIO (Shanghai). For plasmid transfection, cells were seeded into 6-well plates at a density of 3 × 10⁵ cells per well. After 24 h, 2.5 µg plasmid DNA was combined with 5 µL Lipofectamine 3000 and 5 µL P3000 reagent and applied to each well. PKM2 and its deletion constructs were transfected into 293T cells, whereas Spi1 and its point mutants were introduced into Spi1-knockout (Spi1-KO) RAW 264.7 cells. Empty pcDNA3.1(+) vector was added as needed to equalize total DNA input. For siRNA-mediated knockdown, cells were transfected with 50 nM siRNA using Lipofectamine 3000 according to the manufacturer’s instructions, with nontargeting siRNA used as a negative control.

Generation of macrophage-specific AAV8 vectors

Recombinant AAV8 vectors were engineered to achieve macrophage-specific gene expression under the control of the CD68 promoter. Coding sequences encoding mouse Mas1 and Spi1 harboring a K208A point mutation generated by site-directed mutagenesis were inserted into the AAV backbone, generating AAV8 CD68 Mas1 and AAV8 CD68 Spi1 K208A, respectively. An AAV8 CD68 GFP construct was used as a control. All plasmids were verified by Sanger sequencing prior to viral packaging. Vector production was carried out in HEK293T cells via triple-plasmid cotransfection, followed by purification through ultracentrifugation. Viral genome titers were determined by quantitative PCR and adjusted to approximately 1 × 10¹² GC/mL. Endotoxin levels in the final preparations were confirmed to be below established thresholds for in vivo administration.

Western blot analysis

Liver tissue samples (40 mg) or cultured cell lysates were prepared using RIPA buffer supplemented with 100 mM PMSF. Protein concentrations were determined using an enhanced BCA assay kit (Thermo Fisher Scientific, Waltham, MA, USA). Equal amounts of protein were resolved by SDS–PAGE and transferred onto activated PVDF membranes (Millipore, Burlington, MA, USA). Membranes were blocked with 5% nonfat milk for 2 h at room temperature and incubated with primary antibodies overnight at 4 °C. After three washes with TBST (5 min each), the membranes were incubated with HRP-conjugated secondary antibodies for 2 h at room temperature, followed by additional washing steps. Immunoreactive bands were visualized using a high-sensitivity ECL substrate (Yamay, Shanghai, China) and captured with a Bio-Rad chemiluminescence imaging system (Bio-Rad, Hercules, CA, USA). A detailed list of antibodies used in this study is provided in Supplementary Table 3.

Coimmunoprecipitation (Co-IP)

Cell lysates were incubated with the indicated primary antibodies at 4 °C overnight to allow immune complex formation. Protein–antibody complexes were subsequently captured using Pierce Protein A/G magnetic beads (Thermo Fisher Scientific, Waltham, MA, USA). After extensive washing to remove nonspecifically bound proteins, immunoprecipitates were eluted and resolved by SDS–PAGE. Protein–protein interactions were then assessed by immunoblotting.

Real-time quantitative PCR

Total RNA was extracted from mouse liver tissues and BMDMs using RNA Isolater Total RNA Extraction Reagent (Vazyme, Nanjing, China) according to the manufacturer’s instructions. The RNA concentration was measured by NanoDrop spectrophotometry (NanoDrop Technologies, Wilmington, USA). RNA purity was assessed by the 260/280 absorbance ratio. Samples with ratios between 1.8 and 2.0 were selected for analysis. Reverse transcription was carried out using a commercial cDNA synthesis kit (Takara, Shiga, Japan). Each reaction used 1 μg of total RNA. Quantitative PCR analysis was performed using SYBR Green qPCR Master Mix (Vazyme, Nanjing, China). Amplification was conducted on a CFX-96 Real-Time PCR System (Bio-Rad, Hercules, CA, USA) following the manufacturer’s protocols. Relative gene expression was calculated using the 2^−ΔΔCt method. ACTB (β-actin) was used as the internal reference gene. Amplification efficiency was confirmed by standard curve analysis. Primer sequences are provided in Supplementary Table 4.

Immunofluorescence

Mouse liver tissue sections were prepared for immunostaining. Fixation was performed in 4% paraformaldehyde for 24 h. Paraffin embedding was applied after fixation. Primary antibodies were applied to the sections. Incubation was carried out overnight at 4 °C. Fluorophore-conjugated secondary antibodies were applied to the sections. Nuclear staining was performed using DAPI (ab104139, Abcam, Cambridge, UK). For immunofluorescence analysis of BMDMs, cells were washed with PBS and fixed in 4% paraformaldehyde at 37 °C for 15 min. Samples were then blocked with 5% FBS containing 0.1% Triton X-100 for 1 h at room temperature, followed by incubation with primary antibodies overnight at 4 °C. After secondary antibody incubation for 1 h at 37 °C, nuclei were counterstained with DAPI. Fluorescence images were acquired using a laser scanning confocal microscope (Leica, Wetzlar, Germany).

Multiplex immunohistochemistry (mIHC)

Liver tissue specimens were fixed in 10% polymethyl methacrylate. Paraffin embedding was performed after fixation. Paraffin blocks were cut into 4 μm sections. Sections were placed on glass slides. Slides were heated at 60 °C for 1 h for deparaffinization. Xylene treatment was applied after heating. Tissue rehydration was performed using graded ethanol solutions at 100%, 95%, and 70%. Sections were postfixed in 4% paraformaldehyde for 15 min. Membrane permeabilization was performed using 0.5% Triton X-100 for 20 min. Antigen retrieval was conducted in citrate buffer at 95 °C for 20 min. Slides were cooled to room temperature after heating. Endogenous peroxidase activity was blocked using 3% H₂O₂ for 15 min. Nonspecific binding was blocked with 10% goat serum for 30 min. Primary antibodies were applied to the sections. Incubation was performed in a humidified chamber at 37 °C for 1 h. Slides were washed after incubation. HRP-conjugated goat anti-rabbit IgG secondary antibody was applied for 10 min at room temperature. Heat-induced epitope retrieval was repeated after each staining cycle to remove residual antibodies. Multiplex immunohistochemistry was performed using the AlphaTSA Multiplex IHC kit (AXT34100011, AlphaX, Beijing, China) following the manufacturer’s instructions. Nuclear counterstaining was completed with DAPI for 10 min, and multispectral images were acquired using a Zeiss AxioScan7 scanner.

Micro-CT analysis

Micro-CT imaging and quantitative analysis were conducted using the SkyScan 1276 system (Bruker MicroCT, Kontich, Belgium) with dedicated analysis software. Scanning was performed at a detector resolution of 1788 × 1272 pixels and an isotropic voxel size of 20 µm under the following parameters: 100 kV and 200 µA. Two-dimensional grayscale images were reconstructed into three-dimensional tomographic datasets using proprietary reconstruction algorithms. Adipose tissue segmentation included the anatomical region extending from the upper margin of the lungs to the distal femur, with exclusion of the head and limbs owing to minimal fat content. A uniform threshold was applied across all samples to identify adipose tissue, and manual delineation based on this threshold was used to define fat compartments. Volumetric quantification of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) was performed using iterative algorithms embedded within the three-dimensional reconstruction software.

Transmission electron microscopy (TEM)

Liver tissues were excised into approximately 1 mm³ blocks and fixed in 4% glutaraldehyde (Servicebio Technology, Wuhan, China) at 4 °C for 2 h. Following rinsing in 0.1 mol/L phosphate buffer, samples were postfixed in 1% osmium tetroxide. Specimens were then dehydrated through a graded acetone series, infiltrated, embedded, and polymerized. Ultrathin sections (~70 nm) were prepared using an ultramicrotome, stained sequentially with uranyl acetate and lead citrate, and examined by TEM.

Histological analysis

Paraffin-embedded liver sections were stained with hematoxylin and eosin (H&E; Servicebio Technology, Wuhan, China). Frozen liver sections embedded in OCT were stained with Oil Red O (Servicebio Technology) for visualization of neutral lipid droplets. Images were captured using a light microscope (Olympus), with a minimum of three sections analyzed per mouse. All histopathological evaluations were conducted by investigators blinded to the experimental group allocation. Hepatic lesions were graded using the NAFLD Activity Score (NAS) established by the NASH Clinical Research Network. The NAS represents the composite of three histological parameters: steatosis (scored 0–3 based on the proportion of hepatocytes containing lipid droplets: <5%, 5–33%, 33–66%, or >66%), lobular inflammation (scored 0–3 as none, mild, moderate, or severe), and hepatocellular ballooning (scored 0–2 as absent, few, or many). This scoring system provides a validated semiquantitative index of disease activity.

Senescence-associated β-galactosidase

Senescence-associated β-galactosidase staining was performed using a commercial kit (Servicebio Technology, Wuhan, China) in accordance with the manufacturer’s instructions. Frozen liver sections and cultured cells were fixed at room temperature for 15 min, washed three times with PBS, and incubated with staining solution at 37 °C for 14 h. After air drying, senescence-associated β-galactosidase–positive signals were visualized using a bright-field microscope (Mshot, Guangzhou, China).

Biochemical analysis

Hepatic injury was assessed by measuring serum alanine aminotransferase levels. Commercial assay kits from Nanjing Jiancheng Bioengineering Institute were used. Hepatic injury was also assessed by measuring serum aspartate aminotransferase levels. The same supplier provided the assay kits. Triglyceride concentrations were measured in liver tissue homogenates. Triglyceride concentrations were also measured in serum samples. Colorimetric assay kits from the same manufacturer were used. Total cholesterol concentrations were measured in liver tissue homogenates. Total cholesterol concentrations were also measured in serum samples. The same colorimetric assay kits were applied. Nonesterified fatty acid levels were determined using a specific assay kit. The kit was obtained from Solarbio (BC0595, Beijing, China). All measurements followed the manufacturers’ instructions exactly.

Enzyme-linked immunosorbent assay (ELISA)

Serum concentrations of IL-6, IL-1β, and TNF-α were quantified using a commercial mouse ELISA kit (Abs520010, Absin, Shanghai, China) following the supplier’s protocols.

Lactate measurement

Lactate levels were measured using the Lactate Assay Kit II (Sigma‒Aldrich, St. Louis, MO, USA). The assay followed the manufacturer’s instructions. Tissue samples were prepared for analysis. Cell pellets were also prepared for analysis. Samples were homogenized in four volumes of lactate assay buffer. Homogenates were centrifuged. Supernatants were collected after centrifugation. Supernatants were used for lactate quantification.

Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR)

The OCR and ECAR were measured using the Seahorse XF Cell Mito Stress Test Kit (#103015-100, Seahorse, Santa Clara, CA, USA). The ECAR was also measured using the Seahorse XF Glycolysis Stress Test Kit. BMDMs were seeded into XF96 cell culture microplates. Cells received the indicated treatments. Metabolic flux parameters were recorded using a Seahorse XFe96 Analyzer (Agilent, Santa Clara, CA, USA). Mitochondrial stress testing was performed in a stepwise manner. Cells were exposed to 1 μM oligomycin. Cells were exposed to 1 μM FCCP (carbonyl cyanide p-trifluoromethoxyphenylhydrazone). Cells were exposed to 150 nM rotenone. OCR values were recorded after each injection. All OCR values were normalized to total protein content per well. Basal OCR was defined as the difference between measurements obtained before oligomycin exposure and after oligomycin exposure. The maximal OCR was defined as the difference between the FCCP-stimulated OCR and the rotenone-inhibited OCR. Glycolytic stress testing was conducted using sequential substrate addition. Cells were treated with 10 mM glucose. Cells were treated with 1 μM oligomycin. Cells were treated with 50 mM 2-deoxy-glucose (2-DG). ECAR values were recorded after each addition. All ECAR values were normalized to the protein content per well. The basal ECAR was defined as the difference between ECAR values measured before glucose addition and after glucose addition. The maximal ECAR was defined as the difference between ECAR values measured before glucose addition and after oligomycin treatment.

LC‒MS/MS analysis

Proteins were extracted from BMDMs and incubated with anti-Mas1 antibodies in combination with Pierce Protein A/G magnetic beads to selectively precipitate Mas1-containing protein complexes. Following SDS–PAGE separation, gel bands corresponding to the molecular weight of Mas1 were excised after Coomassie Brilliant Blue staining. Proteins within the excised gel fragments were enzymatically digested into peptides, purified, and subsequently subjected to LC–MS/MS analysis performed by Genechem Co., Ltd (Shanghai, China). Peptide separation was achieved using HPLC, and mass spectrometric analysis was conducted on a Q Exactive HF-X mass spectrometer coupled to an Easy-nLC system (Thermo Fisher Scientific, Waltham, MA, USA). Raw data were processed using Proteome Discoverer version 2.2 (Thermo Fisher Scientific) for protein identification, followed by database searches performed with the Mascot 2.6 search engine.

Mass spectrometry analysis

BMDMs were cultured in 10-cm dishes and stimulated with LPS/PA in the presence of lactate, after which cell lysates were collected the following day. Lysates were incubated overnight at 4 °C with anti-Spi1 antibody to immunoprecipitate Spi1-associated proteins, enabling enrichment of lactylated Spi1 complexes. After Coomassie blue staining, gel bands within the 25–50 kDa range were excised and subjected to mass spectrometric analysis. Peptides were analyzed using a nanoliquid chromatography system coupled to a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific). Electrospray ionization was performed at 2.0 kV, and intact peptide ions were detected in the Orbitrap at a resolution of approximately 60,000. MS/MS fragmentation was achieved by higher-energy collisional dissociation (HCD) with a normalized collision energy of 27, and fragment ions were analyzed at a resolution of approximately 30,000. Data-dependent acquisition was applied, selecting the 20 most intense precursor ions from each full MS scan for MS/MS analysis with a dynamic exclusion time of 20 s.

CRISPR/Cas9-mediated editing of Spi1 in RAW 264.7 cells

A dual-vector CRISPR/Cas9 approach was employed to generate Spi1 knockout (KO) RAW 264.7 cells. Two single-guide RNAs (sgRNAs) targeting Spi1 were designed (sgRNA-1: 5′-GCACGTCCTCGATACTCCCA-3′; sgRNA-2: 5′-AGTTCTCAAACTCGTTGTTG-3′). The corresponding oligonucleotides were annealed and cloned and inserted into expression vectors, which were subsequently cotransfected into RAW 264.7 cells using Lipofectamine 3000. Puromycin selection was initiated 24 h after transfection. Surviving cells were serially diluted and seeded into 96-well plates for clonal expansion. Individual clones were screened after 2 weeks, and successful Spi1 deletion was confirmed by PCR amplification and Sanger sequencing.

To introduce the endogenous Spi1 K208A point mutation in RAW 264.7 cells, a CRISPR/Cas9 ribonucleoprotein (RNP)-mediated knock-in strategy was applied. An sgRNA targeting exon 5 of mouse Spi1 (5′-GTTCTCGTCCAAGCACAAGG-3′) was complexed with recombinant Cas9 protein. RAW 264.7 cells were coelectroporated with the RNP complex and a single-stranded donor oligonucleotide (ssODN) harboring the c. A622G and c. A623C substitutions (AAG → GCG) to promote homology-directed repair. Clonal cell lines were expanded, genomic DNA was isolated, and the targeted locus was amplified by PCR. Precise introduction of the Spi1 K208A mutation was verified by Sanger sequencing.

Chromatin immunoprecipitation (ChIP)

ChIP assays were performed to evaluate Spi1 binding to the promoter regions of MMP9, TNF-α, IL-1β, and CCL2 using a commercial ChIP kit (ab500, Abcam, Cambridge, UK) according to the manufacturer’s instructions. Briefly, RAW 264.7 cells were fixed with 1% paraformaldehyde for 15 min to crosslink protein–DNA complexes. Nuclei were isolated, and chromatin was fragmented by sonication to obtain appropriately sized DNA fragments. Immunoprecipitation was carried out using an anti-Spi1 antibody (ab227835, Abcam, Cambridge, UK) or control IgG, followed by quantitative PCR analysis. Primer sequences targeting Spi1-binding regions within the MMP9, TNF-α, IL-1β, and CCL2 promoters are listed in Supplementary Table 5.

Luciferase activity assays

The promoter region of CCL2 was cloned and inserted into a luciferase reporter construct. Spi1 WT and Spi1 K208A RAW 264.7 cells, along with control RAW 264.7 cells, were seeded into 96-well plates and cotransfected with the corresponding plasmids. After 24 h of incubation under normoxic conditions, the cells were transferred to hypoxic conditions for an additional 24 h. Luciferase activity was quantified using a dual-luciferase reporter assay system (Promega, E2920, Madison, WI, USA), with Renilla luciferase activity serving as an internal normalization control.

Flow cytometry

For flow cytometric analysis, liver tissues were excised and enzymatically digested with collagenase IV and pronase E at 37 °C for 30 min to generate single-cell suspensions. The resulting suspensions were further purified by density gradient centrifugation, washed, and resuspended in FACS buffer. For surface marker staining, 100 µL of cell suspension was incubated with Fc block (2 µL; 553141, BD Biosciences, San Jose, CA, USA) for 15 min at room temperature, followed by staining with fluorophore-conjugated antibodies listed in Supplementary Table 2 for 30 min at 4 °C. For intracellular antigen detection, cells were washed, fixed, and permeabilized using the Fixation/Permeabilization Kit (554714, BD Biosciences) and subsequently incubated with the indicated antibodies at 4 °C for 30 min. After a final wash with 1 mL FACS buffer, the cells were centrifuged at 300 × g for 5 min at 4 °C, resuspended in 0.5 mL FACS buffer, and analyzed using a BD Celesta Cell Analyzer (BD Biosciences). Data acquisition and analysis were performed with FlowJo v10.8.1 software (TreeStar, Ashland, OR, USA). Compensation and gating strategies were defined using isotype controls, single-stained controls, and fully stained samples.

Virtual screening

The AlphaFold-predicted structure of mouse Mas1 (AF P30554 F1) was used as the receptor model. The putative ligand-binding pocket (Site1; site score = 1.070) was identified using Schrödinger SiteMap and subsequently refined with the Protein Preparation Wizard, including hydrogen atom addition, side-chain optimization, and restrained energy minimization. A docking grid measuring 20 Å × 20 Å × 20 Å was centered on Site 1. Three compound libraries—the MedChemExpress Bioactive Compounds Library Plus (HY-L001P; 25,000 compounds), the Traditional Chinese Medicine Active Compound Library (HY-L065; 2900 compounds), and the MedChemExpress 50K Diversity Library (HY-L901; 50,000 compounds)—were prepared using Schrödinger LigPrep to generate energy-minimized three-dimensional conformers. Virtual screening was conducted using the Glide docking workflow. Initially, all compounds were screened in high-throughput virtual screening (HTVS) mode; the top 15% were advanced to standard precision (SP) docking, and the top 15% from SP were further subjected to extra precision (XP) docking. Compounds exhibiting XP docking scores below −12.7 kcal·mol⁻¹ and demonstrating stable binding conformations upon visual inspection were retained. The top 20-ranked candidates were selected for experimental validation. Docking poses were visualized using PyMOL.

Molecular dynamics simulation and docking analysis

Protein–protein docking between Mas and PKM2 was performed using AlphaFold3. Complexes with low predicted confidence scores were excluded, and high-confidence models were selected for subsequent molecular dynamics (MD) simulations. MD simulations were conducted with Gromacs 2022.4 using the AMBER14SB force field under periodic boundary conditions at constant temperature and pressure. Covalent bond constraints involving hydrogen atoms were applied using the LINCS algorithm with a 2 fs integration time step. Long-range electrostatic interactions were calculated with the particle mesh Ewald (PME) method using a cutoff of 1.2 nm, whereas short-range nonbonded interactions were truncated at 10 Å and updated every 10 steps. The temperature was maintained at 298 K using a V-rescale thermostat, and the pressure was controlled at 1 bar with a Berendsen barostat. Prior to the 100 ns production run, systems were equilibrated for 100 ps under both NVT and NPT ensembles. Trajectory frames were recorded every 10 ps. Structural dynamics were analyzed using VMD and PyMOL, and binding free energies were estimated with the g_mmpbsa package. The calculated MMPBSA binding free energy for the Mas–PKM2 complex in its stable state is provided in Supplementary Table 6.

Surface plasmon resonance (SPR) analysis

SPR was employed to evaluate the interaction between TFDG (MedChemExpress) and recombinant mouse Mas1 protein using a Biacore T200 system (GE Healthcare, USA). An anti-Mas1 antibody was included as a positive reference. Recombinant mouse Mas1 protein was obtained from Cusabio (CSB-EP013505MO1, Wuhan, China). All analyzes were performed on a CM5 sensor chip (Cytiva, MA, USA) with phosphate-buffered saline (PBS) serving as the running buffer. Analyte solutions were injected at a constant flow rate of 20 μL/min for 100 s to allow association, followed by a 180 s dissociation phase. Association and dissociation were conducted in analyte buffer. Eight injection cycles were performed using increasing analyte concentrations. Signals from the reference flow cell were subtracted to correct for nonspecific binding. Kinetic parameters, including the association rate constant (ka), dissociation rate constant (kd), and equilibrium dissociation constant (KD), were determined by global fitting to a 1:1 Langmuir binding model using Biacore T200 Evaluation Software (version 2.0, GE Healthcare).

Pharmacokinetic analysis

TFDG or MM@NP-TFDG was administered intravenously to mice at a dose of 10 mg/kg. Blood samples were collected from the retro-orbital plexus at 5, 15, and 30 min and at 1, 2, 3, 4, 6, 12, and 24 h post administration. Plasma concentrations of TFDG were quantified by LC–MS/MS. Pharmacokinetic parameters were calculated using Phoenix WinNonlin software (v8.1, Certara).

Cellular thermal shift assay (CETSA)

BMDMs were treated with 5 μM TFDG or vehicle control (0.1% dimethyl sulfoxide, DMSO) for 12 h. Cells were subsequently washed twice with ice-cold PBS and lysed in radioimmunoprecipitation assay (RIPA) buffer supplemented with a protease inhibitor cocktail. Lysates were aliquoted, maintained on ice, and subjected to a controlled temperature gradient ranging from 37 to 62 °C in 4 °C increments, with each temperature held for 3 min using a programmable thermal cycler. Selected aliquots were further subjected to three freeze–thaw cycles alternating between liquid nitrogen and room temperature (25 °C) to ensure complete protein denaturation. After centrifugation at 20,000 × g for 20 min at 4 °C, clarified supernatants were mixed with Laemmli sample buffer, heated at 95 °C for 5 min, and resolved by SDS–PAGE. Following transfer to membranes, immunoblotting was performed using an anti-Mas primary antibody at a dilution of 1:1000.

Isolation of macrophage membranes

Macrophage membranes were isolated from RAW264.7 cells using a Membrane Protein Extraction Kit (Beyotime, Shanghai, China). Cells were incubated with prechilled membrane protein extraction buffer for 15 min and subsequently homogenized 10–20 times using a Dounce homogenizer. Following homogenization, nuclei and intact cells were removed by low-speed centrifugation (800 × g, 10 min). The resulting supernatant was further centrifuged at high speed (15,000 × g, 30 min) to pellet the membrane fraction. The membrane protein concentration was quantified using a bicinchoninic acid (BCA) assay. The isolated membrane fraction was dispersed in an ultrasonic bath for 15 min, after which macrophage membrane vesicles were generated by extruding the suspension 11 times through 400 nm polycarbonate porous membranes using a mini-extruder (Avanti Polar Lipids, Alabaster, AL, USA).

Preparation of NP-TFDG

PLGA nanoparticles (NPs) were fabricated by nanoprecipitation using a carboxylic acid–terminated PLGA copolymer (LAP, Fort Collins, CO, USA). Briefly, PLGA was dissolved in acetone at a concentration of 10 mg/mL, and 1 mL of the polymer solution was rapidly introduced into 2 mL of deionized water under continuous stirring. Acetone was subsequently removed by rotary evaporation under reduced pressure. For the generation of TFDG-loaded nanoparticles, TFDG (10% w/w; MCE, China) was incorporated into the PLGA solution prior to nanoprecipitation.

Preparation of MM@NP-TFDG

For the preparation of membrane-coated nanoparticles (MM@NP-TFDG), macrophage membranes were fused with NP-TFDG using a mini-extruder for at least 20 extrusion cycles. The suspension was subsequently sonicated in a water bath at 40 kHz and 100 W for 2 min to enhance the membrane coating efficiency. The resulting MM@NP-TFDG were purified through repeated washing with PBS. Western blot analysis was performed to verify the presence of characteristic macrophage membrane surface markers on macrophage membranes, NP-TFDG, and MM@NP-TFDG. For morphological characterization, nanoparticles were negatively stained with 1% uranyl acetate and examined by transmission electron microscopy (TF20, FEI, Hillsboro, OR, USA). The particle size distribution and ζ potential of NP-TFDG and MM@NP-TFDG were determined using a Nano-Zeta Analyzer (NanoBrook 90plus PALS, Brookhaven, Holtsville, NY, USA).

Statistical analysis

Statistical analyzes were performed using Prism software (GraphPad Software Inc., San Diego, CA, USA). Quantitative data are presented as the mean ± standard deviation. Comparisons between two groups were conducted using unpaired two-tailed Student’s t tests. For analyzes involving more than two groups, one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test or two-way ANOVA followed by Tukey’s multiple comparisons test was applied as appropriate. A P value < 0.05 was considered statistically significant.

Data availability

All data and materials are available within the submitted article or in public repositories as described below. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository with the dataset identifier PXD074700 (https://proteomecentral.proteomexchange.org). The metabolomics data have been deposited in the OMIX database of the China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number OMIX015164 (https://ngdc.cncb.ac.cn/omix/). The scRNA-sequencing data generated in this study are publicly available in the Genome Sequence Archive at the National Genomics Data Center,^47,48 China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences under accession number CRA038847 (https://ngdc.cncb.ac.cn/gsa/).

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Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (No. U22A20275, 82370627, 82300702), Shanghai Municipal Science and Technology Commission Natural Science Foundation Project (No. 23ZR1457900), and Shanghai Municipal Health Commission Clinical Research Project (No. 202340185). This work was also supported by the China Postdoctoral Science Foundation (No. 2023M732649). The authors thank Singleron Biotechnology Co., Ltd (Suzhou, China) for providing scRNA-seq. The authors are grateful to Gene Denovo Biotechnology Co., Ltd (Guangzhou, China) for assisting in the metabolite and bioinformatics analyses. The authors sincerely thank all the patients who participated in this study.

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These authors contributed equally: Luying Zhao, Shujing Xu, Shikai Qiao, Zhe Wang, Shenglan Wang

Authors and Affiliations

Department of Gastroenterology and Hepatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
Luying Zhao, Shujing Xu, Shikai Qiao, Zhe Wang, Shenglan Wang, Chun Liu, Shuo Zhang, Li Chen, Xiaokun Zhang, Lu Xia, Changqing Yang & Jing Li
Department of Hepatobiliary Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Naval Military Medical University, Shanghai, China
Peng Wang
Central Laboratory, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
Xianghua Sun
Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, China
Shanshan Li
Department of Hepatobiliary Surgery, Wuxi No.2 People’s Hospital, Central Hospital Affiliated to Jiangnan University, Jiangsu, China
Chengxi Hu & Yongping Zhou

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Contributions

Z.L.Y., L.J., Y.C.Q., and X.L. designed the research. Z.L.Y., X.S.J., Q.S.K., W.Z., and W.S.L. conducted the research. Z.L.Y., L.C., Z.S., W.P., S.X.H., L.S.S., C.L., Z.X.K., H.C.X. and Z.Y.P. analyzed the data. Z.L.Y., X.S.J., Q.S.K., W.Z., and W.S.L. wrote this paper. L.J., Y.C.Q., and X.L. reviewed the paper. All the authors have read and approved the article.

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Correspondence to Lu Xia, Changqing Yang or Jing Li.

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Zhao, L., Xu, S., Qiao, S. et al. Myeloid Mas drives pyruvate kinase M2-mediated Spi1 lactylation to fuel inflammatory senescence in MASLD. Sig Transduct Target Ther 11, 186 (2026). https://doi.org/10.1038/s41392-026-02704-6

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Received: 24 September 2025
Revised: 21 February 2026
Accepted: 19 March 2026
Published: 19 May 2026
Version of record: 19 May 2026
DOI: https://doi.org/10.1038/s41392-026-02704-6