Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) emerges as a prevalent liver condition in the western world, affecting roughly one-third of the population^1-3. Its incidence continues to climb due to its strong association with obesity, type 2 diabetes, and the metabolic syndrome^4-6. MASLD includes a spectrum of liver disorders, from metabolic dysfunction-associated fatty liver (MAFL), often referred to as steatosis, to metabolic dysfunction-associated steatohepatitis (MASH)⁷. MASH, characterized by steatosis, inflammation, ballooning injury, and varying degrees of fibrosis, marks the initial critical stage of MASLD⁸. Kupffer cells (KCs) are liver-resident macrophages located in the hepatic sinusoids^9,10. Derived from the embryo’s yolk sac, KCs can self-renew through proliferation in adult homeostasis¹¹. Studies suggest that KCs contribute to triglyceride storage during MASH progression, as demonstrated by depleting CD207⁺ KCs in CD207-DTR mice using diphtheria toxin (DT) or by using CD207^ΔBcl2l1 mice to stimulate a low EmKCs status¹². Upon KCs death, monocyte-derived macrophages (MoMFs) gradually seed the KCs pool and eventually replace the deceased KCs¹³. These monocyte-derived KCs (MoKCs) tend to be more inflammatory than their embryo-derived counterparts (EmKCs), altering the liver’s response in MASH, eventually limiting triglyceride storage and contributing to liver cell damage^12,14. Researchers, including our team, have observed a gradual decline in KCs during MASLD development^12,14-17. However, little is known regarding the dynamic loss of KCs and metabolic changes behind KCs death during MASLD.

Emerging evidence highlights the fundamental role of glucose metabolism in regulating macrophages function, polarization, and survival^18-21. Metabolic reprogramming is a well-established hallmark of macrophage activation and function in various contexts²². Within the unique metabolic milieu of the MASLD liver, characterized by lipotoxicity, insulin resistance, and altered nutrient fluxes, it is plausible that KCs metabolism is profoundly perturbed. Whether such metabolic shifts contribute directly to the observed KCs loss, and if so, through which specific pathways, is a crucial unanswered question.

Therefore, this study aims to investigate the hypothesis that specific metabolic changes, particularly in glucose utilization, underpin the susceptibility of KCs to death during MASLD progression. We sought to define the glucose metabolic alterations occurring in KCs as MASLD develops and to establish a direct causal link between these metabolic reprogramming events and KCs demise. Elucidating these mechanisms is essential for understanding MASLD pathogenesis and identifying novel therapeutic targets aimed at preserving hepatic immune homeostasis.

Materials and methods

Animal experiments and procedures

Animals Chil1^-/- (strain no. T014402), Chil1^flox//flox (strain no. T013652), and Clec4f-cre (strain no. T036801) mice with a C57BL/6J background were purchased from GemPharmatech. Accordingly, C57BL/6J mice (strain no. N000013) were used as wild-type (WT) mice. To generate Clec4f^△Chil1 mice, Chil1^flox//flox mice were crossed with Clec4f-cre mice and knock out efficiency was examined in KCs previously¹⁷. All mouse colonies were maintained at the Animal Core Facility of Yunnan University. The animal studies were approved by the Yunnan University Institutional Animal Care and Use Committee (IACUC, Approval No. YNU20220314). Male mice aged 6-8 weeks were used in this study.

Construction of MASLD/MASH mouse model Mice were provided a high-fat and high-cholesterol diet (Research Diet, d12108c, 40 kcal% fat and 1.25% cholesterol) or a high-fat diet (Research Diet, d12492, 60 kcal% fat). Another group of mice was fed a methionine and choline deficient diet (Research Diet, A02082002BR) for 6 weeks. Throughout the feeding period, the body weight and food consumption of the mice were observed and recorded weekly. Once the dietary intervention was completed, the mice were euthanized. Liver and murine serum samples were collected for further analysis. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in the serum, as well as cholesterol (TC) and triglyceride (TG) levels in both serum and liver tissues, were quantified using commercially available kits (Nanjing Jiancheng Bioengineering Institute). Genotyping Sample preparation and procedure were conducted as previously described¹⁷.

Kupffer Cell Processing and Metabolomic Profiling

Primary KCs were isolated from livers of HFHC-fed mice at 0, 4, and 8 weeks using established protocols. After a 15-min adherence period, cells were washed twice with ice-cold PBS and detached using 1 mL ice-cold PBS followed by gentle scraping. Cell suspensions were transferred to 1.5 mL tubes, and 3 × 10⁶ cells were pelleted by centrifugation (1,000 × g, 5 min). Supernatants were discarded, and pellets snap-frozen in liquid nitrogen. Metabolites were extracted from frozen pellets with 80% methanol (vol/vol) and analyzed via LC–MS/MS using an AB Sciex 6500 Plus QTRAP mass spectrometer coupled to an ExionLC system. All metabolomic processing and data analysis were performed by BioDeep (https://www.biodeep.cn).

Isotope Tracing

For ¹³C-tracing experiments, BMDMs were isolated from WT and Chil1^−/− mice, cultured to maturity, and replated. After 12 hours, cells were incubated for 12 hours in glucose-depleted medium supplemented with 10% dialyzed FBS and 15 mmol/L universally labeled [U-¹³C]glucose (Cambridge Isotope Laboratories, CLM-1396-1). Cells were subsequently washed twice with ice-cold glucose-free medium. Following supernatant removal, metabolites were extracted using pre-cooled 80% (vol/vol) methanol for cell lysis.

Metabolite separation was performed using a Vanquish UHPLC system (Thermo Fisher Scientific) equipped with an Amide column (Waters). The mobile phase consisted of: (A) 10 mM ammonium acetate and 0.3% ammonia in water, and (B) 10 mM ammonium acetate and 0.3% ammonia in 90% acetonitrile. Metabolites were separated using a linear gradient elution. Metabolites were ionized, and mass spectrometry data were acquired using an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific). Targeted metabolite analysis was performed by LC–MS/MS using a QTRAP 5500+ system (SCIEX) coupled to an ExionLC AD UHPLC system (SCIEX). Isotope tracing analysis and metabolomic data processing were conducted by BioDeep using the BioDeep Platform (https://www.biodeep.cn).

Spatial Analysis of KCs Death

To evaluate the spatial distribution of KCs death along the portal-central axis, the distance between the portal vein (PV) and central vein (CV) was measured in histological sections. The PV-CV axis was systematically divided into three equidistant zones (periportal, intermediate, and pericentral) for regional analysis. KCs death was quantified by isolating fluorescence signals corresponding to cell death markers (Tunel) through channel thresholding and noise reduction. The positive area and integrated fluorescence intensity were measured within each zone, excluding vascular structures to focus on parenchymal KCs populations. This zonal approach enabled comparative assessment of KCs death patterns across different hepatic microenvironments.

Diagnosis of MASLD activity score

Murine MASLD activity was assessed histologically using the NAFLD Activity Score (NAS) on hematoxylin and eosin (H&E) stained liver sections following features: hepatocyte ballooning degeneration (0-2), lobular inflammation (0-3), and steatosis grade (0-3)²³. The individual scores were summed to yield the total NAS (range 0-8) per animal. A NAS ≥ 5 was considered diagnostic for steatohepatitis (MASH), NAS ≤ 3 indicated not-MASH, and NAS = 4 was indeterminate.

Data Presentation and Statistical Analysis

Data in graph figures are presented as mean ± standard error of the mean (SEM). Statistical analyses were performed using SPSS Statistics (Version 22). For comparisons between two groups, an unpaired two-tailed Student’s t-test was used, while one-way analysis of variance (ANOVA) was applied for comparisons involving three or more groups. A p-value < 0.05 was considered statistically significant, with p-values indicated where applicable. All cell culture experiments were repeated at least three times independently. Figure 7 was created using the Figdraw platform (www.figdraw.com).

Additional Methods

Additional detailed methods can be found in the Supporting Information.

Results

The death of Kupffer cells is a pathological characteristic during MASLD

To systematically investigate KCs death, we established an MASLD mouse model by feeding C57Bl/6J mice a high-fat high-cholesterol diet (HFHC) (Figure S1A). After 4 or 16 weeks of HFHC feeding, we conducted Hematoxylin/eosin (H&E), Oil Red o and Sirius red staining to assess immune cell infiltration, fat accumulation, and liver fibrosis. At 4 weeks of HFHC feeding, we observed a slight increase in lipid droplets, with no apparent signs of liver inflammation or fibrosis in hepatocytes (Figure S1A). However, by 16 weeks of HFHC feeding, both lipid droplets and immune cell infiltration had significantly increased, though liver fibrosis was still absent and MASLD activity score is below 4 (Figure S1A). Moreover, the body weight of mice fed HFHC gradually increased over the feeding period (Figure S1B). Analysis of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum and liver cholesterol or liver triglyceride (TG) levels revealed significant increases compared to mice fed a normal chow diet (NCD), except for serum TG, which is similar between NCD and HFHC-fed mice (Figure S1D). These findings collectively indicate the successful establishment of an early MASLD mouse model, without fibrosis development yet.

Subsequently, we investigated KCs death by labeling KCs with antibodies targeting C-type lectin domain family 4 (Clec4f), a protein specific to KCs¹³, and employing TdT-mediated dUTP Nick-End Labeling (Tunel), a method for detecting dead cells. Compared to baseline (0 weeks, prior to HFHC diet), KCs death was evident as early as 4 weeks, with nearly 60% of KCs undergoing cell death by 16 weeks post HFHC feeding (Figure 1A, 1B). To further validate KCs death during MASLD progression, we performed co-staining of the apoptotic marker cleaved caspase-3 (Cl-Casp3) with Clec4f. This analysis confirmed a significant increase in apoptotic KCs during HFHC diet feeding (Figure S2A).Considering the potential recruitment of monocyte-derived macrophages (MoMFs) into the liver, which may acquire KC features, including Clec4f expression^12,14, we included another marker specifically expressed by KCs, T cell immunoglobulin mucin protein 4 (Timd-4)^12,24. Flow cytometry analysis of liver nonparenchymal cells (NPCs) was performed to discriminate between KCs and MoMFs. KCs were identified as CD45⁺ F4/80^hi CD11b^low Timd4⁺ cells, while MoMFs were identified as CD45⁺ F4/80⁺ CD11b^hi Timd4^- cells (Figure 1C). Consistent with the staining results, the number of KCs decreased as early as 4 weeks and continued to decline until 16 weeks on the HFHC diet (Figure 1C, 1D). The infiltration of MoMFs into the liver occurred gradually and followed the onset of KCs death (Figure S2B, S2C). To assess proliferation-associated Tunel false positivity, we evaluated Ki67 expression in dying KCs (Tunel⁺Timd4⁺). Two critical observations emerged: Proliferating KCs (Ki67⁺Timd4⁺) were rare under baseline and HFHC conditions. Only a minor fraction (<20%) of Tunel⁺Timd4⁺ KCs co-expressed Ki67, indicating that the majority of Tunel⁺ KCs represent true cell death events rather than proliferation-related artifacts (Figure S2D).

Figure 1.

Furthermore, we examined KCs death in several other MASLD mouse models, including mice fed a high-fat diet (HFD) for 16 weeks²⁵ and a methionine/choline deficient diet (MCD) for 6 weeks¹². Co-staining of Clec4f and Tunel in liver sections revealed a notable increase in KCs death in both the HFD and MCD groups compared to the control group fed a normal chow diet (NCD) (Figure 1E, 1F). These findings collectively confirm that progressive KCs death is a pathological hallmark of MASLD observed in various dietary-induced models.

Kupffer cells exhibit spatially-patterned vulnerability in MASLD

To assess relative susceptibility of hepatic cell populations, diet-induced cell death was quantified via Tunel co-staining with lineage markers: HNF4α (hepatocytes), Desmin (hepatic stellate cells; HSCs), and Iba1 (monocyte-derived macrophages; MoMFs) (Figure 2A–C). Hepatocyte death showed minimal change during the initial 4 weeks but increased modestly by 16 weeks (Figure 2A). In contrast, HSCs and MoMFs mortality rose progressively throughout MASLD progression, with significant increases detectable as early as 4 weeks (Figure 2B, 2C). Notably, KCs exhibited the highest cell death incidence among all evaluated populations, highlighting their exceptional vulnerability.

Figure 2.

We next determined whether KCs mortality displays spatial zonation. Leveraging the liver’s lobular architecture—organized into periportal (PP, zone 1), midzonal (Mid, zone 2), and pericentral (PC, zone 3) hepatocyte zones²⁶—we performed co-staining for Timd4 (KCs marker), Tunel, and glutamine synthetase (GS; central vein marker) (Figure 2D). KCs death increased temporally across all zones, consistent with prior Clec4f/Tunel data. Strikingly, spatial analysis revealed significant periportal predominance, with PP mortality rates exceeding GS⁺ PC zones at 16 weeks of HFHC feeding (Figure 2D). These findings establish KCs as the most vulnerable hepatic population in MASLD and uncover a spatially-distinct periportal mortality pattern.

Kupffer cells exhibit metabolic reprogramming with increased glycolysis during early MASLD

To elucidate glucose metabolism changes behind KCs death during MASLD, we isolated KCs from mice at various time points during HFHC feeding (0, 4, 8, and 16 weeks). The purity of isolated KCs was confirmed by Timd4 immunofluorescence staining, with Timd4+ cells reaching over 90% (Figure S3A). Subsequently, we conducted a qRT-PCR assay to assess the mRNA expression levels of key enzymes involved in glycolysis (Slc2a1, Hk3, Pfkfb3, Pkm), the pentose phosphate pathway (PPP) (G6pd, 6pdg), glycogenolysis (Pygl), and glycogenesis (Gys1, Ugp2). Our data revealed that the mRNA expression of rate-limiting enzymes associated with fast glucose metabolism, such as glycolysis and PPP, significantly increased as early as 8 weeks after HFHC feeding initiation (Figure S3B). While mRNA expression of glycolysis rate-limiting enzymes remained elevated, PPP expression began to decline by 16 weeks after HFHC feeding (Figure S3B). Conversely, enzymes linked to slow glucose metabolism, such as oxidative phosphorylation (Idh1, Ogdh), did not exhibit significant changes (Figure S3B). Furthermore, mRNA expression of glycogenesis and glycogenolysis rate-limiting enzymes started to decrease at 8 and 16 weeks after HFHC feeding, respectively, suggesting that glucose uptake becomes the primary source of glucose metabolism in KCs during this period (Figure S3B). In addition, we investigated the mRNA expression of rate-limiting enzymes involved in β-oxidation (Acadm, Hadh) but found no significant differences (Figure S3B). The data suggest a time-dependent metabolic inflexibility, where impaired glycogen handling and mitochondrial inertia drive sustained glycolytic dependence in KCs, which exacerbate KCs vulnerability (especially in periportal zones, where glycogen storage is predominant²⁷).

To validate KCs-specific metabolic alterations in MASLD, we performed metabolomic analysis on primary KCs from male wild-type mice fed an HFHC diet for 0, 4, or 8 weeks (Figure 3A). Given that KCs mortality peaked at 8 weeks in our model (Figure 2D), we focused on these early time points to capture initiating metabolic shifts. Principal component analysis (PCA) of KCs metabolites revealed distinct, diet duration-dependent clustering, indicating profound remodeling of the global metabolic landscape (Figure 3B). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified glucose metabolism pathways—including glycolysis and the pentose phosphate pathway (PPP)—as the most significantly upregulated during MASLD progression (Figure 3C, 3D). This glycolytic activation was further corroborated by time-dependent accumulation of key intermediates (glucose, Phosphatidylethylamine [PEA], Phosphoenolpyruvate [PEP], fructose-1,6-bisphosphate [FBP], and lactate [LA]) in heatmap analysis (Figure 3E). Critically, we observed progressive increases in death-executing metabolites generated through these glucose metabolism pathways: redox disruptors (GSSG, FAD), mitochondrial toxins (methylmalonic acid), and apoptosis mediators (Hcy) -all exhibiting temporal coupling with glycolytic intermediates (Figure 3F). This demonstrates that KCs undergo rapid glycolytic reprogramming during early MASLD pathogenesis that actively generates cytotoxic effectors, coinciding with their peak vulnerability.

Figure 3.

Excessive glucose metabolic activity contributes to Kupffer cell death

To investigate the direct role of glucose activation in KCs death, isolated Kupffer cells were subjected to in vitro metabolic perturbations. First, KCs were treated with combinations of glucose and palmic acid (PA) to model MASLD pathology. Physiological glucose (5.5 mM) combined with PA increased KCs death by approximately 10%. Strikingly, elevating glucose to serum levels observed during HFHC feeding (10 mM) further increased KCs death by approximately 27%. This was evidenced by increased Cleaved Caspase-3 (Cl-Casp3) staining (Figure 4A) and elevated Cl-Casp3 protein levels via Western blot (Figure 4B).

Figure 4.

Second, we treated KCs with the PDK1 activator PS48, which directly stimulates glycolysis. The results indicated a markedly increased KCs death compared to blank or DMSO vehicle controls (Figure 4C, 4D). Finally, Oligomycin (ATP synthase inhibitor) was used to force glycolytic reliance by blocking mitochondrial ATP production. Oligomycin treatment similarly induced significant KCs death (Figure 4E and F), phenocopying the effects of direct glycolytic activation. Collectively, these data demonstrate that both excessive glycolytic flux (induced by high glucose or PDK1 activation) and impaired mitochondrial ATP production (inducing compensatory glycolysis) promote KCs death.

Enhanced glycolytic flux in Chil1^-/- macrophages

To investigate whether hyperactivated glycolysis drives KCs death in vivo, we focused on Chitinase 3-like 1 (Chi3l1; gene Chil1)—a glucose uptake regulator conserved across macrophage populations, including KCs and BMDMs¹⁷. Given the limited availability of primary KCs for intensive isotopic tracing ([U-¹³C]glucose assays) and the shared susceptibility of MoMFs to MASLD-induced death (Figure 2C), we employed Chil1^-/- BMDMs as a validated model to dissect Chi3l1-dependent metabolic mechanisms. Uniformly labeled [U-¹³C]glucose tracer analysis (Figure 5A) in Chil1^-/- BMDMs revealed genotype-specific metabolic reprogramming: PCA showed distinct clustering versus wild-type (WT) (Figure 5B), while heatmaps demonstrated pronounced accumulation of glycolytic intermediates (Figure 5C). Metabolic flux quantification confirmed significantly elevated U-¹³C enrichment in glycolytic metabolites—including glucose (Glc), fructose-6-phosphate (F6P), 3-phosphoglycerate (3PGA), 2-phosphoglycerate (2PGA), phosphoenolpyruvate (PEP), pyruvate (PA), and lactate (LA)—whereas glucose-6-phosphate (G6P), fructose-1,6-bisphosphate (FBP), and pentose phosphate pathway (PPP) intermediates (ribulose-5-phosphate [Ru5P], ribose-5-phosphate [R5P], sedoheptulose-7-phosphate [S7P]) remained unchanged. Dihydroxyacetone phosphate (DHAP) was the sole PPP-linked metabolite showing increased enrichment (Figure 5D), indicating Chil1 deletion selectively hyperactivates glycolysis without engaging the PPP. Moreover, extracellular acidification rates (ECAR) analysis also showed significantly elevated glycolytic capacity in Chil1^-/- BMDMs (Figure 5 E, 5F).

Figure 5.

Given our prior demonstration that Chi3l1 functions mainly as a secretory regulator of macrophages glucose metabolism¹⁷. Therefore, we also added recombinant Chi3l1(rChi3l1) supplementation group. rChi3l1 supplementation reversed hyper-glycolytic flux, restoring levels comparable to WT (Figure S4A-S4D). Glycolytic intermediates (Glc, F6P, FBP, 3PGA, 2PGA, PEP, PA, LA, G6P) showed reduced mass isotopologue distributions (Figure S4B, S4C). PPP intermediates (Ru5P, R5P, S7P, DHAP) remained unaffected (Figure S4B, S4C), reinforcing pathway specificity. Functionally, rChi3l1 significantly reduced lactate dehydrogenase (LDH) activity in high glucose-treated BMDMs, confirming attenuated glycolytic output (Figure S4D). Collectively, these data establish that Chi3l1 deletion specifically enhances glycolytic flux—but not PPP activity—in macrophages, positioning it as a mechanistic model for investigating glycolysis-driven KCs death.

Enhanced glycolysis accelerated Kupffer cell death in MASLD

Next, we employed Chil1^-/- mice as a mechanistic model to investigate glycolysis-driven KCs death. First, we isolated primary KCs from WT and Chil1^−/− mice and treated them with PA. Chil1^−/−KCs exhibited significantly increased susceptibility to PA-induced cell death compared to WT controls in vitro (Figure 6A). This heightened vulnerability was quantified through elevated Cl-Casp3 immunostaining (Figure 6B) and increased LDH release (Figure 6C), confirming enhanced cell death. We previously reported that KCs death increased in Chil1^-/- (Chil1 is deficient in whole body) mouse during MASLD¹⁷. To definitively determine the cellular source of Chi3l1 in MASLD livers, we performed systematic immunohistochemical analysis of serially sectioned tissues from mice fed an HFHC diet for 16 weeks. Consecutive sections were independently probed for Chi3l1 or lineage-specific markers (HNF4α, Desmin, Iba1), enabling cellular localization through morphological alignment across sequential slices. The results revealed predominant Chi3l1 expression in hepatic macrophages, especially KCs (Figure S5A). Moreover, Chi3l1 expression was significantly elevated in KCs during MASLD progression, as evidenced by co-staining with F4/80 and Timd4 (Figure S5B). These findings suggest Chi3l1 might be a KCs-autonomous regulator protein. To determine cell-autonomous effects, we generated KCs-specific Chil1 knockout mice (Clec4f^ΔChil1 mice) and analyzed their response to HFHC feeding. Flow cytometry of non-parenchymal cells (NPCs) revealed significant lower of the KCs compartment (CD45⁺ F4/80^hi CD11b^low Timd4⁺) in Clec4f^ΔChil1 mice after 16 weeks of HFHC diet compared to Clec4f^cre controls (Figure 6D). This KCs loss was corroborated by Timd4/Tunel co-staining, which showed elevated KCs death in Clec4f^ΔChil1 livers (Figure 6F, 6G). Collectively, these findings demonstrate that genetic ablation of Chil1 amplifies glycolytic flux, sensitizes KCs to lipotoxic stress in vitro, and drives KCs depletion through accelerated cell death in vivo, establishing Chi3l1 as a critical cell-autonomous regulator of KCs survival in MASLD.

Figure 6.

Discussion

This study establishes that KCs death is an early pathological feature of MASLD across multiple dietary models, exhibiting significantly greater susceptibility in KCs compared to other hepatic cell types. Through integrated transcriptomic, metabolomic, and functional analyses, we reveal that KCs undergo profound metabolic reprogramming characterized by progressive activation of glycolytic metabolism during MASLD development. Crucially, we provide compelling in vitro evidence that direct glycolytic activation (via high glucose, the PDK1 agonist PS48) or enforced glycolytic dependence (via mitochondrial ATP synthase inhibition with oligomycin) significantly exacerbates KCs apoptosis. Complementing this, in vivo studies utilizing genetic ablation of Chi3l1 – a KCs glucose-uptake inhibitor– amplifies glycolytic metabolism and accelerates diet-induced KCs loss. Collectively, these findings delineate hyperactivation of glycolysis as a central mechanism underpinning the unique vulnerability and depletion of KCs in MASLD (Figure 7).

Figure 7.

Our observation of preferential periportal KCs death aligns with the portal vein’s role as the primary entry point for dietary nutrients, including glucose and lipids^26,28. This spatial pattern suggests that KCs in this zone face the earliest and highest concentrations of metabolic stressors from the HFHC diet, potentially overwhelming their regulatory capacity. The heightened susceptibility of KCs relative to other hepatic cell types likely stems from their specialized functions as resident macrophages, including constant immune surveillance and phagocytosis, which may impose distinct metabolic demands and sensitivities^29,30. While metabolic reprogramming towards glycolysis is a recognized feature of activated macrophages (often linked to pro-inflammatory phenotypes)¹⁵, our findings reveal a critical divergence: in KCs during MASLD, sustained glycolytic hyperactivation culminates not in sustained activation, but in apoptotic cell death. This highlights a unique metabolic vulnerability specific to KCs in the context of chronic nutrient overload.

Previous studies demonstrate Chi3l1’s pro-fibrotic role in advanced MASH, where it stimulates hepatic stellate cells via IL-13Rα2 (CDAA-HFAT model)³¹, inhibits macrophage apoptosis through Fas/Akt signaling (CCL4 and MCD models)³², and directly promotes fibrogenesis in aging livers³³. In contrast, our work reveals Chi3l1’s protective metabolic function in early MASLD using an HFHC model that recapitulates steatohepatitis without fibrosis (Fig S1A). While myeloid-specific deletion (Lysm^ΔChil1 mice) showed no metabolic phenotype¹⁷, KCs-specific ablation (Clec4f^ΔChil1) accelerated KCs loss and steatosis progression. This establishes Chi3l1 as a KCs-autonomous guardian that restrains glycolytic overload—a mechanism distinct from its anti-apoptotic effects in fibrotic contexts. This apparent duality arises from disease-stage specificity: In pre-fibrotic MASLD, KCs-derived Chi3l1 sustains metabolic homeostasis by preventing lethal hyper-glycolysis. In established fibrosis, MoMFs-derived Chi3l1 drives ECM remodeling. Our data thus reconcile prior contradictions: Chi3l1’s role transitions from hepatoprotective in early disease (via KCs survival) to pro-fibrotic in advanced MASH (via MoMFs-HSCs crosstalk), with outcomes determined by cellular source and pathological context.

Our recent study demonstrated that KCs exhibit a glucose-hungry metabolic phenotype, making them uniquely dependent on Chi3l1-mediated regulation. In contrast, MoMFs maintain a relatively glucose-independent metabolic program¹⁷. Complementing these findings, current data confirm that Chi3l1 loss resulted in increased glycolytic flux (measured by ¹³C-glucose tracing) in KCs and markedly increased sensitivity to lipotoxicity in vitro and accelerated death in vivo. This establishes Chi3l1 as a critical cell-autonomous regulator of KCs glucose metabolism, restraining glycolysis and promoting survival under metabolic stress. Identifying Chi3l1 as a guardian of KCs glucose homeostasis provides crucial insight into their metabolic resilience. While Chi3l1 regulates glucose uptake across macrophage subtypes (including BMDMs), its loss is selectively detrimental to KCs due to their unique reliance on glucose metabolism. This dichotomy arises from intrinsic differences in metabolic programming, not divergent Chi3l1 functions.

While Chi3l1 regulates glucose uptake across macrophage subtypes (including BMDMs), its loss disproportionately compromises KCs survival due to their intrinsic metabolic inflexibility—not divergent Chi3l1 functions. This mechanistic distinction explains why MoMFs (and BMDMs) tolerate Chi3l1 deficiency: their metabolic plasticity allows adaptation to glucose fluctuations, whereas KCs undergo energetic crisis when glycolytic control fails¹⁷. Crucially, our experimental data demonstrate that: Chi3l1 loss hyperactivates glycolytic flux in BMDMs (confirmed by U¹³C-glucose tracing), establishing a conserved regulatory mechanism relevant to KCs. This metabolic dysregulation directly increases KCs sensitivity to lipotoxicity in vitro and accelerates death in vivo. KCs-specific Chi3l1 ablation (Clec4f^ΔChil1) triggered progressive KCs depletion under metabolic stress. Collectively, these findings establish Chi3l1 as a cell-autonomous guardian of KCs glucose homeostasis.

Several limitations of this study should be acknowledged. Firstly, while we utilized multiple dietary models (HFHC, HFD, MCD), the findings are derived from mouse studies. Validation in human MASLD/NASH samples, particularly assessing spatial KCs death patterns and Chi3l1 expression/metabolic signatures in human KCs, is essential. Secondly, while we establish glycolysis as a key driver, the precise downstream mechanisms linking hyper-glycolysis to apoptosis in KCs remain to be fully elucidated. Potential mediators include excessive reactive oxygen species (ROS) generation, lactate accumulation and acidosis, alterations in NAD+/NADH ratios, or insufficient ATP production despite high flux if mitochondrial capacity is compromised. Thirdly, our study focused primarily on glucose metabolism; the potential contribution of altered fatty acid oxidation or amino acid metabolism to KCs death warrants further exploration.

Despite these limitations, our findings have significant translational implications. They suggest that interventions aimed at modulating KCs glucose metabolism, rather than merely suppressing inflammation, could be a novel therapeutic strategy for MASLD. Potential approaches include enhancing Chi3l1 signaling or activity, selectively inhibiting key glycolytic drivers (e.g., PDK1) within KCs, or providing metabolic support to bolster KCs survival under stress. Preserving the KCs population could help maintain immune homeostasis, dampen chronic inflammation, and potentially slow or prevent MASLD progression. Future research should focus on: 1) Validating the human relevance of KCs metabolic vulnerability and Chi3l1’s role; 2) Defining the exact glycolytic checkpoints and downstream effectors triggering KCs apoptosis; 3) Developing and testing targeted strategies to modulate KCs glycolysis in vivo; and 4) Investigating the long-term functional impact of preventing KCs death on overall MASLD histopathology and progression.

In conclusion, this study uncovers a fundamental mechanism of MASLD pathogenesis: metabolic reprogramming towards hyper-glycolysis renders Kupffer cells uniquely susceptible to apoptosis, leading to their progressive depletion, particularly in the periportal region. Chi3l1 serves as a critical endogenous regulator restraining this lethal metabolic shift. Targeting this KCs-specific metabolic vulnerability represents a promising avenue for developing novel therapies aimed at preserving hepatic immune function and halting MASLD progression.

Data availability

Data availability All data generated or analysed during this study are included in the manuscript and supporting files;source data files have been provided. All reagents developed in this study are available upon reasonable request.

Acknowledgements

We thank Dr. Bin Qi (Yunnan University) for suggestions and discussion. We thank Guangxun Meng (The Shanghai Institute of Immunity and Infection of the Chinese Academy of Sciences) for providing us with L929 cells. We thank Cynthia Ju (UTHealth) for advice in manuscript submission.

Additional files

Supplementary materials and methods

Supplementary Figures