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Nuclear PHGDH regulates macrophage polarization through transcriptional repression of GLUD1 and GLS2 in breast cancer
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Pei Wang1, Xin Du1, Zhiren Han1, Jiaxin Zhong1, Jiayu Yuan2, Lin Jiang2, Beinan Han1, Wenkui Fu1, Hongde Li3, Hai Hu2, 3, 4, Zhenkun Na3
Cancer Biology & Medicine | 2025, 22(5) : 502 - 524
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Cancer Biology & Medicine | 2025, 22(5): 502-524
• Original Article •
Nuclear PHGDH regulates macrophage polarization through transcriptional repression of GLUD1 and GLS2 in breast cancer
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Pei Wang1, Xin Du1, Zhiren Han1, Jiaxin Zhong1, Jiayu Yuan2, Lin Jiang2, Beinan Han1, Wenkui Fu1, Hongde Li3, Hai Hu2, 3, 4, Zhenkun Na3
Affiliations
  • 1Department of Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
  • 2Postgraduate Training Base Alliance of Wenzhou Medical University, Zhejiang Cancer Hospital, Hangzhou 310022, China
  • 3Hangzhou Institute of Medicine, Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou 310018, China
  • 4Breast Cancer Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, China
About Author:

Author contributions

Conceived and designed the analysis: Pei Wang, Hai Hu, Zhenkun Na, Xin Du, Hongde Li.

Collected the data: Pei Wang, Jiaxin Zhong, Beinan Han.

Contributed data or analysis tools: Pei Wang, Jiayu Yuan, Lin Jiang, Wenkui Fu.

Performed the analysis: Pei Wang, Xin Du, Zhiren Han.

Wrote the paper: Pei Wang.

Published: 2025-05-15 doi: 10.20892/j.issn.2095-3941.2024.0398
Outline
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Objective:

Tumor-associated macrophages (TAMs) exhibit heterogeneous properties including anti-tumorigenic and pro-tumorigenic phenotypes. The rate-limiting enzyme in de novo serine biosynthesis, 3-phosphoglycerate dehydrogenase (PHGDH), has a well-established role in cellular metabolism, yet its specific role in macrophages remains unknown.

Methods:

Metabolomics assays were conducted to assess metabolite composition and dynamics in macrophages. Changes in polarization and immunosuppressive markers were validated with qRT-PCR. Bioinformatics was used to analyze immune cell subsets and associated metabolic pathways. Finally, ChIP-qPCR and co-immunoprecipitation assays were performed to elucidate the downstream regulatory mechanisms of PHGDH.

Results:

Serine metabolism was found to be downregulated in TAMs in breast cancer. Functional studies revealed that PHGDH inhibition promotes an M2-like phenotype and immunosuppressive functions in macrophages. Furthermore, PHGDH was found to undergo nuclear translocation during macrophage polarization. Mechanistically, nuclear PHGDH was found to regulate GLUD1 and GLS2 transcription via interaction with the transcription factor STAT3. Rescue experiments demonstrated that glutamine supplementation and STAT3 inhibition reversed the effects of PHGDH on macrophage function.

Conclusions:

Our findings reveal a previously unrecognized non-canonical metabolic function of PHGDH, thus providing potential therapeutic targets in the tumor microenvironment for reversing malignant progression.

Breast cancer  /  tumor-associated macrophages  /  serine metabolism  /  polarization
Pei Wang, Xin Du, Zhiren Han, Jiaxin Zhong, Jiayu Yuan, Lin Jiang, Beinan Han, Wenkui Fu, Hongde Li, Hai Hu, Zhenkun Na. Nuclear PHGDH regulates macrophage polarization through transcriptional repression of GLUD1 and GLS2 in breast cancer[J]. Cancer Biology & Medicine, 2025 , 22 (5) : 502 -524 . DOI: 10.20892/j.issn.2095-3941.2024.0398
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Introduction
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Breast cancer is the most prevalent malignant tumor type and the leading cause of cancer-related mortality among women globally. This disease exhibits diverse biological characteristics in terms of pathology, genomics, gene expression, and the tumor microenvironment (TME)1. The optimization of therapy depends on the tumor subtype, anatomic cancer stage, and individual patient preferences2. Decreasing breast cancer mortality requires effectively addressing metastatic disease and overcoming resistance to systemic therapies3.
The focus of recent research on metastasis and drug resistance has expanded from solely tumor cells to the inflammatory TME4. The intricate, multi-layered crosstalk between malignant and non-malignant cells in the TME is critical in breast cancer initiation and progression. The TME contributes to breast cancer heterogeneity and is instrumental in driving malignant progression5. Consequently, numerous strategies targeting the non-malignant cells and elements within the TME have been explored6.
Among the various cellular components of the TME, tumor-associated macrophages (TAMs) are critical in mediating the inflammatory process. TAMs are essential in tumor biology, by promoting tumor angiogenesis, metastasis, and immune evasion, and thereby driving tumor progression and contributing to therapeutic resistance7. Additionally, macrophages exhibit phenotypic heterogeneity and plasticity through a process known as polarization. In this dynamic process, macrophages react to stimuli from the surrounding microenvironment and adopt a distinct functional phenotype8. Macrophages are typically categorized into 2 functional states: the classically activated (M1) phenotype, which exhibits anti-tumorigenic properties, and the alternatively activated (M2) phenotype, which supports tumorigenesis9. Most TAMs in the TME have an M2-like phenotype. An emerging therapeutic strategy involves harnessing the plasticity of macrophages to reprogram them toward a tumoricidal phenotype. Reprogramming TAMs has been suggested to enhance the efficacy of immunotherapies by generating robust anti-tumor responses10.
The TME’s limited differentiated vascular system impairs nutrient and oxygen delivery, and requires immune cells to compete with rapidly proliferating cancer cells for essential resources11. These conditions induce metabolic adaptations in infiltrating immune cells that promote immune tolerance and suppress anti-tumor immunity. Targeting cellular metabolism offers promising therapeutic avenues in cancer treatment, particularly by modulating immune cell metabolism to enhance pro-inflammatory responses and optimize the efficacy of cancer therapies.
Immune cells establish distinct metabolic profiles in response to activation, adaptation to different tissue environments, inflammation, or disease12. In tumors, TAM reprogramming toward an anti-tumorigenic phenotype is tightly regulated at multiple levels, including transcriptional control and metabolic reprogramming13. Pro-tumorigenic TAMs, akin to alternatively activated (M2) macrophages, exhibit enhanced fatty acid uptake and glutamine metabolism, thus leading to elevated oxidative phosphorylation activity14. In contrast, anti-tumorigenic TAMs undergo metabolic shifts similar to those in classically activated (M1) macrophages, and are characterized by upregulated aerobic glycolysis, diminished oxidative metabolism, and tricarboxylic acid (TCA) cycle disruptions. These metabolic alterations underpin their anti-tumorigenic functions15. The divergent bioenergetic demands of M1 and M2 macrophages are increasingly recognized as critical regulatory mechanisms that influence macrophage behavior, thereby offering potential avenues for the development of future cancer therapies.
Serine can be obtained through both exogenous intake and de novo synthesis pathways16, and phosphoglycerate dehydrogenase (PHGDH) is the initial rate-limiting enzyme in endogenous serine synthesis. PHGDH has been reported to have inconsistent effects on macrophage polarization; consequently, serine metabolism might have varying regulatory effects under different conditions. In this study, we applied metabolomics analysis methods in both benign and malignant inflammatory microenvironments, and found that glucose-serine-glycine-one-carbon metabolism differed significantly among TAMs. On the basis of these results, we further investigated the signaling pathway and molecular mechanisms associated with the above metabolic abnormalities in macrophages regulated in the microenvironment. Our results demonstrated that PHGDH undergoes nuclear translocation during polarization and regulates the transcription of GLUD1 and GLS2 by interacting with the transcription factor STAT3. In summary, our findings clarify abnormalities in key metabolic regulatory pathways that may serve as markers of disease progression and provide guidance regarding therapeutic strategies.
Materials and methods
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Cells and cell culture
Six human cell lines were used: THP-1 [American Type Culture Collection (ATCC), Cat. TIB-202], HEK293T (ATCC, Cat. CRL-3216), MCF-7 (ATCC, Cat. HTB-22), MDA-MB-231 (ATCC, Cat. HTB-26), HCC1428 (ATCC, Cat. CRL-2327), and T-47D (ATCC, Cat. HTB-133). HEK293T, MCF-7, and MDA-MB-231 cells were cultured in DMEM (Gibco, Cat. 11965118) supplemented with 10% fetal bovine serum (Gibco, Cat. 10437028) and 1% penicillin‒streptomycin (100 U/mL) (Gibco, Cat. 15140122). THP1, HCC1428, and T-47D were maintained in RPMI1640 (Gibco, Cat. 12633012) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin.
To initiate differentiation into M0 macrophage phenotype, we treated THP-1 cells with 100 ng/mL phorbol-12-myristate-13-acetate (PMA; MCE, Cat. HY-18739) for 24 h. M0 macrophages were then stimulated with 100 ng/mL lipopolysaccharide (MCE, Cat. HY-D1056) and 20 ng/mL IFN-γ (MCE, Cat. HY-P7025A) for 48 h to achieve M1 differentiation. For M2 polarization, M0 macrophages were stimulated with 20 ng/mL IL-4 (MCE, Cat. HY-P70445) and 20 ng/mL IL-13 (MCE, Cat. HY-P70568) for 48 h. For induction of TAMs, MDA-MB-231 cell conditioned medium (MB231-CM) was collected and filtered. The MB231-CM was then combined in a 1:1 ratio with complete DMEM to create a medium suitable for TAM culture. M0 macrophages were then treated with MB231-CM for 48 h to generate TAMs. Correct differentiation into M1 and M2 type macrophages was confirmed through gene expression analysis of specific markers (such as IL1β, IL-6, TNF-α, IL-10, CD163, AGR1, TGF-β1, and CCL22).
Isolation and culture of mouse bone marrow-derived macrophages
C57BL/6 mice 6–8 weeks of age were euthanized and subsequently soaked in 75% ethanol. The skin from the lower part of the body was removed, and the tissue from the legs was carefully dissected. The bilateral femurs and tibias of the mice were isolated, and the muscles and tissues attached to the bones were removed. The bones were then immersed in 75% ethanol for 5 min and soaked in cold PBS for another 5 min. The cleaned bones were separated at the knee joint, and the ends of the leg bones were cut. Bone marrow cells were obtained by flushing of the femurs and tibias with cold induction medium. The cells were subsequently centrifuged at 1,500 rpm for 15 min and then resuspended with red cell lysate for approximately 2 min. DMEM was used to terminate the lysis process. After centrifugation at 300 g for 10 min, the cells were resuspended in DMEM containing 20% L929 conditioned medium. The medium was changed every 3 days, and bone marrow-derived macrophages (BMDMs) were induced to mature after 6–7 days. MB231-CM was then used to stimulate BMDMs for further analysis.
Stable cell line generation, and plasmid or small interfering RNA transfection
The PHGDH overexpression plasmid pcDNA3.1 and PHGDH small interfering RNA (siRNA) were synthesized by IGE Biotechnology, Guangzhou, China. BMDM cells were cultured in 6-well plates, and 3 μg plasmid or 50 nM siRNA was mixed with Advanced DNA Transfection Reagent (Zeta Life, USA, Cat. AD600150). The mixtures were added to 6-well plates after being incubated at room temperature for 15 min. After 48 or 72 h, transgene efficiency was assessed with quantitative real-time reverse transcriptase PCR (qRT-PCR) and Western blot analysis.
Lentiviral vectors were constructed by IGE Biotechnology, Guangzhou, China. The lentiviral vector and packaging plasmid were transfected into HEK293T cells to generate a viral suspension, which was subsequently used to transfect THP-1 cells. The viability of these cells was determined with Western blot and qRT-PCR.
Mouse studies
C57BL/6 mice were purchased from GemPharmatech and raised in the SPF animal facility at the Laboratory Animal Resource Center of Sun Yat-sen University. Female C57BL/6 mice 6–8 weeks of age were used. The mouse breast cancer cell line EO771 (ATCC, Cat. CRL-3461) was obtained from the ATCC and grown according to the standard protocols on the ATCC website. PHGDH overexpressing BMDMs (OE-PHGDH) and control BMDMs (OE-NC) were treated with IL-4/IL-13 for 24 h to induce polarization into immunosuppressive macrophages. Subsequently, they were cocultured with EO771 cells and injected into the fourth mammary fat pad on the right side in mice. EO771 cells (2 × 105) were mixed with 4 × 105 BMDMs in 100 μL PBS and used to induce the formation of subcutaneous tumors in mice. For macrophage ablation, mice received intraperitoneal injection of clodronate liposomes (150 μL/animal, twice per week) (Yeasen, Cat. 40337ES08). For tumor growth analysis, tumors were measured every 3 days with calipers, and tumor volume was calculated with the formula length × width × width/2. The mice were sacrificed 16 days after injection, and the tumors were removed for measurement and photography. The animal experimentation procedures received ethical approval under MSLT-2024-0121.
Immunohistochemistry staining
Mouse tissues were processed for formalin fixation, paraffin embedding, and tissue block sectioning. After deparaffinization and hydration, the sections were treated with H2O2 to block internal peroxidase activity. Citrate buffer (pH = 6) or Tris EDTA buffer (pH = 9) was subsequently used to retrieve antigens. Slides were blocked with goat serum for 30 min, then incubated overnight with rabbit anti-Ki-67 (Abcam, Cat. ab15580, 1:500) primary antibodies at 4°C. The next day, sections were incubated with goat anti-rabbit HRP (Abcam, Cat. ab205718, 1:2,000) for 1 h, then stained with hematoxylin and 3,3′-diaminobenzidine (Dako, Cat. ab64238) solutions. The images were captured under an OLYMPUS DP22 microscope, and ImageJ was used to assess the density of the immunohistochemistry staining.
Dual-luciferase reporter assays
The promoter sequences of GLUD1 and GLS2 in the pGL3-basic plasmid were synthesized by Generay Biotech, Shanghai, China. GLUD1 in the pGL3-basic plasmid and GLS2 in the pGL3-basic plasmid were transfected into TAMs induced by THP1 cells. The cells were treated as indicated in the figures and subsequently collected with passive lysis buffer. Luciferase activity was measured with a dual luciferase assay kit (Promega, Cat. E1910).
Analysis of immune infiltration in breast tumors
The infiltration of immune cells in the TME was quantified with the CIBERSORT analytical tool17. Gene expression data for 22 immune cell types were obtained from the CIBERSORT database. Subsequently, the data were uploaded to the CIBERSORT web platform and processed with the default signature matrix and enriched with 1,000 permutations to enhance the reliability of the results. With the CIBERSORT algorithm, a P-value was calculated for each deconvoluted sample with Monte Carlo sampling, thereby confirming the statistical significance and credibility of our results with high precision.
Correlation between serine metabolism and immune cell infiltration
The transcriptomic data and sample information for TCGA breast cancer data were obtained from the Xena database18. The CIBERSORT algorithm was used to assess immune cell infiltration. The correlations of PHGDH, PSPH, and PSAT expression with the infiltration levels of various immune cells were assessed with Pearson correlation analysis.
Metabolomics
THP1 cells were plated at a density of 3 × 106 cells per well in 6-well plates, washed, and harvested with 1 mL ice-cold 80% methanol. After being placed in a −80°C freezer for 30 min, the THP1 cells were transferred to a 2 mL tube. The dish was washed with 80% cold acetonitrile to ensure complete collection of cells. The samples were then rotary mixed at 4°C for 30 min and centrifuged at 12,000 rpm for 15 min at 4°C. The supernatant was collected and subsequently normalized by protein concentration for further analysis with BCA Protein Assay Kits (Thermo Fisher Scientific, Cat. 23225). The precipitate was kept at −80°C until mass spectrometry. For detection of intracellular metabolites, liquid chromatography-mass spectrometry was performed on an Agilent 1290 Infinity II UHPLC system (Santa Clara) at the Department of Oncology, Sun Yat-sen Memorial Hospital. The raw data were processed in MassHunter Workstation Software (version B.08.00, Agilent), and the peak areas of target compounds were integrated and output for quantitative calculation.
qRT-PCR
THP1 cells were plated at a density of 3 × 106 cells per well in a 6-well culture plate, then washed and harvested with an RNA extraction kit (Thermo Fisher Scientific, Cat. 12183020). Subsequently, cDNA was generated from 1 μg total RNA according to the manufacturer’s instructions (Vazyme, Cat. R223-01). Real-time PCR was performed with qRT-PCR SYBR Master Mix (Vazyme, Cat. Q311-02).
Immunoblotting
THP1 cells were plated at a density of 3 × 106 cells per well in 6-well plates, lysed in RIPA buffer, and quantified with a BCA protein assay kit (Thermo Fisher Scientific, Cat. 23227). Subsequently, cell lysates (20 μg protein) were separated with SDS-PAGE and then transferred to polyvinylidene fluoride membranes (Millipore, Cat. IPVH00010). All membranes were blocked with 5% bovine serum albumin or nonfat milk in TBST buffer for 1 h, then incubated with the following primary antibodies: rabbit anti-PHGDH (Cell Signaling Technology, Cat. 66350, 1:1,000), rabbit anti-Arg1 (Cell Signaling Technology, Cat. 93668, 1:1,000), and rabbit anti-β-actin (Cell Signaling Technology, Cat. 4967, 1:1,000). After being washed with TBST buffer, the membrane was incubated with goat anti-rabbit IgG H&L (HRP) (ab6721, 1:5,000, Abcam, UK), then detected with enhanced chemiluminescence reagent (Thermo Fisher Scientific, Cat. 34580).
Nuclear and cytoplasmic protein was prepared with a Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, Cat. P0028) according to the manufacturer’s instructions. Proteins from cell lysates were analyzed through Western blot with the following antibodies: rabbit anti-PHGDH (Cell Signaling Technology, Cat. 66350, 1:1,000), rabbit anti-histone H3 (Cell Signaling Technology, Cat. 9715, 1:1,000), and rabbit anti-GAPDH (Cell Signaling Technology, Cat. 2118, 1:1,000).
Co-immunoprecipitation
Proteins were extracted from TAMs induced by THP1 cells (1 × 107) with IP lysis buffer (Thermo Fisher Scientific, Cat. 87787). Next, cell lysates were incubated with rabbit anti-PHGDH antibody (Cell Signaling Technology, Cat. 66350) at 4°C overnight. On the second day, the samples were mixed with Protein A/G agarose beads (Santa Cruz, Cat. sc-2003) for 2–3 h, then washed. The beads were boiled in 5× SDS-PAGE loading buffer (Fude, Cat. FD006) for 5 min and analyzed with Western blot. Mouse anti-STAT3 (Cell Signaling Technology, Cat. 9139, 1:1,000) and mouse anti-PHGDH (Abcam, Cat. ab57030, 1:1,000) served as the primary antibodies, whereas goat anti-Mouse IgG H&L (HRP) (Abcam ab6721, Cat. ab7063, 1:5,000) served as secondary antibodies for protein complex detection.
Chromatin immunoprecipitation (ChIP) and ChIP-qPCR
THP1 cells (1 × 107) were seeded in 100-mm dishes and treated with 1% formaldehyde (Sigma, USA, Cat. F8775) for crosslinking. Subsequently, 2.5 M glycine (Sigma, USA, Cat. G7126) was used to stop the crosslinking for 5 min at room temperature, and sonication was performed with a VirTis VirSonic 50 Ultrasonic Cell Disrupter at 4°C to shear chromatin into small fragments (150–300 bp). A 1% volume of the total sample was saved as input control. The anti-PHGDH antibody (Cell Signaling Technology, Cat. 66350) and rabbit IgG (Cell Signaling Technology, Cat. 2729) were added to the samples and rotary mixed at 4°C overnight. On the second day, chromatin immunoprecipitation (ChIP)-Grade Protein A/G Magnetic Beads (Thermo Fisher Scientific, Cat. 26162) were added to pull down the target protein, and elution buffer was used to resuspend the beads and isolate DNA.
Real-time PCR was performed to analyze the amount of bound DNA, and the enrichment value was calculated with the percentage input method. Primers covering the PHGDH binding site of the GLUD1 or GLS2 gene promoter region were used for qRT-PCR.
Immunofluorescence microscopy
Before immunofluorescence analysis, THP-1 cells (4 × 105) were treated with PMA for 24 h to induce differentiation into the M0 macrophage phenotype. After PMA treatment, the previously suspended THP-1 cells adhered to the culture surface. Subsequently, the cells were treated with MB231-CM for 48 h to induce TAMs. For immunofluorescence assays, M0 and TAMs were fixed with 4% paraformaldehyde on confocal dishes, then permeabilized with PBS containing 0.1% Triton X-100 (AR-0341, Dingguocs, Beijing, China) for 20 min. The cells were blocked with 5% bovine serum albumin in TBST buffer for 1 h at room temperature and then incubated with rabbit anti-PHGDH (1:500; Cell Signaling Technology, Cat. 32770) primary antibodies overnight at 4°C. On the second day, the cells were incubated with Alexa Fluor 488-labeled goat anti-rabbit IgG (1:200; Beyotime, Cat. A0423) secondary antibodies at room temperature for 1 h. Images were captured with a confocal microscope system (Zeiss LSM 780, Jena, Germany). Analysis was conducted in ImageJ.
Mouse single-cell RNA sequencing data analysis
For single-cell RNA sequencing (scRNA-seq) analysis, 6- to 8-week-old female BALB/c mice were used. The mouse breast cancer cell line 4T1 (ATCC, Cat. CRL-2359) was obtained from the ATCC and grown according to the standard protocols on the ATCC website. Approximately 1 × 106 4T1 cells in 100 μL PBS were injected into the fourth mammary fat pad in 6- to 8-week-old female BALB/c mice. The tumors were harvested for single-cell sequencing analysis after 4 weeks, to investigate the serine metabolic pathway in the TME.
For preparation of single-cell suspensions, mouse tumor tissue was dissected and minced with sterile scissors. The minced tissue was then transferred into a tube. Collagenase was used to digest the tissue at 37°C under shaking for 30 min. The cell suspension was subsequently filtered through a 40 μm mesh filter for single-cell sequencing analysis.
For alignment and expression matrix construction, raw FASTQ data were mapped to the GRCm39 reference genome (ENSEMBL release) with the 10× Genomics CellRanger pipeline (v8.0.0), and the expression count matrix was constructed via the cellranger count function with default settings.
For quality control, low-quality cells were removed according to the following criteria: (1) cells with > 300 detected genes; (2) cells with < 30% mitochondrial counts; and (3) cells with < 5% hemoglobin gene counts. Doublets were removed with the DoubletFinder pipeline, and genes expressed in fewer than 25 cells were excluded. A total of 19,338 genes and 14,051 cells passed quality control and were used in subsequent analysis.
For integration, dimensionality reduction, and cell annotation, the expression matrix was normalized and scaled with the Seurat pipeline, and sample integration was performed with Harmony. Dimensionality reduction was performed via the RunPCA function, and the top 30 PCs were used for UMAP analysis. A nearest-neighbor graph was generated, and cell clusters were defined with the Louvain algorithm at a resolution of 0.15. Cluster-specific marker genes were identified with Seurat’s FindAllMarkers function, and clusters were annotated according to the biological relevance of their marker genes.
Statistical analysis
All continuous variables are represented as mean ± SD. Non-parametric variables were compared with the Chi-squared test. For parametric variables, a 2-tailed Student’s t-test was used to indicate differences between 2 groups, or one-way analysis of variance was used for more than 2 groups. Kaplan–Meier analysis and log-rank test were used for survival analysis. P < 0.05 was considered statistically significant.
Results
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M2 macrophages predict the prognosis of patients with breast cancer
Breast cancer is the most common malignancy in women and is classified as an inflammation-associated tumor19,20. To investigate the distribution of immune cells in breast cancer samples, we used the CIBERSORT algorithm on the TCGA BRCA database to quantify and compare the abundance of 22 distinct immune cell types. Macrophages emerged as the predominant immune cell subset in the TME (Figure 1A). Clinical evidence has established a significant correlation between TAMs and poor prognosis across various types of cancer21. We extended this analysis to breast cancer, for which survival analysis revealed that M1 macrophage infiltration had no substantial influence on overall survival outcomes. In contrast, lower infiltration of M2 macrophages was associated with a more favorable prognosis (Figure 1B). Additionally, the proportion of M2 macrophages progressively increased with advancing tumor stage, whereas no significant variation in M1 macrophage infiltration was observed across stages (Figure 1C). Together, these findings highlighted that macrophages are the predominant immune cell infiltrate, and M2 macrophage infiltration correlates with prognosis in patients with breast cancer.
Tumor-associated macrophages undergo metabolic reprogramming
Macrophages are highly plastic cells capable of altering their functional phenotype through polarization. In cancer, the TME actively reprograms macrophage metabolism, thereby regulating macrophage differentiation, polarization, mobilization, and the antitumor response. However, the metabolic alterations in TAMs are not fully understood.
To investigate the metabolic reprogramming in TAMs, we first differentiated THP-1 cells into M0 macrophages with PMA, then exposed them to conditioned medium from MDA-MB-231 tumor cell lines to mimic the TME in vitro. To confirm the induction effect, we incorporated proinflammatory (M1-like) and immunosuppressive (M2-like) macrophages as controls. The expression of proinflammatory cytokines, including IL1β, IL-6, and TNF-α, and immunosuppressive genes, such as IL-10, CD163, AGR1, TGF-β1, and CCL22, was measured. The expression of proinflammatory cytokines was comparable between M0 and TAMs. The transcriptional levels of immunosuppressive genes were greater in TAMs than M0 (Figure 2A). These results suggested that MDA-MB-231 conditioned medium promoted TAM polarization toward an M2-like phenotype. Subsequently, we used LC-MS/MS to profile the metabolic landscape of TAMs, and categorized the identified metabolites into 21 distinct chemical classes according to the Kyoto Encyclopedia of Genes and Genomes (KEGG). The numbers and percentage of metabolites in each chemical category are depicted in Figure 2B, thus illustrating the comprehensive coverage of pathway modules in this study. The glycolysis pathway was relatively more active in M1 macrophages (Figure 2C), whereas the tricarboxylic acid cycle was relatively more prominent in M2 macrophages (Figure 2D); in contrast, the metabolic pattern of TAMs was more consistent with M2 macrophages in the heatmap (Figure 2E). KEGG-based metabolic pathway enrichment analyses of differentially expressed metabolites with MetaboAnalyst 5.0 revealed that these metabolites were enriched primarily in cysteine and methionine metabolism pathways, and glycine, serine, and threonine metabolism pathways (Figure 2F). De novo serine synthesis branches off from glycolysis, and the glycolytic intermediate 3-phosphoglycerate is directed into the serine synthesis pathway (SSP). Serine plays a crucial role in providing 1-carbon units to the folate cycle, thus supporting nucleotide synthesis and NADPH production22. Under certain circumstances, 1-carbon units can be integrated into the methionine cycle by recycling homocysteine to methionine (Figure 2G).
Simultaneously, we developed a preclinical model of triple-negative breast cancer by implanting 4T1 cells into BALB/c mice and subsequently conducted scRNA-seq. Notably, the key enzymes involved in the serine metabolic pathway (PHGDH, PSAT1, and PSPH) were significantly enriched in macrophages among the immune cells (Figure 2H and 2I).
The glycolysis branch pathways - the pentose phosphate pathway (PPP) and SSP and 1-carbon metabolism are down-regulated in tumor-associated macrophages
LC-MS/MS metabolomics analysis revealed a significant decrease in SSP metabolism after MB231-CM treatment (Figure 3A). A significant reduction in glycolytic flux was observed in TAMs, particularly affecting downstream pathways such as the PPP and one-carbon metabolism, was observed in TAMs (Figure 3B and 3C).
The restriction of exogenous serine or inhibition of the SSP influences macrophage polarization through various mechanisms23–27. These conflicting findings suggest that serine metabolism, including both exogenous uptake and de novo synthesis, exerts diverse regulatory effects on macrophage behavior under various conditions. Furthermore, previous studies have demonstrated that exogenous serine supplementation cannot compensate for the impairment in cell growth induced by PHGDH deletion28, thus indicating that the function of the SSP extends beyond serine provision and may potentially include the activation of key transcription factors involved in polarization. We assessed the expression of key SSP enzymes at the gene level and observed that MB231-CM treatment downregulated these enzymes, as confirmed by qRT-PCR (Figure 3D).
Given the low expression of PHGDH in the MDA-MB-231 cell line, we hypothesized that crosstalk in serine metabolism might potentially exist between tumor cells and macrophages. THP1 cells were treated with conditional medium from 4 breast tumor cell lines, each with varying PHGDH expression. We observed an approximately 50% decrease in PHGDH mRNA expression in macrophages after stimulation with these media (Figure 3E), as further supported by Western blot findings (Figure 3F). Additionally, an increase in Arg1 protein levels confirmed the induction effect in TAMs (Figure 3F). These results suggested that PHGDH expression in tumor cells does not directly regulate serine metabolism in macrophages.
To further investigate the serine metabolic pathways in human samples, we conducted CIBERSORT immune infiltration analysis of primary breast cancer data, and observed a significant negative correlation between the expression of SSP metabolic enzymes and M2 macrophage infiltration levels (Figure 3G). Collectively, these findings indicated that serine metabolism is downregulated in TAMs in breast cancer.
PHGDH inhibition promotes the M2-like phenotype and immunosuppressive function in macrophages
To further elucidate the biological function of PHGDH in macrophages, we used a lentiviral vector system to achieve conditional expression of the PHGDH gene in THP-1 cells. The knockdown and overexpression of PHGDH were confirmed with qRT-PCR and Western blot (Figure 4A and 4B). Subsequently, we examined the influence of PHGDH on the expression of M2-like markers in TAMs treated with MB231-CM. Knockdown of PHGDH significantly upregulated the mRNA levels of M2-associated genes (IL-10, CCL18, CCL22, TGF-β, and CD206), as determined with qRT-PCR. In contrast, PHGDH overexpression markedly suppressed the expression of these M2 marker genes (Figure 4E). Furthermore, inhibition of PHGDH led to a significant increase in expression of the immune checkpoint genes PD-L1 and PD-L2 (Figure 4G).
Additionally, we investigated the function of PHGDH in mouse BMDMs. We transfected PHGDH specific siRNA (siPHGDH) and overexpression plasmid to regulate the expression of PHGDH (Figure 4C and 4D). Subsequently, we conducted the same experiment and obtained consistent results in BMDMs. Compared with the control group, the PHGDH overexpression group exhibited significantly downregulated expression of M2-associated genes (Figure 4F) and immune checkpoint genes (PD-L1 and PD-L2) (Figure 4H). In contrast, the expression of M2 markers and immune checkpoint genes (PD-L1 and PD-L2) in the siPHGDH group was significantly upregulated with respect to the control group. The consistent results in TAMs induced by THP1 and BMDM suggested that PHGDH inhibition promotes an M2-like phenotype and immunosuppressive function.
Additionally, we explored the relationship between PHGDH expression and macrophage-associated markers (TGF-β and TNF-α) in human breast cancer datasets (TCGA). We observed a significant negative correlation between PHGDH expression and the M2 macrophage marker TGF-β (R = −0.12, P < 0.001) and a positive correlation with the M1 macrophage marker TNF-α (R = 0.11, P < 0.001) in patients with breast cancer (Figure 4I). A weak correlation was found between PHGDH expression and other macrophage-associated markers (Figure S1).
To explore whether PHGDH might regulate macrophage polarization and consequently tumor growth in vivo, we transfected BMDMs (polarized by IL-4 and IL-13) with either a PHGDH-overexpressing plasmid or a control plasmid, and mixed them with EO771 cells. Subsequently, the mixed cells were injected into C57BL/6 mice (n = 7) (Figure 4J). Overexpression of PHGDH resulted in significantly lower tumor growth and tumor weight than observed in control tumors (Figure 4K). Consistently, fewer Ki67+ proliferating cells were found in tumors with PHGDH overexpression than in control tumors (Figure 4L). Collectively, these results suggested that PHGDH inhibition enhances M2-like macrophage polarization and immunosuppression function.
PHGDH knockdown induces metabolic changes in TAMs
To explore the effects of PHGDH on global metabolic remodeling, we performed LC-MS-based metabolomics analysis in TAMs treated with MB231-CM (Figure 5A). Beyond the observed decrease in serine-glycine-1-carbon pathway activity (Figure 5C), we identified significant metabolomic alterations in response to PHGDH suppression. Enrichment analysis of differentially expressed metabolites was performed with MetaboAnalyst 5.0. In PHGDH knockdown TAMs, we observed diminished activity of the glycolysis metabolism pathway, which is upstream of PHGDH (Figure 5D). Moreover, PHGDH knockdown led to a significant upregulation in arginine and glutamine levels (Figure 5C). Metabolites in the TCA cycle, compared with other metabolic pathways, were particularly affected by PHGDH knockdown (Figure 5B). Previous studies have established that pro-inflammatory macrophages depend primarily on glycolysis29 and the PPP14 for energy production, whereas M2 macrophages are more reliant on oxidative phosphorylation. Additionally, arginine and glutamine metabolism30 have been shown to promote M2 polarization through multiple mechanisms. Collectively, these findings suggest that PHGDH knockdown induces metabolic reprogramming, and metabolic stress drives M2 polarization of macrophages.
The observation that PHGDH inhibition affected the TCA cycle prompted us to investigate the underlying mechanisms in greater detail. Notably, TCA cycle metabolites were elevated after the formation of the α-ketoglutarate (Figure 5E). In the mitochondria, glutamine is converted to glutamate, which subsequently serves as a precursor to α-ketoglutarate. Approximately one-third of the carbon atoms in the intermediates of the TCA cycle originate from glutamine (Figure 5F). Therefore, we hypothesized that PHGDH might influence macrophage polarization via glutamine metabolism. In addition, M2 macrophages preferentially use glutamine and fatty acids as energy sources, rather than glucose12. TAMs display elevated levels of glutamine transporters and metabolic enzymes31. Glutamine deprivation results in significant functional alterations in M2 macrophages.
PHGDH undergoes nuclear translocation, and negatively regulates GLUD1 and GLS2 after MB231-CM treatment
Next, we aimed to elucidate the function of PHGDH in glutamine metabolism. We analyzed the expression level of key metabolic enzymes involved in the conversion of glutamine to α-ketoglutarate through qRT-PCR and Western blot. Notably, PHGDH knockdown macrophages exhibited elevated expression of GLUD1 and GLS2 (Figure 6A and 6B), whereas macrophages with PHGDH overexpression showed significantly lower expression levels of these genes than observed in control cells (Figure 6C and 6D). Subsequently, we performed dual luciferase reporter assays to verify PHGDH’s regulation of GLUD1 and GLS2 transcription. The transcription of GLUD1 and GLS2 was significantly enhanced by PHGDH loss-of-function (Figure 6E). ChIP-qPCR assays confirmed direct binding of PHGDH to the promoter regions of GLUD1 or GLS2 genes in macrophages (Figure 6F). These findings suggested that PHGDH suppresses the transcription of GLUD1 and GLS2, thereby modulating glutamine-α-ketoglutarate metabolism in macrophages. Our results therefore revealed a previously unrecognized function of PHGDH in macrophages.
On the basis of our finding that manipulation of PHGDH regulated the expression of GLUD1 and GLS2, we speculated that nuclear localization might be essential for PHGDH’s transcriptional regulatory functions. As expected, the nuclear translocation of PHGDH was confirmed through confocal fluorescence microscopy during the polarization of M0 to TAMs (Figure 6G). This finding was further supported by consistent results from nuclear/cytosolic fragmentation (Figure 6H).
We further hypothesized that nuclear PHGDH might cooperate with specific transcription factors in regulating GLUD1 and GLS2 transcription. To identify these potential transcriptional partners, we conducted a sequential analysis of transcription factors implicated in GLUD1 and GLS2 regulation, then performed co-immunoprecipitation assays in TAMs to identify proteins binding PHGDH (Figure 6I). Nuclear PHGDH was found to mediate GLUD1 and GLS2 transcription through interactions with the transcription factor STAT3, as reported previously32,33. STAT3, a known key driver of macrophage polarization, integrates multiple signaling pathways34,35. Consequently, macrophages with nuclear PHGDH display a novel role in regulating glutamine metabolism independently of enzyme activity.
PHGDH inhibits macrophage function in a glutamine-dependent manner
Previous studies have established the critical role of glutamine in macrophage activation13. In agreement with those findings, glutamine deprivation resulted in down-regulation of immune and polarization markers (Figure 7A). Given the confirmed interaction between PHGDH and GLUD1/GLS2, we propose that PHGDH might drive metabolic reprogramming in macrophages via the modulation of glutamine catabolism. Up-regulation of GLS2 and GLUD1 controls α-ketoglutarate accumulation via glutaminolysis, thereby promoting M2 polarization via Jumonji domain-containing protein D3 (Jmjd3)-dependent demethylation at H3K27me330,36 (Figure 7B). Indeed, glutamine deprivation counteracted the effects of sh-PHGDH in enhancing macrophage polarization and immunosuppressive function (Figure 7C). Notably, glutamine deprivation did not affect the expression levels of CCL22 in macrophages with PHGDH knockdown. These findings suggested that PHGDH inhibition enhances macrophage function by activating the glutamine catabolism pathway. Considering the verified interaction between PHGDH and STAT3, we further treated PHGDH knockdown cells with the STAT3 inhibitor W1131. qRT-PCR analysis demonstrated that W1131 effectively mitigated the effects of PHGDH knockdown on macrophage function (Figure 7D), thus suggesting that PHGDH drives metabolic reprogramming in macrophages through interaction with the transcription factor STAT3.
Discussion
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Ongoing advancements in immunotherapy have markedly improved treatment outcomes for many patients with breast cancer in the past 2 decades. The rise of immune checkpoint inhibitors, antibody-drug conjugates, and cancer vaccines has driven an increase in immunotherapy-based clinical trials37. However, clinical observations have underscored that the TIME composition critically modulates the effectiveness of immunotherapy38. Given the critical roles of TAMs in modulating antitumor immunity, various therapeutic strategies targeting TAMs have been developed, including approaches for their repolarization, reprogramming, and depletion. Despite these efforts, the elimination of TAMs presents a substantial challenge, because it compromises macrophages’ primary phagocytic and antigen-presenting function39. Therefore, reprogramming or repolarization of immunosuppressive TAMs into immunostimulatory phenotypes is a promising research avenue.
In this study, we conducted metabolomics analyses of both benign and malignant inflammatory microenvironments in breast cancer. Our findings demonstrated significant downregulation of the glucose-serine-glycine-1-carbon metabolism pathway in TAMs after treatment with conditioned media from 4 breast cancer cell lines with varying PHGDH expression. The results suggested that PHGDH expression in tumor cells does not directly regulate serine metabolism in macrophages, in agreement with prior findings40. Contrary to previous reports that PHGDH suppression promotes M1 macrophage polarization by increasing expression of IGF1 via S-adenosylmethionine, and that PHGDH contributes to the maintenance of an M2-like macrophage phenotype by activating mTORC1 signaling26,27, our results demonstrated that PHGDH undergoes nuclear translocation during polarization. After translocation to the nucleus, PHGDH represses the M2-like macrophage polarization phenotype and immunosuppressive functions via glutamine metabolic pathways (Figure 8). Furthermore, our analysis of TCGA data revealed a negative correlation between the expression of metabolic enzymes in the SSP and the levels of M2 macrophage infiltration in primary tumor samples of breast cancer. Additionally, we observed a negative correlation between PHGDH expression and the M2 macrophage marker TGF-β in patients with breast cancer. This study provides a theoretical basis for reversing the malignant progression of tumors by targeting the metabolic pathways of immune inflammatory cells involved in malignant transformation.
Given the critical roles of both exogenous and endogenous serine synthesis in regulating cellular dynamics across cancer cells, lymphocytes, and endothelial cells41, treatment with PHGDH inhibitors, and dietary restriction of serine and glycine, are being explored as promising anti-tumor therapies42. Recent studies have further implicated serine metabolism in macrophage polarization43. Previous research has demonstrated that serine supports M1 macrophage polarization through mechanisms involving mTOR signaling and the synthesis of S-adenosylmethionine and glutathione23. Furthermore, de novo serine synthesis has been found to mediate inflammatory responses by regulating IL-1β production through PHGDH-mediated NAD+ accumulation25. However, some studies have reported that inhibiting PHGDH promotes M1 macrophage polarization; therefore, the roles of PHGDH and serine metabolism in TAMs remain unresolved. The contradictory effects of PHGDH on macrophage polarization might stem from variations in the disease models and tissue microenvironments. The increasing application of single-cell transcriptomics, epigenomics, metabolomics, and spatial multi-omics technologies has revealed the molecular and functional diversity of TAMs44. A comprehensive map of human macrophage diversity and development has identified 15 distinct macrophage subtypes45. Prior scRNA-seq studies have also challenged the view that alternative activation is the primary mechanism driving TAM heterogeneity46. Further research is required to elucidate the function of PHGDH in different macrophage subsets.
In recent years, extensive evidence has emerged indicating that the functional regulation of metabolic enzymes extends beyond their canonical roles in specific pathways and includes non-canonical or non-metabolic activities. These enzymes can be spatially and temporally regulated, and consequently act as regulatory molecules by modulating the activities of transcription factors47. The inability of exogenous serine supplementation to rescue the cell growth impairment caused by PHGDH depletion suggests that the function of PHGDH extends beyond serine production. A previous study has reported that PHGDH undergoes nuclear translocation after phosphorylation by p38 under glucose deficiency conditions. Nuclear PHGDH exerts a repressive effect on nuclear NAD+ levels, owing to its enhanced catalytic activity for malate oxidation and NADH generation via AMPK activation28. In this study, we demonstrated that PHGDH undergoes nuclear translocation during M0 macrophage polarization to TAMs, and inhibits TAM function in a glutamine-dependent manner. Specifically, nuclear PHGDH represses the transcription of GLUD1 and GLS2 transcription through direct interaction with the transcription factor STAT3 (Figure 8), which acts as a critical regulator of macrophage plasticity by integrating multiple signaling pathways34.
Several limitations of this study should be acknowledged. First, our research focused primarily on cellular level investigations. However, to comprehensively elucidate the complexity of human cancer biology, further studies at the clinical level will be essential. Additionally, further research is needed to identify the specific nutrient conditions that facilitate PHGDH nuclear translocation and to determine the molecular modifications required for this process.
Conclusions
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In summary, our study elucidated the mechanism through which PHGDH regulates macrophage function and consequently reshapes the anti-tumor response. These findings highlight PHGDH as a potential diagnostic and therapeutic target for breast cancer and may provide a critical theoretical foundation for the development of novel therapies targeting inflammatory cells in the TME.
Funding
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  • This work was supported by grants from the National Key R&D Program of China (Grant No. 2022YFC3401001), National Natural Science Foundation of China (Grant Nos. 82025026 and 82230091 to H.H.), Key R&D Program of Zhejiang (Grant No. 2024C03160), and GuangDong Basic and Applied Basic Research Foundation (Grant Nos. 2023A1515012412 and 2023A1515011214).
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Appendix
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Year 2025 volume 22 Issue 5
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Article Info
doi: 10.20892/j.issn.2095-3941.2024.0398
  • Receive Date：2024-09-15
  • Online Date：2026-11-07
  • Published：2025-05-15
Article Data
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History
  • Received：2024-09-15
  • Accepted：2024-11-14
Funding
This work was supported by grants from the National Key R&D Program of China (Grant No. 2022YFC3401001), National Natural Science Foundation of China (Grant Nos. 82025026 and 82230091 to H.H.), Key R&D Program of Zhejiang (Grant No. 2024C03160), and GuangDong Basic and Applied Basic Research Foundation (Grant Nos. 2023A1515012412 and 2023A1515011214).
Affiliations
    1Department of Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
    2Postgraduate Training Base Alliance of Wenzhou Medical University, Zhejiang Cancer Hospital, Hangzhou 310022, China
    3Hangzhou Institute of Medicine, Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou 310018, China
    4Breast Cancer Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, China

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Correspondence to: Hai Hu and Zhenkun Na, E-mail: and
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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