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Hyperglycemia Enhances Immunosuppression and Aerobic Glycolysis of Pancreatic Cancer Through Upregulating Bmi1-UPF1-HK2 Pathway

  • Author Footnotes
    ∗ Authors share co-first authorship.
    Shihong Wu
    Footnotes
    ∗ Authors share co-first authorship.
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Author Footnotes
    ∗ Authors share co-first authorship.
    Haoxiang Zhang
    Footnotes
    ∗ Authors share co-first authorship.
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Author Footnotes
    ∗ Authors share co-first authorship.
    Chenggang Gao
    Footnotes
    ∗ Authors share co-first authorship.
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Jiaoshun Chen
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Hehe Li
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Zibo Meng
    Affiliations
    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, Heidelberg, Germany
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  • Jianwei Bai
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Qiang Shen
    Affiliations
    Department of Interdisciplinary Oncology, Louisiana State University Health Sciences Center, New Orleans, Louisiana
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  • Heshui Wu
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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  • Tao Yin
    Correspondence
    Correspondence Address correspondence to: Tao Yin, MD, PhD, Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. Tel: +86 027-85351631.
    Affiliations
    Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    Search for articles by this author
  • Author Footnotes
    ∗ Authors share co-first authorship.
Open AccessPublished:July 18, 2022DOI:https://doi.org/10.1016/j.jcmgh.2022.07.008

      Background & Aims

      Accumulating evidence strongly suggests that hyperglycemia promotes the progression of pancreatic cancer (PC). Approximately 80% of patients with PC are intolerant to hyperglycemic conditions. In this study, we define the role of Bmi1, a stemness-related oncogene, in controlling the Warburg effect, and immune suppression under hyperglycemia conditions.

      Methods

      The diabetes mellitus model was established by intraperitoneal injection of streptozotocin. The role of the hyperglycemia-Bmi1-HK2 axis in glycolysis-related immunosuppression was examined in both orthotopic and xenograft in vivo models. Evaluation of immune infiltrates was carried out by flow cytometry. Human PC cell lines, SW1990, BxPC-3, and CFPAC-1, were used for mechanistic in vitro studies.

      Results

      Through bioinformatics analysis, we found that hyperglycemia was strongly related to aerobic glycolysis, immunosuppression, and cancer cell stemness. High glucose condition in the tumor microenvironment promotes immune suppression by upregulating glycolysis in PC cells, which can be rescued via knockdown Bmi1 expression or after 2-deoxy-D-glucose treatment. Through gain-/loss-of-function assessments, we found that Bmi1 upregulated the expression of UPF1, which enhanced the stability of HK2 mRNA and thereby increased the expression of HK2. The role of the hyperglycemia-Bmi-HK2 pathway in the inhibition of antitumor immunity was further verified via the immune-competent and immunodeficient mice model. We also demonstrated that hyperglycemia promotes the expression of Bmi1 by elevating the intracellular acetyl-CoA levels and histone H4 acetylation levels.

      Conclusions

      Our results suggest that the previously unreported Bmi1-UPF1-HK2 pathway contributes to PC progression and immunosuppression, which may bring in new targets for developing effective therapies to treat patients with PC.

      Graphical abstract

      Keywords

      Abbreviations used in this paper:

      ACLY (ATP-citrate lyase), ChIP (chromatin immunoprecipitation assay), CSC (cancer stem cell), DC (dendritic cell), 2-DG (2-deoxy-D-glucose), DM (diabetes mellitus), DMEM (Dulbecco’s Modified Eagle Medium), H3ac (acetylation of histones H3), H4ac (acetylation of histones H4), IFNγ (interferon gamma), IHC (immunohistochemical), IL (interleukin), MAC (total macrophages), MDSCs (myeloid-derived suppressor cells), NC (nonsilencing), NK (natural killer), PBS (phosphate-buffered saline), PC (pancreatic cancer), PRC1 (polycomb group complex 1), RT-qPCR (real-time quantitative polymerase chain reaction), shRNA (short hairpin RNA), siRNA (small interfering RNA), STZ (streptozocin), TME (tumor microenvironment), VPA (valproic acid)
      Patients with pancreatic cancer complicated with diabetes mellitus have lower overall survival than those without. In this study, we define the role of Bmi1, a stemness-related oncogene, in controlling the Warburg effect, and immune suppression under hyperglycemia conditions through the unreported Bmi1-UPF1-HK2 pathway.
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      Results

      Hyperglycemia Promotes Immunosuppression in Patients With PC by Enhancing Aerobic Glycolysis

      Glucose is the most important energy source molecule for cancer cells, and abnormal blood glucose level may significantly affect the metabolic propgram/reprogram of cancer cells. To investigate the association of hyperglycemia, aerobic glycolysis, and immunosuppression, we assessed the intensity of multiple metabolism pathways and infiltration of immune subsets in samples from a cohort (Array Expression /E-MTAB-6134), in which 309 samples were collected from consecutive patients who underwent surgery from September 1996 through December 2010 at 4 academic hospitals in Europe. In patients with PC, the level of glycolysis was negatively correlated with infiltration of CD8+ T cells and dendritic cells (DCs), suggesting an immunosuppressive status (Figure 1, A). Compared with the low-glycolysis cohorts, high-glycolysis cohorts had poorer CD8+ T cell infiltration (P < .001), DC infiltration (P < .001), and B cell infiltration (P < .001) (Figure 1, B). In addition, patients in the low-glycolysis cohorts had a better prognosis than those in the high-glycolysis cohorts (P < .001) (Array Expression /E-MTAB-6134) (Figure 1, C).
      Figure thumbnail gr1
      Figure 1Association of aerobic glycolysis with PC immune evasion, survival, and cancer stemness. (A) Network plot depicting the relationship between multiple metabolisms and infiltrating immune subsets. (B) Violin plot depicting the status of infiltrating immune cells of samples with high or low glycolysis. (C) Survival analysis of patients with differential glycolytic statuses.
      In vitro, the interferon-gamma (IFNγ)-positive cytotoxic T lymphocyte population was significantly lower in the high-glucose cohorts than those in the normal-glucose cohorts (Figure 2, A). Jurkat T cells cultured with differential glucose media for 48 hours showed dose-dependent increases of secreted interleukin (IL)-2 levels (Figure 2, B). This increase was blocked in co-culture with PC cells, and high glucose condition suppressed the Jurkat T cell’s secretion of IL-2 compared with normal control cells (Figure 2, C). Each cell line showed that addition of glucose dose-dependently increased glucose uptake and lactate production (Figure 2, D), indicating upregulated glycolysis. Real-time quantitative polymerase chain reaction (RT-qPCR) analysis of mRNA showed upregulated SLC2A1, HK2, PKM2, and LDHA following high glucose treatment in PC cells cultured 48 hours under differential glucose concentrations (Figure 2, E). Western blot confirmed the protein levels matching the mRNA results (Figure 2, F). We then further investigated whether hyperglycemia promoted immune evasion via upregulating aerobic glycolysis in PC cells by determining whether inhibition of aerobic glycolysis rescues the activity of T lymphocytes and the infiltration of T-cells in xenografts. The SW1900 cells were pretreated with or without 2-deoxy-D-glucose (2-DG), a glycolysis inhibitor, for 24 hours and were then co-cultured with primary human T cells or Jurkat T-cells under differential glycemic conditions. As shown in Figure 2, G, 2-DG administration significantly rescued the immune effectors from the inhibitory effect of high glucose condition.
      Figure thumbnail gr2
      Figure 2Hyperglycemia promotes T-cell immunosuppression in mice bearing PC by enhancing aerobic glycolysis. (A) Flow cytometriy analysis of activated cytotoxic T-cell (CD8+ IFN γ+) population in co-culture with primary T-cells and pancreatic cancer cells in high glucose (25 mM glucose) culture condition compared with normal (5.5 mM glucose) culture conditions. (B) Bar graphs depicting levels of soluble IL-2 of Jurkat T-cells cultured in media with differential glucose concentrations. (C) Bar graphs depicting levels of soluble IL-2 of Jurkat T-cells in high glucose or normal conditions with or without the conditional media of PC cells. (D) Lactate production and uptake of 2-DG were measured in the medium of PC cells treated with differential concentrations of glucose for 48 hours. (E) qPCR analysis for SLC2A1, HK2, PKM2, and LDHA in PC cells treated with different concentrations of glucose for 48 hours. (F) Western blot for SLC2A1, HK2, PKM2, and LDHA proteins in PC cells treated with differential concentrations of glucose for 48 hours. (G) Bar graphs depicting flow cytometry analysis of activated cytotoxic T-cell (INFγ+ and CD8+) population and levels of soluble IL-2 in co-culture with primary T-cells and PC cells pretreated with or without 2-DG (5 mM) in high glucose culture conditions (25 mM glucose) compared with normal culture conditions (5.5 mM glucose). (H) Experimental schema of generating hyperglycemic mouse model and alterations in mouse blood glucose levels after STZ treatment. (I) Representative pictures of the subcutaneous tumors for immune-competent C57BL/6 mice in the euglycemic group, hyperglycemic group, and hyperglycemic 2-DG-treated group. (J) Changes in tumor volume of different groups. (K) Flow cytometry analysis of infiltrating immune effectors in the subcutaneous tumors of euglycemic group, hyperglycemic group, and hyperglycemic 2-DG-treated group. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. ∗∗∗∗P < .0001; ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.
      The in vitro results and the bioinformatic analysis result were further corroborated by in vivo assessments. A hyperglycemic mouse model was generated using streptozocin (STZ) to explore the role of hyperglycemia in immunosuppression in PC. The hyperglycemic mice had higher blood glucose levels than those in the control group (Figure 2, H). Compared with the euglycemic mice, tumors in hyperglycemic mice had greater tumor growth (Figure 2, I‒J). Flow cytometry analysis showed sparser infiltration of immune effector cells, including CD3+CD8+ T cells, M1 macrophages, and DC cells in the xenografts. Subsequent immunophenotyping of mice showed that the CD3+CD8+ T cells were in an immunosuppressive state, exhibiting increased IFNγ and PD1 expression. The proportion of immunosuppressive cells, including Treg cells, total macrophages (MACs), M2 macrophages, and myeloid-derived suppressor cells (MDSCs) was dramatically higher in hyperglycemic mice (Figure 2, K).
      Treatment with 2-DG or phosphate-buffered saline (PBS) was initiated 7 days after injecting Panc-02 cells in the right flank of hyperglycemic mice. The 2-DG treatment reduced subcutaneous tumor growth and reduced tumor weight in hyperglycemic mice compared with PBS-treated control mice (Figure 2, I‒J). As shown in Figure 2, K, 2-DG administration partially rescued the immune effectors from hyperglycemic inhibition. Infiltrated immune cells in the subcutaneous tumors, including total CD3+ T cells, CD3+CD8+ T cells, IFN-γ+ of CD8+ T, and DC cells, were higher in the 2-DG-treated hyperglycemic mice than in the PBS-treated control mice. The proportion of immunosuppressive cells such as CD4+ Treg cells and MDSC was dramatically decreased. The PD-1+ of CD8+ T was also decreased in the 2-DG-treated group.

      Correlation of Cancer Stemness, Aerobic Glycolysis, and Immune Evasion in Patients With PC

      To further explore potential mechanisms regulating glycolysis and immune escape of PC, we explored the correlations between different signaling pathways, glycolysis, and immune infiltration in PC tissue samples through bioinformatics analysis using a publicly available database Array Expression/E-MTAB-1791, which contains RNA expression profiles of 195 pancreatic ductal adenocarcinoma frozen tissue. Interestingly, we found strong cross-correlations among stem cell signaling, glycolysis, and immune infiltration in PC (Figure 3, A).
      Figure thumbnail gr3
      Figure 3High glucose condition promotes the stemness of PC cells. (A) Heat map depicting the relationship among different signaling pathways, glycolysis, and immune infiltration in pancreatic tumors. (B) Network anaylis depicting the relationship among genes involved in stemness, glycolysis, and immune infiltration. (C) Western blot for Nanog, Sox2, Klf4, and Oct4 protein expression in PC cells and CD133 and CD24 expression on the cell surface in SW1990, Bxpc-3, and CFPAC-1cells treated with differential glucose treatment for 48 hours. (D) Sphere-forming ability of PC cells of generation 1 and generation 2 in the serum-free medium under high glucose or normal glucose conditions, scale bar, 100 μm. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.
      To clarify the complicated cross correlations, we conducted correlation analysis among genes involved in stemness, glycolysis, and abundance of infiltrating immune cells. We found that stemness genes were strongly and positively related to glycolytic genes and that the latter exhibited inverse relationship to specific immune subsets including all subtypes of CD8+ T-cells, CD4+ T-cells, DCs, and NK cells, except Th2 cells, immature DCs, and macrophages, representing pro-tumor immune cell subsets (Figure 3, B). These results suggest a potential relationship among cancer stemness, aerobic glycolysis, and immune evasion.
      To explore the potential effect of hyperglycemia on cancer stemness properties of PC cells, Western blotting, flow cytometry, and sphere formation assays were performed. Hyperglycemia enhanced the expression of the pluripotent cell markers including Nanog and Sox2, as well as the stemness markers including Oct4 and Klf4. We also noted that hyperglycemia promoted the expression of the pancreatic cancer stem cell (CSC) surface markers including CD133 and CD24 (Figure 3, C). Sphere formation assay further supported the above results (Figure 3, D). These findings demonstrate that a high glucose TME could potentially help PC cells to obtain more cancer stem-like properties.

      High Glucose Condition Upregulated Bmi1 and Bmi1 Contributed to the High Glucose-enhanced Aerobic Glycolysis in PC Cells

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      • Pandey A.
      • Chinnaiyan A.M.
      ONCOMINE: a cancer microarray database and integrated data-mining platform.
      ,
      • Badea L.
      • Herlea V.
      • Dima S.O.
      • Dumitrascu T.
      • Popescu I.
      Combined gene expression analysis of whole-tissue and microdissected pancreatic ductal adenocarcinoma identifies genes specifically overexpressed in tumor epithelia.
      (Figure 4, A), which is associated with poor prognosis in PC.
      • Song W.
      • Tao K.
      • Li H.
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      • Song Z.
      • Li J.
      • Shi H.
      • Li X.
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      • Dou K.
      Bmi-1 is related to proliferation, survival and poor prognosis in pancreatic cancer.
      Consistent with previous results,
      • Duan Q.
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      High glucose promotes pancreatic cancer cells to escape from immune surveillance via AMPK-Bmi1-GATA2-MICA/B pathway.
      high glucose condition dose-dependently increased Bmi1 mRNA and protein levels (Figure 4, B‒C).
      Figure thumbnail gr4
      Figure 4Bmi1 was upregulated in high glucose conditions and contributed to the high glucose-enhanced aerobic glycolysis in PC cells. (A) In silico analysis demonstrating Bmi1 mRNA expression level in normal pancreatic tissue samples (n = 39) and pancreatic ductal adenocarcinoma tissue samples (n = 39). (B) Western blot of Bmi1 protein in PC cells treated with differential concentrations of glucose for 48 hours. (C) Bmi1 mRNA expression in the cells in A. (D) Western blot of Bmi1 protein in PC cells transfected with vector or Bmi1 plasmid. (E) Lactate production was measured in the supernatant of PC cells transfected with vector or Bmi1 plasmid. (F) Uptake of glucose was measured by flow cytometry in PC cells transfected with vector or Bmi1 plasmid. (G) Western blot of Bmi1 protein in PC cells transfected with vector or Bmi1 plasmid under high glucose or normal conditions. (H) Lactate production was measured in the medium of PC cells after transfection with ncRNA or Bmi1 siRNA under high glucose or normal conditions. (I) Uptake of glucose was measured by flow cytometry in PC cells after transfection with ncRNA or Bmi1 siRNA under high glucose or normal conditions. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.
      Metabolic reprogramming and cancer stemness are 2 closely related hallmarks of cancer.
      • Hanahan D.
      • Weinberg R.A.
      Hallmarks of cancer: the next generation.
      CSCs have a relatively flexible metabolism, allowing them to adjust to variable TMEs.
      • Perusina Lanfranca M.
      • Thompson J.K.
      • Bednar F.
      • Halbrook C.
      • Lyssiotis C.
      • Levi B.
      • Frankel T.L.
      Metabolism and epigenetics of pancreatic cancer stem cells.
      ,
      • Flavahan W.A.
      • Wu Q.
      • Hitomi M.
      • Rahim N.
      • Kim Y.
      • Sloan A.E.
      • Weil R.J.
      • Nakano I.
      • Sarkaria J.N.
      • Stringer B.W.
      • Day B.W.
      • Li M.
      • Lathia J.D.
      • Rich J.N.
      • Hjelmeland A.B.
      Brain tumor initiating cells adapt to restricted nutrition through preferential glucose uptake.
      Whether Bmi1 mediates high glucose enhancement of aerobic glycolysis in PC cells was determined by measuring lactate production and glucose uptake in PC cells overexpressing Bmi1 in high-glucose conditions. As shown in Figure 4, D‒F, Bmi1 over-expression increased lactate production and glucose uptake in PC cells. Measuring glucose uptake and lactate production during Bmi1 silencing in PC cells cultured in differential glucose media showed that, in high-glucose conditions, Bmi1 silencing suppressed the upregulation of aerobic glycolysis (Figure 4, G‒I).

      Bmi1 Promotes Aerobic Glycolysis in PC Cells via UPF1-HK2 Signaling Pathway

      We further explored the mechanism underlying Bmi1, the stem cell factor, promoting glycolysis in PC. Protein expression of SLC2A1, HK2, PKM2, and LDHA was measured after Bmi1 upregulation in PC cells. Ectopic expression of Bmi1 increased protein levels of HK2, whereas SLC2A1, PKM2, and LDHA did not respond to Bmi1 overexpression (Figure 5, A). Knocked-down Bmi1 expression dramatically blocked HK2 upregulation under high-glucose treatment in all 3 PC cell lines (Figure 5, B).
      Figure thumbnail gr5
      Figure 5Bmi1-UPF1-HK2 pathway promotes aerobic glycolysis in PC cells. (A) Western blot of Bmi1, SLC2A1, HK2, PKM2, and LDHA protein in PC cells transfected with vector or Bmi1 plasmid. (B) Western blot of Bmi1 and HK2 protein in PC cells after transfection with ncRNA or Bmi1 siRNA under different glycemic conditions. (C) The GEPIA web tool was adopted to search for the correlation between the expression of Bmi1 and UPF1 in mRNA level in pancreatic ductal adenocarcinoma samples. P values as indicated in the Figure. (D) The GEPIA web tool was adopted to search for the correlation between the expression of UPF1 and HK2 at mRNA levelsin pancreatic ductal adenocarcinoma samples. P-values as indicated in the Figure. (E) Western blot of Bmi1 and UPF1 protein in PC cells transfected with vector or Bmi1 plasmid. (F) Western blot of Bmi1, UPF1, and HK2 protein in sh-control or sh-UPF1 PC cells transfected with vector or Bmi1 plasmid. (G) RNA immunoprecipitation (RIP) assay was used to detect the binding between UPF1 and HK2 mRNA. (H) HK2 mRNA half-life measured by qRT-PCR after actinomycin D treatment. (I) Bar graphs depicting lactate production with or without 2-DG (5 mM) in PC cells after transfection with ncRNA or Bmi1 oeRNA. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. (J) Bar graphs depicting flow cytometric analysis of activated cytotoxic T-cell (INFγ+ and CD8+) population in co-culture of primary T-cells and PC cells transfected with vector or Bmi1 plasmid pretreated with or without 2-DG (5 mM). (K) Bar graphs depicting the levels of soluble IL-2 of Jurkat T-cells with the conditional medium of PC cells transfected with vector or Bmi1 plasmid pretreated with or without 2-DG (5 mM). ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.
      To explore Bmi1 induction of HK2 expression, we searched the GEPIA2 website (http://gepia2.cancer-pku.cn/#index) for genes co-expressed with Bmi1 and HK2 in PC. UPF1, an RNA-binding protein, was found to closely correlate with Bmi1 and HK2 in PC (Figure 5, C‒F). Transient expression of Bmi1 in PC cells promoted UPF1 protein expression as compared with cells with vector control (Figure 5, E). UPF1 silencing suppressed the Bmi1-induced upregulation of HK2 expression (Figure 5, F). Because UPF1 is an RNA-binding protein, whether UPF1 promotes HK2 expression by stabilizing its mRNA was determined. An RNA immunoprecipitation experiment showed that the relative enrichment level of HK2 mRNA was higher in the anti-UPF1 samples than that in the anti-normal group (Figure 5, G), indicating that UPF1 promoted the stability of HK2 mRNA. To confirm this finding, the half-life of HK2 mRNA was measured after knocking-down of UPF1. The half-life of HK2 mRNA in the UPF1-silenced group was decreased (Figure 5, H). These results suggest that high-glucose-induced Bmi1 upregulation promotes aerobic glycolysis by inducing HK2 in an UPF1-dependent manner.
      To test whether Bmi1 promotes aerobic glycolysis through HK2, PC cells were treated with 2-DG, which cannot be further metabolized via glycolysis but accumulates and non-competitively inhibits HK and competitively inhibits PGI, after overexpression of Bmi1. Treatment with 2-DG blocked the upregulated lactate production that was induced by Bmi1 overexpression (Figure 5, I). Taken together, these results demonstrate that the Bmi1-UPF1-HK2 signaling pathway plays a key role in regulating aerobic glycolysis in PC cells.

      Bmi1-HK2 Pathway is Involved in Immunosuppression

      Whether Bmi1-HK2 signaling pathway is involved in aerobic glycolysis-induced immunosuppression was first examined by co-culturing activated human primary T cells with PC cells in vitro. Flow cytometry analysis showed that 2-DG treatment reversed the inhibitory effects of Bmi1 on the activity of T cells (Figure 5, J‒K).
      We then verified this hypothesis in vivo. To exclude the direct effect of 2-DG on glycolysis on immune cells in the tumor microenvironment, we employed rescue experiments via direct knock-down or overexpression of HK2 with lentivirus infection approach and validated the impact of Bmi1-HK2 activation/downregulation on immunosuppression. Stable Panc-02 cells transfected with nonsilencing (NC), OE-Bmi1, KD-Bmi1, OE-Bmi1/KD-HK2, KD-Bmi1/OE-HK2, or KD-HK2 lentivirus were injected into the C57BL/6 mice pancreas tail by laparotomy (Figure 6, A‒B). Under euglycemic conditions, OE-Bmi1 expression in vivo promoted the tumorigenesis and KD-HK2 expression partly rescued this effect. KD-HK2 expression or KD-Bmi1 expression in vivo suppressed the tumorigenesis induced by hyperglycemic conditions, and OE-HK2 rescued the potential therapeutic efficacy of KD-Bmi1 (Figure 6, C). T-cells, DC, MDSC and MAC were evaluated by flow cytometry analysis (Figure 6, E). Our results show that Bmi1-overexpressed tumors had less infiltrated CD3+ T cells, CD3+CD8+ T cells, DC cells, and higher CD8+PD1+ T cells, and MDSC than controls. Moreover, this effect could be reversed by KD-HK2. Under hyperglycemic conditions, Bmi1 inhibition could recover the anti-tumor immune cell infiltration including CD3+CD8+ T cells. Enhanced CD8+ T cells function (IFN-γ+CD8+ T cells and GZMB+CD8+ T cells) and reduced infiltration of CD4+ Treg, PD-1+CD8+T cells, and MAC were also detected. Importantly, this effect can be reversed by OE-HK2. Taken together, our results demonstrate that Bmi1-HK2 activation inhibits the function of immune effectors in the TME of PC.
      Figure thumbnail gr6
      Figure 6Bmi1-HK2 signaling is involved in T cell immunosuppression in PC. (A) Experimental schema of generating hyperglycemic mouse model. (B) Alterations in blood glucose levels after STZ treatment in mice. (C) Representative pictures of the orthotopic tumors. (D) Changes in tumor volume and tumor weight in animals. (E) Flow cytometry analysis of the infiltration of immune effectors (CD3+T, CD3+CD4+ T, Treg, CD3+CD8+T, IFN-γ+ CD8+T, GZMB+ CD8+T, PD1+ CD8+T, KI67+ CD8+T, Perforin+ CD8+T, DC, MDSC, MAC, M1 macrophages, M2 macrophages) in the orthotopic tumors in different groups. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. ∗∗∗∗P < .0001; ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.

      High Glucose Condition Induces Bmi1 Expression Through ATP-citrate Lyase-dependent Acetyl-CoA

      We further explored the mechanism underlying Bmi1 induction under a high glucose environment. Because acetyl-CoA is the necessary donor substrate of histone lysine acetyltransferase (KAT) enzymes,
      • Lee J.V.
      • Carrer A.
      • Shah S.
      • Snyder N.W.
      • Wei S.
      • Venneti S.
      • Worth A.J.
      • Yuan Z.F.
      • Lim H.W.
      • Liu S.
      • Jackson E.
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      • Haas N.B.
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      • Chodosh L.A.
      • Garcia B.A.
      • Stanger B.Z.
      • Feldman M.D.
      • Blair I.A.
      • Wellen K.E.
      Akt-dependent metabolic reprogramming regulates tumor cell histone acetylation.
      the intracellular acetyl-CoA levels of PC cells were determined in different glycemic conditions. High-glucose treatment dose-dependently increased intracellular acetyl-CoA levels. Western blot analysis of ATP-citrate lyase (ACLY), which synthesizes acetyl-CoA, showed that PC cells treated with high glucose had higher levels of ACLY than those treated with normal glucose (Figure 7, A).
      Figure thumbnail gr7
      Figure 7Acetyl-CoA is required for Bmi1 induction under high glucose condition in PC cells. (A) Bar graphs depicting levels of intracellular acetyl-CoA and Western blot of ACLY protein in PC cells under different glycemic conditions. (B) Western blot of ACLY and Bmi1 proteins in PC cells transfected with ncRNA or ACLY siRNA. (C) Bar graphs depicting levels of intracellular acetyl-CoA in PC cells transfected with ncRNA or ACLY siRNA under high glucose or normal conditions. (D) Western blot of ACLY and Bmi1 proteins in PC cells transfected with ncRNA or ACLY siRNA under high glucose or normal conditions. (E) Western blot of histone H3ac and H4ac proteins in PC cells under different glycemic conditions. (F) Western blot of ACLY, histone H3ac, and H4ac proteins in PC cells transfected with ncRNA or ACLY siRNA under different glycemic conditions. (G) Bar graph depicting levels of intracellular acetyl-CoA in PC cells treated with acetate. (H) Western blot of histone H3ac, histone H4ac, and Bmi1 proteins in PC cells treated with acetate. (I) Western blot of ACLY, histone H3ac, histone H4ac, and Bmi1 proteins in ACLY knockdown PC cells treated with or without acetate. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.
      To test whether hyperglycemic-induced Bmi1 upregulation was due to the accumulation of intracellular acetyl-CoA, we tested 3 ACLY-specific small interfering RNAs (siRNAs) in 3 PC cell lines (SW1990, Bxpc-3, and CFPAC-1) (Figure 7, A). The most significant knockdown effect was found with siACLY#003, which was employed in the following studies (Figure 7, B). ACLY knockdown prevented intracellular acetyl-CoA increase due to hyperglycemia in SW1990, Bxpc-3, and CFPAC-1 cells (Figure 7, C). High-glucose-induced Bmi1 overexpression was also inhibited by ACLY knockdown (Figure 7, D), suggesting that Bmi1 induction is acetyl-CoA-dependent. High-glucose treatment increased the acetylation of histones H3 (H3ac) and H4 (H4ac) (Figure 7, E); this effect was inhibited by the knockdown of ACLY (Figure 7, F).
      Acetyl-CoA levels were increased by acetate supplementation to test how high glucose levels promote Bmi1 expression in PC cells. Levels of intracellular acetyl-CoA, H3ac, H4ac, and Bmi1 expression increased after acetate supplementation (Figure 7, G‒H). Moreover, acetate supplementation reversed the suppression of H3ac, H4ac, and Bmi1 expression in PC cells caused by ACLY silencing (Figure 7, I). Therefore, increased intracellular acetyl-CoA is essential for high-glucose-induced Bmi1 expression in PC cells.

      Bmi1 Expression is Enhanced by Upregulating Histone H4 Acetylation

      Valproic acid (VPA), an inhibitor of histone deacetylase I, increases Bmi1 expression in PC cells
      • Li H.
      • Zhang Z.
      • Gao C.
      • Wu S.
      • Duan Q.
      • Wu H.
      • Wang C.
      • Shen Q.
      • Yin T.
      Combination chemotherapy of valproic acid (VPA) and gemcitabine regulates STAT3/Bmi1 pathway to differentially potentiate the motility of pancreatic cancer cells.
      and was used to determine whether histone acetylation was involved in the high glucose–induced Bmi1 expression in PC cells. To test whether acetylation of histone H3 and H4 is required for enhanced Bmi1 expression, PC cells were treated with VPA or C646, a p300 acetyltransferase inhibitor. VPA promoted Bmi1 expression and upregulated H3ac and H4ac levels in PC cells (Figure 8, A). Furthermore, low-dose VPA reversed the inhibitory effect of ACLY silencing on H3 and H4 acetylation and Bmi1 expression in PC cells (Figure 8, B). C646 treatment of PC cells suppressed the H3 and H4 acetylation and Bmi1 expression induced by high-glucose treatment (Figure 8, C) and reversed the increase of H3 and H4 acetylation and Bmi1 expression caused by acetate supplementation (Figure 8, D).
      Figure thumbnail gr8
      Figure 8Histone acetylation is required for Bmi1 induction under high glucose condition in PC cells. (A) Western blot of histone H3ac, histone H4ac, and Bmi1 proteins in PC cells treated with or without VPA under different glycemic conditions. (B) Western blot of ACLY, histone H3ac, histone H4ac, and Bmi1 proteins in ACLY knockdown PC cells treated with or without VPA. (C) Western blot of histone H3ac, histone H4ac, and Bmi1 proteins in PC cells treated with or without C646 under different glycemic conditions. (D) Western blot of histone H3ac, histone H4ac, and Bmi1 proteins in PC cells treated with or without C646 under different concentrations of acetate. (E) Western blot of KAT2A, histone H3ac, and Bmi1 proteins in PC cells transfected with ncRNA or KAT2A siRNA under different glycemic conditions. (F) Western blotof KAT5, histone H4ac, and Bmi1 proteins in PC cells transfected with ncRNA or KAT5 siRNA under different glycemic conditions. (G) Examination of histone H4 acetylation status in the Bmi1 gene promoter region in PC cells under high glucose or normal conditions using ChIP.
      Because high glucose, ACLY knockdown, acetate supplementation, VPA, and C646 affected both H3ac and H4ac, the KATs that are responsible for H3/H4 acetylation were knocked down. Knockdown of KAT5, which acetylates H4, inhibited high-glucose-induction of Bmi1 expression, but KAT2A, which acetylates H3, did not (Figure 8, E‒F). To confirm this, chromatin immunoprecipitation assays (ChIPs) were conducted using SW1990 cells cultured in normal or high-glucose media. Measurement of H3ac and H4ac levels in the Bmi1 gene promoter region showed that H4ac levels were higher in cells treated with high glucose than in those treated with normal glucose (Figure 8, G). These results suggest that a high-glucose environment promotes Bmi1 expression in PC cells through upregulating H4 acetylation levels in the promoter region of the Bmi1 gene.

      The Role of Hyperglycemia-induced Bmi1 Under Immunodeficient Environment

      To verify the in vitro findings and evaluate the role of hyperglycemia and Bmi1 in tumorigenesis under an immunodeficient environment, subcutaneous Panc-02 model in immunodeficient BALB/c nude mice was built with stable Panc-02 cells infected by NC, OE-Bmi1, and KD-Bmi1 lentivirus in both euglycemia and hyperglycemic mice (Figure 9, A). Compared with the euglycemic mice, the tumors in hyperglycemic mice had greater tumor growth (Figure 9, B).
      Figure thumbnail gr9
      Figure 9The role of hyperglycemia-induced Bmi1 expression under immunodeficient environment. (A) Experimental schema of generating the immunodeficient BALB/c nude mice model; and alterations in blood glucose levels after STZ treatment. (B) Representative pictures of the orthotopic tumors. (C) Changes in tumor volume and tumor weight in different groups. . (D) The lactic acid concentration and glucose concentration of tumor tissues from normal and diabetes animal models. (E) IHC staining to evaluate the ACLY, KAT4, H4ac, and Bmi1 protein level in euglycemia-NC and hyperglycemia-NC. (F) IHC staining to evaluate Bmi1, UPF1, and HK2 protein level in KD-Bmi1, NC, and OE-Bmi1 in the euglycemia model. (G) Schematic diagram showing the effect of the hyperglycemia-Bmi1-HK2 pathway on the Warburg effect and TME. Data presented in the graphs represented means ± standard deviation from 3 parallel experiments. ∗∗∗∗P < .0001; ∗∗∗P < .001; ∗∗P < .01; ∗P < .05.
      As shown in Figure 9, D, hyperglycemia increased the content of glucose and lactate in subcutaneous tumor tissues. As determined using an immunohistochemical (IHC) assessment, hyperglycemia increased the expression level of ACLY, KAT5, H4ac, and Bmi1 (Figure 9, E). Bmi1 has been well-known for its abilities in controlling stem cell self-renewal and tissue homeostasis. Therefore, both in OE-Bmi1 under euglycemia model or KD-Bmi1 under hypereuglycemia model, Bmi1 contributed some degree of proliferation of tumor cells in immunodeficient mice model (Figure 9, B). However, we can find that KD-Bmi1 completely inhibited tumor outgrowth promoted by hyperglycemia in immunocompetent mice (Figure 5, C‒D). Although in immunodeficient mice, KD-Bmi1 only partially inhibit tumor outgrowth promoted by hyperglycemia (Figure 9, B‒C). We believe that this difference in rescue experiments was caused by Bmi1-mediated immunosuppression, as supported by our data. The expression of UPF1 and HK2 was regulated by Bmi-1 in vivo (Figure 9, F). Next, we validated UPF1 and HK2 expression in KD-Bmi1, NC, and OE-Bmi1 cells by performing IHC staining (Figure 9, F).

      Discussion

      Hyperglycemia is associated with most patients with PC, suggesting a role of glucose level in PC progression, immune suppression, and therapeutic outcomes. This study focused on the interactions between hyperglycemia and PC carcinogenesis, leading to the discovery of a previously unreported Bmi1-UPF1-HK2 signaling pathway in pancreatic carcinogenesis. This pathway regulates aerobic glycolysis, leading to enhanced Warburg effect and the establishment of an immunosuppressive TME, by upregulating intracellular acetyl-CoA and histone H4 acetylation to upregulate Bmi1 expression (Figure 7, G). Results presented in this paper provide evidence that a high glucose condition in pancreatic TME facilitates PC to evade immune surveillance via upregulating aerobic glycolysis in PC cells. These findings enhance our understanding of PC progression and immune suppression and provide insights for developing targeted cancer and immune therapies for this deadly malignancy.
      The relationship between diabetes and PC is complicated. Hyperglycemia is characteristic of diabetes, a risk factor for PC, and associated with poor survival among PC patients.
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      Although the mechanisms by which high glucose condition affects metabolic reprogramming have yet to be fully determined, this study suggests that hyperglycemia induces Bmi1 expression by binding of H4ac in the promoter of Bmi1 gene. This upregulates the Bmi1-UPF1-HK2 signaling pathway, which promotes aerobic glycolysis and lactate production in PC cells and leads to immune suppression. This suggests that Bmi1 is a novel potential therapeutic target for patients with PC complicated with diabetes.
      Glucose is an important energy source for cancer cells. When immersed in an environment with excessive nutrients, cancer cells tend to upregulate their nutrient uptake and adopt aerobic glycolysis to generate intermediate metabolites,
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      Moreover, novel tumor cell targeting drug delivery systems make it possible to target and inhibit tumor glycolysis directly and precisely in experimental setting.
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      To validate this hypothesis, Bmi1 was silenced in PC cells, which suppressed the effects of high-glucose upregulation of aerobic glycolysis. Our analyses showed that HK2 expression was induced by high-glucose treatment and that Bmi1 silencing blocked the HK2 upregulation. Overexpression of Bmi1 in vivo influenced an assortment of tumor-infiltrating immune cells and established immune-suppressive TME. Importantly, inhibition of HK2 activity by 2-DG or knockdown HK2 expression blocked the high-glucose induction of aerobic glycolysis and Bmi1-induced immune suppression. Under hyperglycemic conditions, knockdown Bmi1 expression in vivo suppressed the anti-tumor immune cell infiltrate, and this effect was also reversed by overexpression of HK2. Evidence from this study supports a relationship among hyperglycemia-induced Bmi1-HK2 pathway activity, Warburg effect enhancement, and cancer cell immunosuppression. In in vitro studies, we have used multiple human PC cells to maximally represent human PC, including SW1990 (with Kras G12D mutation), CFPAC-1 (with Kras G12V mutation), and BxPC-3 (representing wild-type Kras). Our highly consistent results in multiple experiments demonstrate that the correlation and hyperglycemia and aberrant aerobic glycolysis in PC through Bmi1-UPF1-HK2 pathway is not dependent on Kras mutation. Further, recent literature strongly supports the notion that Bmi-1 not only contributes to regulating immunity in pancreatic cancer, but also in other cancer types.
      • Jia L.
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      BMI1 Inhibition eliminates residual cancer stem cells after PD1 blockade and activates antitumor immunity to prevent metastasis and relapse.
      Epigenetic changes adapt environmental alterations, gene expression, and phenotypic state of cells.
      • Wainwright E.N.
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      Epigenetics and cancer stem cells: unleashing, hijacking, and restricting cellular plasticity.
      Cancer cells are particularly sensitive and can adapt to the changes occurred in TME, including fluctuations in energy substrate availability.
      • Gut P.
      • Verdin E.
      The nexus of chromatin regulation and intermediary metabolism.
      Cancer cells partially decide to proliferate or undergo apoptosis based on nutrient availability. In addition, metabolites such as acetyl-CoA, S-adenosylmethionine, and flavin adenine dinucleotide are utilized by chromatin-modifying enzymes to affect the transcriptional network that regulates gene expression.
      • Shi Y.
      • Shi Y.
      Metabolic enzymes and coenzymes in transcription–a direct link between metabolism and transcription?.
      ,
      • Ladurner A.G.
      Rheostat control of gene expression by metabolites.
      Acetyl-CoA serves as the donor for histone acetylation and is reliant on intracellular acetyl-CoA availability,
      • Wellen K.E.
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      • Bui T.V.
      • Cross J.R.
      • Thompson C.B.
      ATP-citrate lyase links cellular metabolism to histone acetylation.
      • Carrer A.
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      Metabolism and epigenetics: a link cancer cells exploit.
      • Shi L.
      • Tu B.P.
      Acetyl-CoA and the regulation of metabolism: mechanisms and consequences.
      which is dynamically regulated by glucose availability in TME.
      • Lee J.V.
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      Akt-dependent metabolic reprogramming regulates tumor cell histone acetylation.
      ACLY is a metabolic enzyme found in both cytosol and nucleus that cleaves citrate to generate acetyl-CoA, and plays an essential role in regulating histone acetylation.
      • Lee J.V.
      • Carrer A.
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      ,
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      Metabolism and epigenetics: a link cancer cells exploit.
      In PC, a high level of histone acetylation is associated with poor survival.
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      • Izetti P.
      • Pereira M.P.
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      • Bravosi C.P.
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      • Prolla P.A.
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      Because Bmi1 expression in PC cells was promoted by VPA, a deacetylase inhibitor, whether high glucose promotes Bmi1 expression in PC cells by upregulation of histone acetylation in an ACLY-dependent manner was tested. Our results show that the H4ac level at the promoter region of Bmi1 gene in high glucose-treated cells was significantly higher than that in control cells. In addition, a high glucose TME-enhanced sphere formation ability of PC cells and upregulated the expression of CSC markers including CD133, CD24, Klf4, and Oct4, and pluripotent cell markers including Nanog and Sox2. Taken together, our results suggest that nutrients in pancreatic TME promote cancer stemness via epigenetic modulation, and acetyl-CoA mediates environmental perturbations.
      These results demonstrate that Bmi1 regulates the Warburg effect in PC cells and enhances the immunosuppressive TME by targeting HK2. Furthermore, hyperglycemic TME also modulates the epigenetics of PC cells and promotes Bmi1 expression. Epigenetic mechanisms may play a key role in the metabolic alterations and crosstalk between TME and cancer stem-like cell properties. Our research suggests that BMI1 may be a promising target to ameliorate the immunosuppressive microenvironment, and glycemic control is crucial for patients with PC with DM.

      Materials and Methods

      Cell Lines

      Human PC cell lines, SW1990 (with Kras G12D mutation) and BxPC-3 (representing wild-type Kras), were obtained from the American Type Culture Collection (Manassas, VA). Human PC cell line, CFPAC-1 (with Kras G12V mutation), 119 and murine ductal pancreas adenocarcinoma cell line, Panc-02 (representing wild-type Kras with homozygous null mutantion of Smad4), were obtained from Procell (Procell Bio-Tech, Shanghai, China). The cells were tested to confirm the absence of Mycoplasma contamination by using the MycoBlue Mycoplasma Detector (Vazyme, Nanjing, China). All cell lines were cultured in Dulbecco’s Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (Gibco Invitrogen, Grand Island, NY) and 100 U/mL penicillin/streptomycin (Beyotime Biotechnology, Shanghai, China). General cell culture was performed under normoglycemia condition (5.5 mM D-glucose). To mimic hyperglycemic condition in tissue culture, 2 diabetogenic glucose concentrations (15 mM and 25 mM) were chosen.

      Quantitative Real-time Polymerase Chain Reaction Assay

      qRT-PCR was performed as previously described.
      • Valk-Lingbeek M.E.
      • Bruggeman S.W.
      • van Lohuizen M.
      Stem cells and cancer; the polycomb connection.
      ,
      • Pannala R.
      • Leirness J.B.
      • Bamlet W.R.
      • Basu A.
      • Petersen G.M.
      • Chari S.T.
      Prevalence and clinical profile of pancreatic cancer-associated diabetes mellitus.
      The data was interpreted using the 2-ΔΔCT method with β-actin as a control. The primer sequences are provided in the Supplementary Materials.

      Western Blot Analysis

      Western blot was performed as previously described.
      • Duan Q.
      • Li H.
      • Gao C.
      • Zhao H.
      • Wu S.
      • Wu H.
      • Wang C.
      • Shen Q.
      • Yin T.
      High glucose promotes pancreatic cancer cells to escape from immune surveillance via AMPK-Bmi1-GATA2-MICA/B pathway.
      ,
      • Li H.
      • Zhang Z.
      • Gao C.
      • Wu S.
      • Duan Q.
      • Wu H.
      • Wang C.
      • Shen Q.
      • Yin T.
      Combination chemotherapy of valproic acid (VPA) and gemcitabine regulates STAT3/Bmi1 pathway to differentially potentiate the motility of pancreatic cancer cells.
      Extraction of proteins was performed with a subcellular structure membrane protein and cytoplasmic protein extraction kit (Boster Biological Technology, China, #AR0155), according to the manufacturer’s instructions. Details of the primary antibodies used are provided in the Supplementary Materials.

      Determination of Intracellular Acetyl-CoA Levels

      Intracellular acetyl-CoA levels in the PC cells were assessed using an acetyl-CoA assay kit from Sigma (#MAK039) following the manufacturer’s protocol.

      Cell Transfection and Lentivirus Infection

      The siRNAs for ACLY , Bmi1, KAT2A, KAT5, and negative control were designed and synthesized by Ribobio (Guangzhou, China). The Bmi1 cDNA overexpression (pcDNA3.1-Bmi1) and empty cDNA vectors (pcDNA3.1-NC) were designed and synthesized by GenePharma (Shanghai, China). Lipofectamine 2000 (Invitrogen, Waltham, MA) was used for cell transfection as per manufacturer’s protocol. After transfection for 4 to 6 hours, the medium was replaced with a regular medium. The siRNA sequences are shown in the Supplementary Materials.
      NC-short hairpin RNA (shRNA) lentivirus (puromycin resistance), Bmi1-shRNA lentivirus (puromycin resistance), HK2-shRNA lentivirus (blasticidin resistance), Bmi1-overexpression lentivirus (puromycin resistance), and HK2-overexpression lentivirus (blasticidin resistance) were purchased from GeneChem (Shanghai, China). The viral supernatants were used to infect Panc-02 cells. Stable overexpressing-Bmi1(OE-Bmi1), knockdown-Bmi1(KD-Bmi1), OE-Bmi1/knockdown-HK2 (OE-Bmi1/KD-HK2), KD-Bmi1/overexpressing-HK2 (KD-Bmi1/OE-HK2), or KD-HK2 cells were constructed by lentiviral infection. Stable cell lines were selected with 0.5 μg/mL puromycin (OE-Bmi1 cells and KD-Bmi1 cells), 2.5μg/mL blasticidin (KD-HK2 cells), or 0.5 μg/mL puromycin + 2.5 μg/mL blasticidin (OE-Bmi1/KD-HK2 cells and KD-Bmi1/OE-HK2 cells) for 2 days.

      Chromatin Immunoprecipitation Assay

      ChIP was performed using Simple ChIP Enzymatic Chromatin IP Kit (Agarose Beads) (Cell Signaling Technology, Danvers, MA, #22188), following the manufacturer’s protocol. After culturing at 5.5 or 25 mM glucose for 48 hours, ChIP was performed using anti-Histone4ac (Millipore, #17-630) or anti-IgG antibody as a control. The bound DNA fragments were amplified using Bmi1 promoter-specific primers. The primer sequences used in the ChIP-qPCR are shown in the Supplementary Materials.

      Lactate Concent, Glucose Concent, and Glucose Uptake Measurements

      Lactate and glucose uptake measurements were performed as previously described.
      • Zhao H.
      • Wu S.
      • Li H.
      • Duan Q.
      • Zhang Z.
      • Shen Q.
      • Wang C.
      • Yin T.
      ROS/KRAS/AMPK signaling contributes to gemcitabine-induced stem-like cell properties in pancreatic cancer.
      Lactate in the supernatant and tumor tissues was determined using the lactic acid assay kit (Nanjing Jiancheng Bio, Nanjing, China, #A019-2-1) as per manufacturer’s protocol and correlated with the total protein in each sample. Glucose concent in tumor tissues was determined using the Glucose Content Assay Kit (Solarbio, Beijing, China, # BC2505), as per manufacturer’s protocol and correlated with the total protein in each sample. For assessment of glucose uptake, 2-NBDG (2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)Amino)-2-deoxyglucose) (Cayman Chemical, Ann Arbor, MI) uptake was analyzed by flow cytometry.

      Flow Cytometry Analysis

      Prior to harvest, activated human primary T cells (Stemcell Technologies) were treated for 24 hours and 4 hours, respectively, with a PMA/ionomycin mixture (MultiSciences, Hangzhou, China, #70-CS1001) and brefeldin A (BFA)(Sigma-Aldrich, MO, USA, #B5936). After washing thrice with PBS, the T cells were incubated with CD8 antibody (BD Pharmingen, #555367) at 4 °C in the dark for 30 minutes. The T cells were fixed and permeabilized using the Fixation/Permeabilization Set protocol (eBioscience, CA, #00-5123-43, #00-5223-56, #00-8333-56) and incubated with IFNγ antibody (BD Pharmingen, #554700) in the dark for 30 minutes. After washing twice, the T cells were analyzed through flow cytometry.
      Single-cell suspensions of tumor-infiltrated immune cells from animal experiments were prepared. To minimize non-specific binding, cells were then incubated with anti-CD16/CD32 antibody (BD Pharmingen, #553141). All cell surface reactions (CD45, CD3, CD4, CD8, CD25, I-A/I-E, CD11c, CD11b, F4/80, PD-1, CD86, Ly-6G, and Ly-6C) were performed at 4 °C for 30 minutes. The permeabilization step allowing intracellular staining (KI-67, IFN-γ, GZMB, Perforin, and Foxp3) was performed according to the manufacturer’s recommendations (BD Pharmingen, # 562574, #550583). After being washed thrice, the sample cells were suspended in PBS at a concentration of 106/200 μL and analyzed by flow cytometry. The fluorochrome-conjugated antimouse antibodies used in the present study are listed in the Supplementary Materials, and the gating strategy is shown in Figure 10.

      Sphere-forming Assay

      PC cells were grown in serum-free DMEM/F12 media supplemented with 2% B-27 (Gibco, #17504044), heparin (4 μg/mL), epidermal growth factor (20 ng/mL) (R&D, #236-EG-200), and fibroblast growth factor (20 ng/mL) (PEPROTECH, #AF-100-18C). DMEM/F12 media with different glucose concentrations were made by mixing equal volumes of DMEM low-glucose media (Gibco) and F12 media (Gibco) supplemented with D-glucose (Aladdin Industrial Corporation, Shanghai, China, #G116304). The assay was performed using 500 cells per well on ultra-low attachment 6-well plates. After 7 days, the cultures were analyzed under a phase-contrast light microscope for sphere formation.

      RNA Immunoprecipitation Assay

      RNA immunoprecipitation was performed using an anti-UPF1 antibody (Proteintech, Wuhan, China, #23379-1-AP) and the RBP Immunoprecipitation Kit (Sigma-Aldrich, St Louis, MO, #17-700) following the manufacturer’s protocol.

      Murine Models

      All experiments involving animals in this research followed the ethical standards set by the Institutional Animal Care and Use Committee of Tongji Medical College, Huazhong University of Science and Technology. Five-week-old male C57BL/6 mice and 5-week-old BALB/c Nude mice were purchased from China Three Gorges University. C57BL/6 mice and BALB/c Nude mice were divided randomly into control and diabetes groups. For the diabetes group, mice were injected for 5 consecutive days with 50 mg/kg STZ (Sigma, St. Louis, MO, #S0130) dissolved in cold fresh sodium citrate buffer (pH = 4.5). Sannuo glucometer (Sannuo, Changsha, China) was used to measure the glucose levels in blood samples taken from tail vein. Mice with blood glucose levels ≥11.1 mmol/L were selected as the diabetic mice.

      Subcutaneous Panc-02 Model in Immune-competent C57BL/6 Mice

      For evaluating the effects of hyperglycemia and hyperglycemia-induced aerobic glycolysis in immune suppression. Subcutaneous Panc-02 tumors (n = 5 in each group) were generated in immune-competent C57BL/6 mice. Tumors were created by injecting 106 cells in 100 μL PBS under the skin of the left flank in both control and diabetes groups. Seven days after injection, 2-DG (5 mM, 50 μL, Sigma-Aldrich, #D8375) or PBS (50 μL) were locally injected around the tumor mass at multiple points every 2 days for 6 times total. Three weeks after injection of the stable Panc-02 cells, the mice were sacrificed.

      Orthotopic Panc-02 Model in Immune-competent C57BL/6 Mice

      To evaluate the function of Bmi1-HK2 in hyperglycemia-induced immune suppression, Panc-02 cells transfected with NC, OE-Bmi1, KD-Bmi1, OE-Bmi1/KD-HK2, KD-Bmi1/OE-HK2, or KD-HK2 lentivirus were injected into the C57BL/6 mouse pancreas tail in both control and diabetes groups by laparotomy. Briefly, mice (n = 4 in each group) were anesthetized with an intraperitoneal injection of 1.25% tribromoethanol (20 μL/g mouse). Fur was shaved in the left-flank surgical field using an electric clipper, and the region of the incision was sterilized with betadine. PC cells were injected (1 × 106 cells) in 10 μL PBS + 10 μL Matrigel in the tail of pancreas that was exposed under the left-flank incision. Three weeks after injection of the stable Panc-02 cells, the mice were sacrificed.

      Subcutaneous Panc-02 Model in Immunodeficient BALB/c Nude Mice

      To evaluate the role of Bmi1 in tumorigenesis under an immunodeficient environment, Panc-02 cells transfected with NC, OE-Bmi1, KD-Bmi1 lentivirus were injected subcutaneously (1 × 106 cells/100 μL/mouse) into the left flank of BALB/c nude mice in both control and diabetes groups. Two weeks after injection of the stable Panc-02 cells, the mice were sacrificed.

      Immunohistochemistry

      IHC was performed as previously described.
      • Zhao H.
      • Wu S.
      • Li H.
      • Duan Q.
      • Zhang Z.
      • Shen Q.
      • Wang C.
      • Yin T.
      ROS/KRAS/AMPK signaling contributes to gemcitabine-induced stem-like cell properties in pancreatic cancer.
      Antibody information is available in the Supplemental Materials. The average immunity group image gradation analysis integral light density of Bmi1, UPF1, HK2, ACLY, KAT5, and H4ac in 5 randomly selected areas for each group were calculated by using Imagepro Plus 6.0 (Media Cybernetics, Inc).

      mRNA Half-life Assay

      PC cells transfected with ncRNA or UPF1 siRNA were treated with 5 μg/mL actinomycin D (Sellect, USA, #S8964). PC cells were then collected at 0, 1, 2, 4, 8, and 16 hours after the supplementation of actinomycin D, and qPCR was performed to measure the levels of HK2 mRNA.

      Statistical Analysis

      Statistical power was calculated with GraphPad Prism 9.3.1. The results are shown as the mean ± standard deviation. Comparisons between 2 groups or multiple groups were analyzed using the Student t test or 1-way analysis of variance. All tests were 2-sided; a P-value < .05 was considered statistically significant.

      Gene Expression Datasets

      The transcriptome data of 2 independent cohorts (E-MTAB-6134 and E-MTAB-1791) used in this study was obtained from public domain (ArrayExpress, https://www.ebi.ac.uk/arrayexpress).

      Activity Detection of Molecular Metabolic Pathways

      Gene Set Variation Analysis used the Gene Set Variation Analysis package of R-3.6.1.
      • Hanzelmann S.
      • Castelo R.
      • Guinney J.
      GSVA: gene set variation analysis for microarray and RNA-seq data.
      ,
      • Yang C.S.
      • Stampouloglou E.
      • Kingston N.M.
      • Zhang L.
      • Monti S.
      • Varelas X.
      Glutamine-utilizing transaminases are a metabolic vulnerability of TAZ/YAP-activated cancer cells.
      Enrichment score was considered to be the measure of metabolic intensity for subsequent analyses and calculations (Supplementary Table 1 and c2.all.v7.1.symbols from Molecular Signatures Database v7.2).

      Inferred Abundances of Immune Subsets

      Xcell (http://xCell.ucsf.edu/) was used to infer 64 immune and stromal cell types based on single-sample Gene Set Enrichment Analysis.
      • Aran D.
      • Hu Z.
      • Butte A.J.
      xCell: digitally portraying the tissue cellular heterogeneity landscape.
      The default parameter P-value < .2 was used as the criterion of reliability.

      Survival Analysis

      Overall survival time was used as the single criterion for survival analysis. Survival curves were drawn using the survival package of R.

      Correlation Analyses

      Correlations between continuous variables were tested using the Hmisc package (version 4.2-0) of R, based on the Spearman correlation coefficient.

      Variation Analysis

      Variation in infiltrating immune cell proportions between samples with high- or low-level intensity of glycolysis was determined using the Kruskal-Wallis test.

      Acknowledgment

      The authors thank the Huazhong University of Science & Technology Analytical & Testing center, Medical sub-center for the technical support. The authors thank Dr Long Yu and Mr Libo Luo for their help in flow cytometry technical support.

      CRediT Authorship Contributions

      Wu Shihong, MD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Methodology: Lead; Resources: Lead; Visualization: Lead; Writing – original draft: Lead; Writing – review & editing: Lead)
      Haoxiang Zhang, MD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Methodology: Lead; Resources: Lead; Software: Lead; Visualization: Lead; Writing – original draft: Lead; Writing – review & editing: Lead)
      Chenggang Gao, MD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Methodology: Lead; Resources: Lead; Software: Lead; Visualization: Lead; Writing – original draft: Lead)
      Jiaoshun Chen, MD (Data curation: Equal; Methodology: Equal; Resources: Equal; Visualization: Equal; Writing – original draft: Equal; Writing – review & editing: Equal)
      Hehe Li, MD (Conceptualization: Equal; Data curation: Equal; Methodology: Equal; Resources: Equal; Writing – review & editing: Equal)
      Zibo Meng, MD (Methodology: Equal; Resources: Equal; Visualization: Equal; Writing – review & editing: Equal)
      Jianwei Bai, MD (Methodology: Equal; Resources: Equal; Visualization: Equal; Writing – review & editing: Equal)
      Heshui Wu, MD, PhD (Supervision: Equal; Validation: Equal; Writing – review & editing: Equal)
      Qiang Shen, MD, PhD (Visualization: Equal; Writing – review & editing: Lead)
      Yin Tao, MD, PhD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Funding acquisition: Lead; Methodology: Lead; Project administration: Lead; Resources: Lead; Supervision: Lead; Validation: Lead; Visualization: Lead; Writing – original draft: Lead; Writing – review & editing: Lead)

      Supplementary Material

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