Multiomic characterization of pancreatic cancer-associated macrophage polarization reveals deregulated metabolic programs driven by the GM-CSF–PI3K pathway

  1. Seth Boyer
  2. Ho-Joon Lee  Is a corresponding author
  3. Nina Steele
  4. Li Zhang
  5. Peter Sajjakulnukit
  6. Anthony Andren
  7. Matthew H Ward
  8. Rima Singh
  9. Venkatesha Basrur
  10. Yaqing Zhang
  11. Alexey I Nesvizhskii
  12. Marina Pasca di Magliano
  13. Christopher J Halbrook  Is a corresponding author
  14. Costas A Lyssiotis  Is a corresponding author
  1. Department of Molecular & Integrative Physiology, University of Michigan, United States
  2. Department of Cell and Developmental Biology, University of Michigan, United States
  3. Department of Surgery, University of Michigan, United States
  4. Department of Molecular Biology and Biochemistry, University of California, Irvine, United States
  5. Department of Pathology, University of Michigan, United States
  6. Department of Computational Medicine and Bioinformatics, University of Michigan, United States
  7. Rogel Cancer Center, University of Michigan, United States
  8. Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan, United States
6 figures and 7 additional files

Figures

Figure 1 with 1 supplement
In vitro modeling and characterization of pancreatic tumor-educated macrophages (TEMs).

(A) Schematic of bone marrow-derived macrophage (BMDM) differentiation and polarization. (B) Left: principal component analysis of transcriptomics and proteomics of BMDMs treated with macrophage colony-stimulating factor (M-CSF; M0), lipopolysaccharide (LPS; M1), interleukin-4 (IL4; M2), or conditioned media from Kras-Off (3 or 5 days) or Kras-On (TEM) pancreatic ductal adenocarcinoma (PDA) cells; right: intracellular and extracellular metabolomics from media (Dulbecco’s modified Eagle medium, DMEM + 10% FBS), M0, M1, M2, or TEM (Kras-On media was mock treated or boiled before TEM culture). Transcriptomics and metabolomics samples were collected in biological triplicate; proteomics in biological duplicate. (C) RNA sequencing (RNA-seq)-measured mean-centered expression of classical M1 genes Nos2 and Slc2a1 across M0, M1, M2, and TEM phenotypes; n = 3. (D) RNA-seq-measured mean-centered expression of classical M2 genes Il4i1, Chil3, and Arg1 across M0, M1, M2, and TEM phenotypes; n = 3. (E) Heat map array of differential markers of each subtype from proteomics (1916 proteins), transcriptomics (9470 transcript), and intracellular (82 metabolites) and extracellular (46 metabolites) metabolomics. We highlight TEM markers in the black boxes. Error bars in (C) and (D) are mean ± standard deviation (SD); significance comparisons are relative to TEM subtype and were calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 1—figure supplement 1
Transcriptomic analyses of macrophages markers.

(A) Quantitative polymerase chain reaction (qPCR) of classic M1 markers Il12b and Tnfa in M0, M1, M2, and tumor-educated macrophage (TEM) subtypes; n = 3. (B) qPCR of classic M2 markers Fizz1, Chil3, Arg1, and Il4i1 in M0, M1, M2, and TEM subtypes; n = 3. (C) qPCR of Nos2 and Slc2a1 in M0, M1, M2, and TEM subtypes; n = 3. (D) Gene set enrichment analysis (GSEA) plots of metabolic pathways selectively upregulated in TEMs. Error bars are mean ± standard deviation (SD); significance comparisons are relative to TEM subtype and were calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 2 with 2 supplements
Metabolism and cytokine signaling are distinctive features of pancreatic tumor-educated macrophages (TEMs).

(A) RNA sequencing (RNA-seq) mean-centered expression of TEM cytokine-related signatures, Ccr1 and Ccr5; n = 3. (B) A bar plot of the numbers of up- and downregulated markers that are metabolic enzymes, present in both protein and gene analyses, for each subtype. (C) Table of 18 upregulated TEM markers from B and their corresponding KEGG (Kyoto Encyclopedia of Genes and Genomes) class. Note Acly as an enzyme of interest. (D) RNA-seq-measured mean-centered expression of TEM enzyme signatures Txnip and Acly; n = 3. (E) Heat map of the top 20 positively and negatively correlated proteins from the proteomics data for TXNIP, ACLY, and ARG1. Error bars in (A) and (D) are mean ± standard deviation (SD); significance comparisons are relative to TEM subtype and were calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; ****p < 0.0001.

Figure 2—figure supplement 1
Transcriptomic and proteomic analyses of macrophage markers.

(A) Quantitative polymerase chain reaction (qPCR) of tumor-educated macrophage (TEM) cytokine signatures Ccr1 and Ccr5 in M0, M1, M2, and TEM subtypes; n = 3. (B) Proteomics mean-centered abundance of CCR1 and CCR5 in M0, M1, M2, and TEM subtypes; n = 2. (C) qPCR of TEM markers Txnip and Acly in M0, M1, M2, and TEM subtypes; n = 3. (D) Proteomics mean-centered abundance of TXNIP, ACLY, and ARG1 in M0, M1, M2, and TEM subtypes; n = 2. Error bars are mean ± standard deviation (SD).

Figure 2—figure supplement 2
Multiomic pathway analyses.

(A) Proteomics and transcriptomics box plot of Acly, metabolomics box plot of citrate, and transcriptomics box plot of Slc25a1 for M0, M1, M2, Kras-Off 3 days, Kras-Off 5 days, and Kras-On tumor-educated macrophage (TEM) subtypes. (B) Proteomics box plots of PLCB2 and PRKAB1 and transcriptomics box plots of 15 PI3K-related genes. (C) STRING-based pathway analysis of Txnip-correlated proteins in TEM. (D) STRING-based pathway analysis of Txnip-correlated genes in TEM. *‘Off3/5d’ = ‘noDOX_3/5d’ = Kras-Off TEM subtype; ‘plusDOX’ = Kras-On TEM subtype. Error bars are mean ± standard deviation (SD), significance comparisons are relative to TEM subtype and were calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; Significance comparisons are relative to TEM subtype and were calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 3 with 1 supplement
Expression of tumor-educated macrophage (TEM) markers in human pancreatic tumor-associated macrophages.

(A) UMAP plot of myeloid populations in a human pancreatic tumor. (B) UMAP plots of TEM markers ACLY, TXNIP, and PI3KCD in human pancreatic tumor-associated macrophages (TAMs). (C) UMAP plots of PI3K-related genes expressed in human pancreatic TAMs (DDIT4, PRKAB1, LAMB3, LAMB2, THBS1, VEGFA, PLCB2, and ITGB7). (D) Expression of murine pancreatic TEM markers in macrophages from human tumors (pancreatic ductal adenocarcinoma [PDAC] TAMs) compared to macrophages from adjacent ‘normal’ tissue (AdjNorm), as analyzed by single-cell RNA sequencing.

Figure 3—figure supplement 1
Expression of pancreatic tumor-educated macrophage (TEM) markers in human tumors.

(A) Violin plots of TEM markers Txnip, Acly, and Pik3cd expressed in human pancreatic tumors. (B) Violin plots of PI3K-related genes expressed in human pancreatic tumors (Prkab1, Lamb3, Lamb2, Ddit4, Vegfa, Thbs1, Plcb2, and Itgb7).

Figure 4 with 1 supplement
The polarization of pancreatic tumor-educated macrophages (TEMs) is dependent on mutant Kras signaling in pancreatic cancer cells.

(A) Schematic of bone marrow-derived macrophage (BMDM) differentiation, iKras*3 cell Kras-On and Kras-Off conditioned media generation, and Kras-On and Kras-Off TEM polarization. (B) Western blot of MAPK pathway proteins ERK and pERK in Kras-expressing and 5-day Kras-extinguished iKras*3 cells. (C) Transcriptomics and proteomics heat maps of the differential markers in Figure 1E for Kras-On and 5-day Kras-Off TEMs. (D) RNA sequencing (RNA-seq)-measured mean-centered expression of TEM signatures Arg1, Acly, Txnip, Ccr1, and Ccr5; n = 3. Error bars are mean ± standard deviation (SD); significance was calculated using using a Student’s t-test; ***p < 0.001, ****p < 0.0001.

Figure 4—figure supplement 1
Regulation of tumor-educated macrophage (TEM) gene expression by mutant Kras in pancreatic ductal adenocarcinoma (PDA) cells.

(A) Gene set enrichment analysis (GSEA) of pancreatic TEMs plus or minus Kras expression (doxycycline, dox) for 5 days. (B) Quantitative polymerase chain reaction (qPCR) validation of TEM enzyme and cytokine markers Txnip, Acly, Arg1, Ccr5, and Ccr1 in Kras-On vs Kras-Off TEMs; n = 3. (C) qPCR of Csf2 expression (n = 6) and ELISA of granulocyte–macrophage colony-stimulating factor (GM-CSF) release (n = 3) in Kras-extinguished iKras*3 cells and two KPC PDA lines, KPC-MT3 and KPC7940. Error bars are mean ± standard deviation (SD); in (C), significance values were calculated using a Student’s t-test; in C, comparisons are relative to TEM subtype and significance was calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; **p < 0.01, ****p < 0.0001.

Figure 5 with 1 supplement
Kras in pancreatic ductal adenocarcinoma (PDA) polarizes pancreatic tumor-educated macrophages (TEMs) by way of granulocyte–macrophage colony-stimulating factor (GM-CSF) and lactate.

(A) Quantitative polymerase chain reaction (qPCR) measurement of Csf2 expression and ELISA for GM-CSF release; n = 3 in murine PDA cell line iKras*3. (B) Liquid chromatography (LC)/mass spectrometry (MS)-measured extracellular lactate abundance from Kras-expressing and -extinguished iKras*3 cells plotted as median-centered values; n = 3. (C) RNA sequencing (RNA-seq)-measured expression of myeloid GM-CSF-responsive genes, Cish, Pim1, and Ccl17 in Kras-On and Kras-Off TEMs; n = 3. (D) RNA-seq-measured expression of lactate-responsive genes, Il1b and Il6 in Kras-On and Kras-Off TEMs; n = 3. (E, F) RNA-seq-measured expression of genes responsive to acidic extracellular pH in Kras-On and Kras-Off TEMs; n = 3. (G) qPCR-measured expression of TEM markers Arg1, Txnip, Acly, Ccr1, and Ccr5 in M0 macrophages treated with either lactate, GM-CSF, or the combination; n = 3. Error bars are mean ± standard deviation (SD); significance values in (A–F) were calculated using a Student’s t-test; in (G), comparisons are relative to TEM subtype, and significance was calculated using one-way analysis of variance (ANOVA) with Dunnett’s post hoc test; *p < 0.05; **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 5—figure supplement 1
Effects of mutant Kras on extracellular metabolism in pancreatic ductal adenocarcinoma (PDA) cells.

(A) Heat map of significant (p < 0.01) extracellular metabolites released from Kras-expressing vs. Kras-extinguished iKras*3 cells. (B) Enrichment analysis of Kras-expressing vs. Kras-extinguished iKras*3 extracellular metabolomics. (C) Extracellular lactate abundance from iKras*3 cells ± doxycycline (dox) as measured by enzymatic fluorescence assay; n = 3. Error bars are mean ± standard deviation (SD), significance values were calculated using a Student’s t-test; ****p < 0.0001.

Figure 6 with 1 supplement
Pancreatic ductal adenocarcinoma (PDA)-derived granulocyte–macrophage colony-stimulating factor (GM-CSF) promotes tumor-educated macrophage (TEM) polarization and dictates their metabolism through the PI3K–AKT pathway.

(A) Western blot of pACLY/ACLY, pAKT/AKT, TXNIP, and ARG1 in bone marrow-derived macrophages (BMDMs) treated with either macrophage colony-stimulating factor (M-CSF), iKras*3 cell-conditioned media + vehicle, iKras*3 cell-conditioned media + pan AKT inhibitor MK-2206, or iKras*3 cell-conditioned media + pan-PI3K inhibitor BKM120. (B) Western blot of pACLY/ACLY, pAKT/AKT, TXNIP, and ARG1 in BMDMs treated with either M-CSF, iKras*3 cell-conditioned media + vehicle, iKras*3 cell-conditioned media + MK-2206, iKras*3 cell-conditioned media + BKM120, iKras*3 cell-conditioned media + IgG control, or iKras*3 cell-conditioned media + GM-CSF-neutralizing antibody. (C) Heat map of differentially abundant metabolites in TEMs treated with either anti-GM-CSF or IgG control; n = 3. (D) Bar graph of TCA and related metabolites in TEMs treated with either anti-GM-CSF or IgG control; n = 3. (E) Schematic of TEM polarization model. Replicates and quantitation of the westerns in (A, B) are presented in Figure 6—figure supplement 1A. Error bars are mean ± standard deviation (SD); significance was calculated using a Student’s t-test; *p < 0.05.

Figure 6—figure supplement 1
Granulocyte–macrophage colony-stimulating factor (GM-CSF) neutralization disrupts the tumor-educated macrophage (TEM) metabolic phenotype.

(A) Bar graphs of pACLY quantitation relative to ACLY expression in TEM mock, MK-2206, BKM120, IgG, and anti-GM-CSF groups; quantitated from three independently prepared westerns. Representative westerns in Figure 6. (B) Bar graphs of TXNIP phosphorylation quantitation from Figure 6A (left) and TXNIP expression quantitation in TEM mock, IgG, and anti-GM-CSF groups; latter are quantitated from three independently prepared westerns. Error bars are mean ± standard deviation (SD); significance for comparison between two groups was performed using the Student’s t-test; significance for comparisons with three groups is relative to TEM subtype and were calculated using one-way analysis of variance (ANOVA) with Tukey’s post hoc; *p < 0.05, **p < 0.01.

Additional files

Supplementary file 1

Differential expression or abundance analysis detailing the markers that distinguish each subtype from Figure 1E.

https://cdn.elifesciences.org/articles/73796/elife-73796-supp1-v2.xlsx
Supplementary file 2

Gene set enrichment analysis (GSEA) of KEGG gene sets from the RNA-seq dataset comparing tumor-educated macrophages (TEMs) to M0, M1, and M2 macrophages.

https://cdn.elifesciences.org/articles/73796/elife-73796-supp2-v2.xlsx
Supplementary file 3

Enrichr analysis of the proteomics dataset comparing tumor-educated macrophages (TEMs) to M0, M1, and M2 macrophages.

https://cdn.elifesciences.org/articles/73796/elife-73796-supp3-v2.xlsx
Supplementary file 4

Gene set enrichment analysis (GSEA) of KEGG gene sets from tumor-educated macrophages (TEMs between the Kras-On and Kras-Off) conditions.

https://cdn.elifesciences.org/articles/73796/elife-73796-supp4-v2.xlsx
Supplementary file 5

Raw metabolomics data from Figure 1 and Figure 5—figure supplement 1D.

https://cdn.elifesciences.org/articles/73796/elife-73796-supp5-v2.xlsx
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https://cdn.elifesciences.org/articles/73796/elife-73796-transrepform1-v2.docx
Source data 1

Raw western blot images.

https://cdn.elifesciences.org/articles/73796/elife-73796-data1-v2.pptx

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  1. Seth Boyer
  2. Ho-Joon Lee
  3. Nina Steele
  4. Li Zhang
  5. Peter Sajjakulnukit
  6. Anthony Andren
  7. Matthew H Ward
  8. Rima Singh
  9. Venkatesha Basrur
  10. Yaqing Zhang
  11. Alexey I Nesvizhskii
  12. Marina Pasca di Magliano
  13. Christopher J Halbrook
  14. Costas A Lyssiotis
(2022)
Multiomic characterization of pancreatic cancer-associated macrophage polarization reveals deregulated metabolic programs driven by the GM-CSF–PI3K pathway
eLife 11:e73796.
https://doi.org/10.7554/eLife.73796