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Cytosolic pH regulates proliferation and tumour growth by promoting expression of cyclin D1

Abstract

Enhanced growth and proliferation of cancer cells are accompanied by profound changes in cellular metabolism. These metabolic changes are also common under physiological conditions, and include increased glucose fermentation accompanied by elevated cytosolic pH (pHc)1,2. However, how these changes contribute to enhanced cell growth and proliferation is unclear. Here, we show that elevated pHc specifically orchestrates an E2F-dependent transcriptional programme to drive cell proliferation by promoting cyclin D1 expression. pHc-dependent transcription of cyclin D1 requires the transcription factors CREB1, ATF1 and ETS1, and the histone acetyltransferases p300 and CBP. Biochemical characterization revealed that the CREB1–p300/CBP interaction acts as a pH sensor and coincidence detector, integrating different mitotic signals to regulate cyclin D1 transcription. We also show that elevated pHc contributes to increased cyclin D1 expression in malignant pleural mesotheliomas (MPMs), and renders these cells hypersensitive to pharmacological reduction of pHc. Taken together, these data demonstrate that elevated pHc is a critical cellular signal regulating G1 progression, and provide a mechanism linking elevated pHc to oncogenic activation of cyclin D1 in MPMs, and possibly other cyclin D1~dependent tumours. Thus, an increase of pHc may represent a functionally important, early event in the aetiology of cancer that is amenable to therapeutic intervention.

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Fig. 1: NHE1 activity regulates pHc and promotes cell-cycle progression through early G1.
Fig. 2: High pHc regulates cyclin D1 transcription in parallel or downstream of CREB activation.
Fig. 3: Sensing of pHc by the CREB–p300 interaction regulates cyclin D1 expression.
Fig. 4: Elevated pHc mediates oncogenic activation of cyclin D1 in MPMs.

Data availability

Raw data and materials are available from the authors upon request. Full scans of all western blots are available in the supplementary material. Gene-expression data are accessible at GEO (https://www.ncbi.nlm.nih.gov/geo/), accession no. GSE145833. Additional data analysis for RNA-seq experiments is provided in Supplementary Tables 923. Raw data for targeted and untargeted metabolomics experiments are included as Supplementary Table 7 and 8, respectively. Data from the publicly available datasets used in this study can be accessed at: TCGA (https://www.cancer.gov/tcga), cBioPortal (http://www.cbioportal.org), TGCA pan cancer atlas (https://portal.gdc.cancer.gov) and Ensembl (https://www.ensembl.org/index.html) Source data are provided with this paper.

References

  1. Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Article  CAS  Google Scholar 

  2. Flinck, M., Kramer, S. H. & Pedersen, S. F. Roles of pH in control of cell proliferation. Acta Physiol. (Oxf). 223, e13068 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Karmazyn, M., Avkiran, M. & Fliegel, L. The Sodium-Hydrogen Exchanger, From Molecule to its Role in Disease (Springer, 2003).

  4. Moolenaar, W. H. Effects of growth factors on intracellular pH regulation. Annu. Rev. Physiol. 48, 363–376 (1986).

    Article  CAS  PubMed  Google Scholar 

  5. Hagag, N., Lacal, J. C., Graber, M., Aaronson, S. & Viola, M. V. Microinjection of ras p21 induces a rapid rise in intracellular pH. Mol. Cell Biol. 7, 1984–1988 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Pouyssegur, J., Sardet, C., Franchi, A., L’Allemain, G. & Paris, S. A specific mutation abolishing Na+/H+ antiport activity in hamster fibroblasts precludes growth at neutral and acidic pH. Proc. Natl Acad. Sci. USA 81, 4833–4837 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Foster, D. A., Yellen, P., Xu, L. & Saqcena, M. Regulation of G1 cell cycle progression: distinguishing the restriction point from a nutrient-sensing cell growth checkpoint(s). Genes Cancer 1, 1124–1131 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Iadevaia, V., Liu, R. & Proud, C. G. mTORC1 signaling controls multiple steps in ribosome biogenesis. Semin. Cell Dev. Biol. 36, 113–120 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Schwarz, C. et al. A precise CDK activity threshold determines passage through the restriction point. Mol. Cell 69, 253–264 e255 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rader, J. et al. Dual CDK4/CDK6 inhibition induces cell-cycle arrest and senescence in neuroblastoma. Clin. Cancer Res. 19, 6173–6182 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Tetsu, O. & McCormick, F. Beta-catenin regulates expression of cyclin D1 in colon carcinoma cells. Nature 398, 422–426 (1999).

    Article  CAS  PubMed  Google Scholar 

  12. Yan, Y., Li, X., Kover, K., Clements, M. & Ye, P. CREB participates in the IGF-I-stimulation cyclin D1 transcription. Dev. Neurobiol. 73, 559–570 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Sakamoto, K. M. & Frank, D. A. CREB in the pathophysiology of cancer: implications for targeting transcription factors for cancer therapy. Clin. Cancer Res. 15, 2583–2587 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Liu, F., Yang, X., Geng, M. & Huang, M. Targeting ERK, an Achilles’ Heel of the MAPK pathway, in cancer therapy. Acta Pharm. Sin. B 8, 552–562 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cardinaux, J. R. et al. Recruitment of CREB binding protein is sufficient for CREB-mediated gene activation. Mol. Cell Biol. 20, 1546–1552 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chan, H. M. & La Thangue, N. B. p300/CBP proteins: HATs for transcriptional bridges and scaffolds. J. Cell Sci. 114, 2363–2373 (2001).

    Article  CAS  PubMed  Google Scholar 

  17. Vo, N. & Goodman, R. H. CREB-binding protein and p300 in transcriptional regulation. J. Biol. Chem. 276, 13505–13508 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Jin, Q. et al. Distinct roles of GCN5/PCAF-mediated H3K9ac and CBP/p300-mediated H3K18/27ac in nuclear receptor transactivation. EMBO J. 30, 249–262 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Raja, D. A. et al. pH-controlled histone acetylation amplifies melanocyte differentiation downstream of MITF. EMBO Rep. 21, e48333 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Shih, H. M. et al. A positive genetic selection for disrupting protein-protein interactions: identification of CREB mutations that prevent association with the coactivator CBP. Proc. Natl Acad. Sci. USA 93, 13896–13901 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Radhakrishnan, I. et al. Structural analyses of CREB–CBP transcriptional activator–coactivator complexes by NMR spectroscopy: implications for mapping the boundaries of structural domains. J. Mol. Biol. 287, 859–865 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Smiechowski, M. Theoretical pK(a) prediction of O-phosphoserine in aqueous solution. Chem. Phys. Lett. 501, 123–129 (2010).

    Article  CAS  Google Scholar 

  23. Amith, S. R. & Fliegel, L. Regulation of the Na+/H+ exchanger (NHE1) in breast cancer metastasis. Cancer Res. 73, 1259–1264 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Zhu, J. & Thompson, C. B. Metabolic regulation of cell growth and proliferation. Nat. Rev. Mol. Cell Biol. 20, 436–450 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Birkeland, E., Koch, L. & Dechant, R. Another consequence of the Warburg effect? Metabolic regulation of Na+/H+ exchangers may link aerobic glycolysis to cell growth. Front. Oncol. https://doi.org/10.3389/fonc.2020.01561 (2020).

  26. Webb, B. A., Chimenti, M., Jacobson, M. P. & Barber, D. L. Dysregulated pH: a perfect storm for cancer progression. Nat. Rev. Cancer 11, 671–677 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Galenkamp, K. M. O. et al. Golgi acidification by NHE7 regulates cytosolic pH homeostasis in pancreatic cancer cells. Cancer Discov. 10, 822–835 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tozzi, M. et al. Proton pump inhibitors reduce pancreatic adenocarcinoma progression by selectively targeting H+, K+-ATPases in pancreatic cancer and stellate cells. Cancers (Basel) 12, 640 (2020).

    Article  CAS  Google Scholar 

  29. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Guo, G. et al. Whole-exome sequencing reveals frequent genetic alterations in BAP1, NF2, CDKN2A, and CUL1 in malignant pleural mesothelioma. Cancer Res. 75, 264–269 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Sobhani, N., Corona, S. P., Zanconati, F. & Generali, D. Cyclin dependent kinase 4 and 6 inhibitors as novel therapeutic agents for targeted treatment of malignant mesothelioma. Genes Cancer 8, 495–496 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pors, J., Naso, J., Berg, K. & Churg, A. Cyclin D1 immunohistochemical staining to separate benign from malignant mesothelial proliferations. Mod. Pathol. 33, 312–318 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Aelony, Y., Yao, J. F. & King, R. R. Prognostic value of pleural fluid pH in malignant epithelial mesothelioma after talc poudrage. Respiration 73, 334–339 (2006).

    Article  PubMed  Google Scholar 

  34. DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 11–20 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Dechant, R., Saad, S., Ibanez, A. J. & Peter, M. Cytosolic pH regulates cell growth through distinct GTPases, Arf1 and Gtr1, to promote Ras/PKA and TORC1 activity. Mol. Cell 55, 409–421 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Orij, R., Brul, S. & Smits, G. J. Intracellular pH is a tightly controlled signal in yeast. Biochim. Biophys. Acta 1810, 933–944 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Carlton, J. G. & Cullen, P. J. Coincidence detection in phosphoinositide signaling. Trends Cell Biol. 15, 540–547 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shin, J. J. H. et al. pH biosensing by PI4P regulates cargo sorting at the TGN. Dev. Cell. 52, 461–476.e4 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. White, K. A. et al. β-Catenin is a pH sensor with decreased stability at higher intracellular pH. J. Cell Biol. 217, 3965–3976 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Harguindey, S., Koltai, T. & Reshkin, S. J. Curing cancer? Further along the new pH-centric road and paradigm. Oncoscience 5, 132–133 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Swietach, P., Vaughan-Jones, R. D., Harris, A. L. & Hulikova, A. The chemistry, physiology and pathology of pH in cancer. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 369, 20130099 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Sharma, M. et al. pH gradient reversal: an emerging hallmark of cancers. Recent Pat. Anticancer Drug Discov. 10, 244–258 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Perona, R., Portillo, F., Giraldez, F. & Serrano, R. Transformation and pH homeostasis of fibroblasts expressing yeast H+-ATPase containing site-directed mutations. Mol. Cell Biol. 10, 4110–4115 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Perona, R. & Serrano, R. Increased pH and tumorigenicity of fibroblasts expressing a yeast proton pump. Nature 334, 438–440 (1988).

    Article  CAS  PubMed  Google Scholar 

  45. Rojas, E. A. et al. Amiloride, an old diuretic drug, is a potential therapeutic agent for multiple myeloma. Clin. Cancer Res. 23, 6602–6615 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lampert, F. et al. The multi-subunit GID/CTLH E3 ubiquitin ligase promotes cell proliferation and targets the transcription factor Hbp1 for degradation. eLife 7, e35528 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Eeckhoute, J., Carroll, J. S., Geistlinger, T. R., Torres-Arzayus, M. I. & Brown, M. A cell-type-specific transcriptional network required for estrogen regulation of cyclin D1 and cell cycle progression in breast cancer. Genes Dev. 20, 2513–2526 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Oki, S. et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 19, e46255 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  56. Tallon de Lara, P. et al. Gemcitabine synergizes with immune checkpoint inhibitors and overcomes resistance in a preclinical model and mesothelioma patients. Clin. Cancer Res. 24, 6345–6354 (2018).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank P. Kimmig, S. Gilberto, K. F. Marquart, W. Kovacs, M. Stoffel, A. Smith and members of the Peter, Curioni and Opitz laboratories for helpful discussions and comments on the manuscript; B. Vrugt, Ch. Mittmann and M. Glönkler, Department of Pathology and Molecular Pathology, University Hospital Zurich, for help with establishment of NHE1 and cyclin D1 IHC, the Functional Genomics Center Zürich (FGCZ) for support with RNA-seq and metabolomics and M. Okoniewski (Scientific IT Services ETH) for help with bioinformatic analyses. Work in the Dechant, Peter, Curioni and Opitz laboratories is funded by the SNF, ETHZ (ETH research grant 28 17-2 to RD) and the Medical University of Zurich.

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Authors and Affiliations

Authors

Contributions

R.D. conceptualized the study; performed formal analysis and visualization; wrote the manuscript with contributions of all authors. L.M.K., E.S.B., S.B., X.H. and R.D. performed experiments; L.M.K., E.S.B., M.M., S.H. and R.D. analysed data. A.J.I. performed metabolomics analysis. R.D., I.O., A.C.-F. and M.P. supervised the study.

Corresponding author

Correspondence to Reinhard Dechant.

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The authors declare no competing interest.

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Peer review information Primary Handling Editor: George Caputa.

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Extended data

Extended Data Fig. 1 NHE1 activity regulates pHc and promotes cell cycle progression through early G1.

(ac) FCS and glucose availability cooperatively regulate pHc. Cells were starved for 24 hours, stimulated with the indicated conditions and pHc was determined. (a) Pooled single cell data of independent experiments N=3, n=30 cells per experiment, two-sided t-Test) are shown as box plot (MATLAB) with median, 25th and 75th percentile and whiskers extending to most extreme points indicated. (b) The difference in pHc relative to the starved control (-/-) (b, c) was plotted as mean ± S.E.M. alongside the histogram of single cell measurements. In the histograms, the median pHc was indicated. For all pHc measurements data are pooled single cell data of independent experiments N=3, n=30 cells per experiment, two-sided t-Test. (d) NHE1 is the most abundant NHE localized to the plasma membrane. Relative expression levels of NHE1-9 were determined by qPCR (mean ± S.E.M. of independent experiments, N=3, two-sided t-Test). Subcellular localization of the different NHE proteins is indicated. (eg) NHE1 activity is required for RB phosphorylation. (e) The fraction of cells with RB phosphorylation at Ser780 from the experiment shown in Fig. 1f is shown (mean ± S.E.M. of independent experiments N= 3, two-sided t-Test). (f) Cells treated as in Fig. 1f were stained for RBP-Ser807/811 and the fraction of cells with RB phosphorylation (mean ± S.E.M. of independent experiments N=3, two-sided t-Test) (f) and representative images are shown. Scale bar represents 33 µm. (g) Cells were subjected to siRNA against NHE1 and RB phosphorylation was determined after 72 hours by immunofluorescence or western blot. The fraction of RB positive cells (mean ± S.E.M. of independent experiments N=3, two-sided t-Test) and representative images are shown. Scale bar represents 100 µm. (h, i) RPE-1 cells were grown in media of different NaHCO3 concentration to adjust extracellular pH to the indicated values and relative growth by MTT (mean of independent experiments N=2) (h), the fraction of cells with RB phosphorylation (mean of independent experiments N=2) and representative images (i) are shown. Scale bar represents 100 µm.

Extended Data Fig. 2 Pharmacological inhibition of NHE activity regulates RB phosphorylation in different cell lines.

Cells treated as in Fig. 1f were stained for RBP-Ser780 and the fraction of cells with RB phosphorylation was determined in the absence or presence of DMA for the indicated cell lines (mean ± S.E.M. of independent experiments N= 3, two-sided t-Test). Scale bar represents 100 µm.

Extended Data Fig. 3 pHc does not globally affect cellular metabolism.

(ac) RPE-1 cells were starved and the relative abundance of metabolites was determined 4 hours after stimulation with the indicated conditions as described in Materials and Methods. Selected metabolites from energy (a) and central carbon metabolism (b) are shown as mean ± S.E.M.; N=4. P-values for all pairwise comparisons between conditions are listed in the Supplementary Table S7. (c) Unsupervised clustering and (d) PCA analysis were calculated using MATLAB (The MathWorks) from metabolite datasets generated using Progenesis QI software (Nonlinear Dynamics) to demonstrate the highly similar metabolic response upon stimulation in the presence or absence of DMA.

Extended Data Fig. 4 NHE activity is required for early G1 progression.

(a, b) Elevated pHc is required within the first 8 hours of the cell-cycle. Extracts of cells as in Fig. 1g were blotted for RB phosphorylation and cyclin A expression as markers for cell-cycle progression. Blots representative of three independent experiments are shown. (b) Elevated pHc is required to drive cell-cycle progression before initiation of S phase. Cells were starved (-/-) and treated with FCS glucose for the indicated time points and BrdU incorporation was measured to follow DNA synthesis. Results of a representative experiment from three independent experiments with percentage of cells in G1, S and G2/M phase are shown. (c, d) pHc regulates a subset of FCS and glucose dependent genes required for G1 progression. Cells were starved for 24 hours, treated with FCS glucose in the presence or absence of DMA for 5 hours and analyzed by RNAseq. (c) Venn diagram indicating the overlap of starvation and DMA-regulated genes. P-value demonstrating statistical significance of co-regulated genes based on a hypergeometric distribution is shown. (d) Genes repressed upon starvation and DMA treatment and co-regulated genes in both conditions were analyzed by GO-term enrichment using ShinyGO (http://bioinformatics.sdstate.edu/go/). Significantly enriched GO-terms were identified for the individual groups and plotted as -log10(FDR).

Source data

Extended Data Fig. 5 Regulation of cyclin D1 transcription requires CREB1/ATF1 and ETS1 transcription factors.

(ac) An elevated pHc is required for cyclin D1 expression. (a) HFF-1 cells were starved and cyclin D1 expression was determined by western blotting 6 hours after stimulation with the indicated conditions. (b) RPE-1 cells were grown and cyclin D1 and phosphorylated RB was determined by western blotting upon treatment with DMA at the indicated time points. (c) RPE-1 cells were subjected to siRNA against NHE1 and cyclin D1 abundance was determined after 72 hours by western blot (d and e) CDK4cyclin D1 activity is required for G1/S progression. Cells were treated as in Fig. 1f and cell-cycle progression was determined by (d) western blotting and (e) FACS analysis and upon treatment with the CDK4cyclin D1 inhibitor Ribociclib at the indicated times. (f) pHc regulates cyclin D1 transcription rather than mRNA stability. Cells were treated with the indicated inhibitors for 4 hours and relative mRNA levels of cyclin D1 were determined (mean ± S.E.M. of independent experiments N=4, two-sided t-Test) (g) CREB1, ATF1 and ETS1 are required for cyclin D1 expression. Cells were transduced with Lentiviral particles expressing shRNA constructs against the indicated genes and expression of transcription factors and cyclin D1 protein levels were determined by western blotting. For all western blots in this figure, blots representative of three independent experiments are shown.

Source data

Extended Data Fig. 6 Regulation of cyclin D1 transcription requires p300/CBP.

(a, b) Reduced activity of p300/CBP decreases cyclin D1 expression and acetylation of histone H3 at K27. (a) Cells were subjected to shRNA against the indicated transcription factors or co-activators and histone acetylation at H3-K27 was determined by western blotting. (b) Cells were grown as in Fig. 3b and cyclin D1 mRNA levels were determined by 4 hours after stimulation with the indicated conditions by qPCR (mean ± S.E.M. of 3 independent experiments, two-sided t-Test). (c) Cells were grown as in Fig. 3a and the efficiency of shRNA mediated knock-down of p300 and CBP and cyclin D1 protein levels were determined by western blotting. (d) DMA treatment and p300/CBP inhibition does not generally affect histone acetylation. Cells were grown as in Fig. 3c and acetylation of histone H3 at K9 was determined by western blotting. (eg) Starvation, inhibition of NHE activity and p300/CBP activity trigger a similar gene expression program. Cells were grown as in Fig. 1h and gene expression was determined by RNAseq. Volcano plots of differentially expressed genes upon stimulation with FCS glucose relative to (e) starvation, (f) DMA treatment and (g) C646 treatment is shown. Co-regulated genes under all conditions are indicated in red. (h) Venn diagram showing number of significantly higher expressed genes under the indicated conditions. For all western blots in this figure, blots representative of three independent experiments are shown.

Source data

Extended Data Fig. 7 pHc is regulated by Akt activity and active glucose metabolism.

(a) Cells as in Fig. 3g were grown and relative biomass accumulation was scored by MTT 72 hours after DMA treatment. (mean ± S.E.M. of independent experiments N=6, two-sided t-Test). (b) Cells as in Fig. 3g were grown and RB phosphorylation was scored 24 hours after DMA treatment. (mean ± S.E.M. of independent experiments N=3, two-sided t-Test). (c) CREB1 peptides interact with the KIX domain of CBP. A result of a pull-down of the KIX domain of CBP with biotinylated peptides representative for 6 independent experiments is shown. (d) RPE-1 cells were starved, stimulated with the indicated conditions and changes in cytosolic pH (mean ± S.E.M. of pooled single cell data from independent experiments, N=3, was plotted alongside the histogram of single cell measurements, n=30 cells per experiment) was determined. (e) RPE-1 cells were starved, stimulated with FCS and glucose in the presence of DMA and C646 and Akt-dependent phosphorylation of TSC2 was determined by western blotting. Blots representative of three independent experiments are shown. (f) Elevated pHc requires active glucose metabolism. pHc as determined in Fig. 3j is plotted from pooled single cell data of independent experiments N=3, n=30 cells per experiment are shown as box plot (MATLAB) with median, 25th and 75th percentile and whiskers extending to most extreme points not considered outliers, and outliers (+) indicated, two-sided t-Test.

Source data

Extended Data Fig. 8 NHE1 activity is critical for cell-cycle progression in PDAC cell lines.

(a) Cells of the indicated genotype expressing pHluorin were starved for 24 hours and pHc was determined 30 min following stimulation with the indicated conditions. For all pHc measurements data are pooled single cell data of independent experiments N=3 for PANC-1, N=3 for MIA Paca-2), n=30 cells per experiment, two-sided t-Test. Box plot (MATLAB) with median, 25th and 75th percentile, whiskers extending to most extreme points not considered outliers, and outliers (+) indicated. (b) Cells as in (a) were starved, treated with DMA for 6 hours and cyclin D1 and RB phosphorylation were determined by western blotting. (c) Cells were subjected to siRNA against NHE1 and cyclin D1, RB phosphorylation and NHE1 abundance were determined by western blotting. (d) Cyclin D1, but not NHE1 expression correlates with survival in PDAC cell lines. Data obtained from TCGA were independently analyzed for a correlation of cyclin D1 and NHE1 mRNA and survival and Kaplan Meier analysis was performed using log-rank test (N=85 patients with high expression of NHE1 or cyclin D1, respectively, N=96 patient with low expression of these markers). Hazard Ratio for NHE1 (with confidence interval) HR = 1.08 (0.72 - 1.62); for cyclin D1 HR = 2.05 (1.36 - 3.10).

Source data

Extended Data Fig. 9 MPMs may be caused by hyperactivation of the NHE1/cyclin D1 axis.

(a) MPMs are associated with high CDK4cyclin D activity. Percentage of patients with the indicated mutations was determined from 2 patient cohorts as described in Materials and Methods. Only mutations in genes linked to the cyclin D/RB pathway are shown. (b) Cells were grown and treated as in Fig. 4c and cyclin D1 protein abundance was analyzed by western blot. Blots representative of three independent experiments are shown. (c) Cells were grown and phosphorylation of RB was analyzed upon DMA treatment (mean ± SEM of independent experiments, N=4). Scale bar represents 100 µm. (d) Cells subjected to siRNA mediated knockdown of NHE1 and cyclin D1 and NHE1 protein levels were analyzed by western blot (mean ± S.E.M. of 3 independent experiments). (e) NHE1 is the most abundant NHE isoform in MPM cell lines. Steady state mRNA levels of the indicated genes relative to NHE1 were determined by qPCR (mean ± SEM of independent experiments, N=3).

Source data

Extended Data Fig. 10 NHE1 expression correlates with cyclin D1 expression and survival in MPM patients.

(a) Representative images of histological sections stained as in Fig. 4f with high (N=45 patients) and low NHE1 (N=51 patients) expression with the corresponding cyclin D1 stainings. Scale bar represents 100 µm. (b) Kaplan Meier analysis of survival data based on high (N=49 patients) or low (N=37 patients) NHE1 expression from the TCGA-Meso cohort using log-rank test. Hazard Ratio for (with confidence interval) HR = 1.95 (1.16 - 3.30) (c) Cyclin D1 expression for patient cohorts with high and low NHE1 expression as determined in (b) Box plot (MATLAB) with median, 25th and 75th percentile, whiskers extending to most extreme points not considered outliers, and outliers (+) indicated.

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Koch, L.M., Birkeland, E.S., Battaglioni, S. et al. Cytosolic pH regulates proliferation and tumour growth by promoting expression of cyclin D1. Nat Metab 2, 1212–1222 (2020). https://doi.org/10.1038/s42255-020-00297-0

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