Skip to main content
SpringerLink
Log in

A Genomic Analysis of Cellular Responses and Adaptions to Extracellular Acidosis

  • Chapter
  • First Online:
Molecular Genetics of Dysregulated pH Homeostasis

  • 569 Accesses

Abstract

Even though lactic acidosis is a prominent feature of solid tumors, we have limited understanding of the mechanisms by which lactic acidosis influences the genetic, epigenetic, proteomic, and metabolic phenotypes of cancer cells. This chapter aims to (1) briefly outline the tumor microenvironment and how acidity relates to its biology; (2) briefly discuss traditional hypothesis-driven or single-gene studies that have explored cancer cells’ responses to acidosis or lactic acidosis; (3) explain what we have learned from “-omics” approaches that have been applied to studying cellular response to acid and lactic acid; and (4) reflect on the projections of these studies in (2) and (3) to in vivo human tumor biology and how we can use this information to better inform disease treatments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vaupel P (2004) Tumor microenvironmental physiology and its implications for radiation oncology. Semin Radiat Oncol 14(3):198–206

    PubMed  Google Scholar 

  2. Webb BA, Chimenti M, Jacobson MP, Barber DL (2011) Dysregulated pH: a perfect storm for cancer progression. Nat Rev Cancer 11(9):671–677

    PubMed  CAS  Google Scholar 

  3. Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev Cancer 4(11):891–899

    PubMed  CAS  Google Scholar 

  4. Cardone RA, Casavola V, Reshkin SJ (2005) The role of disturbed pH dynamics and the Na+/H+ exchanger in metastasis. Nat Rev Cancer 5(10):786–795

    PubMed  CAS  Google Scholar 

  5. Gulledge CJ, Dewhirst MW (1996) Tumor oxygenation: a matter of supply and demand. Anticancer Res 16(2):741–749

    PubMed  CAS  Google Scholar 

  6. Helmlinger G, Yuan F, Dellian M, Jain RK (1997) Interstitial pH and pO2 gradients in solid tumors in vivo: high-resolution measurements reveal a lack of correlation. Nat Med 3(2):177–182

    PubMed  CAS  Google Scholar 

  7. Schornack PA, Gillies RJ (2003) Contributions of cell metabolism and H+ diffusion to the acidic pH of tumors. Neoplasia 5(2):135–145

    PubMed  CAS  PubMed Central  Google Scholar 

  8. Vaupel P, Hockel M (2000) Blood supply, oxygenation status and metabolic micromilieu of breast cancers: characterization and therapeutic relevance. Int J Oncol 17(5):869–879

    PubMed  CAS  Google Scholar 

  9. Robey IF, Baggett BK, Kirkpatrick ND, Roe DJ, Dosescu J, Sloane BF, Hashim AI, Morse DL, Raghunand N, Gatenby RA et al (2009) Bicarbonate increases tumor pH and inhibits spontaneous metastases. Cancer Res 69(6):2260–2268

    PubMed  CAS  PubMed Central  Google Scholar 

  10. Adams DJ (2005) The impact of tumor physiology on camptothecin-based drug development. Curr Med Chem Anticancer Agents 5(1):1–13

    PubMed  CAS  Google Scholar 

  11. Mueller-Klieser W, Walenta S (1993) Geographical mapping of metabolites in biological tissue with quantitative bioluminescence and single photon imaging. Histochem J 25(6):407–420

    PubMed  CAS  Google Scholar 

  12. Thews O, Kelleher DK, Vaupel PW (1995) Modulation of spatial O2 tension distribution in experimental tumors by increasing arterial O2 supply. Acta Oncol 34(3):291–295

    PubMed  CAS  Google Scholar 

  13. Kallinowski F, Schlenger KH, Runkel S, Kloes M, Stohrer M, Okunieff P, Vaupel P (1989) Blood flow, metabolism, cellular microenvironment, and growth rate of human tumor xenografts. Cancer Res 49(14):3759–3764

    PubMed  CAS  Google Scholar 

  14. Dewhirst MW, Klitzman B, Braun RD, Brizel DM, Haroon ZA, Secomb TW (2000) Review of methods used to study oxygen transport at the microcirculatory level. Int J Cancer 90(5):237–255

    PubMed  CAS  Google Scholar 

  15. Brizel DM, Schroeder T, Scher RL, Walenta S, Clough RW, Dewhirst MW, Mueller-Klieser W (2001) Elevated tumor lactate concentrations predict for an increased risk of metastases in head-and-neck cancer. Int J Radiat Oncol Biol Phys 51(2):349–353

    PubMed  CAS  Google Scholar 

  16. Schwickert G, Walenta S, Sundfor K, Rofstad EK, Mueller-Klieser W (1995) Correlation of high lactate levels in human cervical cancer with incidence of metastasis. Cancer Res 55(21):4757–4759

    PubMed  CAS  Google Scholar 

  17. Walenta S, Salameh A, Lyng H, Evensen JF, Mitze M, Rofstad EK, Mueller-Klieser W (1997) Correlation of high lactate levels in head and neck tumors with incidence of metastasis. Am J Pathol 150(2):409–415

    PubMed  CAS  PubMed Central  Google Scholar 

  18. Walenta S, Wetterling M, Lehrke M, Schwickert G, Sundfor K, Rofstad EK, Mueller-Klieser W (2000) High lactate levels predict likelihood of metastases, tumor recurrence, and restricted patient survival in human cervical cancers. Cancer Res 60(4):916–921

    PubMed  CAS  Google Scholar 

  19. Walenta S, Mueller-Klieser WF (2004) Lactate: mirror and motor of tumor malignancy. Semin Radiat Oncol 14(3):267–274

    PubMed  Google Scholar 

  20. Formby B, Stern R (2003) Lactate-sensitive response elements in genes involved in hyaluronan catabolism. Biochem Biophys Res Commun 305(1):203–208

    PubMed  CAS  Google Scholar 

  21. Stern R, Shuster S, Neudecker BA, Formby B (2002) Lactate stimulates fibroblast expression of hyaluronan and CD44: the Warburg effect revisited. Exp Cell Res 276(1):24–31

    PubMed  CAS  Google Scholar 

  22. Lu H, Forbes RA, Verma A (2002) Hypoxia-inducible factor 1 activation by aerobic glycolysis implicates the Warburg effect in carcinogenesis. J Biol Chem 277(26):23111–23115

    PubMed  CAS  Google Scholar 

  23. Fukumura D, Xu L, Chen Y, Gohongi T, Seed B, Jain RK (2001) Hypoxia and acidosis independently up-regulate vascular endothelial growth factor transcription in brain tumors in vivo. Cancer Res 61(16):6020–6024

    PubMed  CAS  Google Scholar 

  24. Xu L, Fidler IJ (2000) Acidic pH-induced elevation in interleukin 8 expression by human ovarian carcinoma cells. Cancer Res 60(16):4610–4616

    PubMed  CAS  Google Scholar 

  25. Semenza GL (2002) HIF-1 and tumor progression: pathophysiology and therapeutics. Trends Mol Med 8(Suppl 4):S62–S67

    PubMed  CAS  Google Scholar 

  26. Harris AL (2002) Hypoxia–a key regulatory factor in tumor growth. Nat Rev Cancer 2(1):38–47

    PubMed  CAS  Google Scholar 

  27. Huang WC, Swietach P, Vaughan-Jones RD, Ansorge O, Glitsch MD (2008) Extracellular acidification elicits spatially and temporally distinct Ca2+ signals. Curr Biol 18(10):781–785

    PubMed  CAS  Google Scholar 

  28. Shi Q, Le X, Wang B, Abbruzzese JL, Xiong Q, He Y, Xie K (2001) Regulation of vascular endothelial growth factor expression by acidosis in human cancer cells. Oncogene 20(28):3751–3756

    PubMed  CAS  Google Scholar 

  29. Mekhail K, Gunaratnam L, Bonicalzi ME, Lee S (2004) HIF activation by pH-dependent nucleolar sequestration of VHL. Nat Cell Biol 6(7):642–647

    PubMed  CAS  Google Scholar 

  30. Graham RM, Frazier DP, Thompson JW, Haliko S, Li H, Wasserlauf BJ, Spiga MG, Bishopric NH, Webster KA (2004) A unique pathway of cardiac myocyte death caused by hypoxia-acidosis. J Exp Biol 207(Pt 18):3189–3200

    PubMed  CAS  Google Scholar 

  31. Moellering RE, Black KC, Krishnamurty C, Baggett BK, Stafford P, Rain M, Gatenby RA, Gillies RJ (2008) Acid treatment of melanoma cells selects for invasive phenotypes. Clin Exp Metastasis 25(4):411–425

    PubMed  CAS  Google Scholar 

  32. Zieker D, Schafer R, Glatzle J, Nieselt K, Coerper S, Northoff H, Konigsrainer A, Hunt TK, Beckert S (2008) Lactate modulates gene expression in human mesenchymal stem cells. Langenbecks Arch Surg 393(3):297–301

    PubMed  Google Scholar 

  33. Nowik M, Lecca MR, Velic A, Rehrauer H, Brandli AW, Wagner CA (2008) Genome-wide gene expression profiling reveals renal genes regulated during metabolic acidosis. Physiol Genomics 32(3):322–334

    PubMed  CAS  Google Scholar 

  34. Wojtkowiak JW, Rothberg JM, Kumar V, Schramm KJ, Haller E, Proemsey JB, Lloyd MC, Sloane BF, Gillies RJ (2012) Chronic autophagy is a cellular adaptation to tumor acidic pH microenvironments. Cancer Res 72(16):3938–3947

    PubMed  CAS  PubMed Central  Google Scholar 

  35. Chen JL, Merl D, Peterson CW, Wu J, Liu PY, Yin H, Muoio DM, Ayer DE, West M, Chi JT (2010) Lactic acidosis triggers starvation response with paradoxical induction of TXNIP through MondoA. PLoS Genet 6(9):e1001093

    PubMed  PubMed Central  Google Scholar 

  36. Chen JL, Lucas JE, Schroeder T, Mori S, Wu J, Nevins J, Dewhirst M, West M, Chi JT (2008) The genomic analysis of lactic acidosis and acidosis response in human cancers. PLoS Genet 4(12):e1000293

    PubMed  PubMed Central  Google Scholar 

  37. Wu H, Ding Z, Hu D, Sun F, Dai C, Xie J, Hu X (2012) Central role of lactic acidosis in cancer cell resistance to glucose deprivation-induced cell death. J Pathol 227(2):189–199

    PubMed  CAS  Google Scholar 

  38. Dong L, Li Z, Leffler NR, Asch AS, Chi JT, Yang LV (2013) Acidosis activation of the proton-sensing GPR4 receptor stimulates vascular endothelial cell inflammatory responses revealed by transcriptome analysis. PLoS One 8(4):e61991

    PubMed  CAS  PubMed Central  Google Scholar 

  39. Raj S, Scott DR, Nguyen T, Sachs G, Kraut JA (2013) Acid stress increases gene expression of proinflammatory cytokines in Madin-Darby canine kidney cells. Am J Physiol Renal Physiol 304(1):F41–F48

    PubMed  CAS  Google Scholar 

  40. Curthoys NP, Taylor L, Hoffert JD, Knepper MA (2007) Proteomic analysis of the adaptive response of rat renal proximal tubules to metabolic acidosis. Am J Physiol Renal Physiol 292(1):F140–F147

    Google Scholar 

  41. Stoltzman CA, Peterson CW, Breen KT, Muoio DM, Billin AN, Ayer DE (2008) Glucose sensing by MondoA:Mlx complexes: a role for hexokinases and direct regulation of thioredoxin-interacting protein expression. Proc Natl Acad Sci U S A 105(19):6912–6917

    PubMed  CAS  PubMed Central  Google Scholar 

  42. Tang X, Lucas JE, Chen JL, LaMonte G, Wu J, Wang MC, Koumenis C, Chi JT (2012) Functional interaction between responses to lactic acidosis and hypoxia regulates genomic transcriptional outputs. Cancer Res 72(2):491–502

    PubMed  CAS  PubMed Central  Google Scholar 

  43. Romero-Ramirez L, Cao H, Nelson D, Hammond E, Lee AH, Yoshida H, Mori K, Glimcher LH, Denko NC, Giaccia AJ et al (2004) XBP1 is essential for survival under hypoxic conditions and is required for tumor growth. Cancer Res 64(17):5943–5947

    PubMed  CAS  Google Scholar 

  44. Rouschop KM, van den Beucken T, Dubois L, Niessen H, Bussink J, Savelkouls K, Keulers T, Mujcic H, Landuyt W, Voncken JW et al (2010) The unfolded protein response protects human tumor cells during hypoxia through regulation of the autophagy genes MAP1LC3B and ATG5. J Clin Invest 120(1):127–141

    PubMed  CAS  PubMed Central  Google Scholar 

  45. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A et al (2005) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439:353–357

    Google Scholar 

  46. Chi JT, Wang Z, Nuyten DS, Rodriguez EH, Schaner ME, Salim A, Wang Y, Kristensen GB, Helland A, Borresen-Dale AL et al (2006) Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med 3(3):e47

    PubMed  PubMed Central  Google Scholar 

  47. Lamb J, Ramaswamy S, Ford HL, Contreras B, Martinez RV, Kittrell FS, Zahnow CA, Patterson N, Golub TR, Ewen ME (2003) A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell 114(3):323–334

    PubMed  CAS  Google Scholar 

  48. Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, Montgomery K, Chi JT, Rijn Mv M, Botstein D, Brown PO (2004) Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol 2(2):E7

    PubMed  PubMed Central  Google Scholar 

  49. Chi J-T, Rodriguez EH, Wang Z, Nuyten DSA, Mukherjee S, de Rijn Mv, de Vijver MJv, Hastie T, Brown PO (2007) Gene expression programs of human smooth muscle cells: tissue-specific differentiation and prognostic significance in breast cancers. PLoS Genet 3(9):e164

    PubMed Central  Google Scholar 

  50. Chang JT, Carvalho C, Mori S, Bild A, Gatza M, Wang Q, Lucase JE, Potti A, Febbo P, West M et al (2009) A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol Cell 34:104–114 (Accepted)

    Google Scholar 

  51. Huang E, Ishida S, Pittman J, Dressman H, Bild A, Kloos M, D’Amico M, Pestell RG, West M, Nevins JR (2003) Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet 34(2):226–230

    PubMed  CAS  Google Scholar 

  52. Mori S, Rempel RE, Chang JT, Yao G, Lagoo AS, Potti A, Bild A, Nevins JR (2008) Utilization of pathway signatures to reveal distinct types of B lymphoma in the Emicro-myc model and human diffuse large B-cell lymphoma. Cancer Res 68(20):8525–8534

    PubMed  CAS  PubMed Central  Google Scholar 

  53. Nevins JR, Potti A (2007) Mining gene expression profiles: expression signatures as cancer phenotypes. Nat Rev Genet 8(8):601–609

    PubMed  CAS  Google Scholar 

  54. West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR (2001) Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A 98(20):11462–11467

    PubMed  CAS  PubMed Central  Google Scholar 

  55. West M, Ginsburg GS, Huang AT, Nevins JR (2006) Embracing the complexity of genomic data for personalized medicine. Genome Res 16(5):559–566

    PubMed  CAS  Google Scholar 

  56. Chen JL, Lucase JE, Schroeder T, Mori S, Nevins JR, Dewhirst MW, West M, Chi JT (2008) Genomic analysis of response to lactic acidosis and acidosis in human cancers. PLoS Genet 4(12):e1000293

    PubMed  PubMed Central  Google Scholar 

  57. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511

    PubMed  CAS  Google Scholar 

  58. Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, Bild A, Iversen ES, Liao M, Chen CM et al (2003) Gene expression predictors of breast cancer outcomes. Lancet 361(9369):1590–1596

    PubMed  CAS  Google Scholar 

  59. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA et al (2000) Molecular portraits of human breast tumours. Nature 406(6797):747–752

    PubMed  CAS  Google Scholar 

  60. Golub TR (2001) Genome-wide views of cancer. N Engl J Med 344(8):601–602

    PubMed  CAS  Google Scholar 

  61. Golub TR (2004) Toward a functional taxonomy of cancer. Cancer Cell 6(2):107–108

    PubMed  Google Scholar 

  62. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537

    PubMed  CAS  Google Scholar 

  63. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009

    PubMed  Google Scholar 

  64. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536

    Google Scholar 

  65. Sweet-Cordero A, Mukherjee S, Subramanian A, You H, Roix JJ, Ladd-Acosta C, Mesirov J, Golub TR, Jacks T (2005) An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 37(1):48–55

    PubMed  CAS  Google Scholar 

  66. Bild A, Febbo PG (2005) Application of a priori established gene sets to discover biologically important differential expression in microarray data. Proc Natl Acad Sci U S A 102(43):15278–15279

    PubMed  CAS  PubMed Central  Google Scholar 

  67. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550

    PubMed  CAS  PubMed Central  Google Scholar 

  68. Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, Alexander KE, Matese JC, Perou CM, Hurt MM, Brown PO et al (2002) Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13(6):1977–2000

    PubMed  CAS  PubMed Central  Google Scholar 

  69. Winter SC, Buffa FM, Silva P, Miller C, Valentine HR, Turley H, Shah KA, Cox GJ, Corbridge RJ, Homer JJ et al (2007) Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers. Cancer Res 67(7):3441–3449

    PubMed  CAS  Google Scholar 

  70. Chi JT, Wang Z, Nuyten DS, Rodriguez EH, Schaner ME, Salim A, Wang Y, Kristensen GB, Helland A, Borresen-Dale AL et al (2006) Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med 3(3):e47

    PubMed  PubMed Central  Google Scholar 

  71. Lucas JE, Kung HN, Chi JT (2010) Latent factor analysis to discover pathway-associated putative segmental aneuploidies in human cancers. PLoS Comput Biol 6(9):e1000920

    PubMed  PubMed Central  Google Scholar 

  72. Freund DM, Prenni JE, Curthoys NP (2013) Proteomic profiling of the mitochondrial inner membrane of rat renal proximal convoluted tubules. Proteomics 13(16):2495–2499

    PubMed  CAS  Google Scholar 

  73. Zaganas I, Spanaki C, Plaitakis A (2012) Expression of human GLUD2 glutamate dehydrogenase in human tissues: functional implications. Neurochem Int 61(4):455–462

    PubMed  CAS  Google Scholar 

  74. Lamonte G, Tang X, Chen JL, Wu J, Ding CKC, Keenan MM, Sangokoya C, Kung HN, Ilkayeva O, Boros LG et al (2014) Acidosis induces reprogramming of cellular metabolism to mitigate oxidative stress. Cancer Metab 1:23

    Google Scholar 

  75. Walmsley SJ, Freund DM, Curthoys NP (2012) Proteomic profiling of the effect of metabolic acidosis on the apical membrane of the proximal convoluted tubule. Am J Physiol Renal Physiol 302(11):F1465–F1477

    PubMed  CAS  PubMed Central  Google Scholar 

  76. Freund DM, Prenni JE, Curthoys NP (2013) Response of the mitochondrial proteome of rat renal proximal convoluted tubules to chronic metabolic acidosis. Am J Physiol Renal Physiol 304(2):F145–F155

    Google Scholar 

  77. Tang X, Lin CC, Spasojevic I, Iversen ES, Chi JT, Marks JR (2014) A joint analysis of metabolomics and genetics of breast cancer. Breast Cancer Res 16 (4):415

    Google Scholar 

  78. Rofstad EK, Mathiesen B, Kindem K, Galappathi K (2006) Acidic extracellular pH promotes experimental metastasis of human melanoma cells in athymic nude mice. Cancer Res 66(13):6699–6707

    PubMed  CAS  Google Scholar 

  79. Martinez-Zaguilan R, Seftor EA, Seftor RE, Chu YW, Gillies RJ, Hendrix MJ (1996) Acidic pH enhances the invasive behavior of human melanoma cells. Clin Exp Metastasis 14(2):176–186

    PubMed  CAS  Google Scholar 

  80. Sauvant C, Nowak M, Wirth C, Schneider B, Riemann A, Gekle M, Thews O (2008) Acidosis induces multi-drug resistance in rat prostate cancer cells (AT1) in vitro and in vivo by increasing the activity of the p-glycoprotein via activation of p38. Int J Cancer 123(11):2532–2542

    PubMed  CAS  Google Scholar 

  81. De Milito A, Canese R, Marino ML, Borghi M, Iero M, Villa A, Venturi G, Lozupone F, Iessi E, Logozzi M et al (2010) pH-dependent antitumor activity of proton pump inhibitors against human melanoma is mediated by inhibition of tumor acidity. Int J Cancer 127(1):207–219

    PubMed  CAS  Google Scholar 

  82. Gatenby RA, Gillies RJ (2008) A microenvironmental model of carcinogenesis. Nat Rev Cancer 8(1):56–61

    PubMed  CAS  Google Scholar 

  83. Fang JS, Gillies RD, Gatenby RA (2008) Adaptation to hypoxia and acidosis in carcinogenesis and tumor progression. Semin Cancer Biol 18(5):330–337

    PubMed  CAS  PubMed Central  Google Scholar 

  84. Hjelmeland AB, Wu Q, Heddleston JM, Choudhary GS, MacSwords J, Lathia JD, McLendon R, Lindner D, Sloan A, Rich JN (2011) Acidic stress promotes a glioma stem cell phenotype. Cell Death Differ 18(5):829–840

    PubMed  CAS  PubMed Central  Google Scholar 

  85. Silva AS, Yunes JA, Gillies RJ, Gatenby RA (2009) The potential role of systemic buffers in reducing intratumoral extracellular pH and acid-mediated invasion. Cancer Res 69(6):2677–2684

    PubMed  CAS  PubMed Central  Google Scholar 

  86. Kaelin WG Jr (2005) The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer 5(9):689–698

    PubMed  CAS  Google Scholar 

  87. Chan DA, Giaccia AJ (2011) Harnessing synthetic lethal interactions in anticancer drug discovery. Nat Rev Drug Discov 10(5):351–364

    PubMed  CAS  PubMed Central  Google Scholar 

  88. Wilson WR, Hay MP (2011) Targeting hypoxia in cancer therapy. Nat Rev Cancer 11(6):393–410

    PubMed  CAS  Google Scholar 

  89. Chan N, Pires IM, Bencokova Z, Coackley C, Luoto KR, Bhogal N, Lakshman M, Gottipati P, Oliver FJ, Helleday T et al (2010) Contextual synthetic lethality of cancer cell kill based on the tumor microenvironment. Cancer Res 70(20):8045–8054

    PubMed  CAS  PubMed Central  Google Scholar 

  90. Sonveaux P, Vegran F, Schroeder T, Wergin MC, Verrax J, Rabbani ZN, De Saedeleer CJ, Kennedy KM, Diepart C, Jordan BF et al (2008) Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J Clin Invest 118(12):3930–3942

    PubMed  CAS  PubMed Central  Google Scholar 

  91. Parks SK, Chiche J, Pouyssegur J (2011) pH control mechanisms of tumor survival and growth. J Cell Physiol 226(2):299–308

    PubMed  CAS  Google Scholar 

  92. Fais S, De Milito A, You H, Qin W (2007) Targeting vacuolar H+ -ATPases as a new strategy against cancer. Cancer Res 67(22):10627–10630

    PubMed  CAS  Google Scholar 

  93. Berns K, Hijmans EM, Mullenders J, Brummelkamp TR, Velds A, Heimerikx M, Kerkhoven RM, Madiredjo M, Nijkamp W, Weigelt B et al (2004) A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428(6981):431–437

    PubMed  CAS  Google Scholar 

  94. Luo B, Cheung HW, Subramanian A, Sharifnia T, Okamoto M, Yang X, Hinkle G, Boehm JS, Beroukhim R, Weir BA et al (2008) Highly parallel identification of essential genes in cancer cells. Proc Natl Acad Sci U S A 105(51):20380–20385

    PubMed  CAS  PubMed Central  Google Scholar 

  95. Schlabach MR, Luo J, Solimini NL, Hu G, Xu Q, Li MZ, Zhao Z, Smogorzewska A, Sowa ME, Ang XL et al (2008) Cancer proliferation gene discovery through functional genomics. Science 319(5863):620–624

    PubMed  CAS  PubMed Central  Google Scholar 

  96. Silva JM, Marran K, Parker JS, Silva J, Golding M, Schlabach MR, Elledge SJ, Hannon GJ, Chang K (2008) Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319(5863):617–620

    PubMed  CAS  PubMed Central  Google Scholar 

  97. Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR, Westbrook TF, Wong KK, Elledge SJ (2009) A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137(5):835–848

    PubMed  CAS  PubMed Central  Google Scholar 

  98. Scholl C, Frohling S, Dunn IF, Schinzel AC, Barbie DA, Kim SY, Silver SJ, Tamayo P, Wadlow RC, Ramaswamy S et al (2009) Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell 137(5):821–834

    PubMed  CAS  Google Scholar 

  99. Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, Sethumadhavan S, Woo HK, Jang HG, Jha AK et al (2011) Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 476:346–350

    Google Scholar 

  100. Meacham CE, Ho EE, Dubrovsky E, Gertler FB, Hemann MT (2009) In vivo RNAi screening identifies regulators of actin dynamics as key determinants of lymphoma progression. Nat Genet 41(10):1133–1137

    PubMed  CAS  PubMed Central  Google Scholar 

  101. Zender L, Xue W, Zuber J, Semighini CP, Krasnitz A, Ma B, Zender P, Kubicka S, Luk JM, Schirmacher P et al (2008) An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 135(5):852–864

    PubMed  CAS  PubMed Central  Google Scholar 

  102. Dekanty A, Romero NM, Bertolin AP, Thomas MG, Leishman CC, Perez-Perri JI, Boccaccio GL, Wappner P (2010) Drosophila genome-wide RNAi screen identifies multiple regulators of HIF-dependent transcription in hypoxia. PLoS Genet 6(6):e1000994

    PubMed  PubMed Central  Google Scholar 

  103. Bergwitz C, Wee MJ, Sinha S, Huang J, DeRobertis C, Mensah LB, Cohen J, Friedman A, Kulkarni M, Hu Y et al (2013) Genetic determinants of phosphate response in Drosophila. PLoS One 8(3):e56753

    PubMed  CAS  PubMed Central  Google Scholar 

  104. Stotz SC, Clapham DE (2012) Anion-sensitive fluorophore identifies the Drosophila swell-activated chloride channel in a genome-wide RNA interference screen. PLoS One 7(10):e46865

    PubMed  CAS  PubMed Central  Google Scholar 

  105. Toret CP, D’Ambrosio MV, Vale RD, Simon MA, Nelson WJ (2014) A genome-wide screen identifies conserved protein hubs required for cadherin-mediated cell-cell adhesion. J Cell Biol 204(2):265–279

    PubMed  CAS  PubMed Central  Google Scholar 

  106. Cheung HW, Cowley GS, Weir BA, Boehm JS, Rusin S, Scott JA, East A, Ali LD, Lizotte PH, Wong TC et al (2011) Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc Natl Acad Sci U S A 108(30):12372–12377

    PubMed  CAS  PubMed Central  Google Scholar 

  107. Birsoy K, Possemato R, Lorbeer FK, Bayraktar EC, Thiru P, Yucel B, Wang T, Chen WW, Clish CB, Sabatini DM (2014) Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature 508(7494):108–112

    PubMed  CAS  PubMed Central  Google Scholar 

  108. Goidts V, Bageritz J, Puccio L, Nakata S, Zapatka M, Barbus S, Toedt G, Campos B, Korshunov A, Momma S et al (2012) RNAi screening in glioma stem-like cells identifies PFKFB4 as a key molecule important for cancer cell survival. Oncogene 31(27):3235–3243

    PubMed  CAS  Google Scholar 

  109. Colombi M, Molle KD, Benjamin D, Rattenbacher-Kiser K, Schaefer C, Betz C, Thiemeyer A, Regenass U, Hall MN, Moroni C (2011) Genome-wide shRNA screen reveals increased mitochondrial dependence upon mTORC2 addiction. Oncogene 30(13):1551–1565

    PubMed  CAS  Google Scholar 

  110. McCleland ML, Adler AS, Deming L, Cosino E, Lee L, Blackwood EM, Solon M, Tao J, Li L, Shames D et al (2013) Lactate dehydrogenase B is required for the growth of KRAS-dependent lung adenocarcinomas. Clin Cancer Res 19(4):773–784

    PubMed  CAS  Google Scholar 

  111. Gerlinger M, Santos CR, Spencer-Dene B, Martinez P, Endesfelder D, Burrell RA, Vetter M, Jiang M, Saunders RE, Kelly G et al (2012) Genome-wide RNA interference analysis of renal carcinoma survival regulators identifies MCT4 as a Warburg effect metabolic target. J Pathol 227(2):146–156

    PubMed  CAS  PubMed Central  Google Scholar 

  112. Pan J, Zhang J, Hill A, Lapan P, Berasi S, Bates B, Miller C, Haney S (2013) A kinome-wide siRNA screen identifies multiple roles for protein kinases in hypoxic stress adaptation, including roles for IRAK4 and GAK in protection against apoptosis in VHL-/- renal carcinoma cells, despite activation of the NF-kappaB pathway. J Biomol Screen 18(7):782–796

    PubMed  CAS  Google Scholar 

  113. Miller JC, Holmes MC, Wang J, Guschin DY, Lee YL, Rupniewski I, Beausejour CM, Waite AJ, Wang NS, Kim KA et al (2007) An improved zinc-finger nuclease architecture for highly specific genome editing. Nat Biotechnol 25(7):778–785

    PubMed  CAS  Google Scholar 

  114. Miller JC, Tan S, Qiao G, Barlow KA, Wang J, Xia DF, Meng X, Paschon DE, Leung E, Hinkley SJ et al (2011) A TALE nuclease architecture for efficient genome editing. Nat Biotechnol 29(2):143–148

    PubMed  CAS  Google Scholar 

  115. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337(6096):816–821

    PubMed  CAS  Google Scholar 

  116. Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, Norville JE, Church GM (2013) RNA-guided human genome engineering via Cas9. Science 339(6121):823–826

    PubMed  CAS  PubMed Central  Google Scholar 

  117. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA et al (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339(6121):819–823

    PubMed  CAS  PubMed Central  Google Scholar 

  118. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelsen TS, Heckl D, Ebert BL, Root DE, Doench JG et al (2014) Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343(6166):84–87

    PubMed  CAS  PubMed Central  Google Scholar 

  119. Wang T, Wei JJ, Sabatini DM, Lander ES (2014) Genetic screens in human cells using the CRISPR-Cas9 system. Science 343(6166):80–84

    PubMed  CAS  PubMed Central  Google Scholar 

  120. Koike-Yusa H, Li Y, Tan EP, Velasco-Herrera Mdel C, Yusa K (2014) Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat Biotechnol 32(3):267–273

    PubMed  CAS  Google Scholar 

Download references

Acknowledgment

Supported by NIH CA125618, CA106520, F31 CA180610 and the Department of Defense W81XWH-12–1-0148 and W81XWH-14-1-0309.

Author information

Authors and Affiliations

  1. Department of Molecular Genetics and Microbiology, Center for Genomic and Computational Biology, Duke Medical Center, 27708, Durham, NC, USA

    Melissa M. Keenan, Chao-Chieh Lin & Jen-Tsan Ashley Chi

Authors
  1. Melissa M. Keenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao-Chieh Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jen-Tsan Ashley Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jen-Tsan Ashley Chi .

Editor information

Editors and Affiliations

  1. Department of Molecular Genetics and Microbiology, Duke Center for Genomic and Computational Biology, Durham, North Carolina, USA

    Jen-Tsan Ashley Chi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Keenan, M., Lin, CC., Chi, JT. (2014). A Genomic Analysis of Cellular Responses and Adaptions to Extracellular Acidosis. In: Chi, JT. (eds) Molecular Genetics of Dysregulated pH Homeostasis. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1683-2_8

Download citation

Publish with us

Policies and ethics

Search

Navigation