International Journal of Legal Medicine

, Volume 127, Issue 2, pp 335–344

Apparent versus true gene expression changes of three hypoxia-related genes in autopsy derived tissue and the importance of normalisation

  • Antje Huth
  • Benedikt Vennemann
  • Tony Fracasso
  • Sabine Lutz-Bonengel
  • Marielle Vennemann
Original Article

Abstract

The aim of our work was to show how a chosen normal-isation strategy can affect the outcome of quantitative gene expression studies. As an example, we analysed the expression of three genes known to be upregulated under hypoxic conditions: HIF1A, VEGF and SLC2A1 (GLUT1). Raw RT-qPCR data were normalised using two different strategies: a straightforward normalisation against a single reference gene, GAPDH, using the 2−ΔΔCt algorithm and a more complex normalisation against a normalisation factor calculated from the quantitative raw data from four previously validated reference genes. We found that the two different normalisation strategies revealed contradicting results: normalising against a validated set of reference genes revealed an upregulation of the three genes of interest in three post-mortem tissue samples (cardiac muscle, skeletal muscle and brain) under hypoxic conditions. Interestingly, we found a statistically significant difference in the relative transcript abundance of VEGF in cardiac muscle between donors who died of asphyxia versus donors who died from cardiac death. Normalisation against GAPDH alone revealed no upregulation but, in some instances, a downregulation of the genes of interest. To further analyse this discrepancy, the stability of all reference genes used were reassessed and the very low expression stability of GAPDH was found to originate from the co-regulation of this gene under hypoxic conditions. We concluded that GAPDH is not a suitable reference gene for the quantitative analysis of gene expression in hypoxia and that validation of reference genes is a crucial step for generating biologically meaningful data.

Keywords

Post-mortem tissue RT-qPCR Normalisation Hypoxia Gene expression 

Supplementary material

414_2012_787_MOESM1_ESM.xlsx (13 kb)
Table S1Detailed information about donors from which samples were obtained. Bars (—) indicate unavailable information. Asterisks (*) indicate the two cases in which no brain tissue was available (XLSX 13 kb)
414_2012_787_MOESM2_ESM.xls (16 kb)
Table S2A summary of all available assay information. Assays were purchased as ready-made assays from Life Technologies and validated before use. Assay efficiencies and amplification rates are within the acceptable range suggested previously [1] (XLS 15 kb)
414_2012_787_MOESM3_ESM.pdf (71 kb)
Table S3Further validation results for the GOI assays using an artificially degraded commercially obtained RNA sample to calculate the influence of degradation on the results. The highest Cq shift was observed for HIF1A in skeletal muscle. Most Cq shifts are below 1 cycle and thus are well within the normal reaction variations (PDF 71 kb)

References

  1. 1.
    Bustin SA, Benes V, Garson JA et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55(4):611–622Google Scholar
  2. 2.
    Thellin O, Zorzi W, Lakaye B et al (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75:291–295Google Scholar
  3. 3.
    Vandesompele J, De Preter K, Pattyn F et al. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. BMC Mol Biol. 3:research0034-research0034.11Google Scholar
  4. 4.
    Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25:169–19Google Scholar
  5. 5.
    Deo SS, Bhagat AR, Shah RN (2011) Genetic variations in nalp1 mRNA expressions in human vitiligo. Indian J Dermatol 56(3):266–71Google Scholar
  6. 6.
    Mauro MO, Sartori D, Oliveira RJ, Ishii PL, Mantovani MS, Ribeiro LR (2011) Activity of selenium on cell proliferation, cytotoxicity, and apoptosis and on the expression of CASP9, BCL-XL and APC in intestinal adenocarcinoma cells. Mutat Res 715(1–2):7–12Google Scholar
  7. 7.
    Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6(4):279–284Google Scholar
  8. 8.
    Schmittgen TD, Zakrajsek BA (2000) Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative PCR. J Biochem Biophys Methods 46:69–81Google Scholar
  9. 9.
    Valenti MT, Bertoldo F, Dalle Carbonare L et al (2006) The effect of biphosphonates on gene expression: GAPDH as a housekeeping or a new target gene? BMC Cancer 6:49Google Scholar
  10. 10.
    Tanic N, Perovic M, Mladenovic A, Ruzdijic S, Kanazir S (2007) Effects of aging, dietary restriction and glucocorticoid treatment on housekeeping gene expression in rat cortex and hippocampus-evaluation by real-time RT-PCR. J Mol Neurosci 32(1):38–46Google Scholar
  11. 11.
    Goidin D, Mamessier A, Staquet MJ, Schmitt D, Berthier-Vergnes O (2001) Ribosomal 18S RNA prevails over glyceraldehyde-3-phosphate dehydrogenase and beta-actin genes as internal standard for quantitative comparison of mRNA levels in invasive and noninvasive human melanoma cell subpopulations. Anal Biochem 295(1):17–21Google Scholar
  12. 12.
    Lupberger J, Kreuzer KA, Baskaynak G, Peters UR, le Coutre P, Schmidt CA (2002) Quantitative analysis of beta-actin, beta-2-microglobulin and porphobilinogen deaminase mRNA and their comparison as control transcripts for RT-PCR. Mol Cell Probes 16:25–30Google Scholar
  13. 13.
    Gorzelniak K, Janke J, Engeli S, Sharma AM (2001) Validation of endogenous controls for gene expression studies in human adipocytes and preadipocytes. Horm Metab Res 33(10):625–627Google Scholar
  14. 14.
    Bonanomi A, Kojic D, Giger B et al (2003) Quantitative cytokine gene expression in human tonsils at excision and during histoculture assessed by standardized and calibrated real-time PCR and novel data processing. J Immunol Methods 283(1–2):27–43Google Scholar
  15. 15.
    Stamova BS, Apperson M, Walker WL et al (2009) Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood. BMC Med Genomics 2:49Google Scholar
  16. 16.
    Meller M, Vadachkoria S, Luthy DA, Williams MA (2005) Evaluation of housekeeping genes in placental comparative expression studies. Placenta 26(8–9):601–607Google Scholar
  17. 17.
    Lagnease J, John R, Schweizer H, Ebmeyer U, Keilhoff G (2008) Selection of reference genes for quantitative real-time PCR in rat asphyxial cardiac arrest model. BMC Mol Biol 9:53Google Scholar
  18. 18.
    Kreth S, Heyn J, Grau S, Ketzschmar HA, Egensperger R, Kreth FW (2010) Identification of valid endogenous control genes for determining gene expression in human glioma. Neuro Oncol 12(6):570–579Google Scholar
  19. 19.
    Hsiao LL, Dangond F, Yoshida T et al (2001) A compendium of gene expression in normal human tissues. Physiol Genomics 7(2):97–104Google Scholar
  20. 20.
    Bas A, Forsberg G, Hammarström S, Hammarström M-L (2004) Utility of the housekeeping genes 18SrRNA, β-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes. Scand J of Immunol 59:566–573Google Scholar
  21. 21.
    Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G, Zumla A (2004) Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques, 37(1): 112–4, 116, 118–9Google Scholar
  22. 22.
    Caradec J, Sirab N, Revaud D, Keumeugni C, Loric S (2010) “Desperate house genes”: the dramatic example of hypoxia. Br J Cancer 102:1037–1043Google Scholar
  23. 23.
    Andersen CL, Jensen JL, Orntoft TF (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 64(15):5245–5250Google Scholar
  24. 24.
    Jiang BH, Semenza GL, Bauer C, Marti HH (1996) Hypoxia-inducible factor 1 levels vary exponentially over a physiologically relevant range of O2 tension. Am J Physiol 271(4 Pt 1):C1172–80Google Scholar
  25. 25.
    Ozaki H, Yu AY, Della N, Ozaki K et al (1999) Hypoxia inducible factor-1alpha is increased in ischemic retina: temporal and spatial correlation with VEGF expression. Invest Ophthalmol Vis Sci 40(1):182–9Google Scholar
  26. 26.
    Zhao D, Zhu BL, Ishikawa T, Li DR, Michiue T, Maeda H (2006) Quantitative RT-PCR assays of hypoxia-inducible factor-1alpha, erythropoietin and vascular endothelial growth factor mRNA transcripts in the kidneys with regard to the cause of death in medicolegal autopsy. Leg Med (Tokyo) 8(5):258–63Google Scholar
  27. 27.
    Zhao D, Zhu BL, Ishikawa T, Quan L, Li DR, Maeda H (2006) Real-time RT-PCR quantitative assays and postmortem degradation profiles of erythropoietin, vascular endothelial growth factor and hypoxia-inducible factor 1 alpha mRNA transcripts in forensic autopsy materials. Leg Med (Tokyo) 8(2):132–6Google Scholar
  28. 28.
    Zhao D, Ishikawa T, Quan L et al (2008) Tissue-specific differences in mRNA quantification of glucose transporter 1 and vascular endothelial growth factor with special regard to death investigations of fatal injuries. Forensic Sci Int 177(2–3):176–83Google Scholar
  29. 29.
    Zhu BL, Tanaka S, Ishikawa T et al (2008) Forensic pathological investigation of myocardial hypoxia-inducible factor-1 alpha, erythropoietin and vascular endothelial growth factor in cardiac death. Leg Med (Tokyo) 10(1):11–9Google Scholar
  30. 30.
    Brahimi Horn C, Pousségur J (2006) The role of the hypoxia-inducible factor in tumor metabolism growth and invasion. Bull Cancer 93(8):E73–80Google Scholar
  31. 31.
    Hirota K, Semenza GL (2005) Regulation of hypoxia-inducible factor 1 by prolyl and asparaginyl hydroxylases. Biochem Biophys Res Commun 338(1):610–616Google Scholar
  32. 32.
    Tai TC, Wong-Faull DC, Claycomb R, Wong DL (2009) Hypoxic stress-induced changes in adrenergic function: role of HIF1 alpha. J Neurochem 109(2):513–524Google Scholar
  33. 33.
    Huang LE, Bunn HF (2003) Hypoxia-inducible factor and its biomedical relevance. J Biol Chem 278(22):19575–19578Google Scholar
  34. 34.
    Semenza GL (2007) Hypoxia-inducible factor 1 (HIF1) pathway. Sci STKE. 2007(407): cm8Google Scholar
  35. 35.
    Burrows N, Babur M, Resch J, Williams KJ, Brabant G (2011) Hypoxia-inducible factor in thyroid carcinoma. J Thyroid Res 2011:762905Google Scholar
  36. 36.
    Liu LX, Lu H, Luo Y et al (2002) Stabilization of vascular endothelial growth factor mRNA by hypoxia-inducible factor 1. Biochem Biophys Res Commun 291(4):908–914Google Scholar
  37. 37.
    Loor G, Schumacker PT (2008) Role of hypoxia-inducible factor in cell survival during myocardial ischemia–reperfusion. Cell Death Differ 15(4):686–690Google Scholar
  38. 38.
    Tan SC, Carr CA, Yeoh KK, Schofield CJ, Davies KE, Clarke K (2012) Identification of valid housekeeping genes for quantitative RT-PCR analysis of cardiosphere-derived cells preconditioned under hypoxia or with prolyl-4-hydroxylase inhibitors. Mol Biol Rep 39(4):4857–67Google Scholar
  39. 39.
    Zhong H, Simons JW (1999) Direct comparison of GAPDH, beta-actin, cyclophilin, and 28S rRNA as internal standards for quantifying RNA levels under hypoxia. Biochem Biophys Res Commun 259(3):523–6Google Scholar
  40. 40.
    Said HM, Hagemann C, Stojic J (2007) GAPDH is not regulated in human glioblastoma under hypoxic conditions. BMC Mol Biol 8:55Google Scholar
  41. 41.
    Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8(2):R19Google Scholar
  42. 42.
    Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and 2−ΔΔCt method. Methods 25(4):402–408Google Scholar
  43. 43.
    Koppelkamm A, Vennemann B, Fracasso T, Lutz-Bonengel S, Schmidt U, Heinrich M (2010) Validation of adequate endogenous reference genes for the normalisation of qPCR gene expression data in human post mortem tissue. Int J Leg Med 124(5):371–380Google Scholar
  44. 44.
    Koppelkamm A, Vennemann B, Lutz-Bonengel S, Fracasso T, Vennemann M (2011) RNA integrity in post-mortem samples: influencing parameters and implications on RT-qPCR assays. Int J Legal Med 125(4):573–580Google Scholar
  45. 45.
    Heinrich M, Matt K, Lutz-Bonengel S (2007) Schmidt U (2007) Successful RNA extraction from various human postmortem tissues. Int J Legal Med 121(2):136–42Google Scholar
  46. 46.
    Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29(9):e45Google Scholar
  47. 47.
    Jang M, Kang HJ, Lee SY et al (2009) Glyceraldehyde-3-phosphate, a glycolytic intermediate, plays a key role in controlling cell fate via inhibition of caspase activity. Mol Cells 28(6):559–63Google Scholar
  48. 48.
    Higashimura Y, Nakajima Y, Yamaji R et al (2011) Up-regulation of glyceraldehyde-3-phosphate dehydrogenase gene expression by HIF-1 activity depending on Sp1 in hypoxic breast cancer cells. Arch Biochem Biophys 509:1–8Google Scholar
  49. 49.
    Tricarico C, Pinzani P, Bianchi S et al (2002) Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem 309(2):293–300Google Scholar
  50. 50.
    Lu S, Gu X, Hoestje S (2002) Epner DE (2002) Identification of an additional hypoxia responsive element in the glyceraldehyde-3-phosphate dehydrogenase gene promoter. Biochim Biophys Acta 1574(2):152–6Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antje Huth
    • 1
    • 2
  • Benedikt Vennemann
    • 3
  • Tony Fracasso
    • 4
    • 5
  • Sabine Lutz-Bonengel
    • 1
  • Marielle Vennemann
    • 6
  1. 1.Institute of Legal MedicineFreiburg University Medical CenterFreiburgGermany
  2. 2.Eurofins MedigenomixEbersbergGermany
  3. 3.Institute for Forensic Medicine OldenburgMedical School HannoverOldenburgGermany
  4. 4.University Center of Legal MedicineUniversity of GenevaGenevaSwitzerland
  5. 5.Institute of Legal MedicineUniversity Hospital MünsterMünsterGermany
  6. 6.Institute for Forensic MedicineMedical School HannoverHannoverGermany

Personalised recommendations