Advertisement

Acta Neuropathologica

, Volume 124, Issue 6, pp 893–903 | Cite as

Selection of novel reference genes for use in the human central nervous system: a BrainNet Europe Study

  • Pascal F. Durrenberger
  • Francisca S. Fernando
  • Roberta Magliozzi
  • Samira N. Kashefi
  • Timothy P. Bonnert
  • Isidro Ferrer
  • Danielle Seilhean
  • Brahim Nait-Oumesmar
  • Andrea Schmitt
  • Peter J. Gebicke-Haerter
  • Peter Falkai
  • Edna Grünblatt
  • Miklos Palkovits
  • Piero Parchi
  • Sabina Capellari
  • Thomas Arzberger
  • Hans Kretzschmar
  • Federico Roncaroli
  • David T. Dexter
  • Richard ReynoldsEmail author
Methods Paper

Abstract

The use of an appropriate reference gene to ensure accurate normalisation is crucial for the correct quantification of gene expression using qPCR assays and RNA arrays. The main criterion for a gene to qualify as a reference gene is a stable expression across various cell types and experimental settings. Several reference genes are commonly in use but more and more evidence reveals variations in their expression due to the presence of on-going neuropathological disease processes, raising doubts concerning their use. We conducted an analysis of genome-wide changes of gene expression in the human central nervous system (CNS) covering several neurological disorders and regions, including the spinal cord, and were able to identify a number of novel stable reference genes. We tested the stability of expression of eight novel (ATP5E, AARS, GAPVD1, CSNK2B, XPNPEP1, OSBP, NAT5 and DCTN2) and four more commonly used (BECN1, GAPDH, QARS and TUBB) reference genes in a smaller cohort using RT-qPCR. The most stable genes out of the 12 reference genes were tested as normaliser to validate increased levels of a target gene in CNS disease. We found that in human post-mortem tissue the novel reference genes, XPNPEP1 and AARS, were efficient in replicating microarray target gene expression levels and that XPNPEP1 was more efficient as a normaliser than BECN1, which has been shown to change in expression as a consequence of neuronal cell loss. We provide herein one more suitable novel reference gene, XPNPEP1, with no current neuroinflammatory or neurodegenerative associations that can be used for gene quantitative gene expression studies with human CNS post-mortem tissue and also suggest a list of potential other candidates. These data also emphasise the importance of organ/tissue-specific stably expressed genes as reference genes for RNA studies.

Keywords

Neurodegeneration Validation Normalisation Gene expression studies Post-mortem tissue Internal controls 

Notes

Acknowledgments

We would like to thank all the tissue donors and their families. Also we are grateful to Veronique Sazdovitch and Kasztner Magdolna for technical assistance. We would like also to thank Charles Mein at The Genome Centre (John Vane Science Centre, Queen Mary, University of London, Charterhouse Square, London EC1M 6BQ) for his assistance and expertise. This study was supported by the European Commission under the Sixth Framework Programme (BrainNet Europe II, LSHM-CT-2004-503039). The Multiple Sclerosis and Parkinson’s Disease Tissue Banks at Imperial were supported by the MS Society of Great Britain and Northern Ireland and the Parkinson’s UK respectively.

Supplementary material

401_2012_1027_MOESM1_ESM.xlsx (19 kb)
Supplementary material 1 (XLSX 18 kb)
401_2012_1027_MOESM2_ESM.xlsx (6.2 mb)
Supplementary material 2 (XLSX 6,364 kb)
401_2012_1027_MOESM3_ESM.pdf (294 kb)
Supplementary material 3 (PDF 294 kb)
401_2012_1027_MOESM4_ESM.pdf (73 kb)
Supplementary material 4 (PDF 73 kb)

References

  1. 1.
    Adibhatla RM, Hatcher JF (2008) Altered lipid metabolism in brain injury and disorders. Subcell Biochem 49:241–268PubMedCrossRefGoogle Scholar
  2. 2.
    Albertella MR, Jones H, Thomson W, Olavesen MG, Neville M, Campbell RD (1996) Localisation of eight additional genes in the human major histocompatibility complex, including the gene encoding the casein kinase II beta subunit, and DNA sequence analysis of the class III region. DNA Seq J DNA Seq Mapp 7:9–12Google Scholar
  3. 3.
    Barrachina M, Castano E, Ferrer I (2006) TaqMan PCR assay in the control of RNA normalization in human post-mortem brain tissue. Neurochem Int 49:276–284PubMedCrossRefGoogle Scholar
  4. 4.
    Bazan JF, Weaver LH, Roderick SL, Huber R, Matthews BW (1994) Sequence and structure comparison suggest that methionine aminopeptidase, prolidase, aminopeptidase P, and creatinase share a common fold. Proc Natl Acad Sci USA 91:2473–2477PubMedCrossRefGoogle Scholar
  5. 5.
    Bottomly D, Walter NA, Hunter JE, Darakjian P, Kawane S, Buck KJ, Searles RP, Mooney M, McWeeney SK, Hitzemann R (2011) Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PLoS ONE 6:e17820PubMedCrossRefGoogle Scholar
  6. 6.
    Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25:169–193PubMedCrossRefGoogle Scholar
  7. 7.
    Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–39PubMedCrossRefGoogle Scholar
  8. 8.
    Bustin SA (2004) A–Z of quantitative PCR. International University Line, La JollaGoogle Scholar
  9. 9.
    Bustin SA, Benes V, Nolan T, Pfaffl MW (2005) Quantitative real-time RT-PCR—a perspective. J Mol Endocrinol 34:597–601PubMedCrossRefGoogle Scholar
  10. 10.
    Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622PubMedCrossRefGoogle Scholar
  11. 11.
    Cahoy JD, Emery B, Kaushal A, Foo LC, Zamanian JL, Christopherson KS, Xing Y, Lubischer JL, Krieg PA, Krupenko SA, Thompson WJ, Barres BA (2008) A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci 28:264–278PubMedCrossRefGoogle Scholar
  12. 12.
    Cao Y, Klionsky DJ (2007) Physiological functions of Atg6/Beclin 1: a unique autophagy-related protein. Cell Res 17:839–849PubMedCrossRefGoogle Scholar
  13. 13.
    Chen-Plotkin AS, Geser F, Plotkin JB, Clark CM, Kwong LK, Yuan W, Grossman M, Van Deerlin VM, Trojanowski JQ, Lee VM (2008) Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration. Hum Mol Genet 17:1349–1362PubMedCrossRefGoogle Scholar
  14. 14.
    Colell A, Green DR, Ricci JE (2009) Novel roles for GAPDH in cell death and carcinogenesis. Cell Death Differ 16:1573–1581PubMedCrossRefGoogle Scholar
  15. 15.
    Coulson DT, Brockbank S, Quinn JG, Murphy S, Ravid R, Irvine GB, Johnston JA (2008) Identification of valid reference genes for the normalization of RT qPCR gene expression data in human brain tissue. BMC Mol Biol 9:46PubMedCrossRefGoogle Scholar
  16. 16.
    de Jonge HJ, Fehrmann RS, de Bont ES, Hofstra RM, Gerbens F, Kamps WA, de Vries EG, van der Zee AG, te Meerman GJ, ter Elst A (2007) Evidence based selection of housekeeping genes. PLoS ONE 2:e898PubMedCrossRefGoogle Scholar
  17. 17.
    Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A (2005) The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem 344:141–143PubMedCrossRefGoogle Scholar
  18. 18.
    Diskin T, Tal-Or P, Erlich S, Mizrachy L, Alexandrovich A, Shohami E, Pinkas-Kramarski R (2005) Closed head injury induces upregulation of Beclin 1 at the cortical site of injury. J Neurotrauma 22:750–762PubMedCrossRefGoogle Scholar
  19. 19.
    Durrenberger PF, Fernando S, Kashefi SN, Ferrer I, Hauw JJ, Seilhean D, Smith C, Walker R, Al-Sarraj S, Troakes C, Palkovits M, Kasztner M, Huitinga I, Arzberger T, Dexter DT, Kretzschmar H, Reynolds R (2010) Effects of antemortem and postmortem variables on human brain mRNA quality: a BrainNet Europe study. J Neuropathol Exp Neurol 69:70–81PubMedCrossRefGoogle Scholar
  20. 20.
    Durrenberger PF, Ettorre A, Kamel F, Webb LV, Sim M, Nicholas RS, Malik O, Reynolds R, Boyton RJ, Altmann DM (2012) Innate Immunity in multiple sclerosis white matter lesions: expression of natural cytotoxicity triggering receptor 1 (NCR1). J Neuroinflamm 9:1CrossRefGoogle Scholar
  21. 21.
    Durrenberger PF, Grunblatt E, Fernando FS, Monoranu CM, Evans J, Riederer P, Reynolds R, Dexter DT (2012) Inflammatory pathways in Parkinson’s disease; A BNE Microarray Study. Parkinsons Dis 2012:214714PubMedGoogle Scholar
  22. 22.
    Eisenberg E, Levanon EY (2003) Human housekeeping genes are compact. Trends Genet 19:362–365PubMedCrossRefGoogle Scholar
  23. 23.
    Erlich S, Shohami E, Pinkas-Kramarski R (2006) Neurodegeneration induces upregulation of Beclin 1. Autophagy 2:49–51PubMedGoogle Scholar
  24. 24.
    Fairn GD, McMaster CR (2008) Emerging roles of the oxysterol-binding protein family in metabolism, transport, and signaling. Cell Mol Life Sci 65:228–236PubMedCrossRefGoogle Scholar
  25. 25.
    Hsiao LL, Dangond F, Yoshida T, Hong R, Jensen RV, Misra J, Dillon W, Lee KF, Clark KE, Haverty P, Weng Z, Mutter GL, Frosch MP, Macdonald ME, Milford EL, Crum CP, Bueno R, Pratt RE, Mahadevappa M, Warrington JA, Stephanopoulos G, Gullans SR (2001) A compendium of gene expression in normal human tissues. Physiol Genomics 7:97–104PubMedGoogle Scholar
  26. 26.
    Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284PubMedCrossRefGoogle Scholar
  27. 27.
    Johansson S, Fuchs A, Okvist A, Karimi M, Harper C, Garrick T, Sheedy D, Hurd Y, Bakalkin G, Ekstrom TJ (2007) Validation of endogenous controls for quantitative gene expression analysis: application on brain cortices of human chronic alcoholics. Brain Res 1132:20–28PubMedCrossRefGoogle Scholar
  28. 28.
    Kang R, Zeh HJ, Lotze MT, Tang D (2011) The Beclin 1 network regulates autophagy and apoptosis. Cell Death Differ 18:571–580PubMedCrossRefGoogle Scholar
  29. 29.
    Kasukawa T, Masumoto KH, Nikaido I, Nagano M, Uno KD, Tsujino K, Hanashima C, Shigeyoshi Y, Ueda HR (2011) Quantitative expression profile of distinct functional regions in the adult mouse brain. PLoS ONE 6:e23228PubMedCrossRefGoogle Scholar
  30. 30.
    Korade Z, Kenworthy AK (2008) Lipid rafts, cholesterol, and the brain. Neuropharmacology 55:1265–1273PubMedCrossRefGoogle Scholar
  31. 31.
    Kubista M, Andrade JM, Bengtsson M, Forootan A, Jonak J, Lind K, Sindelka R, Sjoback R, Sjogreen B, Strombom L, Stahlberg A, Zoric N (2006) The real-time polymerase chain reaction. Mol Aspects Med 27:95–125PubMedCrossRefGoogle Scholar
  32. 32.
    Lagace TA, Byers DM, Cook HW, Ridgway ND (1999) Chinese hamster ovary cells overexpressing the oxysterol binding protein (OSBP) display enhanced synthesis of sphingomyelin in response to 25-hydroxycholesterol. J Lipid Res 40:109–116PubMedGoogle Scholar
  33. 33.
    Lanoix D, Lacasse AA, St-Pierre J, Taylor SC, Ethier-Chiasson M, Lafond J, Vaillancourt C (2012) Quantitative PCR pitfalls: the case of the human placenta. Mol BiotechnolGoogle Scholar
  34. 34.
    Lee JW, Beebe K, Nangle LA, Jang J, Longo-Guess CM, Cook SA, Davisson MT, Sundberg JP, Schimmel P, Ackerman SL (2006) Editing-defective tRNA synthetase causes protein misfolding and neurodegeneration. Nature 443:50–55PubMedCrossRefGoogle Scholar
  35. 35.
    Lee S, Jo M, Lee J, Koh SS, Kim S (2007) Identification of novel universal housekeeping genes by statistical analysis of microarray data. J Biochem Mol Biol 40:226–231PubMedCrossRefGoogle Scholar
  36. 36.
    Li X, Lou Z, Zhou W, Ma M, Cao Y, Geng Y, Bartlam M, Zhang XC, Rao Z (2008) Structure of human cytosolic X-prolyl aminopeptidase: a double Mn(II)-dependent dimeric enzyme with a novel three-domain subunit. J Biol Chem 283:22858–22866PubMedCrossRefGoogle Scholar
  37. 37.
    Lowther WT, Matthews BW (2000) Structure and function of the methionine aminopeptidases. Biochim Biophys Acta 1477:157–167PubMedCrossRefGoogle Scholar
  38. 38.
    Mogk A, Schmidt R, Bukau B (2007) The N-end rule pathway for regulated proteolysis: prokaryotic and eukaryotic strategies. Trends Cell Biol 17:165–172PubMedCrossRefGoogle Scholar
  39. 39.
    Penna I, Vella S, Gigoni A, Russo C, Cancedda R, Pagano A (2011) Selection of candidate housekeeping genes for normalization in human postmortem brain samples. Int J Mol Sci 12:5461–5470PubMedCrossRefGoogle Scholar
  40. 40.
    Ruan W, Lai M (2007) Actin, a reliable marker of internal control? Clin Chim Acta 385:1–5PubMedCrossRefGoogle Scholar
  41. 41.
    Schimmel P (2008) Development of tRNA synthetases and connection to genetic code and disease. Protein Sci 17:1643–1652PubMedCrossRefGoogle Scholar
  42. 42.
    Schmittgen TD, Zakrajsek BA (2000) Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods 46:69–81PubMedCrossRefGoogle Scholar
  43. 43.
    Sirover MA (1999) New insights into an old protein: the functional diversity of mammalian glyceraldehyde-3-phosphate dehydrogenase. Biochim Biophys Acta 1432:159–184PubMedCrossRefGoogle Scholar
  44. 44.
    Solanas M, Moral R, Escrich E (2001) Unsuitability of using ribosomal RNA as loading control for Northern blot analyses related to the imbalance between messenger and ribosomal RNA content in rat mammary tumors. Anal Biochem 288:99–102PubMedCrossRefGoogle Scholar
  45. 45.
    Stum M, McLaughlin HM, Kleinbrink EL, Miers KE, Ackerman SL, Seburn KL, Antonellis A, Burgess RW (2011) An assessment of mechanisms underlying peripheral axonal degeneration caused by aminoacyl-tRNA synthetase mutations. Mol Cell Neurosci 46:432–443PubMedCrossRefGoogle Scholar
  46. 46.
    Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A, Patapoutian A, Hampton GM, Schultz PG, Hogenesch JB (2002) Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci USA 99:4465–4470PubMedCrossRefGoogle Scholar
  47. 47.
    Szymanski M, Deniziak M, Barciszewski J (2000) The new aspects of aminoacyl-tRNA synthetases. Acta Biochim Pol 47:821–834PubMedGoogle Scholar
  48. 48.
    Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75:291–295PubMedCrossRefGoogle Scholar
  49. 49.
    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3: RESEARCH0034Google Scholar
  50. 50.
    Vanhoof G, Goossens F, Juliano MA, Juliano L, De Meester I, Hendriks D, Schatteman K, Scharpe S (1997) Human lymphocyte X-prolyl aminopeptidase (aminopeptidase P)-like protein. A new member of the proline peptidase family? Adv Exp Med Biol 421:25–29PubMedGoogle Scholar
  51. 51.
    Warrington JA, Nair A, Mahadevappa M, Tsyganskaya M (2000) Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiol Genomics 2:143–147PubMedGoogle Scholar
  52. 52.
    Weng L, Dai H, Zhan Y, He Y, Stepaniants SB, Bassett DE (2006) Rosetta error model for gene expression analysis. Bioinformatics 22:1111–1121PubMedCrossRefGoogle Scholar
  53. 53.
    Yue Z, Horton A, Bravin M, DeJager PL, Selimi F, Heintz N (2002) A novel protein complex linking the delta 2 glutamate receptor and autophagy: implications for neurodegeneration in lurcher mice. Neuron 35:921–933PubMedCrossRefGoogle Scholar
  54. 54.
    Zhu J, He F, Song S, Wang J, Yu J (2008) How many human genes can be defined as housekeeping with current expression data? BMC Genomics 9:172PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Pascal F. Durrenberger
    • 1
  • Francisca S. Fernando
    • 1
  • Roberta Magliozzi
    • 2
  • Samira N. Kashefi
    • 1
  • Timothy P. Bonnert
    • 3
  • Isidro Ferrer
    • 4
  • Danielle Seilhean
    • 5
    • 6
  • Brahim Nait-Oumesmar
    • 5
    • 6
  • Andrea Schmitt
    • 7
  • Peter J. Gebicke-Haerter
    • 8
  • Peter Falkai
    • 7
  • Edna Grünblatt
    • 9
    • 10
  • Miklos Palkovits
    • 11
  • Piero Parchi
    • 12
  • Sabina Capellari
    • 12
  • Thomas Arzberger
    • 13
  • Hans Kretzschmar
    • 13
  • Federico Roncaroli
    • 1
  • David T. Dexter
    • 1
  • Richard Reynolds
    • 1
    Email author
  1. 1.Wolfson Neuroscience Laboratories, Division of Brain SciencesImperial College LondonLondonUK
  2. 2.Department of Cell Biology and NeuroscienceIstituto Superiore di SanitaRomeItaly
  3. 3.MAIngenuity Systems IncRedwood CityUSA
  4. 4.Institut de NeuropatologiaIdibell, Hospital Universitari de Bellvitge, Universitat de BarcelonaBarcelonaSpain
  5. 5.Laboratoire de Neuropathologie, Groupe Hospitalier Pitié-SalpêtrièreCRICM INSERM UMR-S 975, CNRS UMR7225, UPMC, Sorbonne Universités, APHPParisFrance
  6. 6.Centre de Recherche Institut Du Cerveau Et de La Moelle ÉpinièreUniversité Pierre et Marie Curie UMR-S975, Inserm U975, Cnrs UMR7725ParisFrance
  7. 7.Department of Psychiatry and PsychotherapyLudwigs-Maximilians-University MunichMunichGermany
  8. 8.Medical Faculty Mannheim, Institute of PsychopharmacologyCentral Institute of Mental Health, University of HeidelbergMannheimGermany
  9. 9.Department of Psychiatry, Psychosomatic and PsychotherapyNeurochemistry Laboratory National Parkinson Foundation Centre of Excellence Research Laboratory, University Hospital of WürzburgWürzburgGermany
  10. 10.Neurobiochemistry LaboratoryHospital of Child and Adolescent Psychiatry, University of ZürichZürichSwitzerland
  11. 11.Laboratory of Neuromorphology and Human Brain Tissue BankSemmelweis UniversityBudapestHungary
  12. 12.IRCCS Istituto Delle Scienze Neurologiche and Dipartimento di Scienze NeurologicheUniversità di BolognaBolognaItaly
  13. 13.Centre for Neuropathology and Prion ResearchLudwig-Maximilians-UniversityMunichGermany

Personalised recommendations