Advertisement

Unification of Gene Expression Data for Comparable Analyses Under Stress Conditions

Chapter
Part of the Microbiology Monographs book series (MICROMONO, volume 22)

Abstract

Gene expression is a fundamental biological process in which genotypes rise to phenotypes. As a quantitative measurement, expression of a gene is commonly examined by mRNA abundance that varies in response to different conditions and environmental stimuli. High throughput quantitative measurements of gene expression data have difficulties of reproducibility and comparability due to a lack of standard mRNA quantification references. Efforts have been made to safeguard data fidelity, yet generating quality expression data of inherent value remains a challenge. This not only affects unbiased data assessment and clinical applications but also damages establishing invaluable database resources for the larger scientific community. Unification of multi-source gene expression data is necessary for comparable and comprehensive analyses to gain insight into complex gene interactions and regulatory networks of life events using more integrated approaches of bioinformatics, computational biology and systems biology. Development and application of commonly accepted quantification references to generate comparable expression data are urgently needed. This chapter provides basics and application aspects for comparative gene expression analyses using microbial examples under stress conditions.

Keywords

Gene Expression Data Master Equation Microarray Assay Target Array Assay Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

The author is grateful to Michael A. Cotta and Marsha Ebener for proofreading of the manuscript. This work was supported in part by NIFA National Research Initiative Award 2006-35504-17359. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. USDA is an equal opportunity provider and employer.

References

  1. Applied Biosystems (2004) Absolute quantification: Getting started guide for the 7300/7500 System, Part Number 4347825 Revison A. Foster CityGoogle Scholar
  2. Applied Biosystems (2006) Amplification efficiency of Taqman gene expression assays, Application Note 5 pp Publication 127AP05-03. Foster CityGoogle Scholar
  3. Baeber RD, Harmer DW, Coleman RA, Clark BJ (2005) GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics 21:389–395CrossRefGoogle Scholar
  4. Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, Cunningham ML, Deng S, Dressman HK, Fannin RD, Farin FM, Freedman JH, Fry RC, Harper A, Humble MC, Hurban P, Kavanagh TJ, Kaufmann WK, Kerr KF, Jing L, Lapidus JA, Lasarev MR, Li J, Li Y-J, Lobenhofer EK, Lu X, Malek RL, Milton S, Nagalla SR, O’Malley JP, Palmer VS, Pattee P, Paules RS, Perou CM, Phillips K, Qin L-X, Qiu Y, Quigley SD, Rodland M, Rusyn I, Samson LD, Schwartz DA, Shi Y, Shin J-L, Sieber SO, Slifer S, Speer MC, Spencer PS, Sproles DI, Swenberg JA, Suk WA, Sullivan RC, Tian R, Tennant RW, Todd SA, Tucker CJ, Van Houten B, Weis BK, Xuan S, Zarbl H (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2:351–356PubMedCrossRefGoogle Scholar
  5. Bower NI, Moser RJ, Hill JR, Lehnert SA (2007) Universal reference method for real-time PCR gene expression analysis of preimplantation embryos. Biotechniques 42:199–206PubMedCrossRefGoogle Scholar
  6. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)–toward standards for microarray data. Nat Genet 29:365–371PubMedCrossRefGoogle Scholar
  7. Bustin SA, Benes VJ, Garson A, Hellemants J, HuggettJ KM, Mueller R, Nolan TM, Pfaffl W, Shipley GL, Vandesompele JC, Wittwer T (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:4CrossRefGoogle Scholar
  8. Choi J-C, Tiedje JM (2002) Quantitative detection of microbial genes by using DNA microarrays. Appl Environ Microbiol 68:1425–1430CrossRefGoogle Scholar
  9. Collins ML, Zayati C, DetmerJJ DB, Kolberg JA, Cha T-A, Irvine BD, Tucker J, Urdea MS (1995) Preperation and characterization of RNA standard for use in quantitative branched DNA hybridization assays. Anal Biochem 226:120–129PubMedCrossRefGoogle Scholar
  10. Collins C, Rommens JM, Kowbel D, Godfrey T, Tabnner M, Hwang SI, Polikoff D, Nonet G, Cochran J, Maymbo K, Jay KE, Frooula J, Cloutier T, Kuo WL, Yaswen P, Dairkee S, Giovanola J, Hutchinson GB, Isola J, Kallioniemi OP, Palazzolo M, Martin C, Erricson C, Pinkel D, Albertson D, Li WB, Gray JW (1998) Positional cloning of ZNF217 and NABC1: genes amplified at 20q13.2 and over expressed in breast carcinoma. Proc Natl Acad Sci USA 95:8703–8708PubMedCrossRefGoogle Scholar
  11. Cronin M, Ghosh K, Sistare F, Quackenbush J, Vilker V, O’Connell C (2004) Universal RNA reference materials for gene expression. Clin Chem 50:1464–1471PubMedCrossRefGoogle Scholar
  12. Czechowski T, Bri RP, Stitt M, Scheible W, Udvardi MK (2004) Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: unprecedented sensitivity reveals novel root-and shoot-specific genes. Plant J 38:366–379PubMedCrossRefGoogle Scholar
  13. Dallas PB, Gottardo NG, Firth MJ, Beesley AH, Hoffmann K, Terry PA, Freitas JR, Boag JM, Cummings AJ, Kees UR (2005) Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR – how well do they correlate? BMC Genomics 6:59PubMedCrossRefGoogle Scholar
  14. Ellefsen S, Stenslokken K-O, Sandvik GK, Kristensen TA, Nilsson GE (2008) Improved normalization of real-time reverse transcriptase polymerase chain reaction data using an external RNA control. Anal Biochem 376:83–93PubMedCrossRefGoogle Scholar
  15. ERCC (2005a) The external RNA controls consortium: a progress report. Nat Methods 2:731–734CrossRefGoogle Scholar
  16. ERCC (2005b) Proposed methods for testing and selecting ERCC external RNA controls. BMC Genomics 6:150CrossRefGoogle Scholar
  17. Etienne W, Meyer MH, Peppers J, Meyer RA Jr (2004) Comparison of mRNA gene expression by RT-PCR and DNA microarray. Biotechniques 36:618–626PubMedGoogle Scholar
  18. Fisk DG, Ball CA, Dolinski K, Engel SR, Hong EL, Issel-Tarver L, Schwartz K, Sethuraman A, Botstein D, Cherry JM (2006) Saccharomyces cerevisiae S288C genome annotation: a working hypothesis. Yeast 23:857–865PubMedCrossRefGoogle Scholar
  19. Goldsworthy SM, Goldsworthy TL, Sprankle CS, Butterworth BE (1993) Variation in expression of genes used for normalization of Northern blots after induction of cell proliferation. Cell Prolif 26:511–518PubMedCrossRefGoogle Scholar
  20. Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalization: strategies and considerations. Genes Immun 6:279–284PubMedCrossRefGoogle Scholar
  21. Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson EJ, Garcia GN, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki Martínez-Murillo EF, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Qing Ye S, Yu W (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2:345–350PubMedCrossRefGoogle Scholar
  22. Kakuhata R, Watanabe M, Yamamoto T, Akamine R, Yamazaki N, Kataoka S, Fukuoka S, Ishikawa M, Ooie T, Baba Y, Hori T, Shinohara Y (2007) Possible utilization of in vitro synthesized mRNA s specifically expressed in certain tissues as standard for quantitative evaluation of the results of microarray analysis. J Biochem Biophys Methods 70:755–760PubMedCrossRefGoogle Scholar
  23. Kanno J, Aisaki K, Igarashi K, Nakatsu N, Ono A, Kodma Y, Nagao T (2006) “Per cell” normalization method for mRNA measurement by quantitative PCR and microarrays. BMC Genomics 7:64PubMedCrossRefGoogle Scholar
  24. Kapushesky M, Emam I, Holloway E, Kurnosov P, Zorin A, Malone J, Rustici G, Williams E, Parkinson H, Brazma A (2010) Gene expression atlas at the European Bioinformatics Institute. Nucleic Acids Res 38:D690–D698, Database issuePubMedCrossRefGoogle Scholar
  25. Klein D (2002) Quantification using real-time PCR technology: applications and limitations. Trends Mol Med 8:257–260PubMedCrossRefGoogle Scholar
  26. Lage JM, Hamann S, Gribanov O, Leamon JH, Pejovic T, Lizardi PM (2002) Microgel assessment of nucleic acid integrity and labeling quality in microarray experiments. Biotechniques 32:312–314PubMedGoogle Scholar
  27. Larinov A, Krause A, Miller W (2005) A standard curve based method for relative real time PCR data processing. BMC Bioinformatics 6:62CrossRefGoogle Scholar
  28. Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J (2005) Independence and reproducibility across microarray platforms. Nat Methods 2:337–344PubMedCrossRefGoogle Scholar
  29. Liu W, Saint DA (2002) A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics. Anal Biochem 302:52–59PubMedCrossRefGoogle Scholar
  30. Liu ZL (2010) Unification of gene expression data applying standard mRNA quantification reference for comparable analyses. J Microbial Biochem Technol 12:124–126CrossRefGoogle Scholar
  31. Liu ZL, Slininger PJ (2007) Universal external RNA quality controls for mRNA expression analysis using microbial DNA oligo microarray and real time quantitative RT-PCR. J Microbiol Methods 68:486–496PubMedCrossRefGoogle Scholar
  32. Liu ZL, Palmquist DE, Ma M, Liu J, Alexander NJ (2009a) Application of a master equation for absolute mRNA quantification using qRT-PCR. J Biotechnol 143:10–16PubMedCrossRefGoogle Scholar
  33. Liu ZL, Ma M, Song M (2009b) Evolutionarily engineered ethanologenic yeast detoxifies lignocellulosic biomass conversion inhibitors by reprogrammed pathways. Mol Genet Genomics 282:233–244PubMedCrossRefGoogle Scholar
  34. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 25:402–408PubMedCrossRefGoogle Scholar
  35. Ma M, Liu ZL (2010) Quantitative transcription dynamic analysis reveales candidate genes and key regulators for ethanol tolerance in Saccharomyces cerevisiae. BMC Genomics 10:169Google Scholar
  36. Mohsenzadeh M, Saupe-Thies W, Sterier G, Schroeder T, Francella F, Ruoff P, Rensing L (1998) Temperature adaptation of house keeping and heat shock gene expression in Neurospora crassa. Fungal Genet Biol 25:31–43PubMedCrossRefGoogle Scholar
  37. Novoradovskaya N, Whitfield ML, Basehore L, Novoradovsky SA, Pesich RJ, Usary J, Karaca M, Wong WK, Aprelikova O, Fero M, Perou CM, Botstein D, Braman J (2004) Universal reference RNA as a standard for microarray experiments. BMC Genomics 5:20PubMedCrossRefGoogle Scholar
  38. PE Applied Biosystems (1997) User Bulletin #2 Norwalk. Perkin-Elmer Corp, CT, p 36Google Scholar
  39. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:e45PubMedCrossRefGoogle Scholar
  40. Smith RD, Brown B, Ikonomi P, Schechter AN (2003) Exogenous reference RNA for normalization of real-time quantitative PCR. Biotechniques 34:88–91PubMedGoogle Scholar
  41. Suslov O, Steindler DA (2005) PCR inhibition by reverse transcriptase leads to an overestimation of amplification efficiency. Nucleic Acids Res 33:e181PubMedCrossRefGoogle Scholar
  42. Tichopad A, Dilger M, Schward G, Pfaffl MW (2003) Standardized determination of real-time PCR efficiency from a single reaction set-up. Nucleic Acids Res 31:e122PubMedCrossRefGoogle Scholar
  43. Tricarico C, Pinzani P, Bianchi S, Paglierani M, Distante V, Pazzagli M, Bustin SA, Orlando C (2002) Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem 309:293–300PubMedCrossRefGoogle Scholar
  44. VanGuilder HD, Vrana KE, Freeman WM (2008) Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques 44:619–626PubMedCrossRefGoogle Scholar
  45. Wong ML, Medrano JF (2005) Real-time PCR for mRNA quantitation. Biotechniques 39:1–11CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.National Center for Agricultural Utilization Research, USDA-ARSPeoriaUSA

Personalised recommendations