Microarray Analysis of Metabolically Engineered NS0 Cell Lines Producing Chimeric Antibody

  • G. Khoo
  • F. Falciani
  • M. Al-Rubeai
Conference paper


Large scale gene expression provides a powerful approach to the characterisation of cells transcriptional state. Thousand of genes can be monitored in single experiments generating an unprecedented volume of data. In animal cell technology, this information can be used to assign functions to previously unassociated genes, identify potential process variable targets and generate snapshots of transcriptional activity in response to any environmental factor or cellular trigger. We have used a mouse array representing 15000 genes to assess the expression profile of mouse myeloma cell line NS0 and GS-NS0 producing chimeric antibody. Comparisons of gene profiles were also made with proliferation-controlled (over-expressing p21CIP1) and apoptosis resistant (over-expressing bcl-2) cell lines. There were 19 genes up regulated and 32 genes down regulated in the apoptosis resistant cell line compared to the parental producing cell line. As for the proliferation-controlled cell line, 54 and 147 genes were up and down regulated respectively. Gene ontology was used to understand the biological relevance of differences in gene expression data. Distinct expression signatures, indicative of observed differences in physiology and productivity between the cell lines, were identified. Our study highlights the potential of microarray technology for the analysis recombinant cell lines as affected by product expression, genetic modification and environmental conditions.


Gene Ontology Glutamine Synthetase Chimeric Antibody Large Scale Gene Expression Animal Cell Technology 
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.


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  1. Al-Shahrour F, Díaz-Uriarte R, and Dopazo J. 2004 Bioinfornatics 20, 578–580CrossRefGoogle Scholar
  2. Ashburner, M, Ball C.A, Blake J.A, Botstein D, Butler H, Cherry J.M, Davis A.P, Dolinski K, Dwight S.S, Eppig J.T, Harris M.A, Hill D.P, Issel-Tarver L, Kasarskis A, Lewis S, Matese J.C, Richardson J.E, Ringwald M, Rubin G.M, and Sherlock G. 2000 Nat.Genet 25, 25–29CrossRefGoogle Scholar
  3. Bebbington C.R., Renner G, Thomson S, King D, Abrams D, and Yarranton G.T. 1992 Bio/Technology 10, 169–175CrossRefGoogle Scholar
  4. Benjamini Y and Hochberg Y. 1995 Journal of Royal Statistical Society B 57, 289–300Google Scholar
  5. Herrero J, Al-Shahrour F, Díaz-Uriarte R, Mateos A, Vaquerizas J.M, Santoyo J, and Dopazo J. 2003 Nucleic Acids Research 31, 3461–3467CrossRefGoogle Scholar
  6. Korke, R, Rink A, Seow T.K, Wong K, Beattie C, and Hu W.S. 2002 Journal of Biotechnology 94, 73–92CrossRefGoogle Scholar
  7. Lomax, J and McCray, A. T. 2004 Comparative Functional Genomics 5[354], 361Google Scholar
  8. Mayford, M, Abel T, and Kandel E.R. 1995. Curr.Opin.Neurobiol. 5, 141–148CrossRefGoogle Scholar
  9. Sapirstein, A and Bonventre, J. V. 2000 Biochim.Biophys.Acta 1488, 139–148Google Scholar
  10. Tanaka T.S, Jaradat S.A, Lim M.K, Kargul G.J, Wang X, Grahovac M.J, Pantano S, Sano Y, Piao Y, Nagaraja R, Dol H, Wood III W H., Becker K.G, and Ko M.S. 2000 Proc Natl Acad Sci USA 97, 9127–9132CrossRefGoogle Scholar
  11. Tey B.T, Singh R.P., Piredda L., Piacentini M., and Al-Rubeai M. 2000 Journal of Biotechnology 79, 147–159CrossRefGoogle Scholar
  12. Watanabe S., Shuttleworth J, and Al-Rubeai M. 2002 Biotechnol Bioeng 77, 1–7CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • G. Khoo
    • 1
  • F. Falciani
    • 2
  • M. Al-Rubeai
    • 1
    • 3
  1. 1.Department of Chemical EngineeringUniversity of BirminghamBirminghamUK
  2. 2.School of BiosciencesUniversity of BirminghamEdgbastonUK
  3. 3.Department of Chemical and Biochemical EngineeringUniversity College DublinBelfieldIreland

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