Mammalian Genome

, 18:617 | Cite as

Microarray analysis of gene expression of mouse hepatocytes of different ploidy

  • Pin Lu
  • Sandrine Prost
  • Helen Caldwell
  • Jonathan D. Tugwood
  • Graham R. Betton
  • David J. Harrison


Polyploidisation in hepatocytes has been associated with many physiologic and pathologic processes such as proliferation, metabolism, regeneration, aging, and cancer. We studied gene expression patterns in hepatocytes of different ploidy. Primary hepatocytes were obtained from mice of different ages: young (4–6 weeks old), adult (8–10 weeks old), and older (22–24 weeks old). Diploid (2N), tetraploid (4N), and octoploid (8N) hepatocytes were isolated for studies using a high-density mouse genome microarray. No major changes of gene expression patterns between hepatocytes of different ploidy were found. Fifty genes were identified as differentially expressed in the diploid and tetraploid populations, but the changes were less than twofold either way. Four genes (Gas2, Igfbp2, Nr1i3, and Ccne2) were differentially expressed in tetraploid and octoploid cells. This was confirmed in two age groups, “adult” and “older,” but once again the factors were less than twofold and the expressions of Gas2 and Igfbp2 were more different between age groups than between ploidy classes. Our results show that polyploid hepatocytes are stable and “normal” without aberrant gene expression, unlike what is thought for cancer cells. By contrast to megakaryocytes, hepatocyte polyploidisation is not a differentiation step associated with major changes in gene expression. Our data support the hypothesis that hepatocyte polyploidisation is a protective mechanism against oxidative stress that occurs via a controlled process throughout growth and aging where binucleation is important.


Activin Primary Hepatocyte Constitutive Androstane Receptor Binucleated Cell Polyploid Cell 
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.



The authors thank Shonna Johnston of the Centre for Inflammation Research, University of Edinburgh, for helping with flow-sorting; Garry Beran and Johanna Loughlin for helping with microarray operation; Dr. Kevin Roberston of the Scottish Centre for Genomic Technology and Informatics for helping with the operation of the Agilent Bioanalyzer and initial microarray data analysis; Wellcome Trust Clinical Research Facility, Edinburgh, for providing the real-time PCR facility and technical support. This project was funded by Safety Assessment UK, AstraZeneca, and University of Edinburgh.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Pin Lu
    • 1
  • Sandrine Prost
    • 1
    • 3
  • Helen Caldwell
    • 1
  • Jonathan D. Tugwood
    • 2
  • Graham R. Betton
    • 2
  • David J. Harrison
    • 1
  1. 1.Pathology Division, School of Molecular and Clinical MedicineUniversity of EdinburghEdinburghUK
  2. 2.Molecular Toxicology GroupSafety Assessment UK, AstraZeneca PharmaceuticalsCheshireUK
  3. 3.The Queen’s Medical Research InstituteUniversity of EdinburghEdinburghUK

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