Gene expression during acute allograft rejection: novel statistical analysis of microarray data

  • Stegall M
  • Park W
  • Kim D
 et al. 
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High-throughput microarrays promise a comprehensive analysis of complex biological processes, yet their applicability is hampered by problems of reproducibility and data management. The current study examines some of the major questions of microarray use in a well-described model of allograft rejection. Using the Brown Norway to Lewis heterotopic heart transplant model, highly purified RNA was isolated from cardiac tissue at postoperative days (POD) 3, 5 and 7 and hybridized onto Affymetrix U34A microarrays. Using the log average ratio (LAR), changes in gene expression were monitored at each timepoint and p-values generated through statistical analysis. Microarray data were verified for 13 significant transcripts using RT-PCR. Of the 8800 transcripts studied, 2864 were increased on POD 3, 1418 on POD 5 and 2745 on POD 7. Verifying previous studies, many up-regulated genes appeared to be associated with the inflammatory process and graft infiltrating cells. Down-regulated transcripts included many novel molecules such as SC1 and decorin. LAR analysis provides a useful approach to analyze microarray data. Results were reproducible and correlated well with both RT-PCR and prior studies. Most importantly, these results provide new insights into the pathogenesis of acute rejection and suggest new molecules for future studies

Author-supplied keywords

  • A
  • Analysis
  • Animals
  • Comparative Study
  • Gene Expression Regulation
  • Genes
  • Genetics
  • Graft Rejection
  • Heart
  • Heart Transplantation
  • Heterotopic
  • Immunology
  • Isolation & Purification
  • Methods
  • Oligonucleotide Array Sequence Analysis
  • Pathology
  • Postoperative Period
  • RT-PCR
  • Rats
  • Rats,Sprague-Dawley
  • Rna
  • Surgery
  • Time Factors
  • Transplantation
  • Transplantation,Homologous
  • Transplantation,Isogeneic
  • Use
  • cells
  • gene
  • gene expression
  • im
  • in

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  • PMID: 12482143


  • M Stegall

  • W Park

  • D Kim

  • W Kremers

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