Novel insights into lung transplant rejection by microarray analysis

Abstract

Gene expression microarrays can estimate the prevalence of mRNA for thousands of genes in a small sample of cells or tissue. Organ transplant researchers are increasingly using microarrays to identify specific patterns of gene expression that predict and characterize acute and chronic rejection, and to improve our understanding of the mechanisms underlying organ allograft dysfunction. We used microarrays to assess gene expression in bronchoalveolar lavage cell samples from lung transplant recipients with and without acute rejection on simultaneous lung biopsies. These studies showed increased expression during acute rejection of genes involved in inflammation, apoptosis, and T-cell activation and proliferation. We also studied gene expression during the evolution of airway obliteration in a murine heterotopic tracheal transplant model of chronic rejection. These studies demonstrated specific patterns of gene expression at defined time points after transplantation in allografts, whereas gene expression in isografts reverted back to that of native tracheas within 2 wk after transplantation. These studies demonstrate the potential power of microarrays to identify biomarkers of acute and chronic lung rejection. The application of new genetic, genomic, and proteomic technologies is in its infancy, and the microarray-based studies described here are clearly only the beginning of their application to lung transplantation. The massive amount of data generated per tissue or cell sample has spawned an outpouring of invention in the bioinformatics field, which is developing methodologies to turn data into meaningful and reproducible clinical and mechanistic inferences.

Figure 1.

Cluster analysis of gene expression patterns of bronchoalveolar lavage (BAL) cells from lung transplant recipients with and without acute rejection. Microarray analysis of human BAL cell samples from subjects with no rejection (A+B score 0 or 1) and those with acute rejection (A+B score > 1). RNA from BAL cells was isolated and applied to Affymetrix U133A microarrays (Affymetryx, Inc., Santa Clara, CA) according to a set of standard procedures. Expression levels for 22,283 transcripts, representing 13,267 unique Entrez gene IDs, were estimated using algorithms from Microarray Suite 5.0 and GeneData Expressionist Refiner 4.0 (GeneData, Basel, Switzerland) to preprocess, normalize, and summarize probe level information. SAM (45) clustered the samples into two main groups: one which included all of the acute-rejection samples and the other which included only no-rejection samples. Subjects A and B each contributed one acute-rejection sample and one no-rejection sample; each of these samples clustered with their appropriate groups according to rejection status. Subject C contributed two acute-rejection samples, each of which clustered with other acute-rejection samples. Conversely, subject D had three no-rejection samples that all clustered within the no-rejection group. Subject E contributed two no-rejection samples: one grouped with the majority of no-rejection samples, but one clustered with the acute-rejection samples. Patients: Yellow = individual patients; red = patient A; green = patient B; gray = patient C; purple = patient D; black = patient E. Biopsies: light blue = no rejection; dark blue = acute rejection. Genes: red = up-regulated genes; green = down-regulated genes. Reprinted by permission from Reference .

Figure 2.

Relationship of genes significantly changed in acute rejection to process pathways. The relationship of genes significantly changed in acute rejection (as compared with no rejection) to different biological pathways was investigated using GenMAPP, a computer application designed to visualize gene expression data through preconfigured or custom-developed biological pathways and groupings of genes (47). For this analysis, a delta value of 1.05, which identified 885 genes with a false discovery rate (FDR) of 4.63%, was used to include a larger pool of candidate genes. Six of the 52 preconfigured pathways showed significant changes in expression of some of their component genes. These included pathways for transforming growth factor-β signaling, inflammatory response, apoptosis, nucleotide G-protein–coupled receptors, peptide G-protein–coupled receptors, and the Wnt family of signaling molecules. The inflammatory response pathway is shown here. Genes highlighted in orange are significantly changed; those highlighted in gray are not significantly changed; those in white were not represented on the Affymetrix Human U133A GeneChip. Reprinted by permission from Reference 76.

Figure 3.

Comparison of gene expression patterns of isograft and allograft tracheas harvested at 4, 14, and 25 d after heterotopic transplantation. Principal components analysis was performed on gene expression measurements of all allografts, isografts, and untransplanted tracheas for the most variant transcripts across the different samples. Principal components analysis is a data-reduction technique that derives an ordered set of orthogonal components, each of which is a linear combination of the original variables, and explains the most amount of variability that has not been accounted for by prior components. The first three principal components of the most variant genes on the microarray, based on the coefficient of variation (SD/mean; n = 3,554), were plotted in three dimensions to visually assess the degree to which graft day and type explain the largest sources of variation in the data. The first three principal components accounted for 15.3, 10.7, and 6.9% of the variation in the data, respectively. Normal, untransplanted tracheas (gray) were closest to 14- and 25-d isografts when plotted according to the first three principal components, suggesting that isografts revert back to the normal baseline gene expression of the mouse trachea. Comp. = component. Reprinted by permission from Reference .