Unknown,Transcriptomics,Genomics,Proteomics

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Three non-invasive protein biomarkers for solid-organ transplant rejection found through integrative genomics

ABSTRACT: We integrated three transplant rejection microarray studies examining gene expression in samples from pediatric renal, adult renal, and adult heart transplants. We performed one study ourselves and retrieved two others from the NCBI Gene Expression Omnibus (GEO)(GSE4470 and GSE1563). We identified 45 genes that were upregulated in common in acute rejection. Half were involved in one immune-related pathway. Among ten proteins we tested by serum ELISA, three successfully distinguished acute rejection from stable transplants. These were CXCL9, PECAM1, and CD44, with areas under the receiver operating characteristic curves of 0.844, 0.802, and 0.738, respectively. Immunohistochemistry showed that the PECAM1 protein was increased in acute rejection in renal, liver and heart transplants versus normal tissues. Our results show that integrating publicly-available gene expression data sets is a fast, powerful, and cost-effective way to identify serum-detectable diagnostic biomarkers. For new microarray experiments, we collected 18 acute rejection (AR) and 18 stable (STA) biopsy samples from pediatric renal allograft recipients at Stanford University. Written informed consent was obtained from all the subjects. The study was approved by the Stanford University Institutional Review Board. Nucleic acids from the samples were hybridized to Affymetrix U133 Plus 2.0 microarrays. AR samples were obtained at the time of a biopsy-proven acute rejection episode according to the Banff classification (IA, IIA, IIB). Prior to including the samples in this study, the diagnosis of acute rejection was confirmed by a pathologist. STA samples were obtained from patients with stable graft function during regular post-transplantation follow-up protocol biopsies. All three data sets (pediatric renal, adult renal, and adult heart) were normalized by the quantile-quantile method using dChip software. Ten proteins in serum were measured by using commercial ELISA kits.

ORGANISM(S): Homo sapiens

SUBMITTER: Sue Hsieh

PROVIDER: E-GEOD-14328 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection.

Ling Xuefeng B XB Sigdel Tara K TK Lau Kenneth K Ying Lihua L Lau Irwin I Schilling James J Sarwal Minnie M MM

Journal of the American Society of Nephrology : JASN 20100211 4

Noninvasive methods to diagnose rejection of renal allografts are unavailable. Mass spectrometry followed by multiple-reaction monitoring provides a unique approach to identify disease-specific urine peptide biomarkers. Here, we performed urine peptidomic analysis of 70 unique samples from 50 renal transplant patients and 20 controls (n = 20), identifying a specific panel of 40 peptides for acute rejection (AR). Peptide sequencing revealed suggestive mechanisms of graft injury with roles for pro ...[more]

PMID: 20150539

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Project description:The data set contains 67 array (lymphochip cDNA array), published in N Engl J Med 2003 Jul 10;349(2):125-38. PMID: 12853585. TITLE: Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. BACKGROUND: The causes and clinical course of acute rejection vary, and it is not possible to predict graft outcome reliably on the basis of available clinical, pathological, and genetic markers. We hypothesized that previously unrecognized molecular heterogeneity might underlie some of the variability in the clinical course of acute renal allograft rejection and in its response to treatment. METHODS: We used DNA microarrays in a systematic study of gene-expression patterns in biopsy samples from normal and dysfunctional renal allografts. A combination of exploratory and supervised bioinformatic methods was used to analyze these profiles. : We found consistent differences among the gene-expression patterns associated with acute rejection, nephrotoxic effects of drugs, chronic allograft nephropathy, and normal kidneys. The gene-expression patterns associated with acute rejection suggested at least three possible distinct subtypes of acute rejection that, although indistinguishable by light microscopy, were marked by differences in immune activation and cellular proliferation. Since the gene-expression patterns pointed to substantial variation in the composition of immune infiltrates, we used immunohistochemical staining to define these subtypes further. This analysis revealed a striking association between dense CD20+ B-cell infiltrates and both clinical glucocorticoid resistance (P=0.01) and graft loss (P<0.001). CONCLUSIONS: Systematic analysis of gene-expression patterns provides a window on the biology and pathogenesis of renal allograft rejection. Biopsy samples from patients with acute rejection that are indistinguishable on conventional histologic analysis reveal extensive differences in gene expression, which are associated with differences in immunologic and cellular features and clinical course. The presence of dense clusters of B cells in a biopsy sample was strongly associated with severe graft rejection, suggesting a pivotal role of infiltrating B cells in acute rejection. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: normal vs disease Keywords: disease_state_design Using regression correlation

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Three non-invasive protein biomarkers for solid-organ transplant rejection found through integrative genomics

Publications

Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection.

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