Dataset Information

INTRAGRAFT ANTI-VIRAL-SPECIFIC GENE EXPRESSION AS A DISTINCTIVE TRANSCRIPTIONAL SIGNATURE FOR POLYOMAVIRUS-ASSOCIATED NEPHROPATHY

ABSTRACT: Polyoma virus nephropathy (PVAN) is a common cause of kidney allograft dysfunction and loss. Microscopic descriptions of PVAN are very similar to T-cell mediated rejection (TCMR) and have unclear underlying molecular mechanisms. To identify PVAN-specific gene expression, we analyzed 162 kidney biopsies with and without PVAN for global gene expression. Unsupervised hierarchical clustering analysis of all 162 biopsies revealed high similarity between PVAN and TCMR gene expression. Increasing the stringency for the specificity (p <0.001 and >2-fold expression) between PVAN and TCMR, 158 and 252 unique PVAN and TCMR injury-specific probesets were observed, respectively. While TCMR-specific probeset were overwhelmingly involved in immune response costimulation (CTLA4, CD28, CD86) and TCR (NFATC2, LCP2) signaling, PVAN-specific probesets were mainly related to viral replication process (IFITM1, LTF, NOSIP, RARRES3), RNA polymerase assembly (POLR2l, TAF10, RPS15) and pathogen recognition receptors (C1QA, C3, CFD). A principal component analysis using these genes further confirmed the most optimal separation between the 3 different clinical phenotypes. Validation of 4 PVAN-specific probesets (RPS15, CFD, LTF, and NOSIP) by QPCR and further confirmation by IHC of 2 PVAN-specific proteins with anti-viral function (LTF and IFITM1) was done, showing significantly higher expression within interstitial cellular infiltrates and in tubuli in PVAN specimens as compared to TCMR and NL kidney biopsies. In conclusion, even though PVAN and TCMR kidney allografts share great similarities on gene perturbation, particular PVAN-specific transcripts were identified with well-known anti-viral properties that provide tools for discerning PVAN and AR as well as attractive targets for rational drug design.

ORGANISM(S): Homo sapiens

PROVIDER: GSE72925 | GEO | 2016/09/30

SECONDARY ACCESSION(S): PRJNA295327

REPOSITORIES: GEO

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Project description:Polyoma virus nephropathy (PVAN) is a common cause of kidney allograft dysfunction and loss. Microscopic descriptions of PVAN are very similar to T-cell mediated rejection (TCMR) and have unclear underlying molecular mechanisms. To identify PVAN-specific gene expression, we analyzed 162 kidney biopsies with and without PVAN for global gene expression. Unsupervised hierarchical clustering analysis of all 162 biopsies revealed high similarity between PVAN and TCMR gene expression. Increasing the stringency for the specificity (p <0.001 and >2-fold expression) between PVAN and TCMR, 158 and 252 unique PVAN and TCMR injury-specific probesets were observed, respectively. While TCMR-specific probeset were overwhelmingly involved in immune response costimulation (CTLA4, CD28, CD86) and TCR (NFATC2, LCP2) signaling, PVAN-specific probesets were mainly related to viral replication process (IFITM1, LTF, NOSIP, RARRES3), RNA polymerase assembly (POLR2l, TAF10, RPS15) and pathogen recognition receptors (C1QA, C3, CFD). A principal component analysis using these genes further confirmed the most optimal separation between the 3 different clinical phenotypes. Validation of 4 PVAN-specific probesets (RPS15, CFD, LTF, and NOSIP) by QPCR and further confirmation by IHC of 2 PVAN-specific proteins with anti-viral function (LTF and IFITM1) was done, showing significantly higher expression within interstitial cellular infiltrates and in tubuli in PVAN specimens as compared to TCMR and NL kidney biopsies. In conclusion, even though PVAN and TCMR kidney allografts share great similarities on gene perturbation, particular PVAN-specific transcripts were identified with well-known anti-viral properties that provide tools for discerning PVAN and AR as well as attractive targets for rational drug design. A total of 168 kidney biopsies from kidney transplant patients were used. The kidney biopsies included renal biopsies from PVAN (n=10), T cell mediated cellular rejection (n=26), patients with IFTA (n=59), patients with stable grafts (STA) (n=73). This dataset is part of the TransQST collection.

Project description:Background: Biopsy-based diagnosis is essential for maintaining renal allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods. Methods: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from renal transplant patients, comprising five biopsies with the diagnosis of T-cell-mediated rejection (TCMR), five biopsies for polyomavirus BK nephropathy (BKPyVN), and normal kidney control tissue (STA). Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. Results: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. Conclusions: This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-derived diagnostic model. It costs less than 10 dollars per test.

Dataset Information

INTRAGRAFT ANTI-VIRAL-SPECIFIC GENE EXPRESSION AS A DISTINCTIVE TRANSCRIPTIONAL SIGNATURE FOR POLYOMAVIRUS-ASSOCIATED NEPHROPATHY

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