Human SERPINA1 ELISA Kit

The glomerular basement membrane (GBM) and extra-cellular matrix (ECM) are essential to maintain a functional interaction between the glomerular podocytes and the fenestrated endothelial cells in the formation of the slit diaphragm for the filtration of blood. Dysregulation of ECM homeostasis can cause Focal segmental glomerulosclerosis (FSGS). Despite this central role, alterations in ECM composition during FSGS have not been analyzed in detail yet. Here, we characterized the ECM proteome changes in miR-193a-overexpressing mice, which suffer from FSGS due to suppression of Wilms' tumor 1 (WT1). By mass spectrometry we identified a massive activation of the acute phase response, especially the complement and fibrinogen pathways. Several protease inhibitors (ITIH1, SERPINA1, SERPINA3) were also strongly increased. Complementary analysis of RNA expression data from both miR-193a mice and human FSGS patients identified additional candidate genes also mainly involved in the acute phase response. In total, we identified more than 60 dysregulated, ECM-associated genes with potential relevance for FSGS progression. Our comprehensive analysis of a murine FSGS model and translational comparison with human data offers novel targets for FSGS therapy.

The study looked for an optimal set of genes differentiating between papillary thyroid cancer (PTC) and normal thyroid tissue and assessed the sources of variability in gene expression profiles. The analysis was done by oligonucleotide microarrays (GeneChip HG-U133A) in 50 tissue samples taken intraoperatively from 33 patients (23 PTC patients and 10 patients with other thyroid disease). In the initial group of 16 PTC and 16 normal samples, we assessed the sources of variability in the gene expression profile by singular value decomposition which specified three major patterns of variability. The first and the most distinct mode grouped transcripts differentiating between tumor and normal tissues. Two consecutive modes contained a large proportion of immunity-related genes. To generate a multigene classifier for tumor-normal difference, we used support vector machines-based technique (recursive feature replacement). It included the following 19 genes: DPP4, GJB3, ST14, SERPINA1, LKP4, MET, EVA1, SPUVE, LGALS3, HBB, MKRN2, MRC2, IGSF1, KIAA0830, RXRG, P4HA2, CDH3, IL13RA1, and MTMR4, and correctly discriminated 17 of 18 additional PTC/normal thyroid samples and all 16 samples published in a previous microarray study. Selected novel genes (LKP4, EVA1, TKPRSS4, QPCT, and SLC34A2) were confirmed by Q-PCR. Our results prove that the gene expression signal of PTC is easily detectable even when cancer cells do not prevail over tumor stroma. We indicate and separate the confounding variability related to the immune response. Finally, we propose a potent molecular classifier able to discriminate between PTC and nonmalignant thyroid in more than 90% of investigated samples.