Project description:The UT-A1 urea transporter is crucial to the kidney’s ability to generate concentrated urine. Native UT-A1 from kidney inner medulla (IM) is a heavily glycosylated protein with two glycosylation forms of 97 and 117 kDa. In diabetes, UT-A1 protein abundance, particularly the 117 kD isoform, is significantly increased corresponding to an increased urea permeability in perfused IM collecting ducts, which plays an important role in preventing the osmotic diuresis caused by glucosuria. In this study, using sugar-specific binding lectins, we found that the carbohydrate structure of UT-A1 is also changed under diabetic conditions with increased amounts of sialic acid, fucose, and increased glycan branching. These changes were accompanied by altered UT-A1 association with the galectin proteins, α-galactoside glycan binding proteins. To explore the molecular basis of the alterations of glycan structures, the highly sensitive next generation sequencing (NGS) technology, Illumina RNA-seq, was employed to analyze genes involved in the process of UT-A1 glycosylation using streptozotocin (STZ) - induced diabetic rat kidney as the tissue source. Differential expression analysis combining quantitative PCR revealed that a number of important glycosylation related genes were changed under diabetic conditions. These genes include the glycosyltransferase genes Mgat4a, the sialylation enzymes St3gal1 and St3gal4and glycan binding protein galectin-3, -5, -8 and -9. In contrast, although highly expressed in kidney IM, the glycosyltransferase genes Mgat1, Mgat2, and fucosyltransferase Fut8, did not show any changes. We conclude that the alteration of these glycosylation related genes may contribute to changing the UT-A1 glycan structure, and therefore modulate kidney urea transport activity under diabetic conditions.
Project description:The urea channel Slc14a2 (or UT-A1) mediates vasopressin-regulated urea transport across the inner medullary collecting duct (IMCD). Previously, UT-A1 was found to present in a high molecular weight complex, suggesting UT-A1 is involved in certain protein-protein interactions. The present study sought to identify the proteins that interact with UT-A1 in this complex for a better understanding of how UT-A1 is regulated. Rat IMCD suspensions were treated with or without V2 receptor agonist, dDAVP, followed by in-cell crosslinking using BSOCOES and detergent solubilization. Immunoprecipitation using Dynabeads coated with UT-A1 specific antibody successfully pulled down the UT-A1 proteins. In-gel digestion protocol was carried out to prepare samples for liquid chromatographic mass spectrometry analysis of tryptic peptides using a Velos-Orbitrap mass spectrometer. The peptides passing stringent spectral quality thresholds were quantified (label-free) to identify those with (UTA-1 antibody/preimmune IgG) >4. A total of 128 UT-A1 interacting proteins were identified. Gene Ontology analysis maps the distribution of these proteins throughout major cell compartments: endoplasmic reticulum, Golgi, endosomes, cytosol and plasma membrane. Among them are four protein kinases (Cdc42bpb, Phkb, Camk2d, Mtor) that play roles in vasopressin-regulated phosphorylation of UT-A1. Non-label quantification was also performed to determine the stoichiometry of UT-A3 with UT-A1, the result does not support an oligomeric complex formation of UT-A1/A3. In conclusion, we have provided a refined list of UT-A1 binding proteins which can be useful for further analysis of the vasopressin signaling pathway in regulation of UT-A1 in IMCD.
Project description:We investigated the expression profiles of microRNAs (miRNA) in 26 renal cell carcinoma (RCC) FFPE tissues (3 chRCC, 5 papRCC and 18 ccRCC), 4 urothelial cell carcinomas of the upper urinary tract (UT-UCs) and 20 normal kidneys by using the miRCURY LNA™ microRNA Array, 6th gen (Exiqon, Woburn MA), containing capture probes that target all miRNAs for all species registered in the miRBASE version 16.0. Real-time PCR (qRT-PCR) using appropriate endogenous controls was performed in order to validate the microarray results of the 27 most differentially expressed (DE) miRNAs. We identified 434 miRNAs that were significantly deregulated in all tumours compared to the normal kidney tissues. Overall, 126 miRNAs (29%) showed increased expression and 303 miRNAs (69.8%) had decreased expression in RCCs and UT-UCs vs. the normal kidneys. Specifically, 374, 420, 421 and 409 miRNAs were 90% consistently down-regulated in ccRCC, papRCC, chRCC and UT-UC, respectively. Unsupervised two-way hierarchical clustering (HCL) with Euclidian distance accurately discriminated between RCC and UT-UC. Apart from one chRCC sample that was clustered with ccRCCs, HCL also managed to successfully classify the 3 RCC subtypes among them. Furthermore, it showed that ccRCC is more closely related to papRCC and that both are distinct from chRCC or UT-UC. Ninety-four miRNAs were co-upregulated among ccRCC, papRCC and chRCC; and 11, 44 and 24 miRNAs were specifically up-regulated in each one of the three RCC subtypes (ccRCC, chRCC and papRCC), respectively. On the other hand, 222 miRNAs were co-down-regulated in the three RCC subtypes, whereas 16, 18 and 5 miRNAs were specifically down-regulated in ccRCC, chRCC and papRCC, respectively. When the DE miRNAs in each RCC subtype were combined with those in UT-UC, we identified 89 and 206 miRNAs, respectively, that were up- and down-regulated in all tumor types. miRNA profiling can distinguish between RCC and UT-UC as well as among distinct RCC subtypes. Our data validicate that miRNA expression tends to be down-regulated in RCC versus the normal kidney tissue.