FK506-Binding Protein 2 Participates in Proinsulin Folding.

Apart from chaperoning, disulfide bond formation, and downstream processing, the molecular sequence of proinsulin folding is not completely understood. Proinsulin requires proline isomerization for correct folding. Since FK506-binding protein 2 (FKBP2) is an ER-resident proline isomerase, we hypothesized that FKBP2 contributes to proinsulin folding. We found that FKBP2 co-immunoprecipitated with proinsulin and its chaperone GRP94 and that inhibition of FKBP2 expression increased proinsulin turnover with reduced intracellular proinsulin and insulin levels. This phenotype was accompanied by an increased proinsulin secretion and the formation of proinsulin high-molecular-weight complexes, a sign of proinsulin misfolding. FKBP2 knockout in pancreatic ß-cells increased apoptosis without detectable up-regulation of ER stress response genes. Interestingly, FKBP2 mRNA was overexpressed in ß-cells from pancreatic islets of T2D patients. Based on molecular modeling and an in vitro enzymatic assay, we suggest that proline at position 28 of the proinsulin B-chain (P28) is the substrate of FKBP2's isomerization activity. We propose that this isomerization step catalyzed by FKBP2 is an essential sequence required for correct proinsulin folding.

Characterization of gluten-degrading prolyl endoprotease from Thermococcus kodakarensis.

There is increasing interest in gluten-degrading enzymes for use during food and drink processing. The industrially available enzymes usually work best at low to ambient temperatures. However, food manufacturing is often conducted at higher temperatures. Therefore, thermostable gluten-degrading enzymes are of great interest. We have identified a new thermostable gluten-degrading proline-specific prolyl endoprotease from the archaea Thermococcus kodakarensis. We then cloned and expressed it in Escherichia coli. The prolyl endoprotease was found to have a size of 70.1 kDa. The synthetic dipeptide Z-Gly-Pro-p-nitroanilide was used to characterize the prolyl endoprotease and it had maximum activity at pH 7 and 77°C. The Vmax, Km and kcat values of the purified prolyl endoprotease were calculated to be 3.14 mM/s, 1.10 mM and 54 s-1, respectively. When the immunogenic gluten peptides PQPQLPYPQPQLPY (α-gliadin) and SQQQFPQPQQPFPQQP (γ-hordein) were used as substrates, the prolyl endoprotease was able to degrade these. Furthermore, gluten in wort was reduced when the prolyl endoprotease was used during mashing of barley malt. The discoveries open up new food processing possibilities and further the understanding of proline-specific protease diversity.

In silico approach to predict pancreatic ß-cells classically secreted proteins.

Pancreatic ß-cells, residents of the islets of Langerhans, are the unique insulin-producers in the body. Their physiology is a topic of intensive studies aiming to understand the biology of insulin production and its role in diabetes pathology. However, investigations about these cells' subset of secreted proteins, the secretome, are surprisingly scarce and a list describing islet/ß-cell secretome upon glucose-stimulation is not yet available. In silico predictions of secretomes are an interesting approach that can be employed to forecast proteins likely to be secreted. In this context, using the rationale behind classical secretion of proteins through the secretory pathway, a Python tool capable of predicting classically secreted proteins was developed. This tool was applied to different available proteomic data (human and rodent islets, isolated ß-cells, ß-cell secretory granules, and ß-cells supernatant), filtering them in order to selectively list only classically secreted proteins. The method presented here can retrieve, organize, search and filter proteomic lists using UniProtKB as a central database. It provides analysis by overlaying different sets of information, filtering out potential contaminants and clustering the identified proteins into functional groups. A range of 70-92% of the original proteomes analyzed was reduced generating predicted secretomes. Islet and ß-cell signal peptide-containing proteins, and endoplasmic reticulum-resident proteins were identified and quantified. From the predicted secretomes, exemplary conservational patterns were inferred, as well as the signaling pathways enriched within them. Such a technique proves to be an effective approach to reduce the horizon of plausible targets for drug development or biomarkers identification.