Proteomic analysis of the INS-1E secretome identify novel vitamin D-regulated proteins

Characterization of the Secretome, Transcriptome, and Proteome of Human β Cell Line EndoC-βH1

Early diabetes research is hampered by limited availability, variable quality, and instability of human pancreatic islets in culture. Little is known about the human β cell secretome, and recent studies question translatability of rodent β cell secretory profiles. Here, we verify representativeness of EndoC-βH1, one of the most widely used human β cell lines, as a translational human β cell model based on omics and characterize the EndoC-βH1 secretome. We profiled EndoC-βH1 cells using RNA-seq, data-independent acquisition, and tandem mass tag proteomics of cell lysate. Omics profiles of EndoC-βH1 cells were compared to human β cells and insulinomas. Secretome composition was assessed by data-independent acquisition proteomics. Agreement between EndoC-βH1 cells and primary adult human β cells was ∼90% for global omics profiles as well as for β cell markers, transcription factors, and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-βH1 cells compared to adult β cells. Consistently, similarity was slightly higher with benign nonmetastatic insulinomas. EndoC-βH1 secreted 783 proteins in untreated baseline state and 3135 proteins when stressed with nontargeting control siRNA, including known β cell hormones INS, IAPP, and IGF2. Further, EndoC-βH1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. EndoC-βH1 secretome contained an unexpectedly high proportion of predicted extracellular vesicle proteins. We believe that secretion of extracellular vesicles and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies.

Serum fatty acid-binding protein 5 is a significant factor in hepatocellular carcinoma progression independent of tissue expression level

Fatty acid-binding protein 5 (FABP5) is highly expressed in hepatocellular carcinoma (HCC) tissues and is related to HCC progression. In this study, we analyzed the potential of serum FABP5 (sFABP5) as a tumor marker in HCC and its clinical significance in HCC progression. We compared the sFABP5 concentration in patients with HCC (HCC group) with that of patients with hepatitis without HCC (hepatitis group). Moreover, we measured the FABP5 expression levels in resected HCC tissues (tFABP5) and analyzed their relationship with sFABP5. We also performed cell-based assays using FABP5 knockout and overexpressing HCC cell lines to analyze the effect of extrinsic FABP5 (exFABP5) on HCC cells. We showed that sFABP5 was not a useful tumor marker for HCC, as HCC and sFABP5 were not correlated. However, sFABP5 and tFABP5 significantly correlated with survival after surgery for HCC, while sFABP5 and tFABP5 were independent of each other. In cell-based assays, exFABP5 was taken up by HCC cell lines and positively affected cell survival under glucose-depleted conditions by complementing the endogenous FABP5 function. In conclusion, sFABP5 had a significant impact on HCC progression irrespective of tFABP5 by augmenting cell viability under glucose-depleted conditions. As tFABP5 and sFABP5 are important factors that are independent of each other in HCC progression, both of them should be considered independently in improving the prognosis of patients with HCC.

In silico approach to predict pancreatic β-cells classically secreted proteins

Pancreatic β-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/β-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 β-cells, β-cell secretory granules, and β-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 β-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.

Serum FABP5 concentration is a potential biomarker for residual risk of atherosclerosis in relation to cholesterol efflux from macrophages

Cholesterol efflux capacity (CEC) from macrophages, the first step in the reverse cholesterol transport pathway, is inversely associated with residual risk for atherosclerotic cardiovascular disease. Fatty acid-binding protein 4 (FABP4) and FABP5 are expressed in both adipocytes and macrophages and play significant roles in the development of insulin resistance and atherosclerosis. Both FABP4 and FABP5 are secreted from cells, and their circulating levels are associated with insulin resistance and atherosclerosis. We investigated the association between CEC and levels of FABP4 and FABP5 in 250 subjects without any medications. CEC was positively correlated with HDL cholesterol level and negatively correlated with concentrations of high-sensitivity C-reactive protein (hsCRP) and FABP5, but not FABP4. Multiple regression analysis demonstrated that FABP5 concentration was an independent predictor of CEC after adjustment of age, gender and levels of HDL cholesterol and hsCRP. In 129 of the 250 subjects who underwent carotid ultrasonography, mean intima-media thickness was negatively correlated with CEC and was positively correlated with concentrations of FABP4 and FABP5. In conclusion, in contrast to FABP4, circulating FABP5 is associated with decreased CEC and carotid atherosclerosis, suggesting that FABP5 level is a regulatory factor of CEC and a potential biomarker for residual risk of atherosclerosis.