Journal Article
Research Support, Non-U.S. Gov't
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A simple and rapid method for identifying and semi-quantifying peptide hormones in isolated pancreatic islets by direct-tissue matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

We describe a new, simple, robust and efficient method based on direct-tissue matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry that enables consistent semi-quantitation of peptide hormones in isolated pancreatic islets from normal and diabetic rodents. Prominent signals were measured that corresponded to all the main peptide hormones present in islet-endocrine cells: (α-cells) glucagon, glicentin-related polypeptide/GRPP; (β-cells) insulin I, insulin II, C-peptide I, C-peptide II, amylin; (δ-cells) somatostatin-14; and (PP-cells), and pancreatic polypeptide. The signal ratios coincided with known relative hormone abundances. The method demonstrated that severe insulin deficiency is accompanied by elevated levels of all non-β-cell-hormones in diabetic rat islets, consistent with alleviation of paracrine suppression of hormone production by non-β-cells. It was also effective in characterizing hormonal phenotype in hemizygous human-amylin transgenic mice that express human and mouse amylin in approx. equimolar quantities. Finally, the method demonstrated utility in basic peptide-hormone discovery by identifying a prominent new Gcg-gene-derived peptide (theoretical monoisotopic molecular weight 3263.5 Da), closely related to but distinct from GRPP, in diabetic islets. This peptide, whose sequence is HAPQDTEENARSFPASQTEPLEDPNQINE in Rattus norvegicus, could be a peptide hormone whose roles in physiology and metabolic disease warrant further investigation. This method provides a powerful new approach that could provide important new insights into the physiology and regulation of peptide hormones in islets and other endocrine tissues. It has potentially wide-ranging applications that encompass endocrinology, pharmacology, phenotypic analysis in genetic models of metabolic disease, and hormone discovery, and could also effectively limit the numbers of animals required for such studies.

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