M Maziarz | PubMed Health

PubMed2004

Characterization and functional role of voltage gated cation conductances in the glucagon-like peptide-1 secreting GLUTag cell line.

Glucagon-like peptide-1 (GLP-1) is released from intestinal L-cells in response to nutrient ingestion. It is currently under therapeutic evaluation because it enhances insulin secretion in type 2 diabetes. Previous studies using the GLP-1 secreting cell line GLUTag have shown that the cells are electrically active, and that the action potential frequency is regulated by nutrients. In this study we characterize voltage gated currents underlying this electrical activity and correlate the electrophysiological findings with gene expression determined by microarrays. Whole cell voltage clamp experiments designed to separate different ionic components revealed rapidly inactivating sodium currents sensitive to tetrodotoxin, calcium currents sensitive to nifedipine and omega-conotoxin GVIA, and sustained as well as rapidly inactivating potassium currents, which were sensitive to TEA and 4-AP, respectively. In perforated patch experiments we also observed hyperpolarization-activated currents which were inhibited by ZD7288. The amplitude of the sodium current was approximately 10 times that of the other depolarizing currents and tetrodotoxin abolished action potential firing. In secretion experiments, however, nifedipine, but not tetrodotoxin, omega-conotoxin GVIA or ZD7288, inhibited glucose-induced GLP-1 release. Consistent with this finding, the intracellular Ca2+ response to glucose was impaired by nifedipine but not by tetrodotoxin. Thus, in GLUTag cells, GLP-1 release is not dependent on the firing of Na+-carrying action potentials but requires membrane depolarization and Ca2+ entry through L-type Ca2+ channels. Understanding the characteristics of the currents and the molecular identification of the underlying channels in GLP-1 secreting cells might facilitate the development of agents to enhance GLP-1 secretion in vivo.

Animals↗

PubMed2002

Binary tree-structured vector quantization approach to clustering and visualizing microarray data.

MOTIVATION: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. RESULTS: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach.

Algorithms↗