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Bernd Jagla

Publications and source records attributed to Bernd Jagla.

2 recordsLinked to original sources

Cell-based assays using primary endothelial cells to study multiple steps in inflammation.

Cell-based assays are powerful tools for drug discovery and provide insight into complex signal transduction pathways in higher eukaryotic cells. Information gleaned from assays that monitor a cellular phenotype can be used to elucidate the details of a single pathway and to establish patterns of cross talk between pathways. By selecting the appropriate cell model, cell-based assays can be used to understand the function of a specific cell type in a complex disease process such as inflammation. We have used human umbilical vein endothelial cells to establish three cell-based, phenotypic assays that query different stages of a major signaling pathway activated in inflammation. One assay analyzes the tumor necrosis factor alpha (TNFalpha)-induced translocation of the transcription factor NF-kappaB from the cytoplasm into the nucleus 20 min after stimulation with TNFalpha. Two more assays monitor the expression of E-selectin and VCAM-1, 4 and 24 h after stimulation with TNFalpha. Indirect immunofluorescence and high-throughput automated microscopy were used to analyze cells. Imaging was performed with the IN Cell Analyzer 3000. All assays proved to be highly robust. Z' values between 0.7 and 0.8 make each of the three assays well suited for use in high-throughput screening for drug or probe discovery.

Animals↗

Sequence characteristics of functional siRNAs.

RNA interference in mammalian cells is actively used to conduct genetic screens, to identify and to validate targets, and to elucidate regulators and modifiers of cellular pathways. To ensure the specificity and efficacy of the active 21mer siRNA molecules, it is pertinent to develop a strategy for their rational design. Here we show that most functional siRNAs have characteristic sequence features. We tested 601 siRNAs targeting one exogenous and three endogenous genes. The efficacy of the siRNAs was determined at the protein level. Using a decision tree algorithm in combination with information analysis, our analyses revealed four sets of rules with a mean knockdown efficacy ranging from 60% to 73%. (To distinguish between percentages used to describe the quality of an siRNA and the percentages used to describe parts of data sets we underlined the former throughout this paper.) The best rule comprises an A/U at positions 10 and 19, a G/C at position 1, and more than three A/Us between positions 13 and 19, in the sense strand of the siRNA sequence. Using these rules, there is a 99.9% chance of designing an effective siRNA in a set of three with more than 50% knockdown efficiency in a biological readout.

Base Sequence↗