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Molecular & Cellular Proteomics 3:770-779, 2004.
© 2004 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Microarray Transfection Analysis of Transcriptional Regulation by cAMP-dependent Protein Kinase^*

Tanya M. Redmond, Xiaomei Ren, Ginger Kubish, Stephen Atkins, Sean Low and Michael D. Uhler^,,¶,||

From the Mental Health Research Institute, Neuroscience Graduate Program, and ^¶ Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109-0669

A wide variety of bioinformatic tools have been described to characterize potential transcriptional regulatory mechanisms based on genomic sequence analysis and microarray hybridization studies. However, these regulatory mechanisms are still experimentally verified using transient transfection methods. Current transfection methods are limited both by their large scale and by the low level of efficiency for certain cell types. Our goals were to develop a microarray-based transfection method that could be optimized for different cell types and that would be useful in reporter assays of transcriptional regulation. Here we describe a novel transfection method, termed STEP (surface transfection and expression protocol), which employs microarray-based DNA transfection of adherent cells in the functional analysis of transcriptional regulation. In STEP, recombinant proteins with biological activities designed to enhance transfection are complexed with expression vector DNAs prior to spotting on microscope slides. The recombinant proteins used in STEP complexes can be varied to increase the efficiency for different cell types. We demonstrate that STEP efficiently transfects both supercoiled plasmids and PCR-generated linear expression cassettes. A co-transfection assay using effector expression vectors encoding the cAMP-dependent protein kinase (PKA), as well as reporter vectors containing PKA-regulated promoters, showed that STEP transfection allows detection and quantitation of transcriptional regulation by this protein kinase. Because bioinformatic studies often result in the identification of many putative regulatory elements and signaling pathways, this approach should be of utility in high-throughput functional genomic studies of transcriptional regulation.

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