In budding yeast, ubiquitination of the cyclin-dependent kinase (Cdk) inhibitor Sic1 is catalyzed by the E2 ubiquitin conjugating enzyme Cdc34 in conjunction with an E3 ubiquitin ligase complex composed of Skp1, Cdc53 and the F-box protein, Cdc4 (the SCF Cdc4 complex). Skp1 binds a motif called the F-box and in turn F-box proteins appear to recruit specific substrates for ubiquitination. We find that Skp1 interacts with Cdc53 in vivo, and that Skp1 bridges Cdc53 to three different F-box proteins, Cdc4, Met30, and Grr1. Cdc53 contains independent binding sites for Cdc34 and Skp1 suggesting it functions as a scaffold protein within an E2/E3 core complex. F-box proteins show remarkable functional specificity in vivo: Cdc4 is specific for degradation of Sic1, Grr1 is specific for degradation of the G 1 cyclin Cln2, and Met30 is specific for repression of methionine biosynthesis genes. In contrast, the Cdc34-Cdc53-Skp1 E2/E3 core complex is required for all three functions. Combinatorial control of SCF complexes may provide a basis for the regulation of diverse cellular processes.

Rst1 and Rst2 repress the mating and filamentous growth responses of S. cerevisiae by directly inhibiting Ste12. Activation of Fus3 or Kss1 may cause phosphorylation-dependent release of Ste12 from Rst1/Rst2 and thereby activate Ste12-dependent transcription.

We describe the details of a serial analysis of gene expression (SAGE) library construction and analysis platform that has enabled the generation of >298 high-quality SAGE libraries and >30 million SAGE tags primarily from sub-microgram amounts of total RNA purified from samples acquired by microdissection. Several RNA isolation methods were used to handle the diversity of samples processed, and various measures were applied to minimize ditag PCR carryover contamination. Modifications in the SAGE protocol resulted in improved cloning and DNA sequencing efficiencies. Bioinformatic measures to automatically assess DNA sequencing results were implemented to analyze the integrity of ditag structure, linker or cross-species ditag contamination, and yield of high-quality tags per sequence read. Our analysis of singleton tag errors resulted in a method for correcting such errors to statistically determine tag accuracy. From the libraries generated, we produced an essentially complete mapping of reliable 21-base-pair tags to the mouse reference genome sequence for a meta-library of ∼5 million tags. Our analyses led us to reject the commonly held notion that duplicate ditags are artifacts. Rather than the usual practice of discarding such tags, we conclude that they should be retained to avoid introducing bias into the results and thereby maintain the quantitative nature of the data, which is a major theoretical advantage of SAGE as a tool for global transcriptional profiling.[Supplemental material is available online at www.genome.org.]Serial analysis of gene expression (SAGE) offers a particularly attractive technology for profiling eukaryotic transcriptomes (Velculescu et al. 1995) because of the digital and quantitative nature of the data, its efficient sampling of short sequence tags from known and novel mRNA transcripts, and its theoretically unlimited dynamic range. Numerous improvements to the original technology have been described (Peters et al. 1999;Saha et al. 2002;Gowda et al. 2004;Heidenblut et al. 2004;Wei et al. 2004;Kodzius et al. 2006). These include the production of longer tags, which have improved the specificity of tag-to-gene mapping (Saha et al. 2002;Matsumura et al. 2003), and modifications designed to facilitate library construction from nanogram quantities of total RNA (Peters et al. 1999;Neilson et al. 2000). Recently, the use of SAGE-like procedures to identify regions of the genome interacting with DNA-binding proteins has been described (Impey et al. 2004;Chen and Sadowski 2005;Kim et al. 2005;Loh et al. 2006;Wei et al. 2006). Such approaches represent viable alternatives to ChIP-on-chip (Ren et al. 2000).SAGE is among the few relatively accessible digital gene expression profiling technologies capable of generating comprehensive transcriptome profiles. Nevertheless, challenges associated with laborious library construction and generally limited access to inexpensive automated DNA sequencing have restricted its application to large-scale initiatives. Experiments at our Genome Center ) and e...