BackgroundLife and death decisions of metazoan cells hinge on the balance between the expression of pro- versus anti-apoptotic gene products. The general RNA polymerase II transcription factor, TFIID, plays a central role in the regulation of gene expression through its core promoter recognition and co-activator functions. The core TFIID subunit TAF6 acts in vitro as an essential co-activator of transcription for the p53 tumor suppressor protein. We previously identified a splice variant of TAF6, termed TAF6δ that can be induced during apoptosis.Methodology/Principal FindingsTo elucidate the impact of TAF6δ on cell death and gene expression, we have employed modified antisense oligonucleotides to enforce expression of endogenous TAF6δ. The induction of endogenous TAF6δ triggered apoptosis in tumor cell lines, including cells devoid of p53. Microarray experiments revealed that TAF6δ activates gene expression independently of cellular p53 status.ConclusionsOur data define TAF6δ as a pivotal node in a signaling pathway that controls gene expression programs and apoptosis in the absence of p53.

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Novel microarray technologies such as the AB1700 platform from Applied Biosystems promise significant increases in the signal dynamic range and a higher sensitivity for weakly expressed transcripts. We have compared a representative set of AB1700 data with a similarly representative Affymetrix HG-U133A dataset. The AB1700 design extends the signal dynamic detection range at the lower bound by one order of magnitude. The lognormal signal distribution profiles of these high-sensitivity data need to be represented by two independent distributions. The additional second distribution covers those transcripts that would have gone undetected using the Affymetrix technology. The signal-dependent variance distribution in the AB1700 data is a non-trivial function of signal intensity, describable using a composite function. The drastically different structure of these high-sensitivity transcriptome profiles requires adaptation or even redevelopment of the standard microarray analysis methods. Based on the statistical properties, we have derived a signal variance distribution model for AB1700 data that is necessary for such development. Interestingly, the dual lognormal distribution observed in the AB1700 data reflects two fundamentally different biologic mechanisms of transcription initiation.