Model Systems: Yeast Peroxisome Biogenesis

We are working on identifying and investigating transcriptional regulators through a novel clustering methodology we developed that uses network topology (based the cumulative distribution function) to characterize combinatorial control of multiple biological responses. We applied this methodology to the transcriptional network governing the induction of peroxisome biogenesis to reveal the coordination of different transcriptional responses by parallel combinatorial control.

Rather than focusing on the characterization of individual targets in network clusters, we identified significantly overrepresented gene properties in each network topology cluster. This method of analysis not only provides a measure of confidence in the results, it also broadens the analysis to global trends to reveal insight into systems level regulatory network function. In this manner, complementary high-throughput techniques and principled data integration strategies were used to characterise a dynamic transcriptional regulatory network involving the participation of a regulator in multi-input network motifs to control and synchronize the general stress response and oleic acid metabolism.

We have also used proteomics analyses of the transcriptional complexes and chromatin modifiers bound to promoter elements responsible for fatty acid induced peroxisome biogenesis, single cell imaging and reporter assays to screen a genomic library containing single deletions of each non-essential gene in yeast to identify factors required for robust peroxisome induction.

These experiments have revealed novel signaling components, which are being used to develop a network-based model for the activation of peroxisome biogenesis.