A holistic view on transcriptional regulatory networks in S. cerevisiae: Implications and utilization
Life; perhaps it is bold to start an abstract with this powerful word, but this is where I will start. My research is at the heart of life. How can a single human cell proliferate to become bones, eyes, fingers and, finally, a human being? How can different cells containing the same set of DNA be so versatile? The answer lies within the regulation of genes. To build upon our understanding of gene regulation, I have studied gene transcription and especially transcription factors in a holistic, systems biology way using the model organism Saccharomyces cerevisiae. Translation from S. cerevisiae to humans will help us get both a fundamental understanding of the networks and engineer better cell factories.
Transcription factors play an essential role in transcription as they function to activate and suppress genes in response to stimuli. The transcription factors form transcriptional regulatory networks (TRNs), with intricate cross-talk and overlapping functions balancing the ability of the cells to react to stimuli but at the same time remain as steady as possible. This is a fine-tuned machinery that has a built-in safety feature of self-regulation if the system is perturbed in any way. We study the TRNs with state-of-the-art methods for transcription factor-DNA interaction: Chromatin Immunoprecipitation with exonuclease treatment or ChIP-exo for short. This method provides us with all the DNA interactions of a selected transcription factor at the nucleotide level and to what degree these interactions occurs.
To study these transcriptional regulatory networks, we put the yeast cells under nutrient starvation in fermentation systems. The fermentation system used is the chemostat, which enables a tight control on the environmental parameters, ensures a steady-state in the culture, and allows for high reproducibility. Ensuring that the cell culture is identical in-between runs is important since we can’t study all transcription factors at the same time.
In this thesis, I present studies on transcription factors both individually, or as part of a bigger whole. We investigate stress response, NADPH generation, control over lipid and amino acid metabolism and the glycolytic pathway. Thanks to the different metabolic conditions used to study the transcription factors, we can both determine a core set of genes and genes that are specific for different conditions. We also employ statistical methods and regression models to understand and predict regulatory pathways. While doing so we discover novel functions and modularity and expand the transcriptional regulatory network for all studied transcription factors. We also constructed a multi-paralleled miniaturized chemostat-system to study these transcription factors in a high-throughput fashion. Finally, we have developed a toolbox for analysis of transcription factor data, including visual representation of the DNA binding, comparison of gene transcription and transcription binding between conditions and statistical methods for identifying regulatory pathways that can be used both for a fundamental understanding of TRNs and for better cell factory engineering.