An analytical framework for studying transcriptional regulation
Doctoral thesis, 2020

The state and behavior of any living cell is controlled by a complex interplay of different regulatory processes, with the regulation of transcription playing a major role. When a cell adapts to a new environment it often does that by modulating gene transcript levels, mainly through changes in transcription factor binding events. Therefore, understanding the transcriptional regulation is vital for many biological research fields ranging from understanding cancer metabolism to metabolic engineering.

In this thesis, I present and apply an analytical framework for studying transcriptional regulation in a well-characterized eukaryotic model organism, the yeast S. cerevisiae. The framework is a combination of advanced sequencing methods like Chromatin Immunoprecipitation followed by DNA sequencing (ChIP-seq / ChIP-exo) and Cap Analysis of Gene Expression (CAGE) with bioinformatic approaches.

The relative binding location of transcription factors in relation to the transcription start site is important for interpretation, therefore the transcription start sites of all genes active in multiple controlled growth environments were determined using CAGE. To use and analyze the gathered data in a reliable and efficient way a high-quality bioinformatics pipeline was established.

After establishing the required analytical framework, I employed it in various projects, all aimed to gain a better understanding of yeast transcriptional regulation. In a detailed study of a single transcription factor, I investigated Leu3, the main regulator of leucine biosynthesis. Here, I was able to show that its binding behavior is affected by the availability of leucine in the media, an adaptive behavior that has not been reported before.

Metabolic engineering will be increasingly important to support the needs of our society and in order to help with this, I developed a tool for fine tuning conditional gene expression levels using hybrid promoters. This tool is based on a machine learning approach and can be used to improve productivity in large scale fermentations.

In conclusion, this thesis lays the foundation for future large-scale studies of transcriptional regulation in S. cerevisiae and can also serve as a blueprint on how to study it in different organisms.

transcription factor

ChIP-exo

S. cerevisiae

transcriptional regulation

Opponent: Associate Professor Christopher Workman, Technical University Denmark

Author

Christoph Sebastian Börlin

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

A bioinformatic pipeline to analyze ChIP-exo datasets

Biology Methods and Protocols,; Vol. 4(2019)

Journal article

Börlin CS, Bergenholm D, Kerkhoven EJ, Siewers V & Nielsen J. Analyzing and predicting conditional gene expression changes using transcription factor binding data.

Bergenholm D, Börlin CS, Holland P & Nielsen J. T-rEx: A Saccharomyces cerevisiae transcription factor explorer.

A living cell is exposed to a constantly changing environment and therefore has to continuously adapt. It also has to periodically change between growth and cell division to survive and proliferate in nature. How can such a dynamic response be achieved and regulated? The main determinant for the cellular responses is the concentration of different proteins inside the cell, which are the little machines that perform most tasks. One key mechanism that influences the protein levels is a regulatory process called transcriptional regulation.

Because transcriptional regulation plays such a central role in determining the fate and state of living cells, understanding it is vital for many biological research fields ranging from understanding cancer metabolism to metabolic engineering.

In this thesis, I present the development of a framework to study transcriptional regulation in the yeast Saccharomyces cerevisiae, also known as Baker’s yeast. The framework is based on gathering data on the exact start sites of transcription in different environmental conditions using state-of-the-art sequencing technologies combined with a custom build bioinformatics analytical pipeline.

Additionally, I also highlight how one can use this framework to study the complex mechanisms involved in transcriptional regulation using machine learning. The results from this can then be used to develop online tools for metabolic engineering applications, a field of research that is vital for the transition towards a bio-based economy and a reduced dependency on non-renewable resources.

In conclusion, this thesis lays the foundation for future large-scale studies of transcriptional regulation in S. cerevisiae based on transcription factor binding data and can also serve as a blueprint on how to study this regulatory process in different organisms.

Subject Categories

Biological Sciences

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

ISBN

978-91-7905-332-1

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4799

Publisher

Chalmers University of Technology

Online

Opponent: Associate Professor Christopher Workman, Technical University Denmark

More information

Latest update

8/26/2020