An analytical framework for studying transcriptional regulation

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

The transcription factor Leu3 shows differential binding behavior in response to changing leucine availability

The main transcriptional regulator of leucine biosynthesis in the yeast Saccharomyces cerevisiae is the transcription factor Leu3. It has previously been reported that Leu3 always binds to its target genes, but requires activation to induce their expression. In a recent large-scale study of high-resolution transcription factor binding site identification, we showed that Leu3 has divergent binding sites in different cultivation conditions, thereby questioning the results of earlier studies. Here, we present a follow-up study using chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate the influence of leucine supplementation on Leu3 binding activity and strength. With this new data set we are able to show that Leu3 exhibits changes in binding activity in response to changing levels of leucine availability

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels.\ua0Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels

Controlling gene expression with deep generative design of regulatory DNA

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue

Saccharomyces cerevisiae displays a stable transcription start site landscape in multiple conditions

One of the fundamental processes that determine cellular fate is regulation of gene transcription. Understanding these regulatory processes is therefore essential for understanding cellular responses to changes in environmental conditions. At the core promoter, the regulatory region containing the transcription start site (TSS), all inputs regulating transcription are integrated. Here, we used Cap Analysis of Gene Expression (CAGE) to analyze the pattern of TSSs at four different environmental conditions (limited in ethanol, limited in nitrogen, limited in glucose and limited in glucose under anaerobic conditions) using the Saccharomyces cerevisiae strain CEN.PK113-7D. With this experimental setup, we were able to show that the TSS landscape in yeast is stable at different metabolic states of the cell. We also show that the spatial distribution of transcription initiation events, described by the shape index, has a surprisingly strong negative correlation with measured gene expression levels, meaning that genes with higher expression levels tend to have a broader distribution of TSSs. Our analysis supplies a set of high-quality TSS annotations useful for metabolic engineering and synthetic biology approaches in the industrially relevant laboratory strain CEN.PK113-7D, and provides novel insights into yeast TSS dynamics and gene regulation