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.\ua0\ua0 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

New Initiatives of Japanese Universities in International Cooperation : Focused on Basic Education Support in Developing Countries

In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103, encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae

Expression of cocoa genes in Saccharomyces cerevisiae improves cocoa butter production

Background: Cocoa butter (CB) extracted from cocoa beans (Theobroma cacao) is the main raw material for chocolate production, but CB supply is insufficient due to the increased chocolate demand and limited CB production. CB is mainly composed of three different kinds of triacylglycerols (TAGs), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP, C16:0-C18:1-C16:0), 1-palmitoyl-3-stearoyl-2-oleoyl-glycerol (POS, C16:0-C18:1-C18:0) and 1,3-distearoyl-2-oleoyl-glycerol (SOS, C18:0-C18:1-C18:0). In general, Saccharomyces cerevisiae produces TAGs as storage lipids, which consist of C16 and C18 fatty acids. However, cocoa butter-like lipids (CBL, which are composed of POP, POS and SOS) are not among the major TAG forms in yeast. TAG biosynthesis is mainly catalyzed by three enzymes: glycerol-3-phosphate acyltransferase (GPAT), lysophospholipid acyltransferase (LPAT) and diacylglycerol acyltransferase (DGAT), and it is essential to modulate the yeast TAG biosynthetic pathway for higher CBL production. Results: We cloned seven GPAT genes and three LPAT genes from cocoa cDNA, in order to screen for CBL biosynthetic gene candidates. By expressing these cloned cocoa genes and two synthesized cocoa DGAT genes in S. cerevisiae, we successfully increased total fatty acid production, TAG production and CBL production in some of the strains. In the best producer, the potential CBL content was eightfold higher than the control strain, suggesting the cocoa genes expressed in this strain were functional and might be responsible for CBL biosynthesis. Moreover, the potential CBL content increased 134-fold over the control Y29-TcD1 (IMX581 sct1 ale1 lro1 dga1 with TcDGAT1 expression) in strain Y29-441 (IMX581 sct1 ale1 lro1 dga1 with TcGPAT4, TcLPAT4 and TcDGAT1 expression) further suggesting cocoa GPAT and LPAT genes functioned in yeast. Conclusions: We demonstrated that cocoa TAG biosynthetic genes functioned in S. cerevisiae and identified cocoa genes that may be involved in CBL production. Moreover, we found that expression of some cocoa CBL biosynthetic genes improved potential CBL production in S. cerevisiae, showing that metabolic engineering of yeast for cocoa butter production can be realized by manipulating the key enzymes GPAT, LPAT and DGAT in the TAG biosynthetic pathway

Rational gRNA design based on transcription factor binding data

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system has become a standard tool in many genome engineering endeavors. The endonuclease-deficient version of Cas9 (dCas9) is also a powerful programmable tool for gene regulation. In this study, we made use of Saccharomyces cerevisiae transcription factor (TF) binding data to obtain a better understanding of the interplay between TF binding and binding of dCas9 fused to an activator domain, VPR. More specifically, we targeted dCas9-VPR toward binding sites of Gcr1-Gcr2 and Tye7 present in several promoters of genes encoding enzymes engaged in the central carbon metabolism. From our data, we observed an upregulation of gene expression when dCas9-VPR was targeted next to a TF binding motif, whereas a downregulation or no change was observed when dCas9 was bound on a TF motif. This suggests a steric competition between dCas9 and the specific TF. Integrating TF binding data, therefore, proved to be useful for designing guide RNAs for CRISPR interference or CRISPR activation applications

Increasing cocoa butter-like lipid production of Saccharomyces cerevisiae by expression of selected cocoa genes

Cocoa butter (CB) extracted from cocoa beans mainly consists of three different kinds of triacylglycerols (TAGs), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP, C16:0–C18:1–C16:0), 1-palmitoyl-3-stearoyl-2-oleoyl-glycerol (POS, C16:0–C18:1–C18:0) and 1,3-distearoyl-2-oleoyl-glycerol (SOS, C18:0–C18:1–C18:0), but CB supply is limited. Therefore, CB-like lipids (CBL, which are composed of POP, POS and SOS) are in great demand. Saccharomyces cerevisiae produces TAGs as storage lipids, which are also mainly composed of C16 and C18 fatty acids. However, POP, POS and SOS are not among the major TAG forms in yeast. TAG synthesis is mainly catalyzed by three enzymes: glycerol-3-phosphate acyltransferase (GPAT), lysophospholipid acyltransferase (LPAT) and diacylglycerol acyltransferase (DGAT). In order to produce CBL in S. cerevisiae, we selected six cocoa genes encoding GPAT, LPAT and DGAT potentially responsible for CB biosynthesis from the cocoa genome using a phylogenetic analysis approach. By expressing the selected cocoa genes in S. cerevisiae, we successfully increased total fatty acid production, TAG production and CBL production in some S. cerevisiae strains. The relative CBL content in three yeast strains harboring cocoa genes increased 190, 230 and 196% over the control strain, respectively; especially, the potential SOS content of the three yeast strains increased 254, 476 and 354% over the control strain. Moreover, one of the three yeast strains had a 2.25-fold increased TAG content and 6.7-fold higher level of CBL compared with the control strain. In summary, CBL production by S. cerevisiae were increased through expressing selected cocoa genes potentially involved in CB biosynthesis. © 2017, The Author(s)

Parameter identifiability of fundamental pharmacodynamic models

Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably “good” standard errors may sometimes mask unidentifiability issues

Predictive models of eukaryotic transcriptional regulation reveals changes in transcription factor roles and promoter usage between metabolic conditions

Transcription factors (TF) are central to transcriptional regulation, but they are often studied in relative isolation and without close control of the metabolic state of the cell. Here, we describe genome-wide binding (by ChIP-exo) of 15 yeast TFs in four chemostat conditions that cover a range of metabolic states. We integrate this data with transcriptomics and six additional recently mapped TFs to identify predictive models describing how TFs control gene expression in different metabolic conditions. Contributions by TFs to gene regulation are predicted to be mostly activating, additive and well approximated by assuming linear effects from TF binding signal. Notably, using TF binding peaks from peak finding algorithms gave distinctly worse predictions than simply summing the low-noise and high-resolution TF ChIP-exo reads on promoters. Finally, we discover indications of a novel functional role for three TFs; Gcn4, Ert1 and Sut1 during nitrogen limited aerobic fermentation. In only this condition, the three TFs have correlated binding to a large number of genes (enriched for glycolytic and translation processes) and a negative correlation to target gene transcript levels

Example data set for the ChIPexo Pipeline

Example data set for the ChIP-exo Analytical Pipeline available through GitHub https://github.com/SysBioChalmers/ChIPexo_Pipelin