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Cells are built from a variety of macromolecules and metabolites. Both, the proteome and the metabolome are highly dynamic and responsive to environmental cues and developmental processes. But it is not their bare numbers, but their interactions that enable life. The protein-protein (PPI) and protein-metabolite interactions (PMI) facilitate and regulate all aspects of cell biology, from metabolism to mitosis. Therefore, the study of PPIs and PMIs and their dynamics in a cell-wide context is of great scientific interest. In this dissertation, I aim to chart a map of the dynamic PPIs and PMIs across metabolic and cellular transitions. As a model system, I study the shift from the fermentative to the respiratory growth, known as the diauxic shift, in the budding yeast Saccharomyces cerevisiae. To do so, I am applying a co-fractionation mass spectrometry (CF-MS) based method, dubbed protein metabolite interactions using size separation (PROMIS). PROMIS, as well as comparable methods, will be discussed in detail in chapter 1.
Since PROMIS was developed originally for Arabidopsis thaliana, in chapter 2, I will describe the adaptation of PROMIS to S. cerevisiae. Here, the obtained results demonstrated a wealth of protein-metabolite interactions, and experimentally validated 225 previously predicted PMIs. Applying orthogonal, targeted approaches to validate the interactions of a proteogenic dipeptide, Ser-Leu, five novel protein-interactors were found. One of those proteins, phosphoglycerate kinase, is inhibited by Ser-Leu, placing the dipeptide at the regulation of glycolysis.
In chapter 3, I am presenting PROMISed, a novel web-tool designed for the analysis of PROMIS- and other CF-MS-datasets. Starting with raw fractionation profiles, PROMISed enables data pre-processing, profile deconvolution, scores differences in fractionation profiles between experimental conditions, and ultimately charts interaction networks. PROMISed comes with a user-friendly graphic interface, and thus enables the routine analysis of CF-MS data by non-computational biologists.
Finally, in chapter 4, I applied PROMIS in combination with the isothermal shift assay to the diauxic shift in S. cerevisiae to study changes in the PPI and PMI landscape across this metabolic transition. I found a major rewiring of protein-protein-metabolite complexes, exemplified by the disassembly of the proteasome in the respiratory phase, the loss of interaction of an enzyme involved in amino acid biosynthesis and its cofactor, as well as phase and structure specific interactions between dipeptides and enzymes of central carbon metabolism.
In chapter 5, I am summarizing the presented results, and discuss a strategy to unravel the potential patterns of dipeptide accumulation and binding specificities. Lastly, I recapitulate recently postulated guidelines for CF-MS experiments, and give an outlook of protein interaction studies in the near future.
Synthetische Transkriptionsfaktoren bestehen wie natürliche Transkriptionsfaktoren aus einer DNA-Bindedomäne, die sich spezifisch an die Bindestellensequenz vor dem Ziel-Gen anlagert, und einer Aktivierungsdomäne, die die Transkriptionsmaschinerie rekrutiert, sodass das Zielgen exprimiert wird. Der Unterschied zu den natürlichen Transkriptionsfaktoren ist, sowohl dass die DNA-Bindedomäne als auch die Aktivierungsdomäne wirtsfremd sein können und dadurch künstliche Stoffwechselwege im Wirt, größtenteils chemisch, induziert werden können. Optogenetische synthetische Transkriptionsfaktoren, die hier entwickelt wurden, gehen einen Schritt weiter. Dabei ist die DNA-Bindedomäne nicht mehr an die Aktivierungsdomäne, sondern mit dem Blaulicht-Photorezeptor CRY2 gekoppelt. Die Aktivierungsdomäne wurde mit dem Interaktionspartner CIB1 fusioniert. Unter Blaulichtbestrahlung dimerisieren CRY2 und CIB1 und damit einhergehend die beiden Domänen, sodass ein funktionsfähiger Transkriptionsfaktor entsteht. Dieses System wurde in die Saccharomyces cerevisiae genomisch integriert. Verifiziert wurde das konstruierte System mit Hilfe des Reporters yEGFP, welcher durchflusszytometrisch detektiert werden konnte. Es konnte gezeigt werden, dass die yEGFP Expression variabel gestaltet werden kann, indem unterschiedlich lange Blaulichtimpulse ausgesendet wurden, die DNA-Bindedomäne, die Aktivierungsdomäne oder die Anzahl der Bindestellen, an dem sich die DNA-Bindedomäne anlagert, verändert wurden. Um das System für industrielle Anwendungen attraktiv zu gestalten, wurde das System vom Deepwell-Maßstab auf Photobioreaktor-Maßstab hochskaliert. Außerdem erwies sich das Blaulichtsystem sowohl im Laborstamm YPH500 als auch im industriell oft verwendeten Hefestamm CEN.PK als funktional. Des Weiteren konnte ein industrierelevante Protein ebenso mit Hilfe des verifizierten Systems exprimiert werden. Schlussendlich konnte in dieser Arbeit das etablierte Blaulicht-System erfolgreich mit einem Rotlichtsystem kombiniert werden, was zuvor noch nicht beschrieben wurde.
Plant-derived Transcription Factors for Orthologous Regulation of Gene Expression in the Yeast Saccharomyces cerevisiae
Control of gene expression by transcription factors (TFs) is central in many synthetic biology projects where tailored expression of one or multiple genes is often needed. As TFs from evolutionary distant organisms are unlikely to affect gene expression in a host of choice, they represent excellent candidates for establishing orthogonal control systems. To establish orthogonal regulators for use in yeast (Saccharomyces cerevisiae), we chose TFs from the plant Arabidopsis thaliana. We established a library of 106 different combinations of chromosomally integrated TFs, activation domains (yeast GAL4 AD, herpes simplex virus VP64, and plant EDLL) and synthetic promoters harbouring cognate cis-regulatory motifs driving a yEGFP reporter. Transcriptional output of the different driver / reporter combinations varied over a wide spectrum, with EDLL being a considerably stronger transcription activation domain in yeast, than the GAL4 activation domain, in particular when fused to Arabidopsis NAC TFs. Notably, the strength of several NAC - EDLL fusions exceeded that of the strong yeast TDH3 promoter by 6- to 10-fold. We furthermore show that plant TFs can be used to build regulatory systems encoded by centromeric or episomal plasmids. Our library of TF – DNA-binding site combinations offers an excellent tool for diverse synthetic biology applications in yeast.
COMPASS: Rapid combinatorial optimization of biochemical pathways based on artificial transcription factors
We established a high-throughput cloning method, called COMPASS for COMbinatorial Pathway ASSembly, for the balanced expression of multiple genes in Saccharomyces cerevisiae. COMPASS employs orthogonal, plant-derived artificial transcription factors (ATFs) for controlling the expression of pathway genes, and homologous recombination-based cloning for the generation of thousands of individual DNA constructs in parallel. The method relies on a positive selection of correctly assembled pathway variants from both, in vivo and in vitro cloning procedures. To decrease the turnaround time in genomic engineering, we equipped COMPASS with multi-locus CRISPR/Cas9-mediated modification capacity. In its current realization, COMPASS allows combinatorial optimization of up to ten pathway genes, each transcriptionally controlled by nine different ATFs spanning a 10-fold difference in expression strength. The application of COMPASS was demonstrated by generating cell libraries producing beta-carotene and co-producing beta-ionone and biosensor-responsive naringenin. COMPASS will have many applications in other synthetic biology projects that require gene expression balancing.
CaPRedit: Genome editing using CRISPR-Cas9 and plant-derived transcriptional regulators for the redirection of flux through the FPP branch-point in yeast. Technologies developed over the past decade have made Saccharomyces cerevisiae a promising platform for production of different natural products. We developed CRISPR/Ca9- and plant derived regulator-mediated genome editing approach (CaPRedit) to greatly accelerate strain modification and to facilitate very low to very high expression of key enzymes using inducible regulators. CaPRedit can be implemented to enhance the production of yeast endogenous or heterologous metabolites in the yeast S. cerevisiae. The CaPRedit system aims to faciltiate modification of multiple targets within a complex metabolic pathway through providing new tools for increased expression of genes encoding rate-limiting enzymes, decreased expression of essential genes, and removed expression of competing pathways. This approach is based on CRISPR/Cas9-mediated one-step double-strand breaks to integrate modules containing IPTG-inducible plant-derived artificial transcription factor and promoter pair(s) in a desired locus or loci. Here, we used CaPRedit to redirect the yeast endogenous metabolic flux toward production of farnesyl diphosphate (FPP), a central precursor of nearly all yeast isoprenoid products, by overexpression of the enzymes lead to produce FPP from glutamate. We found significantly higher beta-carotene accumulation in the CaPRedit-mediated modified strain than in the wild type (WT) strain. More specifically, CaPRedit_FPP 1.0 strain was generated, in which three genes involved in FPP synthesis, tHMG1, ERG20, and GDH2, were inducibly overexpressed under the control of strong plant-derived ATFPs. The beta–carotene accumulated in CaPRedit_FPP 1.0 strain to a level 1.3-fold higher than the previously reported optimized strain that carries the same overexpressed genes (as well as additional genetic modifications to redirect yeast endogenous metabolism toward FPP production). Furthermore, the genetic modifications implemented in CaPRedit_FPP 1.0 strain resulted in only a very small growth defect (growth rate relative to the WT is ~ -0.03).
The past decades are characterized by various efforts to provide complete sequence information of genomes regarding various organisms. The availability of full genome data triggered the development of multiplex high-throughput assays allowing simultaneous measurement of transcripts, proteins and metabolites. With genome information and profiling technologies now in hand a highly parallel experimental biology is offering opportunities to explore and discover novel principles governing biological systems. Understanding biological complexity through modelling cellular systems represents the driving force which today allows shifting from a component-centric focus to integrative and systems level investigations. The emerging field of systems biology integrates discovery and hypothesis-driven science to provide comprehensive knowledge via computational models of biological systems. Within the context of evolving systems biology, investigations were made in large-scale computational analyses on transcript co-response data through selected prokaryotic and plant model organisms. CSB.DB - a comprehensive systems-biology database - (http://csbdb.mpimp-golm.mpg.de/) was initiated to provide public and open access to the results of biostatistical analyses in conjunction with additional biological knowledge. The database tool CSB.DB enables potential users to infer hypothesis about functional interrelation of genes of interest and may serve as future basis for more sophisticated means of elucidating gene function. The co-response concept and the CSB.DB database tool were successfully applied to predict operons in Escherichia coli by using the chromosomal distance and transcriptional co-responses. Moreover, examples were shown which indicate that transcriptional co-response analysis allows identification of differential promoter activities under different experimental conditions. The co-response concept was successfully transferred to complex organisms with the focus on the eukaryotic plant model organism Arabidopsis thaliana. The investigations made enabled the discovery of novel genes regarding particular physiological processes and beyond, allowed annotation of gene functions which cannot be accessed by sequence homology. GMD - the Golm Metabolome Database - was initiated and implemented in CSB.DB to integrated metabolite information and metabolite profiles. This novel module will allow addressing complex biological questions towards transcriptional interrelation and extent the recent systems level quest towards phenotyping.