The biobased-economy aims to create a circular biotechnology ecosystem to transition from a fossil fuel-based to a sustainable industry based on biomass. For this, new microbial cell-factories are essential. We present the draft genome of the CEN.PK-derived Saccharomyces cerevisiae SpyCas9 expressing strain (IMX2600), that serve as chassis of new cell-factories.

The yeast Saccharomyces cerevisiae is a widely-used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimizing biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates, and standardized sample preparation procedures ensured reproducibility and accuracy. In addition, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimization of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared with the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step toward better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

Mitochondria fulfil many essential roles and have their own genome, which is expressed as polycistronic transcripts that undergo co- or posttranscriptional processing and splicing. Due to the inherent complexity and limited technical accessibility of the mitochondrial transcriptome, fundamental questions regarding mitochondrial gene expression and splicing remain unresolved, even in the model eukaryote Saccharomyces cerevisiae. Long-read sequencing could address these fundamental questions. Therefore, a method for the enrichment of mitochondrial RNA and sequencing using Nanopore technology was developed, enabling the resolution of splicing of polycistronic genes and the quantification of spliced RNA. This method successfully captured the full mitochondrial transcriptome and resolved RNA splicing patterns with single-base resolution and was applied to explore the transcriptome of S. cerevisiae grown with glucose or ethanol as the sole carbon source, revealing the impact of growth conditions on mitochondrial RNA expression and splicing. This study uncovered a remarkable difference in the turnover of Group II introns between yeast grown in either mostly fermentative or fully respiratory conditions. Whether this accumulation of introns in glucose medium has an impact on mitochondrial functions remains to be explored. Combined with the high tractability of the model yeast S. cerevisiae, the developed method enables to monitor mitochondrial transcriptome responses in a broad range of relevant contexts, including oxidative stress, apoptosis and mitochondrial diseases.

The construction of powerful cell factories requires intensive genetic engineering for the addition of new functionalities and the remodeling of native pathways and processes. The present study demonstrates the feasibility of extensive genome reprogramming using modular, specialized de novo-assembled neochromosomes in yeast. The in vivo assembly of linear and circular neochromosomes, carrying 20 native and 21 heterologous genes, enabled the first de novo production in a microbial cell factory of anthocyanins, plant compounds with a broad range of pharmacological properties. Turned into exclusive expression platforms for heterologous and essential metabolic routes, the neochromosomes mimic native chromosomes regarding mitotic and genetic stability, copy number, harmlessness for the host and editability by CRISPR/Cas9. This study paves the way for future microbial cell factories with modular genomes in which core metabolic networks, localized on satellite, specialized neochromosomes can be swapped for alternative configurations and serve as landing pads for the addition of functionalities.

Although transplantation of single genes in yeast plays a key role in elucidating gene functionality in metazoans, technical challenges hamper humanization of full pathways and processes. Empowered by advances in synthetic biology, this study demonstrates the feasibility and implementation of full humanization of glycolysis in yeast. Single gene and full pathway transplantation revealed the remarkable conservation of glycolytic and moonlighting functions and, combined with evolutionary strategies, brought to light context-dependent responses. Human hexokinase 1 and 2, but not 4, required mutations in their catalytic or allosteric sites for functionality in yeast, whereas hexokinase 3 was unable to complement its yeast ortholog. Comparison with human tissues cultures showed preservation of turnover numbers of human glycolytic enzymes in yeast and human cell cultures. This demonstration of transplantation of an entire essential pathway paves the way for establishment of species-, tissue-, and disease-specific metazoan models.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.

Synthetic Genomics focuses on the construction of rationally designed chromosomes and genomes and offers novel approaches to study biology and to construct synthetic cell factories. Currently, progress in Synthetic Genomics is hindered by the inability to synthesize DNA molecules longer than a few hundred base pairs, while the size of the smallest genome of a self-replicating cell is several hundred thousand base pairs. Methods to assemble small fragments of DNA into large molecules are therefore required. Remarkably powerful at assembling DNA molecules, the unicellular eukaryote Saccharomyces cerevisiae has been pivotal in the establishment of Synthetic Genomics. Instrumental in the assembly of entire genomes of various organisms in the past decade, the S. cerevisiae genome foundry has a key role to play in future Synthetic Genomics developments.

The importance of obtaining comprehensive and accurate information from cellular proteomics experiments asks for a systematic investigation of sample preparation protocols. In particular when working with unicellular organisms with strong cell walls, such as found in the model organism and cell factory Saccharomyces cerevisiae. Here, we performed a systematic comparison of sample preparation protocols using a matrix of different conditions commonly applied in whole cell lysate, bottom-up proteomics experiments. The different protocols were evaluated for their overall fraction of identified spectra, proteome and amino acid sequence coverage, GO-term distribution and number of peptide modifications, by employing a combination of database and unrestricted modification search approaches. Ultimately, the best protocols enabled the identification of approximately 65–70% of all acquired fragmentation spectra, where additional de novo sequencing suggests that unidentified spectra were largely of too low spectral quality to provide confident spectrum matches. Generally, a range of peptide modifications could be linked to solvents, additives as well as filter materials. Most importantly, the use of moderate incubation temperatures and times circumvented excessive formation of modification artefacts. The collected protocols and large sets of mass spectrometric raw data provide a resource to evaluate and design new protocols and guide the analysis of (native) peptide modifications. Significance: The single-celled eukaryote yeast is a widely used model organism for higher eukaryotes in which, for example, the regulation of glycolysis is studied in the context of health and disease. Moreover, yeast is a widely employed cell factory because it is one of the few eukaryotic organisms that can efficiently grow under both aerobic and anaerobic conditions. Large-scale proteomics studies have become increasingly important for single-celled model organisms, such as yeast, in order to provide fundamental understanding of their metabolic processes and proteome dynamics under changing environmental conditions. However, comprehensive and accurate cellular proteomics experiments require optimised sample preparation procedures, in particular when working with unicellular organisms with rigid cell walls, such as found in yeast. Protocols may substantially bias towards specific protein fractions, modify native protein modifications or introduce artificial modifications. That lowers the overall number of spectral identifications and challenges the study of native protein modifications. Therefore, we performed a systematic study of a large array of protocols on yeast grown under highly controlled conditions. The obtained outcomes, the collected protocols and the mass spectrometric raw data enable the selection of suitable sample preparation elements and furthermore support the evaluation of (native) peptide modifications in yeast, and beyond.

Saccharomyces cerevisiae, whose evolutionary past includes a whole-genome duplication event, is characterized by a mosaic genome configuration with substantial apparent genetic redundancy. This apparent redundancy raises questions about the evolutionary driving force for genomic fixation of “minor” paralogs and complicates modular and combinatorial metabolic engineering strategies. While isoenzymes might be important in specific environments, they could be dispensable in controlled laboratory or industrial contexts. The present study explores the extent to which the genetic complexity of the central carbon metabolism (CCM) in S. cerevisiae, here defined as the combination of glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and a limited number of related pathways and reactions, can be reduced by elimination of (iso)enzymes without major negative impacts on strain physiology. Cas9-mediated, groupwise deletion of 35 of the 111 genes yielded a “minimal CCM” strain which, despite the elimination of 32% of CCM-related proteins, showed only a minimal change in phenotype on glucose-containing synthetic medium in controlled bioreactor cultures relative to a congenic reference strain. Analysis under a wide range of other growth and stress conditions revealed remarkably few phenotypic changes from the reduction of genetic complexity. Still, a well-documented context-dependent role of GPD1 in osmotolerance was confirmed. The minimal CCM strain provides a model system for further research into genetic redundancy of yeast genes and a platform for strategies aimed at large-scale, combinatorial remodeling of yeast CCM.

Even for the genetically accessible yeast Saccharomyces cerevisiae, the CRISPR-Cas DNA editing technology has strongly accelerated and facilitated strain construction. Several methods have been validated for fast and highly efficient single editing events, and diverse approaches for multiplex genome editing have been described in the literature by means of SpCas9 or FnCas12a endonucleases and their associated guide RNAs (gRNAs). The gRNAs used to guide the Cas endonuclease to the editing site are typically expressed from plasmids using native Pol II or Pol III RNA polymerases. These gRNA expression plasmids require laborious, time-consuming cloning steps, which hampers their implementation for academic and applied purposes. In this study, we explore the potential of expressing gRNA from linear DNA fragments using the T7 RNA polymerase (T7RNAP) for single and multiplex genome editing in Saccharomyces cerevisiae. Using FnCas12a, this work demonstrates that transforming short, linear DNA fragments encoding gRNAs in yeast strains expressing T7RNAP promotes highly efficient single and duplex DNA editing. These DNA fragments can be custom ordered, which makes this approach highly suitable for high-Throughput strain construction. This work expands the CRISPR toolbox for large-scale strain construction programs in S. cerevisiae and promises to be relevant for other less genetically accessible yeast species.

The construction of microbial cell factories for sustainable production of chemicals and pharmaceuticals requires extensive genome engineering. Using Saccharomyces cerevisiae, this study proposes synthetic neochromosomes as orthogonal expression platforms for rewiring native cellular processes and implementing new functionalities. Capitalizing the powerful homologous recombination capability of S. cerevisiae, modular neochromosomes of 50 and 100 kb were fully assembled de novo from up to 44 transcriptional-unit-sized fragments in a single transformation. These assemblies were remarkably efficient and faithful to their in silico design. Neochromosomes made of non-coding DNA were stably replicated and segregated irrespective of their size without affecting the physiology of their host. These non-coding neochromosomes were successfully used as landing pad and as exclusive expression platform for the essential glycolytic pathway. This work pushes the limit of DNA assembly in S. cerevisiae and paves the way for de novo designer chromosomes as modular genome engineering platforms in S. cerevisiae.

The thermotolerant yeast Ogataea parapolymorpha (formerly Hansenula polymorpha) is an industrially relevant production host that exhibits a fully respiratory sugar metabolism in aerobic batch cultures. NADH-derived electrons can enter its mitochondrial respiratory chain either via a proton-translocating complex I NADH-dehydrogenase or via three putative alternative NADH dehydrogenases. This respiratory entry point affects the amount of ATP produced per NADH/O2 consumed and therefore impacts the maximum yield of biomass and/or cellular products from a given amount of substrate. To investigate the physiological importance of complex I, a wild-type O. parapolymorpha strain and a congenic complex I-deficient mutant were grown on glucose in aerobic batch, chemostat, and retentostat cultures in bioreactors. In batch cultures, the two strains exhibited a fully respiratory metabolism and showed the same growth rates and biomass yields, indicating that, under these conditions, the contribution of NADH oxidation via complex I was negligible. Both strains also exhibited a respiratory metabolism in glucose-limited chemostat cultures, but the complex I-deficient mutant showed considerably reduced biomass yields on substrate and oxygen, consistent with a lower efficiency of respiratory energy coupling. In glucose-limited retentostat cultures at specific growth rates down to ∼0.001 h-1, both O. parapolymorpha strains showed high viability. Maintenance energy requirements at these extremely low growth rates were approximately 3-fold lower than estimated from faster-growing chemostat cultures, indicating a stringent-response-like behavior. Quantitative transcriptome and proteome analyses indicated condition-dependent expression patterns of complex I subunits and of alternative NADH dehydrogenases that were consistent with physiological observations.IMPORTANCE Since popular microbial cell factories have typically not been selected for efficient respiratory energy coupling, their ATP yields from sugar catabolism are often suboptimal. In aerobic industrial processes, suboptimal energy coupling results in reduced product yields on sugar, increased process costs for oxygen transfer, and volumetric productivity limitations due to limitations in gas transfer and cooling. This study provides insights into the contribution of mechanisms of respiratory energy coupling in the yeast cell factory Ogataea parapolymorpha under different growth conditions and provides a basis for rational improvement of energy coupling in yeast cell factories. Analysis of energy metabolism of O. parapolymorpha at extremely low specific growth rates indicated that this yeast reduces its energy requirements for cellular maintenance under extreme energy limitation. Exploration of the mechanisms for this increased energetic efficiency may contribute to an optimization of the performance of industrial processes with slow-growing eukaryotic cell factories.

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

The construction of powerful cell factories requires intensive and extensive remodelling of microbial genomes. Considering the rapidly increasing number of these synthetic biology endeavors, there is an increasing need for DNA watermarking strategies that enable the discrimination between synthetic and native gene copies. While it is well documented that codon usage can affect translation, and most likely mRNA stability in eukaryotes, remarkably few quantitative studies explore the impact of watermarking on transcription, protein expression, and physiology in the popular model and industrial yeast Saccharomyces cerevisiae. The present study, using S. cerevisiae as eukaryotic paradigm, designed, implemented, and experimentally validated a systematic strategy to watermark DNA with minimal alteration of yeast physiology. The 13 genes encoding proteins involved in the major pathway for sugar utilization (i.e., glycolysis and alcoholic fermentation) were simultaneously watermarked in a yeast strain using the previously published pathway swapping strategy. Carefully swapping codons of these naturally codon optimized, highly expressed genes, did not affect yeast physiology and did not alter transcript abundance, protein abundance, and protein activity besides a mild effect on Gpm1. The markerQuant bioinformatics method could reliably discriminate native from watermarked genes and transcripts. Furthermore, presence of watermarks enabled selective CRISPR/Cas genome editing, specifically targeting the native gene copy while leaving the synthetic, watermarked variant intact. This study offers a validated strategy to simply watermark genes in S. cerevisiae.

Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.

Microbial production of chemical compounds often requires highly engineered microbial cell factories. During the last years, CRISPR-Cas nucleases have been repurposed as powerful tools for genome editing. Here, we briefly review the most frequently used CRISPR-Cas tools and describe some of their applications. We describe the progress made with respect to CRISPR-based multiplex genome editing of industrial bacteria and eukaryotic microorganisms. We also review the state of the art in terms of gene expression regulation using CRISPRi and CRISPRa. Finally, we summarize the pillars for efficient multiplexed genome editing and present our view on future developments and applications of CRISPR-Cas tools for multiplex genome editing.

Engineered strains of Saccharomyces cerevisiae are used for industrial production of succinic acid. Optimal process conditions for dicarboxylic-acid yield and recovery include slow growth, low pH, and high CO₂. To quantify and understand how these process parameters affect yeast physiology, this study investigates individual and combined impacts of low pH (3.0) and high CO₂ (50%) on slow-growing chemostat and retentostat cultures of the reference strain S. cerevisiae CEN.PK113-7D. Combined exposure to low pH and high CO₂ led to increased maintenance-energy requirements and death rates in aerobic, glucose-limited cultures. Further experiments showed that these effects were predominantly caused by low pH. Growth under ammonium-limited, energy-excess conditions did not aggravate or ameliorate these adverse impacts. Despite the absence of a synergistic effect of low pH and high CO₂ on physiology, high CO₂ strongly affected genome-wide transcriptional responses to low pH. Interference of high CO₂ with low-pH signaling is consistent with low-pH and high-CO₂ signals being relayed via common (MAPK) signaling pathways, notably the cell wall integrity, high-osmolarity glycerol, and calcineurin pathways. This study highlights the need to further increase robustness of cell factories to low pH for carboxylic-acid production, even in organisms that are already applied at industrial scale.

When faced with environmental changes, microbes often enter a temporary growth arrest during which they reprogram the expression of specific genes to adapt to the new conditions. A prime example of such a lag phase occurs when microbes need to switch from glucose to other, less-preferred carbon sources. Despite its industrial relevance, the genetic network that determines the duration of the lag phase has not been studied in much detail. Here, we performed a genome-wide Bar-Seq screen to identify genetic determinants of the Saccharomyces cerevisiae glucose-to-galactose lag phase. The results show that genes involved in respiration, and specifically those encoding complexes III and IV of the electron transport chain, are needed for efficient growth resumption after the lag phase. Anaerobic growth experiments confirmed the importance of respiratory energy conversion in determining the lag phase duration. Moreover, overexpression of the central regulator of respiration, HAP4, leads to significantly shorter lag phases. Together, these results suggest that the glucose-induced repression of respiration, known as the Crabtree effect, is a major determinant of microbial fitness in fluctuating carbon environments.IMPORTANCE The lag phase is arguably one of the prime characteristics of microbial growth. Longer lag phases result in lower competitive fitness in variable environments, and the duration of the lag phase is also important in many industrial processes where long lag phases lead to sluggish, less efficient fermentations. Despite the immense importance of the lag phase, surprisingly little is known about the exact molecular processes that determine its duration. Our study uses the molecular toolbox of S. cerevisiae combined with detailed growth experiments to reveal how the transition from fermentative to respirative metabolism is a key bottleneck for cells to overcome the lag phase. Together, our findings not only yield insight into the key molecular processes and genes that influence lag duration but also open routes to increase the efficiency of industrial fermentations and offer an experimental framework to study other types of lag behavior.