The oleaginous yeast Yarrowia lipolytica is an emerging host for production of fatty acid-derived chemicals. To enable rapid iterative metabolic engineering of this yeast, there is a need for well-characterized genetic parts and convenient and reliable methods for their incorporation into yeast. Here, the EasyCloneYALI genetic toolbox, which allows streamlined strain construction with high genome editing efficiencies in Y. lipolytica via the CRISPR/Cas9 technology is presented. The toolbox allows marker-free integration of gene expression vectors into characterized genome sites as well as marker-free deletion of genes with the help of CRISPR/Cas9. Genome editing efficiencies above 80% were achieved with transformation protocols using non-replicating DNA repair fragments (such as DNA oligos). Furthermore, the toolbox includes a set of integrative gene expression vectors with prototrophic markers conferring resistance to hygromycin and nourseothricin.

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Conferring methylotrophy on industrial microorganisms would enable the production of diverse products from one-carbon feedstocks and contribute to establishing a low-carbon society. Rebuilding methylotrophs, however, requires a thorough metabolic refactoring and is highly challenging. Only recently was synthetic methylotrophy achieved in model microorganismsEscherichia coli and baker’s yeast Saccharomyces cerevisiae. Here, we have engineered industrially important yeast Yarrowia lipolytica to assimilate methanol. Through rationally constructing a chimeric assimilation pathway, rewiring the native metabolism for improved precursor supply, and laboratory evolution, we improved the methanol assimilation from undetectable to a level of 1.1 g/L per 72 h and enabled methanol-supported cellular maintenance. By transcriptomic analysis, we further found that fine-tuning of methanol assimilation and ribulose monophosphate/xylulose monophosphate (RuMP/XuMP) regeneration and strengthening formate dehydrogenation and the serine pathway were beneficial for methanol assimilation. This work paves the way for creating synthetic methylotrophic yeast cell factories for low-carbon economy.

Fatty alcohols are widely used in various applications within a diverse set of industries, such as the soap and detergent industry, the personal care, and cosmetics industry, as well as the food industry. The total world production of fatty alcohols is over 2 million tons with approximately equal parts derived from fossil oil and from plant oils or animal fats. Due to the environmental impact of these production methods, there is an interest in alternative methods for fatty alcohol production via microbial fermentation using cheap renewable feedstocks. In this study, we aimed to obtain a better understanding of how fatty alcohol biosynthesis impacts the host organism, baker’s yeast Saccharomyces cerevisiae or oleaginous yeast Yarrowia lipolytica . Producing and non-producing strains were compared in growth and nitrogen-depletion cultivation phases. The multi-omics analysis included physiological characterization, transcriptome analysis by RNAseq, 13 Cmetabolic flux analysis, and intracellular metabolomics. Both species accumulated fatty alcohols under nitrogen-depletion conditions but not during growth. The fatty alcohol–producing Y. lipolytica strain had a higher fatty alcohol production rate than an analogous S. cerevisiae strain. Nitrogen-depletion phase was associated with lower glucose uptake rates and a decrease in the intracellular concentration of acetyl–CoA in both yeast species, as well as increased organic acid secretion rates in Y. lipolytica . Expression of the fatty alcohol–producing enzyme fatty acyl–CoA reductase alleviated the growth defect caused by deletion of hexadecenal dehydrogenase encoding genes ( HFD1 and HFD4 ) in Y. lipolytica . RNAseq analysis showed that fatty alcohol production triggered a cell wall stress response in S. cerevisiae . RNAseq analysis also showed that both nitrogen-depletion and fatty alcohol production have substantial effects on the expression of transporter encoding genes in Y. lipolytica . In conclusion, through this multi-omics study, we uncovered some effects of fatty alcohol production on the host metabolism. This knowledge can be used as guidance for further strain improvement towards the production of fatty alcohols.

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

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Protein-based encapsulation systems have a wide spectrum of applications in targeted delivery of cargo molecules and for chemical transformations in confined spaces. By engineering affinity between cargo and container proteins it has been possible to enable the efficient and specific encapsulation of target molecules. Missing in current approaches is the ability to turn off the interaction after encapsulation to enable the cargo to freely diffuse in the lumen of the container. Separation between cargo and container is desirable in drug delivery applications and in the use of capsids as catalytic nanoparticles. We describe an encapsulation system based on the hepatitis B virus capsid in which an engineered high-affinity interaction between cargo and capsid proteins can be modulated by Ca . Cargo proteins are loaded into capsids in the presence of Ca , while ligand removal triggers unbinding inside the container. We observe that confinement leads to hindered rotation of cargo inside the capsid. Application of the designed container for catalysis was also demonstrated by encapsulation of an enzyme with β-glucosidase activity.

Protein-based encapsulation systems have aw ide spectrum of applications in targeted delivery of cargo molecules and for chemical transformations in confined spaces.By engineering affinity between cargo and container proteins it has been possible to enable the efficient and specific encapsulation of target molecules.Missing in current approaches is the ability to turn off the interaction after encapsulation to enable the cargo to freely diffuse in the lumen of the container. Separation between cargo and container is desirable in drug delivery applications and in the use of capsids as catalytic nanoparticles.W ed escribe an encapsulation system based on the hepatitis Bv irus capsid in which an engineered highaffinity interaction between cargo and capsid proteins can be modulated by Ca 2+ .Cargo proteins are loaded into capsids in the presence of Ca 2+ ,while ligand removal triggers unbinding inside the container.W eo bserve that confinement leads to hindered rotation of cargo inside the capsid. Application of the designed container for catalysis was also demonstrated by encapsulation of an enzyme with b-glucosidase activity.Protein-based nanocontainers provide tantalizing opportunities for the encapsulation and targeted delivery of various drugs and imaging probes,t he construction of nanoreactors, and the development of biomaterials. [1] Virus-like particles (VLPs) have been widely used as encapsulation systems. [1] In natural container systems,l oading and release of cargo is typically exquisitely controlled. To facilitate an increased control of loading in engineered VLPs,s everal groups have mimicked natural container systems and introduced sites for noncovalent interactions between cargo and capsid proteins.Fore xample,V LPs have been modified to have highly negatively charged interiors to efficiently encapsulate cargo proteins tagged with positively charged sequence. [2] Specific protein-protein interactions have also been used to drive encapsulation selectivity by adding complementary coiledcoil sequences to capsid and guest proteins. [3] It has also been possible to use native RNA-capsid interactions to drive encapsulation of cargo into virus-derived VLPs. [4] An approach for metal-induced assembly and encapsulation has also been described. [5] An additional level of control might be achieved if the interaction between cargo and capsid proteins can be modulated post-encapsulation. In many applications,it would be highly beneficial to be able to turn off the noncovalent interaction between cargo and capsid after encapsulation. In protein delivery applications,t he cargo would ideally be separated from capsomers once capsids have disassembled. In nanoreactors,d etachment of enzymes attached to the container wall to move freely in the lumen of the capsid could enable more efficient catalysis and facilitate noncovalent interactions between proteins involved in multienzyme reaction cascades.Freely diffusing proteins inside the nanocontainer would also facilitate the study of protein crowding and protein-protein...