TitleEngineering prokaryotic transcriptional activators as metabolite biosensors in yeast io-based production of chemicals and fuels is an attractive avenue to reduce dependence on petroleum. For bio-based production, biocatalysts must often be genetically modified to increase production. However, the current efficiency of genomeengineering methods and parts prospecting allows for unprecedented genotype diversity that vastly outstrips our ability to screen for best cell performance 1,2 .To meet current demand, bioengineers have started to develop genetically encoded devices and systems that enable screening and selection of better-performing biocatalysts in higher throughput. Genetic devices including oscillators, amplifiers and recorders, which have been developed based on fine-tuned relationships between input and output signals, are promising tools for programming and controlling gene expression in living cells [3][4][5] . These devices sense extracellular or intracellular perturbations and actuate cellular decision-making processes akin to logic gates in electrical circuits. Hence, from a diverse set of inputs, molecular gating components such as RNA aptamers and allosterically regulated transcription factors have been engineered to control outputs for applications such as high-throughput screening, actuation on cellular metabolism and evolution-based selection of optimal cell performance [6][7][8] .A key component in many of the reported devices is a ligandinducible transcriptional regulator. Transcriptional regulators are straightforward and powerful components, with many uses in genetic designs. Owing to their modular structure, transcriptional regulators have proven to be versatile platforms for genetically encoded Boolean logic functions 9,10 . In particular, gene switches based on ligand-binding transcriptional repressors bind to genomic targets in the absence of their cognate ligand and thereby repress gene expression of the downstream gene(s), whereas binding between ligand and repressor causes the release of the repressor from the DNA and thereby a derepression 11 . In such 'NOT' gates, simple steric hindrance of RNA polymerase progression, as in the case of the tetracycline-responsive gene switch TetR, have for decades been used for conditional control of gene expression in both prokaryotic and eukaryotic chassis 12,13 . Transcriptional repressors and other artificial transcriptional regulators can be further engineered, for example, via the addition of nuclear localization signals, destabilization domains and transcriptional activation regions, to repurpose conditional repressors into activators [13][14][15] . Though conceptually intriguing and practically relevant, the repurposing of logic gates can suffer from the inherent need for extensive engineering 9,16,17 .Though most ligand-inducible genetic devices adopted for eukaryotes historically have been founded on transcriptional repressors, a hitherto untapped resource for use in genetic designs is ligand-inducible transcriptional activators. Bac...
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
Homologous recombination (HR) in Saccharomyces cerevisiae has been harnessed for both plasmid construction and chromosomal integration of foreign DNA. Still, native HR machinery is not efficient enough for complex and marker-free genome engineering required for modern metabolic engineering. Here, we present a method for marker-free multiloci integration of in vivo assembled DNA parts. By the use of CRISPR/Cas9-mediated one-step double-strand breaks at single, double and triple integration sites we report the successful in vivo assembly and chromosomal integration of DNA parts. We call our method CasEMBLR and validate its applicability for genome engineering and cell factory development in two ways: (i) introduction of the carotenoid pathway from 15 DNA parts into three targeted loci, and (ii) creation of a tyrosine production strain using ten parts into two loci, simultaneously knocking out two genes. This method complements and improves the current set of tools available for genome engineering in S. cerevisiae.
BackgroundTranscriptional reprogramming is a fundamental process of living cells in order to adapt to environmental and endogenous cues. In order to allow flexible and timely control over gene expression without the interference of native gene expression machinery, a large number of studies have focused on developing synthetic biology tools for orthogonal control of transcription. Most recently, the nuclease-deficient Cas9 (dCas9) has emerged as a flexible tool for controlling activation and repression of target genes, by the simple RNA-guided positioning of dCas9 in the vicinity of the target gene transcription start site.ResultsIn this study we compared two different systems of dCas9-mediated transcriptional reprogramming, and applied them to genes controlling two biosynthetic pathways for biobased production of isoprenoids and triacylglycerols (TAGs) in baker’s yeast Saccharomyces cerevisiae. By testing 101 guide-RNA (gRNA) structures on a total of 14 different yeast promoters, we identified the best-performing combinations based on reporter assays. Though a larger number of gRNA-promoter combinations do not perturb gene expression, some gRNAs support expression perturbations up to ~threefold. The best-performing gRNAs were used for single and multiplex reprogramming strategies for redirecting flux related to isoprenoid production and optimization of TAG profiles. From these studies, we identified both constitutive and inducible multiplex reprogramming strategies enabling significant changes in isoprenoid production and increases in TAG.ConclusionTaken together, we show similar performance for a constitutive and an inducible dCas9 approach, and identify multiplex gRNA designs that can significantly perturb isoprenoid production and TAG profiles in yeast without editing the genomic context of the target genes. We also identify a large number of gRNA positions in 14 native yeast target pomoters that do not affect expression, suggesting the need for further optimization of gRNA design tools and dCas9 engineering.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-017-0664-2) contains supplementary material, which is available to authorized users.
Streptomycetes are exploited for production of a wide range of secondary metabolites, and there is much interest in enhancing the level of production of these metabolites. Secondary metabolites are synthesized in dedicated biosynthetic routes, but precursors and co-factors are derived from the primary metabolism. High level production of antibiotics in streptomycetes therefore requires engineering of the primary metabolism. Here we demonstrate this by targeting a key enzyme in glycolysis, phosphofructokinase, leading to improved antibiotic production in Streptomyces coelicolor A3(2). Deletion of pfkA2 (SCO5426), one of three annotated pfkA homologues in S. coelicolor A3(2), resulted in a higher production of the pigmented antibiotics actinorhodin and undecylprodigiosin. The pfkA2 deletion strain had an increased carbon flux through the pentose phosphate pathway, as measured by 13 C metabolic flux analysis, establishing the ATP-dependent PfkA2 as a key player in determining the carbon flux distribution. The increased pentose phosphate pathway flux appeared largely because of accumulation of glucose 6-phosphate and fructose 6-phosphate, as experimentally observed in the mutant strain. Through genome-scale metabolic model simulations, we predicted that decreased phosphofructokinase activity leads to an increase in pentose phosphate pathway flux and in flux to pigmented antibiotics and pyruvate. Integrated analysis of gene expression data using a genome-scale metabolic model further revealed transcriptional changes in genes encoding redox co-factor-dependent enzymes as well as those encoding pentose phosphate pathway enzymes and enzymes involved in storage carbohydrate biosynthesis.
The field of systems biology is often held back by difficulties in obtaining comprehensive, highquality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. on the basis of the integrated analysis of the highthroughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism.
Nutrient sensing and coordination of metabolic pathways are crucial functions for living cells. A combined analysis of the yeast transcriptome, phosphoproteome and metabolome is used to investigate the interactions between the Snf1 and TORC1 pathways under nutrient-limited conditions.
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