L-ascorbic acid producing yeasts learn from plants how to recycle it

The role of GDP-l-galactose phosphorylase in the control of ascorbate biosynthesis

The enzymes involved in l-ascorbate biosynthesis in photosynthetic organisms (the Smirnoff-Wheeler [SW] pathway) are well established. Here, we analyzed their subcellular localizations and potential physical interactions and assessed their role in the control of ascorbate synthesis. Transient expression of C terminal-tagged fusions of SW genes in Nicotiana benthamiana and Arabidopsis thaliana mutants complemented with genomic constructs showed that while GDP-d-mannose epimerase is cytosolic, all the enzymes from GDP-d-mannose pyrophosphorylase (GMP) to l-galactose dehydrogenase (l-GalDH) show a dual cytosolic/nuclear localization. All transgenic lines expressing functional SW protein green fluorescent protein fusions driven by their endogenous promoters showed a high accumulation of the fusion proteins, with the exception of those lines expressing GDP-l-galactose phosphorylase (GGP) protein, which had very low abundance. Transient expression of individual or combinations of SW pathway enzymes in N. benthamiana only increased ascorbate concentration if GGP was included. Although we did not detect direct interaction between the different enzymes of the pathway using yeast-two hybrid analysis, consecutive SW enzymes, as well as the first and last enzymes (GMP and l-GalDH) associated in coimmunoprecipitation studies. This association was supported by gel filtration chromatography, showing the presence of SW proteins in high-molecular weight fractions. Finally, metabolic control analysis incorporating known kinetic characteristics showed that previously reported feedback repression at the GGP step, combined with its relatively low abundance, confers a high-flux control coefficient and rationalizes why manipulation of other enzymes has little effect on ascorbate concentration.

Copper homeostasis as a target to improve Saccharomyces cerevisiae tolerance to oxidative stress

The yeast Saccharomyces cerevisiae is widely used as a cell factory for the biotechnological production of various industrial products. During these processes, yeasts meet different kinds of stressors that often cause oxidative stress and thus impair cell growth. Therefore, the development of robust strains is indispensable to improve production, yield and productivity of fermentative processes. Copper plays a key role in the response to oxidative stress, as cofactor of the cytosolic superoxide dismutase (Sod1) and being contained in metallochaperone and metallothioneines with antioxidant properties. In this work, we observed a higher naturally copper internalization in a robust S. cerevisiae strain engineered to produce the antioxidant l-ascorbic acid (L-AA), compared with the wild type strain. Therefore, we investigated the effect of the alteration of copper homeostasis on cellular stress tolerance. CTR1 and FRE1 genes, codifying for a plasma membrane high-affinity copper transporter and for a cell-surface ferric/cupric reductase, respectively, were overexpressed in both wild type and L-AA cells. Remarkably, we found that the sole FRE1 overexpression was sufficient to increase copper internalization leading to an enhanced stress tolerance toward H₂O₂ exposure, in both strains under investigation. These findings reveal copper homeostasis as a target for the development of robust cell factories.

Temperature-induced lipocalin (TIL): a shield against stress-inducing environmental shocks in Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is a well-established workhorse, either for recombinant or natural products, thanks to its natural traits and easily editable metabolism. However, during a bio-based industrial process it meets multiple stresses generated by operative conditions such as non-optimal temperature, pH, oxygenation and product accumulation. The development of tolerant strains is therefore indispensable for the improvement of production, yield and productivity of fermentative processes. In this regard, plants as resilient organisms are a generous source for fishing genes and/or metabolites that can help the cell factory to counteract environmental constraints. Plants possess proteins named temperature-induced lipocalins, TIL, whose levels in the cells correlates with the tolerance to sudden temperature changes and with the scavenging of reactive oxygen species. In this work, the gene encoding for the Arabidopsis thaliana TIL protein was for the first time expressed in S. cerevisiae. The recombinant strain was compared and analysed against the parental counterpart under heat shock, freezing, exposure to organic acid and oxidative agents. In all the tested conditions, TIL expression conferred a higher tolerance to the stress imposed, making this strain a promising candidate for the development of robust cell factories able to overtake the major impairments of industrial processes.

The Saccharomyces cerevisiae poly(A) binding protein Pab1 as a target for eliciting stress tolerant phenotypes

When exploited as cell factories, Saccharomyces cerevisiae cells are exposed to harsh environmental stresses impairing titer, yield and productivity of the fermentative processes. The development of robust strains therefore represents a pivotal challenge for the implementation of cost-effective bioprocesses. Altering master regulators of general cellular rewiring represents a possible strategy to evoke shaded potential that may accomplish the desirable features. The poly(A) binding protein Pab1, as stress granules component, was here selected as the target for obtaining widespread alterations in mRNA metabolism, resulting in stress tolerant phenotypes. Firstly, we demonstrated that the modulation of Pab1 levels improves robustness against different stressors. Secondly, the mutagenesis of PAB1 and the application of a specific screening protocol on acetic acid enriched medium allowed the isolation of the further ameliorated mutant pab1 A60-9. These findings pave the way for a novel approach to unlock industrially promising phenotypes through the modulation of a post-transcriptional regulatory element.

Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

Background

The robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability.

Results

Based on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation.

Conclusions

We generated simple mathematical models for first-order approximation of chemical production yield from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers.