Evaluation of enzyme-constrained genome-scale model through metabolic engineering of anaerobic co-production of 2,3-butanediol and glycerol by Saccharomyces cerevisiae.

Enzyme-constrained genome-scale models (ecGEMs) have potential to predict phenotypes in a variety of conditions, such as growth rates or carbon sources. This study investigated if ecGEMs can guide metabolic engineering efforts to swap anaerobic redox-neutral ATP-providing pathways in yeast from alcoholic fermentation to equimolar co-production of 2,3-butanediol and glycerol. With proven pathways and low product toxicity, the ecGEM solution space aligned well with observed phenotypes. Since this catabolic pathway provides only one-third of the ATP of alcoholic fermentation (2/3 versus 2 ATP per glucose), the ecGEM predicted a growth decrease from 0.36 h-1 in the reference to 0.175 h-1 in the engineered strain. However, this <3-fold decrease would require the specific glucose consumption rate to increase. Surprisingly, after the pathway swap the engineered strain immediately grew at 0.15 h-1 with a glucose consumption rate of 29 mmol (g CDW)-1 h-1, which was indeed higher than reference (23 mmol (g CDW)-1 h-1) and one of the highest reported for S. cerevisiae. The accompanying 2,3-butanediol- (15.8 mmol (g CDW)-1 h-1) and glycerol (19.6 mmol (g CDW)-1 h-1) production rates were close to predicted values. Proteomics confirmed that this increased consumption rate was facilitated by enzyme reallocation from especially ribosomes (from 25.5 to 18.5 %) towards glycolysis (from 28.7 to 43.5 %). Subsequently, 200 generations of sequential transfer did not improve growth of the engineered strain, showing the use of ecGEMs in predicting opportunity space for laboratory evolution. The observations in this study illustrate both the current potential, as well as future improvements, of ecGEMs as a tool for both metabolic engineering and laboratory evolution.

Genome-scale metabolic modeling reveals metabolic trade-offs associated with lipid production in Rhodotorula toruloides.

Rhodotorula toruloides is a non-conventional, oleaginous yeast able to naturally accumulate high amounts of microbial lipids. Constraint-based modeling of R. toruloides has been mainly focused on the comparison of experimentally measured and model predicted growth rates, while the intracellular flux patterns have been analyzed on a rather general level. Hence, the intrinsic metabolic properties of R. toruloides that make lipid synthesis possible are not thoroughly understood. At the same time, the lack of diverse physiological data sets has often been the bottleneck to predict accurate fluxes. In this study, we collected detailed physiology data sets of R. toruloides while growing on glucose, xylose, and acetate as the sole carbon source in chemically defined medium. Regardless of the carbon source, the growth was divided into two phases from which proteomic and lipidomic data were collected. Complemental physiological parameters were collected in these two phases and altogether implemented into metabolic models. Simulated intracellular flux patterns demonstrated the role of phosphoketolase in the generation of acetyl-CoA, one of the main precursors during lipid biosynthesis, while the role of ATP citrate lyase was not confirmed. Metabolic modeling on xylose as a carbon substrate was greatly improved by the detection of chirality of D-arabinitol, which together with D-ribulose were involved in an alternative xylose assimilation pathway. Further, flux patterns pointed to metabolic trade-offs associated with NADPH allocation between nitrogen assimilation and lipid biosynthetic pathways, which was linked to large-scale differences in protein and lipid content. This work includes the first extensive multi-condition analysis of R. toruloides using enzyme-constrained models and quantitative proteomics. Further, more precise kcat values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies.

Detailed analysis of metabolism reveals growth-rate-promoting interactions between Anaerostipes caccae and Bacteroides spp.

INTRODUCTION: Human gut microbiota species which are next-generation probiotics (NGPs) candidates are of high interest as they have shown the potential to treat intestinal inflammation and other diseases. Unfortunately, these species are often not robust enough for large-scale cultivation, especially in maintaining diversity in co-culture production. OBJECTIVES: In this study, we describe interactions between human gut microbiota species in the cultivation process with unique substrates. We also demonstrated that it is possible to change the species ratio in co-culture by changing the ratio of carbon sources. METHODS: We screened 25 different bacterial species based on their metabolic capabilities. After evaluating unique substrate possibilities, we chose Anaerostipes caccae (A. caccae), Bacteroides thetaiotaomicron (B. thetaiotaomicron), and Bacteroides vulgatus (B. vulgatus) as subjects for further study. D-sorbitol, D-xylose, and D-galacturonic acid were selected as substrates for A. caccae, B. thetaiotaomicron, and B. vulgatus respectively. All three species were cultivated as both monocultures and in co-cultures in serial batch fermentations in an isothermal microcalorimeter. RESULTS: Positive interactions were detected between the species in both co-cultures (A. caccae + B. thetaiotaomicron; A. caccae + B. vulgatus) resulting in higher heat production compared to the sum of the monocultures. The same positive cross-feeding interactions took place in larger-scale cultivation experiments. We confirmed acetate and lactate cross-feeding between A. caccae and B. thetaiotaomicron with flux balance analysis (FBA). CONCLUSION: Changing the ratio of the selected carbon sources in the medium changed the species ratio accordingly. Such robustness is the basis for developing more efficient industrial co-culture processes including the production of NGPs.

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae.

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.

Potassium and Sodium Salt Stress Characterization in the Yeasts Saccharomyces cerevisiae, Kluyveromyces marxianus, and Rhodotorula toruloides.

Biotechnology requires efficient microbial cell factories. The budding yeast Saccharomyces cerevisiae is a vital cell factory, but more diverse cell factories are essential for the sustainable use of natural resources. Here, we benchmarked nonconventional yeasts Kluyveromyces marxianus and Rhodotorula toruloides against S. cerevisiae strains CEN.PK and W303 for their responses to potassium and sodium salt stress. We found an inverse relationship between the maximum growth rate and the median cell volume that was responsive to salt stress. The supplementation of K+ to CEN.PK cultures reduced Na+ toxicity and increased the specific growth rate 4-fold. The higher K+ and Na+ concentrations impaired ethanol and acetate metabolism in CEN.PK and acetate metabolism in W303. In R. toruloides cultures, these salt supplementations induced a trade-off between glucose utilization and cellular aggregate formation. Their combined use increased the beta-carotene yield by 60% compared with that of the reference. Neural network-based image analysis of exponential-phase cultures showed that the vacuole-to-cell volume ratio increased with increased cell volume for W303 and K. marxianus but not for CEN.PK and R. toruloides in response to salt stress. Our results provide insights into common salt stress responses in yeasts and will help design efficient bioprocesses. IMPORTANCE Characterization of microbial cell factories under industrially relevant conditions is crucial for designing efficient bioprocesses. Salt stress, typical in industrial bioprocesses, impinges upon cell volume and affects productivity. This study presents an open-source neural network-based analysis method to evaluate volumetric changes using yeast optical microscopy images. It allows quantification of cell and vacuole volumes relevant to cellular physiology. On applying salt stress in yeasts, we found that the combined use of K+ and Na+ improves the cellular fitness of Saccharomyces cerevisiae strain CEN.PK and increases the beta-carotene productivity in Rhodotorula toruloides, a commercially important antioxidant and a valuable additive in foods.

C/N ratio and carbon source-dependent lipid production profiling in Rhodotorula toruloides.

Microbial oils are lipids produced by oleaginous microorganisms, which can be used as a potential feedstock for oleochemical production. The oleaginous yeast Rhodotorula toruloides can co-produce microbial oils and high-value compounds from low-cost substrates, such as xylose and acetic acid (from hemicellulosic hydrolysates) and raw glycerol (a byproduct of biodiesel production). One step towards economic viability is identifying the best conditions for lipid production, primarily the most suitable carbon-to-nitrogen ratio (C/N). Here, we aimed to identify the best conditions and cultivation mode for lipid production by R. toruloides using various low-cost substrates and a range of C/N ratios (60, 80, 100, and 120). Turbidostat mode was used to achieve a steady state at the maximal specific growth rate and to avoid continuously changing environmental conditions (i.e., C/N ratio) that inherently occur in batch mode. Regardless of the carbon source, higher C/N ratios increased lipid yields (up to 60% on xylose at a C/N of 120) but decreased the specific growth rate. Growth on glycerol resulted in the highest specific growth and lipid production (0.085 g lipids/gDW*h) rates at C/Ns between 60 and 100. We went on to study lipid production using glycerol in both batch and fed-batch modes, which resulted in lower specific lipid production rates compared with turbisdostat, however, fed batch is superior in terms of biomass production and lipid titers. By combining the data we obtained in these experiments with a genome-scale metabolic model of R. toruloides, we identified targets for improvements in lipid production that could be carried out either by metabolic engineering or process optimization.

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints.

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.

Transcriptome analysis of the thermotolerant yeast Kluyveromyces marxianus CCT 7735 under ethanol stress.

The thermotolerant yeast Kluyveromyces marxianus displays a potential to be used for ethanol production from both whey and lignocellulosic biomass at elevated temperatures, which is highly alluring to reduce the cost of the bioprocess. Nevertheless, contrary to Saccharomyces cerevisiae, K. marxianus cannot tolerate high ethanol concentrations. We report the transcriptional profile alterations in K. marxianus under ethanol stress in order to gain insights about mechanisms involved with ethanol response. Time-dependent changes have been characterized under the exposure of 6% ethanol and compared with the unstressed cells prior to the ethanol addition. Our results reveal that the metabolic flow through the central metabolic pathways is impaired under the applied ethanol stress. Consistent with these results, we also observe that genes involved with ribosome biogenesis are downregulated and gene-encoding heat shock proteins are upregulated. Remarkably, the expression of some gene-encoding enzymes related to unsaturated fatty acid and ergosterol biosynthesis decreases upon ethanol exposure, and free fatty acid and ergosterol measurements demonstrate that their content in K. marxianus does not change under this stress. These results are in contrast to the increase previously reported with S. cerevisiae subjected to ethanol stress and suggest that the restructuration of K. marxianus membrane composition differs in the two yeasts which gives important clues to understand the low ethanol tolerance of K. marxianus compared to S. cerevisiae.

Applications of computational modeling in metabolic engineering of yeast.

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.

Protein turnover forms one of the highest maintenance costs in Lactococcus lactis.

Protein turnover plays an important role in cell metabolism by regulating metabolic fluxes. Furthermore, the energy costs for protein turnover have been estimated to account for up to a third of the total energy production during cell replication and hence may represent a major limiting factor in achieving either higher biomass or production yields. This work aimed to measure the specific growth rate (µ)-dependent abundance and turnover rate of individual proteins, estimate the ATP cost for protein production and turnover, and compare this with the total energy balance and other maintenance costs. The lactic acid bacteria model organism Lactococcus lactis was used to measure protein turnover rates at µ = 0.1 and 0.5 h(-1) in chemostat experiments. Individual turnover rates were measured for ~75% of the total proteome. On average, protein turnover increased by sevenfold with a fivefold increase in growth rate, whilst biomass yield increased by 35%. The median turnover rates found were higher than the specific growth rate of the bacterium, which suggests relatively high energy consumption for protein turnover. We found that protein turnover costs alone account for 38 and 47% of the total energy produced at µ = 0.1 and 0.5 h(-1), respectively, and gene ontology groups Energy metabolism and Translation dominated synthesis costs at both growth rates studied. These results reflect the complexity of metabolic changes that occur in response to changes in environmental conditions, and signify the trade-off between biomass yield and the need to produce ATP for maintenance processes.

Exploring the resilience and stability of a defined human gut microbiota consortium: An isothermal microcalorimetric study.

The gut microbiota significantly contributes to human health and well-being. The aim of this study was to evaluate the stability and resilience of a consortium composed of three next-generation probiotics (NGPs) candidates originally found in the human gut. The growth patterns of Akkermansia muciniphila, Bacteroides thetaiotaomicron, and Faecalibacterium prausnitzii were studied both individually and consortium. The growth kinetics of Akkermansia muciniphila (A. muciniphila), Bacteroides thetaiotaomicron (B. thetaiotaomicron), and Faecalibacterium prausnitzii (F. prausnitzii) were characterized both individually and in consortium using isothermal microcalorimetry and 16S ribosomal RNA next-generation sequencing. The consortium reached stability after three passages and demonstrated resilience to changes in its initial composition. The concentration of butyrate produced was nearly twice as high in the consortium compared to the monoculture of F. prausnitzii. The experimental conditions and methodologies used in this article are a solid foundation for developing further complex consortia.

A Conundrum of r-Protein Stability: Unbalanced Stoichiometry of r-Proteins during Stationary Phase in Escherichia coli.

Bacterial ribosomes are composed of three rRNA and over 50 ribosomal protein (r-protein) molecules. r-proteins are essential for ribosome assembly and structural stability and also participate in almost all ribosome functions. Ribosomal components are present in stoichiometric amounts in the mature 70S ribosomes during exponential and early stationary growth phases. Ribosomes are degraded in stationary phase; however, the stability and fate of r-proteins during stationary growth phase are not known. In this study, we report a quantitative analysis of ribosomal components during extended stationary-phase growth in Escherichia coli. We show that (i) the quantity of ribosomes per cell mass decreases in stationary phase, (ii) 70S ribosomes contain r-proteins in stoichiometric amounts, (iii) 30S subunits are degraded faster than 50S subunits, (iv) the quantities of 21 r-proteins in the total proteome decrease during 14 days (short-lived r-proteins) concomitantly with the reduction of cellular RNA, and (e) 30 r-proteins are stable and form a pool of free r-proteins (stable r-proteins). Thus, r-proteins are present in nonstoichiometric amounts in the proteome of E. coli during the extended stationary phase. IMPORTANCE Ribosome degradation has been extensively described from the viewpoint of its main component, rRNA. Here, we aim to complement our knowledge by quantitatively analyzing r-protein degradation and stability both in the ribosomes and in the whole-cell proteome during stationary phase in E. coli. r-proteins are considered to be very stable in the proteome. Here, we show that a specific set of r-proteins are rapidly degraded after release from the rRNA. The degradation of r-proteins is an intriguing new aspect of r-protein metabolism in bacteria.

Development of a dedicated Golden Gate Assembly Platform (RtGGA) for Rhodotorula toruloides.

Rhodotorula toruloides is a potential chassis for microbial cell factories as this yeast can metabolise different substrates into a diverse range of natural products, but the lack of efficient synthetic biology tools hinders its applicability. In this study, the modular, versatile and efficient Golden Gate DNA assembly system (RtGGA) was adapted to the first basidiomycete, an oleaginous yeast R. toruloides. R. toruloides CCT 0783 was sequenced, and used for the GGA design. The DNA fragments were assembled with predesigned 4-nt overhangs and a library of standardized parts was created containing promoters, genes, terminators, insertional regions, and resistance genes. The library was combined to create cassettes for the characterization of promoters strength and to overexpress the carotenoid production pathway. A variety of reagents, plasmids, and strategies were used and the RtGGA proved to be robust. The RtGGA was used to build three versions of the carotenoid overexpression cassette by using different promoter combinations. The cassettes were transformed into R. toruloides and the three new strains were characterized. Total carotenoid concentration increased by 41%. The dedicated GGA platform fills a gap in the advanced genome engineering toolkit for R. toruloides, enabling the efficient design of complex metabolic pathways.

Multi-omics approach to study the growth efficiency and amino acid metabolism in Lactococcus lactis at various specific growth rates.

BACKGROUND: Lactococcus lactis is recognised as a safe (GRAS) microorganism and has hence gained interest in numerous biotechnological approaches. As it is fastidious for several amino acids, optimization of processes which involve this organism requires a thorough understanding of its metabolic regulations during multisubstrate growth. RESULTS: Using glucose limited continuous cultivations, specific growth rate dependent metabolism of L. lactis including utilization of amino acids was studied based on extracellular metabolome, global transcriptome and proteome analysis. A new growth medium was designed with reduced amino acid concentrations to increase precision of measurements of consumption of amino acids. Consumption patterns were calculated for all 20 amino acids and measured carbon balance showed good fit of the data at all growth rates studied. It was observed that metabolism of L. lactis became more efficient with rising specific growth rate in the range 0.10-0.60 h(-1), indicated by 30% increase in biomass yield based on glucose consumption, 50% increase in efficiency of nitrogen use for biomass synthesis, and 40% reduction in energy spilling. The latter was realized by decrease in the overall product formation and higher efficiency of incorporation of amino acids into biomass. L. lactis global transcriptome and proteome profiles showed good correlation supporting the general idea of transcription level control of bacterial metabolism, but the data indicated that substrate transport systems together with lower part of glycolysis in L. lactis were presumably under allosteric control. CONCLUSIONS: The current study demonstrates advantages of the usage of strictly controlled continuous cultivation methods combined with multi-omics approach for quantitative understanding of amino acid and energy metabolism of L. lactis which is a valuable new knowledge for development of balanced growth media, gene manipulations for desired product formation etc. Moreover, collected dataset is an excellent input for developing metabolic models.

Screening and Growth Characterization of Non-conventional Yeasts in a Hemicellulosic Hydrolysate.

Lignocellulosic biomass is an attractive raw material for the sustainable production of chemicals and materials using microbial cell factories. Most of the existing bioprocesses focus on second-generation ethanol production using genetically modified Saccharomyces cerevisiae, however, this microorganism is naturally unable to consume xylose. Moreover, extensive metabolic engineering has to be carried out to achieve high production levels of industrially relevant building blocks. Hence, the use of non-Saccharomyces species, or non-conventional yeasts, bearing native metabolic routes, allows conversion of a wide range of substrates into different products, and higher tolerance to inhibitors improves the efficiency of biorefineries. In this study, nine non-conventional yeast strains were selected and screened on a diluted hemicellulosic hydrolysate from Birch. Kluyveromyces marxianus CBS 6556, Scheffersomyces stipitis CBS 5773, Lipomyces starkeyi DSM 70295, and Rhodotorula toruloides CCT 7815 were selected for further characterization, where their growth and substrate consumption patterns were analyzed under industrially relevant substrate concentrations and controlled environmental conditions in bioreactors. K. marxianus CBS 6556 performed poorly under higher hydrolysate concentrations, although this yeast was determined among the fastest-growing yeasts on diluted hydrolysate. S. stipitis CBS 5773 demonstrated a low growth and biomass production while consuming glucose, while during the xylose-phase, the specific growth and sugar co-consumption rates were among the highest of this study (0.17 h-1 and 0.37 g/gdw*h, respectively). L. starkeyi DSM 70295 and R. toruloides CCT 7815 were the fastest to consume the provided sugars at high hydrolysate conditions, finishing them within 54 and 30 h, respectively. R. toruloides CCT 7815 performed the best of all four studied strains and tested conditions, showing the highest specific growth (0.23 h-1), substrate co-consumption (0.73 ± 0.02 g/gdw*h), and xylose consumption (0.22 g/gdw*h) rates. Furthermore, R. toruloides CCT 7815 was able to produce 10.95 ± 1.37 gL-1 and 1.72 ± 0.04 mgL-1 of lipids and carotenoids, respectively, under non-optimized cultivation conditions. The study provides novel information on selecting suitable host strains for biorefinery processes, provides detailed information on substrate consumption patterns, and pinpoints to bottlenecks possible to address using metabolic engineering or adaptive evolution experiments.

Metabolism Control in 3D-Printed Living Materials Improves Fermentation.

The three-dimensional (3D) printing of cell-containing polymeric hydrogels creates living materials (LMs), offering a platform for developing innovative technologies in areas like biosensors and biomanufacturing. The polymer material properties of cross-linkable F127-bis-urethane methacrylate (F127-BUM) allow reproducible 3D printing and stability in physiological conditions, making it suitable for fabricating LMs. Though F127-BUM-based LMs permit diffusion of solute molecules like glucose and ethanol, it remains unknown whether these are permissible for oxygen, essential for respiration. To determine oxygen permissibility, we quantified dissolved oxygen consumption by the budding yeast-laden F127-BUM-based LMs. Moreover, we obtained data on cell-retaining LMs, which allowed a direct comparison between LMs and suspension cultures. We further developed a highly reliable method to isolate cells from LMs for flow cytometry analysis, cell viability evaluation, and the purification of macromolecules. We found oxygen consumption heavily impaired inside LMs, indicating that yeast metabolism relies primarily on fermentation instead of respiration. Applying this finding to brewing, we observed a higher (3.7%) ethanol production using LMs than the traditional brewing process, indicating improved fermentation. Our study concludes that the present F127-BUM-based LMs are useful for microaerobic processes but developing aerobic bioprocesses will require further research.

Xylose Metabolism and the Effect of Oxidative Stress on Lipid and Carotenoid Production in Rhodotorula toruloides: Insights for Future Biorefinery.

The use of cell factories to convert sugars from lignocellulosic biomass into chemicals in which oleochemicals and food additives, such as carotenoids, is essential for the shift toward sustainable processes. Rhodotorula toruloides is a yeast that naturally metabolises a wide range of substrates, including lignocellulosic hydrolysates, and converts them into lipids and carotenoids. In this study, xylose, the main component of hemicellulose, was used as the sole substrate for R. toruloides, and a detailed physiology characterisation combined with absolute proteomics and genome-scale metabolic models was carried out to understand the regulation of lipid and carotenoid production. To improve these productions, oxidative stress was induced by hydrogen peroxide and light irradiation and further enhanced by adaptive laboratory evolution. Based on the online measurements of growth and CO2 excretion, three distinct growth phases were identified during batch cultivations. Majority of the intracellular flux estimations showed similar trends with the measured protein levels and demonstrated improved NADPH regeneration, phosphoketolase activity and reduced ß-oxidation, correlating with increasing lipid yields. Light irradiation resulted in 70% higher carotenoid and 40% higher lipid content compared to the optimal growth conditions. The presence of hydrogen peroxide did not affect the carotenoid production but culminated in the highest lipid content of 0.65 g/gDCW. The adapted strain showed improved fitness and 2.3-fold higher carotenoid content than the parental strain. This work presents a holistic view of xylose conversion into microbial oil and carotenoids by R. toruloides, in a process toward renewable and cost-effective production of these molecules.

Physical Confinement Impacts Cellular Phenotypes within Living Materials.

Additive manufacturing allows three-dimensional printing of polymeric materials together with cells, creating living materials for applications in biomedical research and biotechnology. However, an understanding of the cellular phenotype within living materials is lacking, which is a key limitation for their wider application. Herein, we present an approach to characterize the cellular phenotype within living materials. We immobilized the budding yeast Saccharomyces cerevisiae in three different photo-cross-linkable triblock polymeric hydrogels containing F127-bis-urethane methacrylate, F127-dimethacrylate, or poly(alkyl glycidyl ether)-dimethacrylate. Using optical and scanning electron microscopy, we showed that hydrogels based on these polymers were stable under physiological conditions, but yeast colonies showed differences in the interaction within the living materials. We found that the physical confinement, imparted by compositional and structural properties of the hydrogels, impacted the cellular phenotype by reducing the size of cells in living materials compared with suspension cells. These properties also contributed to the differences in immobilization patterns, growth of colonies, and colony coatings. We observed that a composition-dependent degradation of polymers was likely possible by cells residing in the living materials. In conclusion, our investigation highlights the need for a holistic understanding of the cellular response within hydrogels to facilitate the synthesis of application-specific polymers and the design of advanced living materials in the future.