Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Saccharomyces”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The oleaginous yeast Rhodosporidium toruloides engineered for biomass hydrolysate-derived (E)-α-bisabolene production

The oleaginous yeast Rhodosporidium toruloides has been exploited for many bioproducts, including several terpenes, owing to its oleaginous nature and biomass inhibitor tolerance. Here, we built upon previous (E)-a-bisabolene work by iteratively stacking the complete mevalonate pathway from Saccharomyces cerevisiae onto a multicopy bisabolene synthase parent strain. Metabolomics and proteomics verified heterologous pathway expression and identified metabolic bottlenecks at three intermediate steps, with candidate feedback-resistant mevalonate kinases screening improving titers 15%. Subtle differences in codon optimization, and preliminary attenuation of competing flux toward lipids resulted in 6-fold, 7-fold higher titers relative to controls, respectively. Media optimization led to modest improvements, with zinc identified as the most promising at 10% titer improvement. Ultimately, high-performance strains were cultivated with corn-stover biomass hydrolysate in microtiter plates at 300g/L total sugar, achieving 20.8g/L bisabolene, the highest reported titer in the literature. A 2L glucose minimal medium bioreactor achieved 19.3 g/L bisabolene and a literature-high productivity of 0.11 g/L/h.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Robust Rapid Cellular Metabolite Sensing Using Benchtop NMR and SABRE-Hyperpolarized [1- 13 C]Pyruvate

Hyperpolarized NMR has emerged as a powerful analytical technique to significantly enhance targeted NMR signals, improving the sensitivity for investigations of unique chemical and biological dynamics. Here, we demonstrate the use of a hyperpolarization strategy based on Signal Amplification By Reversible Exchange (SABRE) to generate highly reproducible doses of a hyperpolarized [1- 13 C]pyruvate probe for benchtop characterization of yeast metabolism. This method allows rapid, scalable, and benchtop preparation of biocompatible hyperpolarized solutions suitable for live-cell experiments. We show that this production can be dove-tailed into a modular, compact workflow to characterize real-time metabolism in cell cultures, using Saccharomyces cerevisiae (Baker’s yeast) as a model organism. With high temporal resolution, we show that this method can resolve the conversion of hyperpolarized [1- 13 C]pyruvate into oxidative decarboxylation products CO 2 and bicarbonate. This conversion exhibits sustained and detectable metabolic activity for over 300 s after introduction of the agent to the cells. We model the metabolite kinetics to show decarboxylation activity and derive estimates of the pH over time from the CO 2 and bicarbonate (carbonic acid buffer system) equilibrium to probe changes in the cellular environment during active metabolism. These results highlight the utility of benchtop SABRE-hyperpolarized [1- 13 C]pyruvate as a scalable, specific probe for metabolic phenotyping of living cells using compact, low-cost instrumentation well-suited for future high-throughput applications across microbial engineering, drug response profiling, and dynamic metabolic screening.

fungi↗
DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗
DOE OSTI2020

Antimicrobial Properties of Corn Stover Lignin Fractions Derived from Catalytic Transfer Hydrogenolysis in Supercritical Ethanol with a Ru/C Catalyst

Converting lignin to value-added products at high yields provides an avenue for making ethanol biorefineries more profitable while reducing the carbon footprint of products generally derived from petroleum. In this study, corn stover lignin was depolymerized by catalytic transfer hydrogenolysis (CTH) in supercritical ethanol with a Ru/C catalyst. The lignin-derived bio-oil was then sequentially extracted utilizing hexane, petroleum ether, chloroform, and ethyl acetate as solvents in order of less polar to polar, and the subsequent bio-oils were characterized using GPC, GC/MS, and HSQC NMR. In this work, results show that the monomers in the bio-oil fractions contained primarily alkylated phenols, hydrogenated hydroxycinnamic acid derivatives, syringol and guaiacol-type lignins created from reductive cleavages of ether linkages, which were sequentially extracted into groups depending on the solvent polarity. The antimicrobial properties of the bio-oils were screened against Gram-positive (Bacillus subtilis, Lactobacillus amylovorus, and Staphylococcus epidermidis) and Gram-negative (Escherichia coli) bacteria and yeast (Saccharomyces cerevisiae) by examining microbial growth inhibition. Results show that CTH-derived bio-oils inhibited all tested organisms at concentrations less than 3 mg/mL. Total monomer concentration and the presence of specific monomers (i.e., syringyl propane) showed correlations to antimicrobial activity, likely due to cell death or membrane damage. This study provides insights into using sequential extraction to fractionate lignin-derived compounds and correlations between the properties of the extracted compounds and their antimicrobial activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

High-Efficiency Conversion of Ionic Liquid-Pretreated Woody Biomass to Ethanol at the Pilot Scale

We report that with a diverse and widely distributed global resource base, woody biomass is a compelling organic feedstock for conversion to renewable liquid fuels. In California, woody biomass comprises the largest fraction of underutilized biomass available for biofuel production, but conversion to fuels is challenged both by recalcitrance to deconstruction and by toxicity toward downstream saccharification and fermentation due to organic acids and phenolic compounds generated during pretreatment. In this study, we optimize pretreatment and scale-up of an integrated one-pot process for deconstruction of California woody biomass using the ionic liquid (IL) cholinium lysinate [Ch][Lys] as a pretreatment solvent. By evaluating the impact of solid loading, solid removal, yeast acclimatization, fermentation temperature, fermentation pH, and nutrient supplementation on final ethanol yields and titers, we achieve nearly full conversion of both glucose and xylose to ethanol with commercial C5-utilizing Saccharomyces cerevisiae. We then demonstrate process scalability in 680 L pilot-scale fermentation, achieving >80% deconstruction efficiency, >90% fermentation efficiency, 27.7 g/L ethanol titer, and >80% ethanol distillation efficiency from the IL-containing hydrolysate post fermentation. This fully integrated process requires no intermediate separations and no intermediate detoxification of the hydrolysate. Using an integrated biorefinery model, current performance results in a minimum ethanol selling price of $\$$8.8/gge. Reducing enzyme loading along with other minor process improvements can reduce the ethanol selling price to $\$$3/gge. This study is the largest scale demonstration of IL pretreatment and biofuel conversion known to date, and the overall biomass-to-ethanol efficiencies are the highest reported to date for any IL-based biomass-to-biofuel conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Deconstruction of Woody Biomass via Protic and Aprotic Ionic Liquid Pretreatment for Ethanol Production

Ionic liquids (ILs) have emerged as important solvents for conversion of lignocellulosic feedstocks to fuels and chemicals due to their ability to enable efficient biomass deconstruction and fractionation. Woody biomass derived from forest and agricultural residues has the potential to be used for production of biofuels and its removal from forests can help mitigate disastrous wildfires in fire-prone states like California. This study evaluated woody biomass types (pine, almond, walnut, and fir) from California as potential biofuel feedstocks. The feedstocks were pretreated with the ILs cholinium lysinate ([Ch][Lys]) and ethanolamine acetate ([EOA][OAc]), followed by enzymatic hydrolysis and fermentation of lignocellulosic sugars to produce ethanol. Under optimal conditions, [EOA][OAc] pretreatment and enzymatic hydrolysis generated glucose and xylose yields in the range of 24-82 and 14-80%, respectively, while glucose and xylose yields for the [Ch][Lys] ranged between 28-83 and 23-80%, respectively. Maximum fermentable sugar was released from almond wood, and the lowest amount was from pine and fir. Further, blends of feedstocks were also explored, and a blend with a mass ratio of 2/2/1 (almond/walnut/pine) resulted in maximum glucose and xylose (>90%) yields using [Ch][Lys]. Fermentation of this hydrolysate using a C5-utilizing strain of Saccharomyces cerevisiae resulted in a maximum ethanol concentration of 17.9 g/L for mixture biomass hydrolysate, corresponding to 60.8% fermentation efficiency. This study represents the first demonstration of the use of these ILs for pretreatment of woody biomass blends that resulted in a high overall conversion efficiency for ethanol production.

09 BIOMASS FUELS↗
DOE OSTI2023

Cell Extracts from Bacteria and Yeast Retain Metabolic Activity after Extended Storage and Repeated Thawing

Cell-free synthetic biology enables rapid prototyping of biological parts and synthesis of proteins or metabolites in the absence of cell growth constraints. Cell-free systems are frequently made from crude cell extracts, where composition and activity can vary significantly based on source strain, preparation and processing, reagents, and other considerations. This variability can cause extracts to be treated as black boxes for which empirical observations guide practical laboratory practices, including a hesitance to use dated or previously thawed extracts. To better understand the robustness of cell extracts over time, we assessed the activity of cell-free metabolism during storage. As a model, we studied conversion of glucose to 2,3-butanediol. We found that cell extracts from Escherichia coli and Saccharomyces cerevisiae subjected to an 18-month storage period and repeated freeze-thaw cycles retain consistent metabolic activity. Here this work gives users of cell-free systems a better understanding of the impacts of storage on extract behavior.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation, we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive split transcription factors in the budding yeast, Saccharomyces cerevisiae. We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automated Strain Construction for Biosynthetic Pathway Screening in Yeast

Automation accelerates the Design-Build-Test-Learn (DBTL) cycle for synthetic biology; however, most strain construction pipelines lack robotic integration. Here, in this study, we present the workflow design and source code for a modular, integrated protocol that automates the Build step in Saccharomyces cerevisiae. We programmed the Hamilton Microlab VANTAGE to integrate off-deck hardware via its central robotic arm, enabling automated steps that increased throughput to 2,000 transformations per week. We developed a user interface with the Hamilton VENUS software to support on-demand parameter customization. As a proof of concept, we screened a gene library in an engineered yeast strain producing verazine, a key intermediate in the biosynthesis of steroidal alkaloids. Our pipeline rapidly identified pathway bottlenecks and genes that enhanced verazine production by 2.0- to 5-fold. This technical note provides resources for synthetic biologists designing yeast workflows for biofoundries to screen libraries for pathway discovery/optimization, combinatorial biosynthesis, and protein engineering.

automation↗
DOE OSTI2020

SPLIT: Stable Protein Coacervation Using a Light Induced Transition

Protein coacervates serve as hubs to concentrate and sequester proteins and nucleotides and thus function as membraneless organelles to manipulate cell physiology. We have engineered a coacervating protein to create tunable, synthetic membraneless organelles that assemble in response to a single pulse of light. Coacervation is driven by the intrinsically disordered RGG domain from the protein LAF-1, and opto-responsiveness is coded by the protein PhoCl, which cleaves in response to 405 nm light. We developed a fusion protein containing a solubilizing maltose-binding protein domain, PhoCl, and two copies of the RGG domain. Several seconds of illumination at 405 nm is sufficient to cleave PhoCl, removing the solubilization domain and enabling RGG-driven coacervation within minutes in cellularsized water-in-oil emulsions. An optimized version of this system displayed light-induced coacervation in Saccharomyces cerevisiae. Finally, the methods described here provide novel strategies for inducing protein phase separation using light.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

macroMS: Image-Guided Analysis of Random Objects by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Mass spectrometry imaging is well-suited to characterizing sample surfaces for their chemical content in a spatially resolved manner. However, when the surface contains small objects with significant empty spaces between them, more efficient approaches to sample acquisition are possible. Image-guided mass spectrometry (MS) enables high-throughput analysis of a diverse range of sample types, such as microbial colonies, liquid microdroplets, and others, by recognizing and analyzing selected location targets in an image. Here, we describe an imaging protocol and macroMS, an online software suite that can be used to enhance MS measurements of macroscopic samples that are imaged by a camera or a flatbed scanner. Furthermore, the web-based tool enables users to find and filter targets from the optical images, correct optical distortion issues for improved spatial location of selected targets, input the custom geometry files into an MS device to acquire spectra at the selected locations, and finally, perform limited data analysis and use visualization tools to aid locating samples containing compounds of interest. Using the macroMS suite, an enzyme mutant library of Saccharomyces cerevisiae and nL droplet arrays of Escherichia coli and Pseudomonas fluorescens have been assayed at a rate of ~2 s/sample.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites

Mass spectrometry analyses have identified tens of thousands of unknown small molecule-associated peaks in different biological specimens. Notably, even the simplest and best studied organisms like Escherichia coli and Saccharomyces cerevisiae yield thousands of unknown peaks. A key question is how many of these reflect actual novel endogenous metabolites. To explore this, Mahieu and Patti used complete 13 C -labeling in E. coli to credential peaks as biological. This reduced the number of unknowns by more than 90%. Here, we carry out similar uniform 13 C-labeling in the Baker’s yeast S. cerevisiae and two less-studied bioenergy-relevant yeasts Rhodotorula toruloides (lipid producer) and Issatchenkia orientalis (organic acid producer). Identification of unknown metabolite peaks and their molecular formulas is facilitated through software tailored for 13 C labeling data and resulting knowledge of carbon atom count. A classification model evaluates the plausibility of each candidate formula, with peaks lacking plausible candidate formulas unlikely to reflect metabolite molecular ions. This approach prioritizes about one hundred candidate abundant unknown metabolites with logical molecular formulas. Most of these are species-specific rather than conserved across yeasts, and more are found in the nonmodel yeasts than S. cerevisiae. Thus, 13 C-labeling data on unknown metabolites highlights the potential for discovering new metabolites and pathways in nonmodel yeasts.

Carbon↗
DOE OSTI2020

Synthetic hybrids of six yeast species

Abstract Allopolyploidy generates diversity by increasing the number of copies and sources of chromosomes. Many of the best-known evolutionary radiations, crops, and industrial organisms are ancient or recent allopolyploids. Allopolyploidy promotes differentiation and facilitates adaptation to new environments, but the tools to test its limits are lacking. Here we develop an iterative method of Hybrid Production (iHyPr) to combine the genomes of multiple budding yeast species, generating Saccharomyces allopolyploids of at least six species. When making synthetic hybrids, chromosomal instability and cell size increase dramatically as additional copies of the genome are added. The six-species hybrids initially grow slowly, but they rapidly regain fitness and adapt, even as they retain traits from multiple species. These new synthetic yeast hybrids and the iHyPr method have potential applications for the study of polyploidy, genome stability, chromosome segregation, and bioenergy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An integrated in vivo/in vitro framework to enhance cell-free biosynthesis with metabolically rewired yeast extracts

Cell-free systems using crude cell extracts present appealing opportunities for designing biosynthetic pathways and enabling sustainable chemical synthesis. However, the lack of tools to effectively manipulate the underlying host metabolism in vitro limits the potential of these systems. Here, we create an integrated framework to address this gap that leverages cell extracts from host strains genetically rewired by multiplexed CRISPR-dCas9 modulation and other metabolic engineering techniques. As a model, we explore conversion of glucose to 2,3-butanediol in extracts from flux-enhanced Saccharomyces cerevisiae strains. We show that cellular flux rewiring in several strains of S. cerevisiae combined with systematic optimization of the cell-free reaction environment significantly increases 2,3-butanediol titers and volumetric productivities, reaching productivities greater than 0.9 g/L-h. We then show the generalizability of the framework by improving cell-free itaconic acid and glycerol biosynthesis. Our coupled in vivo/in vitro metabolic engineering approach opens opportunities for synthetic biology prototyping efforts and cell-free biomanufacturing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production

Abstract Branched-chain amino acid (BCAA) metabolism fulfills numerous physiological roles and can be harnessed to produce valuable chemicals. However, the lack of eukaryotic biosensors specific for BCAA-derived products has limited the ability to develop high-throughput screens for strain engineering and metabolic studies. Here, we harness the transcriptional regulator Leu3p from Saccharomyces cerevisiae to develop a genetically encoded biosensor for BCAA metabolism. In one configuration, we use the biosensor to monitor yeast production of isobutanol, an alcohol derived from valine degradation. Small modifications allow us to redeploy Leu3p in another biosensor configuration that monitors production of the leucine-derived alcohol, isopentanol. These biosensor configurations are effective at isolating high-producing strains and identifying enzymes with enhanced activity from screens for branched-chain higher alcohol (BCHA) biosynthesis in mitochondria as well as cytosol. Furthermore, this biosensor has the potential to assist in metabolic studies involving BCAA pathways, and offers a blueprint to develop biosensors for other products derived from BCAA metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Compositional and temporal division of labor modulates mixed sugar fermentation by an engineered yeast consortium

Abstract Synthetic microbial communities have emerged as an attractive route for chemical bioprocessing. They are argued to be superior to single strains through microbial division of labor (DOL), but the exact mechanism by which DOL confers advantages remains unclear. Here, we utilize a synthetic Saccharomyces cerevisiae consortium along with mathematical modeling to achieve tunable mixed sugar fermentation to overcome the limitations of single-strain fermentation. The consortium involves two strains with each specializing in glucose or xylose utilization for ethanol production. By controlling initial community composition, DOL allows fine tuning of fermentation dynamics and product generation. By altering inoculation delay, DOL provides additional programmability to parallelly regulate fermentation characteristics and product yield. Mathematical models capture observed experimental findings and further offer guidance for subsequent fermentation optimization. This study demonstrates the functional potential of DOL in bioprocessing and provides insight into the rational design of engineered ecosystems for various applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Machine learning reveals genes impacting oxidative stress resistance across yeasts

Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterize the variation in resistance to the ROS-inducing compound tert -butyl hydroperoxide across the ancient yeast subphylum Saccharomycotina and use machine learning (ML) to identify gene families whose sizes are predictive of ROS resistance. The most predictive features are enriched in gene families related to cell wall organization and include two reductase gene families. We estimate the quantitative contributions of features to each species’ classification to guide experimental validation and show that overexpression of the old yellow enzyme (OYE) reductase increases ROS resistance in Kluyveromyces lactis , while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes are hypersensitive to ROS. Altogether, this work provides a framework for how ML can uncover genetic mechanisms underlying trait variation across diverse species and inform trait manipulation for clinical and biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genetic circuit design automation for yeast

Cells can be programmed to monitor and react to their environment using genetic circuits. Design automation software maps a desired circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, in this work we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build circuits with up to 11 regulatory proteins. A simple dynamic model predicts the circuit response over days. Genetic circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗