Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Genome engineering”

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 397 records · Page 22

Genetic and genomic analysis of RNases in model cyanobacteria

Cyanobacteria are diverse photosynthetic microbes with the ability to convert CO 2 into useful products. However, metabolic engineering of cyanobacteria remains challenging because of the limited resources for modifying the expression of endogenous and exogenous biochemical pathways. Fine-tuned control of protein production will be critical to optimize the biological conversion of CO 2 into desirable molecules. Messenger RNA (mRNA) are labile intermediates that play critical roles in determining the translation rate and steady state protein concentrations in the cell. The majority of studies on mRNA turnover have focused on the model heterotrophic bacteria Escherichia coli and Bacillus subtilis . These studies have elucidated many RNA modifying and processing enzymes and have highlighted the differences between these Gram-negative and Gram-positive bacteria, respectively. In contrast, much less is known about mRNA turnover in cyanobacteria. We generated a compendium of the major ribonucleases (RNases) and provide an in-depth analysis of RNase III-like enzymes in commonly studied and diverse cyanobacteria. Furthermore, using targeted gene deletion, we genetically dissected the RNases in Synechococcus sp. PCC 7002, one of the fastest growing and industrially attractive cyanobacterial strains. We found that all three cyanobacterial homologs of RNase III and a member of the RNase II/R family are not essential under standard laboratory conditions, while homologs of RNase E/G, RNase J1/J2, PNPase, and a different member of the RNase II/R family appear to be essential for growth. In conclusion, this work will enhance our understanding of native control of gene expression and will facilitate the development of an RNA-based toolkit for metabolic engineering in cyanobacteria.

59 BASIC BIOLOGICAL SCIENCES↗

Data for Mitochondrial ATP Generation is More Proteome Efficient than Glycolysis

Metabolic efficiency profoundly influences organismal fitness. Heterotrophs, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. While respiration is more energy-efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (i.e. faster). Through quantitative flux analysis and proteomics, we find however that mitochondrial respiration is actually more proteome-efficient than aerobic glycolysis. This is shown across yeasts, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which is valuable for hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth, but outgrow respiratory cells in oxygen limitation. Thus, aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

Metabolomics↗

iNovo479: Metabolic Modeling Provides a Roadmap to Optimize Bioproduct Yield from Deconstructed Lignin Aromatics by Novosphingobium aromaticivorans

Lignin is an abundant renewable source of aromatics and precursors for the production of other organic chemicals. However, lignin is a heterogeneous polymer, so the mixture of aromatics released during its depolymerization can make its conversion to chemicals challenging. Microbes are a potential solution to this challenge, as some can catabolize multiple aromatic substrates into one product. Novosphingobium aromaticivorans has this ability, and its use as a bacterial chassis for lignin valorization could be improved by the ability to predict product yields based on thermodynamic and metabolic inputs. In this work, we built a genome-scale metabolic model of N. aromaticivorans, iNovo479, to guide the engineering of strains for aromatic conversion into products. iNovo479 predicted product yields from single or multiple aromatics, and the impact of combinations of aromatic and non-aromatic substrates on product yields. We show that enzyme reactions from other organisms can be added to iNovo479 to predict the feasibility and profitability of producing additional products by engineered strains. Thus, we conclude that iNovo479 can help guide the design of bacteria to convert lignin aromatics into valuable chemicals.

59 BASIC BIOLOGICAL SCIENCES↗

Methods of identifying and modulating pathogen resistance in plants

Pathogenic fungi from the genus Sphaerulina cause damage to a diverse array of economically important plant species. The present disclosure provides methods of determining whether a plant is susceptible to pathogenic fungi infections. The disclosure further provides methods of engineering pathogenic fungi-resistant plants from susceptible plants using targeted genome editing techniques.

Muchero, Wellington↗

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗

Engineering the hyperthermophilic archaeon Pyrococcus furiosus for 1-propanol production

ABSTRACT Society relies heavily on chemicals traditionally produced through the refinement of fossil fuels. The conversion of renewable biomass to value-added chemicals by microbes, particularly hyperthermophiles (T opt ≥80°C), offers a renewable alternative to this traditional approach. Herein, we describe the engineering of the hyperthermophilic archaeon Pyrococcus furiosus , which grows optimally (T opt ) at 100°C, for the conversion of sugar to 1-propanol. This was accomplished by constructing a hybrid metabolic pathway consisting of two native and seven heterologously produced enzymes to convert acetyl-CoA from carbohydrate metabolism to 1-propanol. A total of eleven foreign genes from two other organisms were utilized, one from the thermophilic bacterium Thermoanaerobacter sp. strain X514 and 10 from the thermoacidophilic archaeon Metallosphaera sedula, both of which grow optimally near 70°C. The recombinant P. furiosus strain produced 1-propanol at similar concentrations (up to ~1 mM) when incubated at 75°C to activate the gene products of Thermoanaerobacter sp. strain X514 and M. sedula and by initially incubating at 95°C for P. furiosus growth and then subsequently returning to 75°C to promote 1-propanol formation. Note that 1-propanol was not produced if the culture was grown only at 95°C. This work has the potential for future optimization through harnessing the genome-scale metabolic model of P. furiosus that was used herein to identify engineering targets to increase 1-propanol titer. IMPORTANCE As petroleum reserves become increasingly strained, the development of renewable alternatives to traditional chemical synthesis becomes more important. In this work, a high-temperature biological system for sugar to 1-propanol conversion was demonstrated by metabolic engineering of the hyperthermophilic archaeon Pyrococcus furiosus (T opt 100°C). The engineered strain produced 1-propanol by temperature shifting from 75°C to 95°C and then back to 75°C to accommodate the temperature ranges for native and foreign proteins associated with 1-propanol biosynthesis. Genome-scale metabolic modeling informed the carbon and reductant flux in the system, identified potential factors limiting 1-propanol production, and revealed potential optimization targets.

O'Quinn, Hailey C. (ORCID:0009000274865755)↗

Microbial Community Analysis & Functional Evaluation in Soils

The overall objective of this proposal was to develop technologies to alter the composition and function of important members of microbial communities. In particular, the overall objective of the microbial community editing portion of the proposal focuses on developing foundational tools and understanding required to predict, alter and design grass rhizosphere communities impacting DOE missions. Specifically, the project is centered on the Microbial Community Analysis & Functional Evaluation in Soils (m-CAFES) to manipulate microbial consortia associated with plants of interest for the bioenergy sector, under the presumption that bacterial communities can be manipulated to enhance plant health. For tasks of specific interest to us, we are focusing on developing novel Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) based technologies (primarily focusing on Aim 1) and their delivery modalities (notably subaim 1.2) to edit specific bacterial genomes of interest to enhance their functionalities, and programmably ablate specific undesirable members of bacterial communities for plant health. We are focusing on engineering bacteriophages (bacterial viruses, for subaim 1.2) to carry programmable CRISPR-Cas systems (subaim 1.1) to target (ablate) or alter (edit) genomes of interest. This will enable us to carry out microbial perturbations that will impact community composition and function and ultimately plant growth and health, to enable the next phase of the project by deploying them in situ (subaims 1.3 and 1.4).

59 BASIC BIOLOGICAL SCIENCES↗

MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm

Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacteria that has the smallest genome among all sequenced aerobic myxobacteria, was easily achieved using the binning software. This work demonstrates that the processes required for recovering genomes from assembled metagenomic datasets can be readily automated, an important advance in understanding the metabolic potential of microbes in natural environments. MaxBin is available at https://sourceforge.net/projects/maxbin/.

59 BASIC BIOLOGICAL SCIENCES↗

Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design

Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

59 BASIC BIOLOGICAL SCIENCES↗

Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

Background: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H-2/CO2, and more importantly on synthesis gas (H-2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. Results: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. Conclusions: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.

Nagarajan, H↗

Data for An End-to-End Pipeline for Succinic Acid Production at an Industrially Relevant Scale Using Issatchenkia orientalis

Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34–90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids.

Metabolomics↗

Comparative functional genomics identifies an iron-limited bottleneck in a Saccharomyces cerevisiae strain with a cytosolic-localized isobutanol pathway

Metabolic engineering strategies have been successfully implemented to improve the production of isobutanol, a next-generation biofuel, in Saccharomyces cerevisiae. Here, we explore how two of these strategies, pathway relocalization and redox cofactor-balancing, affect the performance and physiology of isobutanol producing strains. We equipped yeast with isobutanol cassettes which had either a mitochondrial or cytosolic localized isobutanol pathway and used either a redox-imbalanced (NADPH-dependent) or redox-balanced (NADHdependent) ketol-acid reductoisomerase enzyme. We then conducted transcriptomic, proteomic and metabolomic analyses to elucidate molecular differences between the engineered strains. Pathway localization had a large effect on isobutanol production with the strain expressing the mitochondrial-localized enzymes producing 3.8-fold more isobutanol than strains expressing the cytosolic enzymes. Cofactor-balancing did not improve isobutanol titers and instead the strain with the redox-imbalanced pathway produced 1.5-fold more isobutanol than the balanced version, albeit at low overall pathway flux. Functional genomic analyses suggested that the poor performances of the cytosolic pathway strains were in part due to a shortage in cytosolic Fe–S clusters, which are required cofactors for the dihydroxyacid dehydratase enzyme. We then demonstrated that this cofactor limitation may be partially recovered by disrupting iron homeostasis with a fra2 mutation, thereby increasing cellular iron levels. The resulting isobutanol titer of the fra2 null strain harboring a cytosolic-localized isobutanol pathway outperformed the strain with the mitochondrial-localized pathway by 1.3-fold, demonstrating that both localizations can support flux to isobutanol.

59 BASIC BIOLOGICAL SCIENCES↗

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Defining Determinants and Dynamics and Cellulose Microfibril Biosynthesis, Assembly and Degredation OSP Number: 63079/A001

The central paradigm for converting plant biomass into soluble sugars for subsequent conversion to transportation fuels involves the enzymatic depolymerization of lignocellulosic plant cell walls by microbial enzymes. Despite decades of intensive research, this is still a relatively inefficient process, due largely to the recalcitrance and enormous complexity of the substrate. A major obstacle is still insufficient understanding of the detailed structure and biosynthesis of major wall components, including cellulose. For example, although cellulose is generally depicted as rigid, insoluble, uniformly crystalline microfibrils that are resistant to enzymatic degradation, the in vivo structures of plant cellulose microfibrils are surprisingly complex. Crystallinity is frequently disrupted, for example by dislocations and areas containing chain ends, resulting in “amorphous” disordered regions. Importantly, microfibril structure and the relative proportions of crystalline and non-crystalline disordered surface regions vary substantially and yet the molecular mechanisms by which plants regulate microfibril crystallinity, and other aspects of microfibril architecture, are still entirely unknown. This obviously has a profound effect on susceptibility to enzymatic hydrolysis and so this is a critical area of research in order to characterize and optimize cellulosic biomass degradation. The entire field of cell wall assembly, as distinct from polysaccharide biosynthesis, and the degree to which they are coupled, are relatively unexplored, despite the great potential for major advances in addressing the hurdle of biomass recalcitrance. Our overarching hypothesis was that identification of the molecular machinery that determine microfibril polymerization, deposition and structure will allow the design of more effective degradative systems, and the generation of cellulosic materials with enhanced and predictable bioconversion characteristics. Our experimental framework had been based on the idea that the most effective way to address this long standing and highly complex question is to adopt a broad ‘systems approach’. Accordingly, we assembled a multi-disciplinary collaborative team with collective expertise in plant biology and molecular genetics, polymer structure and chemistry, enzyme biochemistry and biochemical engineering. We used a spectrum of cutting edge technologies, including plant functional genomics, chemical genetics, live cell imaging, advanced microscopy, high energy X-ray spectroscopy and nanotechnology, to study the molecular determinants of cellulose microfibril structure. Importantly, this research effort was closely coupled with an analytical pipeline to characterize the effects of altering microfibril architecture on bioconversion potential, with the goal of generating predictive models to help guide the identification, development and implementation of new feedstocks. This project therefore spanned core basic science and applied research, in line with the goals of the program. Over the course of the project, accomplishments included: - Establishing platforms through reverse and forward genetics to identify and manipulate candidate genes that influence cellulose microfibril synthesis and structure in a model C3 grass, Brachypodium distachyon and a model C4 grass Setaria viridis; Identifying and characterizing the effects of a number of cellulose biosynthesis inhibitors (CBIs), and particularly those that target monocots with the aim of generating resistance loci; Developing protocols for the use of high energy X-ray diffraction (XRD) to study the structure and organization of cellulose microfibrils in plant walls, notably those in Arabidopsis and Brachypodium; Using the chemical and genetic based inhibition strategies to develop new mechanistic models of cellulose microfibril crystallization, and of how altering microfibril architecture influences digestibility.

09 BIOMASS FUELS↗

Modeling of cellulose, hemicellulose and lignin-carbohydrate complex formation and regulation to understand plant cell wall structure

The goal of this project is to increase the fundamental understanding of the plant secondary cell wall in wood formation, using Populus trichocarpa as the model species. The project is built on a systematic transgenic strategy to perturb the expression of targeted cell-wall component genes and transcription factors (TFs). We used a systems biology approach that integrates this genome-wide transgenesis with full genome information to advance our understanding of wood formation and how to engineer woody plants to create robust feedstocks that maximize beneficial traits for biofuel and material production.

09 BIOMASS FUELS↗

Evolutionary Divergence of Duplicated Hsf Genes in Populus

Heat shock transcription factors (Hsfs), which function as the activator of heat shock proteins (Hsps), play multiple roles in response to environmental stress and the development of plants. The Hsf family had experienced gene expansion via whole-genome duplication from a single cell algae to higher plants. Yet, how the Hsf gene family went through evolutionary divergence after genome duplication is unknown. As a model wood species, Populus trichocarpa is widely distributed in North America with various ecological and climatic environments. In this study, we used P. trichocarpa as materials and identified the expression divergence of the PtHsf gene family in developmental processes, such as dormant bud formation and opening, catkins development, and in response to environments. Through the co-expression network, we further discovered the divergent co-expressed genes that related to the functional divergence of PtHsf . Then, we studied the alternative splicing events, single nucleotide polymorphism distribution and tertiary structures of members of the PtHsf gene family. In addition to expression divergence, we uncovered the evolutionary divergence in the protein level which may be important to new function formations and for survival in changing environments. This study comprehensively assessed the evolutionary divergence of a member of the PtHsf gene family after genome duplication, paving the way for further gene function analysis and genetic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

Expression of Gre2p improves tolerance of engineered xylose-fermenting Saccharomyces cerevisiae to glycolaldehyde under xylose metabolism

Engineered S. cerevisiae employing the xylose reductase pathway enables efficient xylose valorization to fuels and chemicals. However, toxicity of thermochemically pretreated biomass hydrolysate on S. cerevisiae is one of the key technical challenges to upgrade biomass-derived sugars including xylose and glucose into high-value products. We investigated the effect of glycolaldehyde, one of the biomass-derived highly toxic aldehyde compounds, and its combinatorial inhibitory effect with other major fermentation inhibitors commonly found in plant hydrolysate such as methylglyoxal, 5-HMF, furfural, vanillin, and acetic acid on engineered xylose-fermenting S. cerevisiae in xylose and/or glucose media. We elucidated that glycolaldehyde and methylglyoxal are the key inhibitory short-aliphatic aldehydes on engineered xylose-fermenting S. cerevisiae in xylose-containing medium. Indeed, the degree of toxicity of these tested fermentation inhibitors varies with the sole carbon source of the medium. We demonstrate that genome integration of an extra copy of autologous GRE2 with its native promotor substantially improved the toxic tolerance of engineered xylose-fermenting S. cerevisiae to major inhibitory compounds including glycolaldehyde in the xylose-containing medium, and xylose-rich, lignocellulosic hydrolysate derived from Miscanthus giganteus, and concurrently improved the ethanol fermentation profile. In conclusion, outcomes of this study will aid the development of next-generation robust S. cerevisiae strains for efficient fermentation of hexose and pentose sugars found in biomass hydrolysate.

09 BIOMASS FUELS↗