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189 records · Page 11

Data for "Decompartmentalization of the yeast mitochondrial metabolism to improve chemical production in Issatchenkia orientalis "

Microbial production of chemicals may suffer from inadequate cofactor provision, a challenge further exacerbated in yeasts due to compartmentalized cofactor metabolism. Here, we perform cofactor engineering through the decompartmentalization of mitochondrial metabolism to improve succinic acid (SA) production in Issatchenkia orientalis . We localize the reducing equivalents of mitochondrial NADH to the cytosol through cytosolic expression of its pyruvate dehydrogenase (PDH) complex and couple a reductive tricarboxylic acid pathway with a glyoxylate shunt, partially bypassing an NADH-dependent malate dehydrogenase to conserve NADH. Cytosolic SA production reaches a titer of 104 g/L and a yield of 0.85 g/g glucose, surpassing the yield of 0.66 g/g glucose constrained by cytosolic NADH availability. Additionally, expressing cytosolic PDH, we expand our I. orientalis platform to enhance acetyl-CoA-derived citramalic acid and triacetic acid lactone production by 1.22- and 4.35-fold, respectively. Our work establishes I. orientalis as a versatile platform to produce markedly reduced and acetyl-CoA-derived chemicals.

bioproducts↗

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

59 BASIC BIOLOGICAL SCIENCES↗

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

09 BIOMASS FUELS↗

Hydropower Biological Evaluation Toolset Best Practice Guide

Field studies using live fish are necessary for the evaluation of turbine biological performance, but they cannot determine the specific hydraulic conditions or physical stresses experienced by the fish, the locations where deleterious conditions occur, or the specific causes of the biological response. Using the Sensor Fish (SF) sensing technology, this deficiency can be overcome because the SF can be released independently or concurrently with live fish directly into operating infrastructure, and it takes high-frequency measurements of hydraulic conditions such as pressure, acceleration, and rotation acting on a body in situ during downstream passage. The Hydropower Biological Evaluation Tools (HBET; Hou et al. 2018) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by the PNNL-developed SF and by live fish. HBET was developed specifically to design SF field studies, process the raw data, and analyze the processed data efficiently and scientifically. Its objectives are to facilitate SF studies focused on characterizing hydraulic conditions and to apply SF data for evaluating the impacts on fish from passage through hydro-structures. HBET allows users to design new studies, analyze data, perform statistical analyses, and evaluate predicted biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to evaluate hydro-structures to enhance environmental sustainability in a cost-effective manner.

13 HYDRO ENERGY↗

Multiplexed Knockouts in the Model Diatom Phaeodactylum by Episomal Delivery of a Selectable Cas9

Marine diatoms are eukaryotic microalgae that play significant ecological and biogeochemical roles in oceans. They also have significant potential as organismal platforms for exploitation to address biotechnological and industrial goals. In order to address both modes of research, sophisticated molecular and genetic tools are required. We presented here new and improved methodologies for introducing CRISPRCas9 to the model diatom Phaeodactylum tricornutum cells and a streamlined protocol for genotyping mutant cell lines with previously unknown phenotypes. First, bacterialconjugation was optimized for the delivery of Cas9 by transcriptionally fusing Cas9 to a selectable marker by the 2A peptide. An episome cloning strategy using both negative and positive selection was developed to streamline CRISPR-episome assembly. Next, cell line picking and genotyping strategies, that utilize manual sequencing curation, TIDE sequencing analysis, and a T7 endonuclease assay, were developed to shorten the time required to generate mutants. Following this new experimental pipeline, both singlegene and two-gene knockout cell lines were generated at mutagenesis efficiencies of 48% and 25%, respectively. Lastly, a protocol for precise gene insertions via CRISPRCas9 targeting was developed using particle-bombardment transformation methods. Overall, the novel Cas9 episome design and improved genotyping methods presented here allow for quick and easy genotyping and isolation of Phaeodactylum mutant cell lines (less than 3 weeks) without relying on a known phenotype to screen for mutants.

59 BASIC BIOLOGICAL SCIENCES↗

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Automated microscale bioreactors (15 mL) can be a useful tool for cell culture engineers. They facilitate the simultaneous execution of a wide variety of experimental conditions while minimizing potential process variability. Applications of this approach include: clone screening, temperature and pH shifts, media and supplement optimization. Furthermore, the small reactor volumes are conducive to large Design of Experiments that investigate a wide range of conditions. This allows upstream processes to be significantly optimized before scale-up where experimentation is more limited in scope due to time and economic constraints. Automated microscale bioreactor systems offer various advantages over traditional small scale cell culture units, such as shake flasks or spinner flasks. However, during pilot scale process development significant care must be taken to ensure that these advantages are realized. When run with care, the system can enable high level automation, can be programmed to run DOE's with a higher number of variables and can reduce sampling time when integrated with a nutrient analyzer or cell counter. Integration of the expert-derived heuristics presented here, with current automated microscale bioreactor experiments can minimize common pitfalls that hinder meaningful results. In the extreme, failure to adhere to the principles laid out here can lead to equipment damage that requires expensive repairs. Furthermore, the microbioreactor systems have small culture volumes making characterization of cell culture conditions difficult. The number and amount of samples taken in-process in batch mode culture is limited as operating volumes cannot fall below 10 mL. This method will discuss the benefits and drawbacks of microscale bioreactor systems.

59 BASIC BIOLOGICAL SCIENCES↗

Spinning the Facts against Genetically Engineered Foods?

A signaling game is developed in order to derive the equilibrium conditions under which special interest groups (SIGs) involved in the controversial debate over genetically engineered (GE) foods have the incentive to truthfully reveal their information or spin facts regarding the health impact of GE foods. Consumers can choose to inspect information provided by SIGs at a cost. The risk of spinning facts is much higher for pro-GE groups, because if it turns out that a certain GE food is unsafe, the penalty will be severe. However, anti-GE groups can still spin facts at low risk even if consumers choose to inspect. This helps explain why some pro-GE groups, particularly the biotech industry, tend to remain silent. Revealing information regarding the safety of GE foods could be counterproductive given pre-existing public skepticism. Consumers may not make “better” decisions with more information provided because more information increases their inspection costs. When it is costlier for consumers to inspect, it is more likely that anti-GE groups will continue to spin facts about the negative health impact of GE foods.

59 BASIC BIOLOGICAL SCIENCES↗