Kevin W. George

Improving Microbial Biogasoline Production in Escherichia coli Using Tolerance Engineering

ABSTRACT Engineering microbial hosts for the production of fungible fuels requires mitigation of limitations posed on the production capacity. One such limitation arises from the inherent toxicity of solvent-like biofuel compounds to production strains, such as Escherichia coli. Here we show the importance of host engineering for the production of short-chain alcohols by studying the overexpression of genes upregulated in response to exogenous isopentenol. Using systems biology data, we selected 40 genes that were upregulated following isopentenol exposure and subsequently overexpressed them in E. coli. Overexpression of several of these candidates improved tolerance to exogenously added isopentenol. Genes conferring isopentenol tolerance phenotypes belonged to diverse functional groups, such as oxidative stress response (soxS, fpr, and nrdH), general stress response (metR, yqhD, and gidB), heat shock-related response (ibpA), and transport (mdlB). To determine if these genes could also improve isopentenol production, we coexpressed the tolerance-enhancing genes individually with an isopentenol production pathway. Our data show that expression of 6 of the 8 candidates improved the production of isopentenol in E. coli, with the methionine biosynthesis regulator MetR improving the titer for isopentenol production by 55%. Additionally, expression of MdlB, an ABC transporter, facilitated a 12% improvement in isopentenol production. To our knowledge, MdlB is the first example of a transporter that can be used to improve production of a short-chain alcohol and provides a valuable new avenue for host engineering in biogasoline production. IMPORTANCE The use of microbial host platforms for the production of bulk commodities, such as chemicals and fuels, is now a focus of many biotechnology efforts. Many of these compounds are inherently toxic to the host microbe, which in turn places a limit on production despite efforts to optimize the bioconversion pathways. In order to achieve economically viable production levels, it is also necessary to engineer production strains with improved tolerance to these compounds. We demonstrate that microbial tolerance engineering using transcriptomics data can also identify targets that improve production. Our results include an exporter and a methionine biosynthesis regulator that improve isopentenol production, providing a starting point to further engineer the host for biogasoline production. The use of microbial host platforms for the production of bulk commodities, such as chemicals and fuels, is now a focus of many biotechnology efforts. Many of these compounds are inherently toxic to the host microbe, which in turn places a limit on production despite efforts to optimize the bioconversion pathways. In order to achieve economically viable production levels, it is also necessary to engineer production strains with improved tolerance to these compounds. We demonstrate that microbial tolerance engineering using transcriptomics data can also identify targets that improve production. Our results include an exporter and a methionine biosynthesis regulator that improve isopentenol production, providing a starting point to further engineer the host for biogasoline production.

Correlation analysis of targeted proteins and metabolites to assess and engineer microbial isopentenol production.

The ability to rapidly assess and optimize heterologous pathway function is critical for effective metabolic engineering. Here, we develop a systematic approach to pathway analysis based on correlations between targeted proteins and metabolites and apply it to the microbial production of isopentenol, a promising biofuel. Starting with a seven‐gene pathway, we performed a correlation analysis to reduce pathway complexity and identified two pathway proteins as the primary determinants of efficient isopentenol production. Aided by the targeted quantification of relevant pathway intermediates, we constructed and subsequently validated a conceptual model of isopentenol pathway function. Informed by our analysis, we assembled a strain which produced isopentenol at a titer 1.5 g/L, or 46% of theoretical yield. Our engineering approach allowed us to accurately identify bottlenecks and determine appropriate pathway balance. Paired with high‐throughput cloning techniques and analytics, this strategy should prove useful for the analysis and optimization of increasingly complex heterologous pathways. Biotechnol. Bioeng. 2014;111: 1648–1658.

Metabolic engineering for the high-yield production of isoprenoid-based C5 alcohols in E. coli

Branched five carbon (C5) alcohols are attractive targets for microbial production due to their desirable fuel properties and importance as platform chemicals. In this study, we engineered a heterologous isoprenoid pathway in E. coli for the high-yield production of 3-methyl-3-buten-1-ol, 3-methyl-2-buten-1-ol, and 3-methyl-1-butanol, three C5 alcohols that serve as potential biofuels. We first constructed a pathway for 3-methyl-3-buten-1-ol, where metabolite profiling identified NudB, a promiscuous phosphatase, as a likely pathway bottleneck. We achieved a 60% increase in the yield of 3-methyl-3-buten-1-ol by engineering the Shine-Dalgarno sequence of nudB, which increased protein levels by 9-fold and reduced isopentenyl diphosphate (IPP) accumulation by 4-fold. To further optimize the pathway, we adjusted mevalonate kinase (MK) expression and investigated MK enzymes from alternative microbes such as Methanosarcina mazei. Next, we expressed a fusion protein of IPP isomerase and the phosphatase (Idi1~NudB) along with a reductase (NemA) to diversify production to 3-methyl-2-buten-1-ol and 3-methyl-1-butanol. Finally, we used an oleyl alcohol overlay to improve alcohol recovery, achieving final titers of 2.23 g/L of 3-methyl-3-buten-1-ol (~70% of pathway-dependent theoretical yield), 150 mg/L of 3-methyl-2-buten-1-ol, and 300 mg/L of 3-methyl-1-butanol.

Impact of Pretreatment Technologies on Saccharification and Isopentenol Fermentation of Mixed Lignocellulosic Feedstocks

In order to enable the large-scale production of biofuels or chemicals from lignocellulosic biomass, a consistent and affordable year-round supply of lignocellulosic feedstocks is essential. Feedstock blending and/or densification offers one promising solution to overcome current challenges on biomass supply, i.e., low energy and bulk densities and significant compositional variations. Therefore, it is imperative to develop conversion technologies that can process mixed pelleted biomass feedstocks with minimal negative impact in terms of overall performance of the relevant biorefinery unit operations: pretreatment, fermentable sugar production, and fuel titers. We processed the mixture of four feedstocks—corn stover, switchgrass, lodgepole pine, and eucalyptus (1:1:1:1 on dry weight basis)—in flour and pellet form using ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate, dilute sulfuric acid (DA), and soaking in aqueous ammonia (SAA) pretreatments. Commercial enzyme mixtures, including cellulases and hemicellulases, were then applied to these pretreated feedstocks at low to moderate enzyme loadings to determine hydrolysis efficiency. Results show significant variations on the chemical composition, crystallinity, and enzymatic digestibility of the pretreated feedstocks across the different pretreatment technologies studied. The advanced biofuel isopentenol was produced during simultaneous saccharification and fermentation (SSF) of pretreated feedstocks using an engineered Escherichia coli strain. Results show that IL pretreatment liberates the most sugar during enzymatic saccharification, and in turn led to the highest isopentenol titer as compared to DA and SAA pretreatments. This study provides insights on developing biorefinery technologies that produce advanced biofuels based on mixed feedstock streams.

Encoding substrates with mass tags to resolve stereospecific reactions using Nimzyme

RATIONALE The nanostructure-initiator mass spectrometry based enzyme assay (Nimzyme) provides a rapid method for screening glycan modifying reactions. However, this approach cannot resolve stereospecific reactions which are common in glycobiology and are typically assayed using lower-throughput methods (gas chromatography/mass spectrometry (GC/MS) or liquid chromatography/tandem mass spectrometry (LC/MS/MS) analysis) often in conjunction with stable isotopically labeled reactants. However, in many applications, library size necessitates the development of higher-throughput screening approaches of stereospecific reactions from crude sample preparations. Therefore, here we test the approach of utilizing Nimzyme linkers with unique masses to encode substrate identity such that this assay can resolve stereospecific reactions. METHODS We utilize the nanostructure-initiator mass spectrometry (NIMS) enzyme assay in conjuction with an accurate mass tagging approach where each reactant is tagged with a unique perfluoronated tail. Mass spectrometric analysis was conducted using conventional MALDI-TOF instrumentation. RESULTS Stereospecific reaction pathways of three stereoisomers (maltose, lactose and cellobiose) to afford the same product glucose were resolved simutaneously due to the presence of unique fluorous tags on both reactants and products. Not only purified enzymes, but also crude cell lysates can be used in this assay. CONCLUSIONS The Nimzyme assay with accurate mass tagging provides a rapid method for screening for targeted stereospecific reactions using mass spectrometry and may be useful for high-throughput screening and functional annotation of a wide range of glycan-modifying enzymes.

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

A numerical simulation of a plunging breaking wave

ONR Program Manager: Dr. Patrick Purtell. ONR Contract Number: N00014-07-C-0184. Computer resources provided by the DoD High Performance Computing Modernization Program at the ERDC DoD Supercomputing Research Center, Vicksburg MS.