Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels are biofuels produced from a renewable carbon source using engineered microorganisms. Two biofuels, ethanol and biodiesel, have been made inroads to displacing petroleum-based fuels, but their penetration has been limited by the amounts that can be used in conventional engines and by cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure, but have had limited penetration due to costs. In this review, we will discuss the advances in engineering microbial metabolism to produce advanced biofuels and prospects for reducing their costs

Small-molecule biosensors for high-throughput metabolic engineering

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. In this presentation we outline a versatile and high-throughput method to evolve and functionalize prokaryotic aTF ligand specificity and transfer functions in a eukaryote chassis, namely baker’s yeast Saccharomyces cerevisiae. From a single round of directed evolution of the aTF ligand-binding domain coupled with various toggled selection regimes, we robustly select aTF variants evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion of function from activation to repression. Importantly, by targeting only the ligand-binding domain, the evolved biosensors display DNA-binding affinities similar to parental aTFs and are functional when ported back into a non-native prokaryote chassis. The developed platform technology thus leverages aTF evolvability for the development of new biosensors with user-defined small-molecule specificities and transfer functions. Finally, the presentation will highlight examples on biosensor applications for high-throughput metabolic engineering. Please click Additional Files below to see the full abstract

ClusterCAD: a computational platform for type I modular polyketide synthase design

ClusterCAD is a web-based toolkit designed to leverage the collinear structure and deterministic logic of type I modular polyketide synthases (PKSs) for synthetic biology applications. The unique organization of these megasynthases, combined with the diversity of their catalytic domain building blocks, has fueled an interest in harnessing the biosynthetic potential of PKSs for the microbial production of both novel natural product analogs and industrially relevant small molecules. However, a limited theoretical understanding of the determinants of PKS fold and function poses a substantial barrier to the design of active variants, and identifying strategies to reliably construct functional PKS chimeras remains an active area of research. In this work, we formalize a paradigm for the design of PKS chimeras and introduce ClusterCAD as a computational platform to streamline and simplify the process of designing experiments to test strategies for engineering PKS variants. ClusterCAD provides chemical structures with stereochemistry for the intermediates generated by each PKS module, as well as sequence- and structure-based search tools that allow users to identify modules based either on amino acid sequence or on the chemical structure of the cognate polyketide intermediate. ClusterCAD can be accessed at https://clustercad.jbei.org and at http://clustercad.igb.uci.edu

<i>In vitro</i> Characterization of Phenylacetate Decarboxylase, a Novel Enzyme Catalyzing Toluene Biosynthesis in an Anaerobic Microbial Community

Anaerobic bacterial biosynthesis of toluene from phenylacetate was reported more than two decades ago, but the biochemistry underlying this novel metabolism has never been elucidated. Here we report results of in vitro characterization studies of a novel phenylacetate decarboxylase from an anaerobic, sewage-derived enrichment culture that quantitatively produces toluene from phenylacetate; complementary metagenomic and metaproteomic analyses are also presented. Among the noteworthy findings is that this enzyme is not the well-characterized clostridial p-hydroxyphenylacetate decarboxylase (CsdBC). However, the toluene synthase under study appears to be able to catalyze both phenylacetate and p-hydroxyphenylacetate decarboxylation. Observations suggesting that phenylacetate and p-hydroxyphenylacetate decarboxylation in complex cell-free extracts were catalyzed by the same enzyme include the following: (i) the specific activity for both substrates was comparable in cell-free extracts, (ii) the two activities displayed identical behavior during chromatographic separation of cell-free extracts, (iii) both activities were irreversibly inactivated upon exposure to O2, and (iv) both activities were similarly inhibited by an amide analog of p-hydroxyphenylacetate. Based upon these and other data, we hypothesize that the toluene synthase reaction involves a glycyl radical decarboxylase. This first-time study of the phenylacetate decarboxylase reaction constitutes an important step in understanding and ultimately harnessing it for making bio-based toluene

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

13C metabolic flux analysis (13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference S. cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1∆ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1∆ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria towards cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1∆ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium

Constructing tailored isoprenoid products by structure-guided modification of geranylgeranyl reductase

The archaeal enzyme geranylgeranyl reductase (GGR) catalyzes hydrogenation of carbon-carbon double bonds to produce the saturated alkyl chains of the organism\u27s unusual isoprenoid-derived cell membrane. Enzymatic reduction of isoprenoid double bonds is of considerable interest both to natural products researchers and to synthetic biologists interested in the microbial production of isoprenoid drug or biofuel molecules. Here we present crystal structures of GGR from Sulfolobus acidocaldarius, including the structure of GGR bound to geranylgeranyl pyrophosphate (GGPP). The structures are presented alongside activity data that depict the sequential reduction of GGPP to H(6)GGPP via the intermediates H(2)GGPP and H(4)GGPP. We then modified the enzyme to generate sequence variants that display increased rates of H(6)GGPP production or are able to halt the extent of reduction at H(2)GGPP and H(4)GGPP. Crystal structures of these variants not only reveal the structural bases for their altered activities; they also shed light onto the catalytic mechanism employed

Characterization of mercury bioremediation by transgenic bacteria expressing metallothionein and polyphosphate kinase

<p>Abstract</p> <p>Background</p> <p>The use of transgenic bacteria has been proposed as a suitable alternative for mercury remediation. Ideally, mercury would be sequestered by metal-scavenging agents inside transgenic bacteria for subsequent retrieval. So far, this approach has produced limited protection and accumulation. We report here the development of a transgenic system that effectively expresses metallothionein (<it>mt-1</it>) and polyphosphate kinase (<it>ppk</it>) genes in bacteria in order to provide high mercury resistance and accumulation.</p> <p>Results</p> <p>In this study, bacterial transformation with transcriptional and translational enhanced vectors designed for the expression of metallothionein and polyphosphate kinase provided high transgene transcript levels independent of the gene being expressed. Expression of polyphosphate kinase and metallothionein in transgenic bacteria provided high resistance to mercury, up to 80 μM and 120 μM, respectively. Here we show for the first time that metallothionein can be efficiently expressed in bacteria without being fused to a carrier protein to enhance mercury bioremediation. Cold vapor atomic absorption spectrometry analyzes revealed that the <it>mt-1 </it>transgenic bacteria accumulated up to 100.2 ± 17.6 μM of mercury from media containing 120 μM Hg. The extent of mercury remediation was such that the contaminated media remediated by the <it>mt-1 </it>transgenic bacteria supported the growth of untransformed bacteria. Cell aggregation, precipitation and color changes were visually observed in <it>mt-1 </it>and <it>ppk </it>transgenic bacteria when these cells were grown in high mercury concentrations.</p> <p>Conclusion</p> <p>The transgenic bacterial system described in this study presents a viable technology for mercury bioremediation from liquid matrices because it provides high mercury resistance and accumulation while inhibiting elemental mercury volatilization. This is the first report that shows that metallothionein expression provides mercury resistance and accumulation in recombinant bacteria. The high accumulation of mercury in the transgenic cells could present the possibility of retrieving the accumulated mercury for further industrial applications.</p

PVN-LOT-414-C-005

UnlabelledEngineering 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.ImportanceThe 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

BayFlux: A Bayesian Method to Quantify Metabolic Fluxes and their Uncertainty at the Genome Scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty