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Author(s): Falk Warnecke [1, 8]; Peter Luginbühl [2, 8]; Natalia Ivanova [1]; Majid Ghassemian [2]; Toby H. Richardson [2, 9]; Justin T. Stege [2]; Michelle Cayouette [2]; Alice C. McHardy [...]

The carbon fluxes in the central metabolism of Geobacter metallireducens strain GS-15 were analyzed using [super 13]C isotopomer modeling. The results indicated that Geobacter metallireducens strain GS-15 utilizes the complete TCA cycle to oxidize acetate to C[O.sub.2] while reducing soluble Fe(III)-nitrilotriacetic acid.

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model). [ABSTRACT FROM AUTHOR]

Flowers of the hop plant provide both bitterness and "hoppy" flavor to beer. Hops are, however, both a water and energy intensive crop and vary considerably in essential oil content, making it challenging to achieve a consistent hoppy taste in beer. Here, we report that brewer's yeast can be engineered to biosynthesize aromatic monoterpene molecules that impart hoppy flavor to beer by incorporating recombinant DNA derived from yeast, mint, and basil. Whereas metabolic engineering of biosynthetic pathways is commonly enlisted to maximize product titers, tuning expression of pathway enzymes to affect target production levels of multiple commercially important metabolites without major collateral metabolic changes represents a unique challenge. By applying state-of-the-art engineering techniques and a framework to guide iterative improvement, strains are generated with target performance characteristics. Beers produced using these strains are perceived as hoppier than traditionally hopped beers by a sensory panel in a double-blind tasting. [ABSTRACT FROM AUTHOR]

Background: Glycolysis breakdowns glucose into essential building blocks and ATP/NAD(P)H for the cell, occupying a central role in its growth and bio-production. Among glycolytic pathways, the Entner Doudoroff pathway (EDP) is a more thermodynamically favorable pathway with fewer enzymatic steps than either the Embden-Meyerhof-Parnas pathway (EMPP) or the oxidative pentose phosphate pathway (OPPP). However, Escherichia coli do not use their native EDP for glucose metabolism. Results: Overexpression of edd and eda in E. coli to enhance EDP activity resulted in only a small shift in the flux directed through the EDP (~20 % of glycolysis flux). Disrupting the EMPP by phosphofructokinase I (pfkA) knockout increased flux through OPPP (~60 % of glycolysis flux) and the native EDP (~14 % of glycolysis flux), while overexpressing edd and eda in this ΔpfkA mutant directed ~70 % of glycolytic flux through the EDP. The downregulation of EMPP via the pfkA deletion significantly decreased the growth rate, while EDP overexpression in the ΔpfkA mutant failed to improve its growth rates due to metabolic burden. However, the reorganization of E. coli glycolytic strategies did reduce glucose catabolite repression. The ΔpfkA mutant in glucose medium was able to cometabolize acetate via the citric acid cycle and gluconeogenesis, while EDP overexpression in the ΔpfkA mutant repressed acetate flux toward gluconeogenesis. Moreover, 13C-pulse experiments in the ΔpfkA mutants showed unsequential labeling dynamics in glycolysis intermediates, possibly suggesting metabolite channeling (metabolites in glycolysis are pass from enzyme to enzyme without fully equilibrating within the cytosol medium). Conclusions: We engineered E. coli to redistribute its native glycolytic flux. The replacement of EMPP by EDP did not improve E. coli glucose utilization or biomass growth, but alleviated catabolite repression. More importantly, our results supported the hypothesis of channeling in the glycolytic pathways, a potentially overlooked mechanism for regulating glucose catabolism and coutilization of other substrates. The presence of channeling in native pathways, if proven true, would affect synthetic biology applications and metabolic modeling. [ABSTRACT FROM AUTHOR]

Here we report the first metatranscriptomic analysis of gene expression and regulation of ‘ Candidatus Accumulibacter’-enriched lab-scale sludge during enhanced biological phosphorus removal (EBPR). Medium density oligonucleotide microarrays were generated with probes targeting most predicted genes hypothesized to be important for the EBPR phenotype. RNA samples were collected at the early stage of anaerobic and aerobic phases (15 min after acetate addition and switching to aeration respectively). We detected the expression of a number of genes involved in the carbon and phosphate metabolisms, as proposed by EBPR models (e.g. polyhydroxyalkanoate synthesis, a split TCA cycle through methylmalonyl-CoA pathway, and polyphosphate formation), as well as novel genes discovered through metagenomic analysis. The comparison between the early stage anaerobic and aerobic gene expression profiles showed that expression levels of most genes were not significantly different between the two stages. The majority of upregulated genes in the aerobic sample are predicted to encode functions such as transcription, translation and protein translocation, reflecting the rapid growth phase of Accumulibacter shortly after being switched to aerobic conditions. Components of the TCA cycle and machinery involved in ATP synthesis were also upregulated during the early aerobic phase. These findings support the predictions of EBPR metabolic models that the oxidation of intracellularly stored carbon polymers through the TCA cycle provides ATP for cell growth when oxygen becomes available. Nitrous oxide reductase was among the very few Accumulibacter genes upregulated in the anaerobic sample, suggesting that its expression is likely induced by the deprivation of oxygen. [ABSTRACT FROM AUTHOR]

New high-throughput culture-independent molecular tools are allowing the scientific community to characterize and understand the microbial communities underpinning environmental biotechnology processes in unprecedented ways. By creatively leveraging these new data sources, microbial ecology has the potential to transition from a purely descriptive to a predictive framework, in which ecological principles are integrated and exploited to engineer systems that are biologically optimized for the desired goal. But to achieve this goal, ecology, engineering and microbiology curricula need to be changed from the very root to better promote interdisciplinarity. [Copyright &y& Elsevier]

Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge is re-engineering a partially reductive PKS module to produce a saturated β-carbon through a reductive loop (RL) exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in drug discovery, offers a viable strategy for RL exchanges. We first introduced a set of donor RLs of diverse genetic origin and chemical substrates  into the first extension module of the lipomycin PKS (LipPKS1). Product titers of these engineered unimodular PKSs correlated with chemical structure similarity between the substrate of the donor RLs and recipient LipPKS1, reaching a titer of 165 mg/L of short-chain fatty acids produced by the host  Streptomyces albus J1074. Expanding this method to larger intermediates that require bimodular communication, we introduced RLs of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid in the engineering of PKSs to produce desired, unnatural products.

Background: Glycolysis breakdowns glucose into essential building blocks and ATP/NAD(P)H for the cell, occupying a central role in its growth and bio-production. Among glycolytic pathways, the Entner Doudoroff pathway (EDP) is a more thermodynamically favorable pathway with fewer enzymatic steps than either the Embden-Meyerhof-Parnas pathway (EMPP) or the oxidative pentose phosphate pathway (OPPP). However, Escherichia coli do not use their native EDP for glucose metabolism., Results: Overexpression of edd and eda in E. coli to enhance EDP activity resulted in only a small shift in the flux directed through the EDP (~20 % of glycolysis flux). Disrupting the EMPP by phosphofructokinase I ( pfkA ) knockout increased flux through OPPP (~60 % of glycolysis flux) and the native EDP (~14 % of glycolysis flux), while overexpressing edd and eda in this Δ pfkA mutant directed ~70 % of glycolytic flux through the EDP. The downregulation of EMPP via the pfkA deletion significantly decreased the growth rate, while EDP overexpression in the Δ pfkA mutant failed to improve its growth rates due to metabolic burden. However, the reorganization of E. coli glycolytic strategies did reduce glucose catabolite repression. The Δ pfkA mutant in glucose medium was able to cometabolize acetate via the citric acid cycle and gluconeogenesis, while EDP overexpression in the Δ pfkA mutant repressed acetate flux toward gluconeogenesis. Moreover, 13 C-pulse experiments in the Δ pfkA mutants showed unsequential labeling dynamics in glycolysis intermediates, possibly suggesting metabolite channeling (metabolites in glycolysis are pass from enzyme to enzyme without fully equilibrating within the cytosol medium)., Conclusions: We engineered E. coli to redistribute its native glycolytic flux. The replacement of EMPP by EDP did not improve E. coli glucose utilization or biomass growth, but alleviated catabolite repression. More importantly, our results supported the hypothesis of channeling in the glycolytic pathways, a potentially overlooked mechanism for regulating glucose catabolism and coutilization of other substrates. The presence of channeling in native pathways, if proven true, would affect synthetic biology applications and metabolic modeling.

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields., ((c) 2009 Wiley Periodicals, Inc.)

We derive a linear recursion relation for the species abundance distribution in a statistical model of ecology and demonstrate the existence of a scaling solution.