Stoichiometric model ofEscherichia coli metabolism: Incorporation of growth-rate dependent biomass composition and mechanistic energy requirements

Evaluation of enzyme-constrained genome-scale model through metabolic engineering of anaerobic co-production of 2,3-butanediol and glycerol by Saccharomyces cerevisiae

Enzyme-constrained genome-scale models (ecGEMs) have potential to predict phenotypes in a variety of conditions, such as growth rates or carbon sources. This study investigated if ecGEMs can guide metabolic engineering efforts to swap anaerobic redox-neutral ATP-providing pathways in yeast from alcoholic fermentation to equimolar co-production of 2,3-butanediol and glycerol. With proven pathways and low product toxicity, the ecGEM solution space aligned well with observed phenotypes. Since this catabolic pathway provides only one-third of the ATP of alcoholic fermentation (2/3 versus 2 ATP per glucose), the ecGEM predicted a growth decrease from 0.36 h-1 in the reference to 0.175 h-1 in the engineered strain. However, this <3-fold decrease would require the specific glucose consumption rate to increase. Surprisingly, after the pathway swap the engineered strain immediately grew at 0.15 h-1 with a glucose consumption rate of 29 mmol (g CDW)-1 h-1, which was indeed higher than reference (23 mmol (g CDW)-1 h-1) and one of the highest reported for S. cerevisiae. The accompanying 2,3-butanediol- (15.8 mmol (g CDW)-1 h-1) and glycerol (19.6 mmol (g CDW)-1 h-1) production rates were close to predicted values. Proteomics confirmed that this increased consumption rate was facilitated by enzyme reallocation from especially ribosomes (from 25.5 to 18.5 %) towards glycolysis (from 28.7 to 43.5 %). Subsequently, 200 generations of sequential transfer did not improve growth of the engineered strain, showing the use of ecGEMs in predicting opportunity space for laboratory evolution. The observations in this study illustrate both the current potential, as well as future improvements, of ecGEMs as a tool for both metabolic engineering and laboratory evolution.

Balancing biomass reaction stoichiometry and measured fluxes in flux balance analysis

MOTIVATION

Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty.

RESULTS

In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes.

AVAILABILITY AND IMPLEMENTATION

The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing

The reconstruction of accurate yet simplified mimetic models of cell membranes is a very challenging goal of synthetic biology. To date, most of the research focuses on the development of eukaryotic cell membranes, while reconstitution of their prokaryotic counterparts has not been fully addressed, and the proposed models do not reflect well the complexity of bacterial cell envelopes. Here, we describe the reconstitution of biomimetic bacterial membranes with an increasing level of complexity, developed from binary and ternary lipid mixtures. Giant unilamellar vesicles composed of phosphatidylcholine (PC) and phosphatidylethanolamine (PE); PC and phosphatidylglycerol (PG); PE and PG; PE, PG and cardiolipin (CA) at varying molar ratios were successfully prepared by the electroformation method. Each of the proposed mimetic models focuses on reproducing specific membrane features such as membrane charge, curvature, leaflets asymmetry, or the presence of phase separation. GUVs were characterized in terms of size distribution, surface charge, and lateral organization. Finally, the developed models were tested against the lipopeptide antibiotic daptomycin. The obtained results showed a clear dependency of daptomycin binding efficiency on the amount of negatively charged lipid species present in the membrane. We anticipate that the models proposed here can be applied not only in antimicrobial testing but also serve as platforms for studying fundamental biological processes in bacteria as well as their interaction with physiologically relevant biomolecules.

Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

System-wide optimization of an orthogonal translation system with enhanced biological tolerance

Over the past two decades, synthetic biological systems have revolutionized the study of cellular physiology. The ability to site-specifically incorporate biologically relevant non-standard amino acids using orthogonal translation systems (OTSs) has proven particularly useful, providing unparalleled access to cellular mechanisms modulated by post-translational modifications, such as protein phosphorylation. However, despite significant advances in OTS design and function, the systems-level biology of OTS development and utilization remains underexplored. In this study, we employ a phosphoserine OTS (pSerOTS) as a model to systematically investigate global interactions between OTS components and the cellular environment, aiming to improve OTS performance. Based on this analysis, we design OTS variants to enhance orthogonality by minimizing host process interactions and reducing stress response activation. Our findings advance understanding of system-wide OTS:host interactions, enabling informed design practices that circumvent deleterious interactions with host physiology while improving OTS performance and stability. Furthermore, our study emphasizes the importance of establishing a pipeline for systematically profiling OTS:host interactions to enhance orthogonality and mitigate mechanisms underlying OTS-mediated host toxicity.

The unraveling of balanced complexes in metabolic networks

Balanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks.

H+-Translocating Membrane-Bound Pyrophosphatase from Rhodospirillum rubrum Fuels Escherichia coli Cells via an Alternative Pathway for Energy Generation

Inorganic pyrophosphatases (PPases) catalyze an essential reaction, namely, the hydrolysis of PP_i, which is formed in large quantities as a side product of numerous cellular reactions. In the majority of living species, PP_i hydrolysis is carried out by soluble cytoplasmic PPase (S-PPases) with the released energy dissipated in the form of heat. In Rhodospirillum rubrum, part of this energy can be conserved by proton-pumping pyrophosphatase (H⁺-PPase^Rru) in the form of a proton electrochemical gradient for further ATP synthesis. Here, the codon-harmonized gene hppa^Rru encoding H⁺-PPase^Rru was expressed in the Escherichia coli chromosome. We demonstrate, for the first time, that H⁺-PPase^Rru complements the essential native S-PPase in E. coli cells. ¹³C-MFA confirmed that replacing native PPase to H⁺-PPase^Rru leads to the re-distribution of carbon fluxes; a statistically significant 36% decrease in tricarboxylic acid (TCA) cycle fluxes was found compared with wild-type E. coli MG1655. Such a flux re-distribution can indicate the presence of an additional method for energy generation (e.g., ATP), which can be useful for the microbiological production of a number of compounds, the biosynthesis of which requires the consumption of ATP.

Producing medium-chain-length polyhydroxyalkanoate from diverse feedstocks by deregulating unsaturated fatty acid biosynthesis in Escherichia coli

The fatty acid metabolism in Escherichia coli has served as a basic metabolic chassis for medium-chain-length polyhydroxyalkanoate (mcl-PHA) production. In this study, the phaG and phaC1 genes from Pseudomonas entomophila L48 were first cloned as pGRN08. E. coli BL21P (E. coli BL21(DE3) ΔptsG) containing pGRN08 was able to produce 23 ± 3 and 7 ± 0 mg/L homopolymer poly(3-hydroxydecanoate)(P(3HD)) from glucose and xylose, respectively. Next, a gene, PSEEN0908 (encoding a putative 3-hydroxyacyl-CoA ligase), from P. entomophila L48 was found to increase the performance of mcl-PHA production. The induction of the fatty acid biosynthesis repressor (FabR), a transcription regulator that represses UFA biosynthesis, in E. coli substantially increased the mcl-PHA production by an order of magnitude from both unrelated and related carbon source conversion. A mcl-PHA concentration of 179 ± 1 mg/L and a content of 5.79 ± 0.16 % were obtained, where 31 mol% was 3-hydroxyoctanoate (3HO) and 69 mol% was 3HD.

Transcriptome Analysis Reveals Potential Mechanisms of L-Serine Production by Escherichia coli Fermentation in Different Carbon–Nitrogen Ratio Medium

L-serine is an industrially valuable amino acid that is widely used in the food, cosmetics and pharmaceutical industries. In this study, transcriptome sequencing technology was applied to analyze the changes in gene expression levels during the synthesis of L-serine in Escherichia coli fermentation. The optimal carbon–nitrogen ratio for L-serine synthesis in E. coli was determined by setting five carbon–nitrogen ratios for shake flask fermentation. Transcriptome sequencing was performed on E. coli fermented in five carbon–nitrogen ratio medium in which a total of 791 differentially expressed genes (DEGs) were identified in the CZ4_vs_CZ1 group, including 212 upregulated genes and 579 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these DEGs showed that the effect of an altered carbon–nitrogen ratio on the fermentability of E. coli was mainly focused on metabolic pathways such as GABAergic synapse and the two-component system (TCS) in which the genes playing key roles were mainly gadB, gadA, glsA, glnA, narH and narJ. In summary, these potential key metabolic pathways and key genes were proposed to provide valuable information for improving glucose conversion during E. coli fermentation.

Lipid tails modulate antimicrobial peptide membrane incorporation and activity

Membrane disrupting antimicrobial peptides (AMPs) are often amphipathic peptides that interact directly with lipid bilayers. AMPs are generally thought to interact mostly with lipid head groups, but it is less clear how the lipid alkyl chain length and saturation modulate interactions with membranes. Here, we used native mass spectrometry to measure the stoichiometry of three different AMPs-LL-37, indolicidin, and magainin-2-in lipid nanodiscs. We also measured the activity of these AMPs in unilamellar vesicle leakage assays. We found that LL-37 formed specific hexamer complexes but with different intermediates and affinities that depended on the bilayer thickness. LL-37 was also most active in lipid bilayers containing longer, unsaturated lipids. In contrast, indolicidin incorporated to a higher degree into more fluid lipid bilayers but was more active with bilayers with thinner, less fluid lipids. Finally, magainin-2 incorporated to a higher degree into bilayers with longer, unsaturated alkyl chains and showed more activity in these same conditions. Together, these data show that higher amounts of peptide incorporation generally led to higher activity and that AMPs tend to incorporate more into longer unsaturated lipid bilayers. However, the activity of AMPs was not always directly related to amount of peptide incorporated.

Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling

Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

This review is a part of the SI 'Genome-Scale Modeling of Microorganisms in the Real World'. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

Plant cell cultures as heterologous bio-factories for secondary metabolite production

Synthetic biology has been developing rapidly in the last decade and is attracting increasing attention from many plant biologists. The production of high-value plant-specific secondary metabolites is, however, limited mostly to microbes. This is potentially problematic because of incorrect post-translational modification of proteins and differences in protein micro-compartmentalization, substrate availability, chaperone availability, product toxicity, and cytochrome p450 reductase enzymes. Unlike other heterologous systems, plant cells may be a promising alternative for the production of high-value metabolites. Several commercial plant suspension cell cultures from different plant species have been used successfully to produce valuable metabolites in a safe, low cost, and environmentally friendly manner. However, few metabolites are currently being biosynthesized using plant platforms, with the exception of the natural pigment anthocyanin. Both Arabidopsis thaliana and Nicotiana tabacum cell cultures can be developed by multiple gene transformations and CRISPR-Cas9 genome editing. Given that the introduction of heterologous biosynthetic pathways into Arabidopsis and N. tabacum is not widely used, the biosynthesis of foreign metabolites is currently limited; however, therein lies great potential. Here, we discuss the exemplary use of plant cell cultures and prospects for using A. thaliana and N. tabacum cell cultures to produce valuable plant-specific metabolites.

Recombinant protein production provoked accumulation of ATP, fructose-1,6-bisphosphate and pyruvate in E. coli K12 strain TG1

Background

Recently it was shown that production of recombinant proteins in E. coli BL21(DE3) using pET based expression vectors leads to metabolic stress comparable to a carbon overfeeding response. Opposite to original expectations generation of energy as well as catabolic provision of precursor metabolites were excluded as limiting factors for growth and protein production. On the contrary, accumulation of ATP and precursor metabolites revealed their ample formation but insufficient withdrawal as a result of protein production mediated constraints in anabolic pathways. Thus, not limitation but excess of energy and precursor metabolites were identified as being connected to the protein production associated metabolic burden.

Results

Here we show that the protein production associated accumulation of energy and catabolic precursor metabolites is not unique to E. coli BL21(DE3) but also occurs in E. coli K12. Most notably, it was demonstrated that the IPTG-induced production of hFGF-2 using a tac-promoter based expression vector in the E. coli K12 strain TG1 was leading to persistent accumulation of key regulatory molecules such as ATP, fructose-1,6-bisphosphate and pyruvate.

Conclusions

Excessive energy generation, respectively, accumulation of ATP during recombinant protein production is not unique to the BL21(DE3)/T7 promoter based expression system but also observed in the E. coli K12 strain TG1 using another promoter/vector combination. These findings confirm that energy is not a limiting factor for recombinant protein production. Moreover, the data also show that an accelerated glycolytic pathway flux aggravates the protein production associated “metabolic burden”. Under conditions of compromised anabolic capacities cells are not able to reorganize their metabolic enzyme repertoire as required for reduced carbon processing.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

Rapid analysis of polyhydroxyalkanoate contents and its monomer compositions by pyrolysis-gas chromatography combined with mass spectrometry (Py-GC/MS)

Here, we report an analysis method for determining PHA (polyhydroxyalkanoates) contents and their monomer composition in microbial cells based on pyrolysis gas chromatography combined with mass spectrometry (Py-GC/MS). Various kinds of microbial cells accumulating different PHA contents and monomer compositions were prepared through the cultivation of Ralstonia eutropha and recombinant Escherichia coli. Py-GC/MS could analyse these samples in a short time without complicated pretreatment steps. Characteristic peaks such as 2-butenoic acid, 2-pentenoic acid, and hexadecanoic acid regarding PHA compositions and cell components were identified. Considering constituents of cells and ratios of peak areas of dehydrated monomers to hexadecanoic acid, a simple equation for estimation of PHA contents in microbial cells was derived. Also, monomer compositions of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) in R. eutropha could be successfully determined based on peak area of 2-butenoic acid and 2-pentenoic acid of Py-GC/MS, which are the corresponding species of 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV) in PHBV. Correlation of results between GC-FID and Py-GC/MS could be fitted very well. This method shows similar results for the samples obtained from same experimental conditions, allowing rapid and reliable analysis. Py-GC/MS can be a promising tool to rapidly screen PHA-positive strains based on polymer contents along with monomer compositions.

Structures of three MORN repeat proteins and a re-evaluation of the proposed lipid-binding properties of MORN repeats

MORN (Membrane Occupation and Recognition Nexus) repeat proteins have a wide taxonomic distribution, being found in both prokaryotes and eukaryotes. Despite this ubiquity, they remain poorly characterised at both a structural and a functional level compared to other common repeats. In functional terms, they are often assumed to be lipid-binding modules that mediate membrane targeting. We addressed this putative activity by focusing on a protein composed solely of MORN repeats-Trypanosoma brucei MORN1. Surprisingly, no evidence for binding to membranes or lipid vesicles by TbMORN1 could be obtained either in vivo or in vitro. Conversely, TbMORN1 did interact with individual phospholipids. High- and low-resolution structures of the MORN1 protein from Trypanosoma brucei and homologous proteins from the parasites Toxoplasma gondii and Plasmodium falciparum were obtained using a combination of macromolecular crystallography, small-angle X-ray scattering, and electron microscopy. This enabled a first structure-based definition of the MORN repeat itself. Furthermore, all three structures dimerised via their C-termini in an antiparallel configuration. The dimers could form extended or V-shaped quaternary structures depending on the presence of specific interface residues. This work provides a new perspective on MORN repeats, showing that they are protein-protein interaction modules capable of mediating both dimerisation and oligomerisation.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Anaplerotic Pathways in Halomonas elongata: The Role of the Sodium Gradient

Salt tolerance in the γ-proteobacterium Halomonas elongata is linked to its ability to produce the compatible solute ectoine. The metabolism of ectoine production is of great interest since it can shed light on the biochemical basis of halotolerance as well as pave the way for the improvement of the biotechnological production of such compatible solute. Ectoine belongs to the biosynthetic family of aspartate-derived amino-acids. Aspartate is formed from oxaloacetate, thereby connecting ectoine production to the anaplerotic reactions that refill carbon into the tricarboxylic acid cycle (TCA cycle). This places a high demand on these reactions and creates the need to regulate them not only in response to growth but also in response to extracellular salt concentration. In this work, we combine modeling and experiments to analyze how these different needs shape the anaplerotic reactions in H. elongata. First, the stoichiometric and thermodynamic factors that condition the flux distributions are analyzed, then the optimal patterns of operation for oxaloacetate production are calculated. Finally, the phenotype of two deletion mutants lacking potentially relevant anaplerotic enzymes: phosphoenolpyruvate carboxylase (Ppc) and oxaloacetate decarboxylase (Oad) are experimentally characterized. The results show that the anaplerotic reactions in H. elongata are indeed subject to evolutionary pressures that differ from those faced by other gram-negative bacteria. Ectoine producing halophiles must meet a higher metabolic demand for oxaloacetate and the reliance of many marine bacteria on the Entner-Doudoroff pathway compromises the anaplerotic efficiency of Ppc, which is usually one of the main enzymes fulfilling this role. The anaplerotic flux in H. elongata is contributed not only by Ppc but also by Oad, an enzyme that has not yet been shown to play this role in vivo. Ppc is necessary for H. elongata to grow normally at low salt concentrations but it is not required to achieve near maximal growth rates as long as there is a steep sodium gradient. On the other hand, the lack of Oad presents serious difficulties to grow at high salt concentrations. This points to a shared role of these two enzymes in guaranteeing the supply of oxaloacetate for biosynthetic reactions.

Curation and Analysis of a Saccharomyces cerevisiae Genome-Scale Metabolic Model for Predicting Production of Sensory Impact Molecules under Enological Conditions

One approach for elucidating strain-to-strain metabolic differences is the use of genome-scale metabolic models (GSMMs). To date GSMMs have not focused on the industrially important area of flavor production and, as such; do not cover all the pathways relevant to flavor formation in yeast. Moreover, current models for Saccharomyces cerevisiae generally focus on carbon-limited and/or aerobic systems, which is not pertinent to enological conditions. Here, we curate a GSMM (iWS902) to expand on the existing Ehrlich pathway and ester formation pathways central to aroma formation in industrial winemaking, in addition to the existing sulfur metabolism and medium-chain fatty acid (MCFA) pathways that also contribute to production of sensory impact molecules. After validating the model using experimental data, we predict key differences in metabolism for a strain (EC 1118) in two distinct growth conditions, including differences for aroma impact molecules such as acetic acid, tryptophol, and hydrogen sulfide. Additionally, we propose novel targets for metabolic engineering for aroma profile modifications employing flux variability analysis with the expanded GSMM. The model provides mechanistic insights into the key metabolic pathways underlying aroma formation during alcoholic fermentation and provides a potential framework to contribute to new strategies to optimize the aroma of wines.

High titer methyl ketone production with tailored Pseudomonas taiwanensis VLB120

Methyl ketones present a group of highly reduced platform chemicals industrially produced from petroleum-derived hydrocarbons. They find applications in the fragrance, flavor, pharmacological, and agrochemical industries, and are further discussed as biodiesel blends. In recent years, intense research has been carried out to achieve sustainable production of these molecules by re-arranging the fatty acid metabolism of various microbes. One challenge in the development of a highly productive microbe is the high demand for reducing power. Here, we engineered Pseudomonas taiwanensis VLB120 for methyl ketone production as this microbe has been shown to sustain exceptionally high NAD(P)H regeneration rates. The implementation of published strategies resulted in 2.1 g L_aq^-1 methyl ketones in fed-batch fermentation. We further increased the production by eliminating competing reactions suggested by metabolic analyses. These efforts resulted in the production of 9.8 g L_aq^-1 methyl ketones (corresponding to 69.3 g L_org^-1 in the in situ extraction phase) at 53% of the maximum theoretical yield. This represents a 4-fold improvement in product titer compared to the initial production strain and the highest titer of recombinantly produced methyl ketones reported to date. Accordingly, this study underlines the high potential of P. taiwanensis VLB120 to produce methyl ketones and emphasizes model-driven metabolic engineering to rationalize and accelerate strain optimization efforts.

Quantitative Connection between Cell Size and Growth Rate by Phospholipid Metabolism

The processes involved in cell growth are extremely complicated even for a single cell organism such as Escherichia coli, while the relationship between growth rate and cell size is simple. We aimed to reveal the systematic link between them from the aspect of the genome-scale metabolic network. Since the growth rate reflects metabolic rates of bacteria and the cell size relates to phospholipid synthesis, a part of bacterial metabolic networks, we calculated the cell length from the cardiolipin synthesis rate, where the cardiolipin synthesis reaction is able to represent the phospholipid metabolism of Escherichia coli in the exponential growth phase. Combined with the flux balance analysis, it enables us to predict cell length and to examine the quantitative relationship between cell length and growth rate. By simulating bacteria growing in various nutrient media with the flux balance analysis and calculating the corresponding cell length, we found that the increase of the synthesis rate of phospholipid, the cell width, and the protein fraction in membranes caused the increase of cell length with growth rate. Different tendencies of phospholipid synthesis rate changing with growth rate result in different relationships between cell length and growth rate. The effects of gene deletions on cell size and growth rate are also examined. Knocking out the genes, such as Δ tktA, Δ tktB, Δ yqaB, Δ pgm, and Δ cysQ, affects growth rate largely while affecting cell length slightly. Results of this method are in good agreement with experiments.

The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models

Systems biology has long been interested in models capturing both metabolism and expression in a cell. We propose here an implementation of the metabolism and expression model formalism (ME-models), which we call ETFL, for Expression and Thermodynamics Flux models. ETFL is a hierarchical model formulation, from metabolism to RNA synthesis, that allows simulating thermodynamics-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. ETFL formulates a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. ETFL is compatible with standard MILP solvers and does not require a non-linear solver, unlike the previous state of the art. It also accounts for growth-dependent parameters, such as relative protein or mRNA content. We present ETFL along with its validation using results obtained from a well-characterized E. coli model. We show that ETFL is able to reproduce proteome-limited growth. We also subject it to several analyses, including the prediction of feasible mRNA and enzyme concentrations and gene essentiality.

Accounting for the effects of genetic expression in genome-scale metabolic models is challenging. Here, the authors introduce a model formulation that efficiently simulates thermodynamic-compliant fluxes, enzyme and mRNA concentration levels, allowing omics integration and broad analysis of in silico cellular physiology.

RNA accumulation in Candida tropicalis based on cofactor engineering

Redox cofactors play an important role in biosynthetic and catabolic reactions and the transfer of energy for the cell. Therefore, studying the relationship between cofactor perturbation and metabolism is a useful approach to improve the yield of target products. To study RNA accumulation and metabolism when intracellular cofactor balance was impaired, the water-forming NADH oxidase (NoxE) from Lactococcus lactis and membrane-bound transhydrogenase (PntAB) from Escherichia coli were expressed in Candidatropicalis no. 121, respectively. Expression of noxE significantly decreased the intracellular NADH/NAD+ ratio, but the NADPH/NADP+ ratio did not differ significantly. PntAB increased the intracellular NADH pool, while the NADPH/NADP+ ratio decreased. The perturbation of the cofactors caused a large redistribution of metabolic fluxes. The biomass and RNA content decreased by 11.0% and 10.6% in pAUR-noxE strain, respectively, while the RNA content increased by 5.5% and the biomass showed no signification difference in pAUR-pntAB strain. Expression of noxE and pntAB led to decreases and increases in the ATP concentration and yield of RNA, respectively, which also indicated that ATP plays an important role in the RNA biosynthesis.

An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.

Dynamic 13C Labeling of Fast Turnover Metabolites for Analysis of Metabolic Fluxes and Metabolite Channeling

Dynamic or isotopically nonstationary ¹³C labeling experiments are a powerful tool not only for precise carbon flux quantification (e.g., metabolic flux analysis of photoautotrophic organisms) but also for the investigation of pathway bottlenecks, a cell's phenotype, and metabolite channeling. In general, isotopically nonstationary metabolic flux analysis requires three main components: (1) transient isotopic labeling experiments; (2) metabolite quenching and isotopomer analysis using LC-MS; (3) metabolic network construction and flux quantification. Labeling dynamics of key metabolites from ¹³C-pulse experiments allow flux estimation of key central pathways by solving ordinary differential equations to fit time-dependent isotopomer distribution data. Additionally, it is important to provide biomass requirements, carbon uptake rates, specific growth rates, and carbon excretion rates to properly and precisely balance the metabolic network. Labeling dynamics through cascade metabolites may also identify channeling phenomena in which metabolites are passed between enzymes without mixing with the bulk phase. In this chapter, we outline experimental protocols to probe metabolic pathways through dynamic labeling. We describe protocols for labeling experiments, metabolite quenching and extraction, LC-MS analysis, computational flux quantification, and metabolite channeling observations.

A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth

Living cells react to changes in growth conditions by re-shaping their proteome. This accounts for different stress-response strategies, both specific (i.e., aimed at increasing the availability of stress-mitigating proteins) and systemic (such as large-scale changes in the use of metabolic pathways aimed at a more efficient exploitation of resources). Proteome re-allocation can, however, imply significant biosynthetic costs. Whether and how such costs impact the growth performance are largely open problems. Focusing on carbon-limited E. coli growth, we integrate genome-scale modeling and proteomic data to address these questions at quantitative level. After deriving a simple formula linking growth rate, carbon intake, and biosynthetic costs, we show that optimal growth results from the tradeoff between yield maximization and protein burden minimization. Empirical data confirm that E. coli growth is indeed close to Pareto-optimal over a broad range of growth rates. Moreover, we establish that, while most of the intaken carbon is diverted into biomass precursors, the efficiency of ATP synthesis is the key driver of the yield-cost tradeoff. These findings provide a quantitative perspective on carbon overflow, the origin of growth laws and the multidimensional optimality of E. coli metabolism.

Model metabolic strategy for heterotrophic bacteria in the cold ocean based on Colwellia psychrerythraea 34H

Colwellia psychrerythraea 34H is a model psychrophilic bacterium found in the cold ocean-polar sediments, sea ice, and the deep sea. Although the genomes of such psychrophiles have been sequenced, their metabolic strategies at low temperature have not been quantified. We measured the metabolic fluxes and gene expression of 34H at 4 °C (the mean global-ocean temperature and a normal-growth temperature for 34H), making comparative analyses at room temperature (above its upper-growth temperature of 18 °C) and with mesophilic Escherichia coli When grown at 4 °C, 34H utilized multiple carbon substrates without catabolite repression or overflow byproducts; its anaplerotic pathways increased flux network flexibility and enabled CO₂ fixation. In glucose-only medium, the Entner-Doudoroff (ED) pathway was the primary glycolytic route; in lactate-only medium, gluconeogenesis and the glyoxylate shunt became active. In comparison, E. coli, cold stressed at 4 °C, had rapid glycolytic fluxes but no biomass synthesis. At their respective normal-growth temperatures, intracellular concentrations of TCA cycle metabolites (α-ketoglutarate, succinate, malate) were 4-17 times higher in 34H than in E. coli, while levels of energy molecules (ATP, NADH, NADPH) were 10- to 100-fold lower. Experiments with E. coli mutants supported the thermodynamic advantage of the ED pathway at cold temperature. Heat-stressed 34H at room temperature (2 hours) revealed significant down-regulation of genes associated with glycolytic enzymes and flagella, while 24 hours at room temperature caused irreversible cellular damage. We suggest that marine heterotrophic bacteria in general may rely upon simplified metabolic strategies to overcome thermodynamic constraints and thrive in the cold ocean.

Absolute Quantification of ppGpp and pppGpp by Double-Spike Isotope Dilution Ion Chromatography-High-Resolution Mass Spectrometry

Guanosine 5'-diphosphate 3'-diphosphate (ppGpp) and guanosine 5'-triphosphate 3'-diphosphate (pppGpp) play a central role in the adaptation of bacterial and plant cells to nutritional and environmental stresses and in bacterial resistance to antibiotics. These compounds have historically been detected and quantified by two-dimensional thin-layer chromatography of ³²P-radiolabeled nucleotides. We report a new method to quantify ppGpp and pppGpp in complex biochemical matrix using ion chromatography coupled to high-resolution mass spectrometry. The method is based on isotopic dilution mass spectrometry (IDMS) using ¹³C to accurately quantify the nucleotides. However, the loss of a phosphate group from pppGpp during the sample preparation process results in the erroneous quantification of ppGpp. This bias was corrected by adding an extra ¹⁵N isotope dilution dimension. This double-spike IDMS method was applied to quantify the ppGpp and pppGpp in Escherichia coli and in a mutant strain deleted for gppA (encoding the ppGpp phosphohydrolase) before and after exposure of both strains to serine hydroxamate, known to trigger the accumulation of these nucleotides.

Model-based metabolism design: constraints for kinetic and stoichiometric models

The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.

Phytoplankton growth rate modelling: can spectroscopic cell chemotyping be superior to physiological predictors?

Climate change has a strong impact on phytoplankton communities and water quality. However, the development of robust techniques to assess phytoplankton growth is still in progress. In this study, the growth rate of phytoplankton cells grown at different temperatures was modelled based on conventional physiological traits (e.g. chlorophyll, carbon and photosynthetic parameters) using the partial least square regression (PLSR) algorithm and compared with a new approach combining Fourier transform infrared-spectroscopy and PLSR. In this second model, it is assumed that the macromolecular composition of phytoplankton cells represents an intracellular marker for growth. The models have comparable high predictive power (R² > 0.8) and low error in predicting new observations. Interestingly, not all of the predictors present the same weight in the modelling of growth rate. A set of specific parameters, such as non-photochemical fluorescence quenching (NPQ) and the quantum yield of carbon production in the first model, and lipid, protein and carbohydrate contents for the second one, strongly covary with cell growth rate regardless of the taxonomic position of the phytoplankton species investigated. This reflects a set of specific physiological adjustments covarying with growth rate, conserved among taxonomically distant algal species that might be used as guidelines for the improvement of modern primary production models. The high predictive power of both sets of cellular traits for growth rate is of great importance for applied phycological studies. Our approach may find application as a quality control tool for the monitoring of phytoplankton populations in natural communities or in photobioreactors.

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. It has been described that breast cancer subtypes present metabolism differences and this fact enables the possibility of using metabolic inhibitors as targeted drugs in specific scenarios. In this study, breast cancer cell lines were treated with metformin and rapamycin, showing a heterogeneous response to treatment and leading to cell cycle disruption. The genetic causes and molecular effects of this differential response were characterized by means of SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes such as transcription. Moreover, flux balance analysis using protein expression values showed that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these diverse approaches provide complementary information and allow us to suggest hypotheses about the response to drugs that target metabolism and their mechanisms of action.

Highly Effective Polyphosphate Synthesis, Phosphate Removal, and Concentration Using Engineered Environmental Bacteria Based on a Simple Solo Medium-Copy Plasmid Strategy

Microbial polyphosphate (polyP) production is vital to the removal of phosphate from wastewater. However, to date, engineered polyP synthesis using genetically accessible environmental bacteria remains a challenge. This study develops a simple solo medium-copy plasmid-based polyphosphate kinase (PPK1) overexpression strategy for achieving maximum intracellular polyphosphate accumulation by environmental bacteria. The polyP content of the subsequently engineered Citrobacter freundii (CPP) could reach as high as 12.7% of its dry weight. The biomass yield of CPP was also guaranteed because of negligible metabolic burden effects resulting from the medium plasmid copy number. Consequently, substantial removal of phosphate (P_i) from the ambient environment was achieved simultaneously. Because of the need for exogenous P_i for in vivo ATP regeneration, CPP could thoroughly remove P_i from synthetic municipal wastewater when it was applied for the "one-step" removal of P_i with a bench-scale sequence batch membrane reactor. Almost all the phosphorus except for that assimilated by CPP for cellular growth could be recovered in the form of more concentrated P_i. Overall, engineering environmental bacteria to overexpress PPK1 via a solo medium-copy plasmid strategy may represent a valuable general option for not only biotechnological research based on sufficient intracellular polyP production but also removal of P_i from wastewater and P_i enrichment.

Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis

Vibrio natriegens is a fast-growing, non-pathogenic bacterium that is being considered as the next-generation workhorse for the biotechnology industry. However, little is known about the metabolism of this organism which is limiting our ability to apply rational metabolic engineering strategies. To address this critical gap in current knowledge, here we have performed a comprehensive analysis of V. natriegens metabolism. We constructed a detailed model of V. natriegens core metabolism, measured the biomass composition, and performed high-resolution ¹³C metabolic flux analysis (¹³C-MFA) to estimate intracellular fluxes using parallel labeling experiments with the optimal tracers [1,2-¹³C]glucose and [1,6-¹³C]glucose. During exponential growth in glucose minimal medium, V. natriegens had a growth rate of 1.70 1/h (doubling time of 24min) and a glucose uptake rate of 3.90g/g/h, which is more than two 2-fold faster than E. coli, although slower than the fast-growing thermophile Geobacillus LC300. ¹³C-MFA revealed that the core metabolism of V. natriegens is similar to that of E. coli, with the main difference being a 33% lower normalized flux through the oxidative pentose phosphate pathway. Quantitative analysis of co-factor balances provided additional insights into the energy and redox metabolism of V. natriegens. Taken together, the results presented in this study provide valuable new information about the physiology of V. natriegens and establish a solid foundation for future metabolic engineering efforts with this promising microorganism.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

Stoichiometric models have been used in the area of metabolic engineering and systems biology for many decades. The early examples of these models include simplified ad hoc built metabolic pathways, and biomass compositions. The development of genome scale models (GEMs) brought a standard to metabolic network modeling. However, the vast amount of detailed biochemistry in GEMs makes it necessary to develop methods to manage the complexity in them. In this study, we developed lumpGEM, a tool that can systematically identify subnetworks from metabolic networks that can perform certain tasks, such as biosynthesis of a biomass building block and any other target metabolite. By generating alternative subnetworks, lumpGEM also accounts for the redundancy in metabolic networks. We applied lumpGEM on latest E. coli GEM iJO1366 and identified subnetworks/lumped reactions for every biomass building block defined in its biomass formulation. We also compared the results from lumpGEM with existing knowledge in the literature. The lumped reactions generated by lumpGEM can be used to generate consistently reduced metabolic network models.

A Model for Designing Adaptive Laboratory Evolution Experiments

The occurrence of mutations is a cornerstone of the evolutionary theory of adaptation, capitalizing on the rare chance that a mutation confers a fitness benefit. Natural selection is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining when to consider an experiment complete and for balancing outcomes with available resources (i.e., laboratory supplies, personnel, and time). To design and to better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to the optimization of ALE experiments. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim, to explain differences in experimental outcomes. Furthermore, a beneficial mutation rate of 10^-6.9 to 10^-8.4 mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in serial passage batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results described here will aid in the design of ALE experiments to fit the exact needs of a project while taking into account the resources required and will lower the barriers to entry for this experimental technique.IMPORTANCE ALE is a widely used scientific technique to increase scientific understanding, as well as to create industrially relevant organisms. The manner in which ALE experiments are conducted is highly manual and uniform, with little optimization for efficiency. Such inefficiencies result in suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in an optimized fashion and can design experiments to generate greater fitness in an accelerated time frame, thereby pushing the limits of what adaptive laboratory evolution can achieve.

Drivers of Bacterial Maintenance and Minimal Energy Requirements

Microbes maintain themselves through a variety of processes. Several of these processes can be reduced or shut down entirely when resource availability declines. In pure culture conditions with ample substrate supply, a relationship between the maximum growth rate and the energy invested in maintenance has been reported widely. However, at the other end of the resources spectrum, bacteria are so extremely limited by energy that no growth occurs and metabolism is constrained to the most essential functions only. These minimum energy requirements have been called the basal power requirement. While seemingly different from each other, both aspects are likely components of a continuum of regulated maintenance processes. Here, we analyze cross-species tradeoffs in cellular physiology over the range of bacterial size and energy expenditure and determine the contributions to maintenance metabolism at each point along the size-energy spectrum. Furthermore, by exploring the simplest bacteria within this framework– which are most affected by maintenance constraints– we uncover which processes become most limiting. For the smallest species, maintenance metabolism converges on total metabolism, where we predict that maintenance is dominated by the repair of proteins. For larger species the relative costs of protein repair decrease and maintenance metabolism is predicted to be dominated by the repair of RNA components. These results provide new insights into which processes are likely to be regulated in environments that are extremely limited by energy.

Regulation of glycolysis in head and neck squamous cell carcinoma

Glycolysis is highly upregulated in head and neck squamous cell carcinoma (HNSCC). HNSCC glycolysis is an important contributor to disease progression and decreases sensitivity to radiation or chemotherapy. Despite therapeutic advances, the survival rates for HNSCC patients remain low. Understanding glycolysis regulation in HNSCC will facilitate the development of effective therapeutic strategies for this disease. In this review, we will evaluate the regulation of altered HNSCC glycolysis and possible therapeutic approaches by targeting glycolytic pathways.

Osmoregulation in the Halophilic Bacterium Halomonas elongata: A Case Study for Integrative Systems Biology

Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem. However, as more of these reconstructions become available, it becomes clear that processes predicted by genome annotation may not reflect the processes that are active in vivo. As a case in point, E. coli is unable to grow aerobically on citrate in spite of having all the necessary genes to do it. It has also been shown that the realization of this genetic potential into an actual capability to metabolize citrate is an extremely unlikely event under normal evolutionary conditions. Moreover, many marine bacteria seem to have the same pathways to metabolize glucose but each species uses a different one. In this work, a metabolic network inferred from genomic annotation of the halophilic bacterium Halomonas elongata and proteomic profiling experiments are used as a starting point to motivate targeted experiments in order to find out some of the defining features of the osmoregulatory strategies of this bacterium. This new information is then used to refine the network in order to describe the actual capabilities of H. elongata, rather than its genetic potential.

Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis

BACKGROUND

Synechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973's fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, ¹³C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions.

RESULTS

The Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO₂ conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions.

CONCLUSIONS

This study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for CO₂ assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.