Why in vivo may not equal in vitro - new effectors revealed by measurement of enzymatic activities under the same in vivo-like assay conditions

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknownme". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteria Escherichia coli using the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.

Chemotactic Interactions Drive Migration of Membraneless Active Droplets

In nature, chemotactic interactions are ubiquitous and play a critical role in driving the collective behavior of living organisms. Reproducing these interactions in vitro is still a paramount challenge due to the complexity of mimicking and controlling cellular features, such as tangled metabolic networks, cytosolic macromolecular crowding, and cellular migration, on a microorganism size scale. Here, we generate enzymatically active cell-sized droplets able to move freely, and by following a chemical gradient, able to interact with the surrounding droplets in a collective manner. The enzyme within the droplets generates a pH gradient that extends outside the edge of the droplets. We discovered that the external pH gradient triggers droplet migration and controls its directionality, which is selectively toward the neighboring droplets. Hence, by changing the enzyme activity inside the droplet, we tuned the droplet migration speed. Furthermore, we showed that these cellular-like features can facilitate the reconstitution of a simple and linear protometabolic pathway and increase the final reaction product generation. Our work suggests that simple and stable membraneless droplets can reproduce complex biological phenomena, opening new perspectives as bioinspired materials and synthetic biology tools.

Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution

Inside cells, various biological systems work cooperatively for homeostasis and self-replication. These systems do not work independently as they compete for shared elements like ATP and NADH. However, it has been believed that such competition is not a problem in codependent biological systems such as the energy-supplying glycolysis and the energy-consuming translation system. In this study, we biochemically reconstituted the coupling system of glycolysis and translation using purified elements and found that the competition for ATP between glycolysis and protein synthesis interferes with their coupling. Both experiments and simulations revealed that this interference is derived from a metabolic tug-of-war between glycolysis and translation based on their reaction rates, which changes the threshold of the initial substrate concentration for the success coupling. By the metabolic tug-of-war, translation energized by strong glycolysis is facilitated by an exogenous ATPase, which normally inhibits translation. These findings provide chemical insights into the mechanism of competition among biological systems in living cells and provide a framework for the construction of synthetic metabolism in vitro.

Calcium oscillations optimize the energetic efficiency of mitochondrial metabolism

Energy transduction is central to living organisms, but the impact of enzyme regulation and signaling on its thermodynamic efficiency is generally overlooked. Here, we analyze the efficiency of ATP production by the tricarboxylic acid cycle and oxidative phosphorylation, which generate most of the chemical energy in eukaryotes. Calcium signaling regulates this pathway and can affect its energetic output, but the concrete energetic impact of this cross-talk remains elusive. Calcium enhances ATP production by activating key enzymes of the tricarboxylic acid cycle while calcium homeostasis is ATP-dependent. We propose a detailed kinetic model describing the calcium-mitochondria cross-talk and analyze it using nonequilibrium thermodynamics: after identifying the effective reactions driving mitochondrial metabolism out of equilibrium, we quantify the mitochondrial thermodynamic efficiency for different conditions. Calcium oscillations, triggered by extracellular stimulation or energy deficiency, boost the thermodynamic efficiency of mitochondrial metabolism, suggesting a compensatory role of calcium signaling in mitochondrial bioenergetics.

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.

Toxicological effects of selenium nanoparticles in laboratory animals: A review

This paper provides a comprehensive summary of the main toxicological studies conducted on selenium nanoparticles (NPs) using laboratory animals, up until February 28, 2023. A literature search revealed 17 articles describing experimental studies conducted on warm-blooded animals. Despite some uncertainties, in vivo studies have demonstrated that selenium NPs have an adverse effect on laboratory animals, as evidenced by several indicators of general toxic action. These effects include reductions of body mass, changes in hepatotoxicity indices (increased enzyme activity and accumulation of selenium in the liver), and the possibility of impairment of fatty acid, protein, lipid, and carbohydrate metabolisms. However, no specific toxic action attributable solely to selenium has been identified. The LOAEL and NOAEL values are contradictory. The NOAEL was 0.22 mg/kg body weight per day for males and 0.33 mg/kg body weight per day for females, while the LOAEL was assumed to be a dose of 0.05 mg/kg of nanoselenium. This LOAEL value is much higher for rats than for humans. The relationship between the adverse effects of selenium NPs and exposure dose is controversial and presents a wide typological diversity. Further research is needed to clarify the absorption, metabolism, and long-term toxicity of selenium NPs, which is critical to improving the risk assessment of these compounds.

LcSAO1, an Unconventional DOXB Clade 2OGD Enzyme from Ligusticum chuanxiong Catalyzes the Biosynthesis of Plant-Derived Natural Medicine Butylphthalide

Butylphthalide, a prescription medicine recognized for its efficacy in treating ischemic strokes approved by the State Food and Drug Administration of China in 2005, is sourced from the traditional botanical remedy Ligusticum chuanxiong. While chemical synthesis offers a viable route, limitations in the production of isomeric variants with compromised bioactivity necessitate alternative strategies. Addressing this issue, biosynthesis offers a promising solution. However, the intricate in vivo pathway for butylphthalide biosynthesis remains elusive. In this study, we examined the distribution of butylphthalide across various tissues of L. chuanxiong and found a significant accumulation in the rhizome. By searching transcriptome data from different tissues of L. chuanxiong, we identified four rhizome-specific genes annotated as 2-oxoglutarate-dependent dioxygenase (2-OGDs) that emerged as promising candidates involved in butylphthalide biosynthesis. Among them, LcSAO1 demonstrates the ability to catalyze the desaturation of senkyunolide A at the C-4 and C-5 positions, yielding the production of butylphthalide. Experimental validation through transient expression assays in Nicotiana benthamiana corroborates this transformative enzymatic activity. Notably, phylogenetic analysis of LcSAO1 revealed that it belongs to the DOXB clade, which typically encompasses genes with hydroxylation activity, rather than desaturation. Further structure modelling and site-directed mutagenesis highlighted the critical roles of three amino acid residues, T98, S176, and T178, in substrate binding and enzyme activity. By unraveling the intricacies of the senkyunolide A desaturase, the penultimate step in the butylphthalide biosynthesis cascade, our findings illuminate novel avenues for advancing synthetic biology research in the realm of medicinal natural products.

Biochemical evidence that the whole compartment activity behavior of GAPDH differs between the cytoplasm and nucleus

Some metabolic enzymes normally occur in the nucleus and cytoplasm. These compartments differ in molecular composition. Since post-translational modification and interaction with allosteric effectors can tune enzyme activity, it follows that the behavior of an enzyme as a catalyst may differ between the cytoplasm and nucleus. We explored this possibility for the glycolytic enzyme glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Homogenates of pristine nuclei and cytoplasms isolated from Xenopus laevis oocytes were used for whole compartment activity profiling in a near-physiological buffer. Titrations of NAD+ revealed similar whole compartment activity profiles for GAPDH in nuclear and cytoplasmic homogenates. Surprisingly however GAPDH in these compartments did not have the same behavior in assays of the dependence of initial velocity (v0) on G3P concentration. First, the peak v0 for nuclear GAPDH was up to 2.5-fold higher than the peak for cytoplasmic GAPDH. Second, while Michaelis Menten-like behavior was observed in all assays of cytoplasm, the v0 versus [G3P] plots for nuclear GAPDH typically exhibited a non-Michaelis Menten (sigmoidal) profile. Apparent Km and Vmax (G3P) values for nuclear GAPDH activity were highly variable, even between replicates of the same sample. Possible sources of this variability include in vitro processing of a metabolite that allosterically regulates GAPDH, turnover of a post-translational modification of the enzyme, and fluctuation of the state of interaction of GAPDH with other proteins. Collectively these findings are consistent with the hypothesis that the environment of the nucleus is distinct from the environment of the cytoplasm with regard to GAPDH activity and its modulation. This finding warrants further comparison of the regulation of nuclear and cytoplasmic GAPDH, as well as whole compartment activity profiling of other enzymes of metabolism with cytosolic and nuclear pools.

Antimelanogenic Effects of Curcumin and Its Dimethoxy Derivatives: Mechanistic Investigation Using B16F10 Melanoma Cells and Zebrafish (Danio rerio) Embryos

Regulation of melanin production via the MC1R signaling pathway is a protective mechanism of the skin of living organisms against exposure to ultraviolet rays. The discovery of human skin-whitening agents has been one of the most intense pursuits of the cosmetic industry. The MC1R signaling pathway is activated by its agonist, alpha-melanocyte stimulating hormone (α-MSH), and mainly regulates melanogenesis. Here, we evaluated the antimelanogenic activities of curcumin (CUR) and its two derivatives, dimethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC), in B16F10 mouse melanoma cells and zebrafish embryos. CUR and BDMC reduced the α-MSH-induced melanin production in B16F10 cells and also downregulated the expression of the melanin-production-related genes Tyr, Mitf, Trp-1, and Trp-2. Moreover, the biological activity of these two compounds against melanogenesis was confirmed in in vivo experiments using zebrafish embryos. However, the highest concentration of CUR (5 µM) resulted in slight malformations in zebrafish embryos, as indicated by acute toxicity tests. In contrast, DMC did not show any biological activity in vitro or in vivo. Conclusively, BDMC is a strong candidate as a skin-whitening agent.

Dynamics of co-substrate pools can constrain and regulate metabolic fluxes

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of these two parameters can allow regulation of flux dynamics in branched and coupled pathways. To support these theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by the dynamics of co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate dynamics is crucial for understanding and engineering metabolic fluxes in cells.

Lacto-Fermented and Unfermented Soybean Differently Modulate Serum Lipids, Blood Pressure and Gut Microbiota during Hypertension

Soy consumption may reduce hypertension but the impact of food processing on the antihypertensive effect is unclear. Hence, we ascertained the effects of lacto-fermented (FSB) and unfermented soybean (USB) consumption on serum atherogenic lipids, hypertension and gut microbiota of spontaneous hypertensive rats (SHR). FSB displayed a strong in vitro angiotensin converting enzyme (ACE) inhibitory ability of 70 ± 5% while USB inhibited 5 ± 3% of the enzyme activity. Consumption of USB reduced serum ACE activity by 19.8 ± 12.85 U while FSB reduced the enzyme activity by 47.6 ± 11.35 U, respectively. FSB significantly improved cholesterol levels and reduced systolic and diastolic blood pressures by 14 ± 3 mmHg and 10 ± 3 mmHg, respectively, while USB only had a marginal impact on blood pressure. Analysis of FSB showed the abundance of ACE inhibitory peptides EGEQPRPFPFP and AIPVNKP (which were absent in USB) and 30 phenolic compounds (only 12 were abundant in USB). Feeding SHR with FSB promoted the growth of Akkermansia, Bacteroides, Intestinimonas, Phocaeicola, Lactobacillus and Prevotella (short chain fatty acid producers) while USB promoted only Prevotellamassilia, Prevotella and Intestimonas levels signifying the prebiotic ability of FSB. Our results show that, relative to USB, FSB are richer in bioactive compounds that reduce hypertension by inhibiting ACE, improving cholesterol levels and mitigating gut dysbiosis.

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

BACKGROUND

Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (K_m), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem).

RESULTS

To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for K_m estimation of kinetic modeling. First, we use a machine learning-based K_m predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted K_m values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R² = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping K_m values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated K_m values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated K_m values, which were close to the measured values.

CONCLUSIONS

MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based K_m predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.

pH dependencies of glycolytic enzymes of yeast under in vivo-like assay conditions

Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S. cerevisiae under in vivo-like conditions at different pH. We demonstrate that glycolytic enzymes exhibit differential pH dependencies, and optima, in the pH range observed during carbon source transitions. The forward reaction of GAPDH shows the highest decrease in activity, 83%, during a simulated feast/famine regime upon glucose removal (cytosolic pH drop from 7.1 to 6.4). We complement our biochemical characterization of the glycolytic enzymes by fitting the V_max to the progression curves of product formation or decay over time. The fitting analysis shows that the observed changes in enzyme activities require changes in V_max , but changes in K_m cannot be excluded. Our study highlights the relevance of pH as a key player in metabolic regulation and provides a large set of quantitative data that can be explored to improve our understanding of metabolism in dynamic environments.

Synergistic deciphering of bioenergy production and electron transport characteristics to screen traditional Chinese medicine (TCM) for COVID-19 drug development

Background

Traditional Chinese medicine (TCM) has been used as an "immune booster” for disease prevention and clinical treatment since ancient China. However, many studies were focused on the organic herbal extract rather than aqueous herbal extract (AHE; decoction). Due to the COVID-19 pandemics, this study tended to decipher phytochemical contents in the decoction of herbs and derived bioactivities (e.g., anti-oxidant and anti-inflammatory properties). As prior works revealed, the efficacy of Parkinson's medicines and antiviral flavonoid herbs was strongly governed by their bioenergy-stimulating proficiency.

Methods

Herbal extracts were prepared by using a traditional Chinese decoction pot. After filtration and evaporation, crude extracts were used to prepare sample solutions for various bioassays. The phytochemical content and bioactivities of AHEs were determined via ELISA microplate reader. Microbial fuel cells (MFCs) were used as a novel platform to evaluate bioenergy contents with electron-transfer characteristics for antiviral drug development.

Significant findings

Regarding 18 TCM herbal extracts for the prevention of SARS and H1N1 influenza, comparison on total polyphenol, flavonoid, condensed tannins and polysaccharides were conducted. Moreover, considerable total flavonoid contents were detected for 11 herb extracts. These AEHs were not only rich in phytonutrient contents but also plentiful in anti-oxidant and anti-inflammatory activities. Herbs with high polyphenol content had higher antioxidant activity. Forsythia suspensa extract expressed the highest inhibition against nitric oxide production for anti-inflammation. MFC bioenergy-stimulating studies also revealed that top ranking COVID-19 efficacious herbs were both bioenergy driven and electron mediated. That is, electron transfer-controlled bioenergy extraction was significant to antiviral characteristics for anti-COVID-19 drug development.

A novel strigolactone receptor antagonist provides insights into the structural inhibition, conditioning, and germination of the crop parasite Striga

Crop parasites of the Striga genera are a major biological deterrent to food security in Africa and are one of the largest obstacles to poverty alleviation on the continent. Striga seeds germinate by sensing small-molecule hormones, strigolactones (SLs), that emanate from host roots. Although SL receptors (Striga hermonthica HYPOSENSITIVE TO LIGHT [ShHTL]) have been identified, discerning their function has been difficult because these parasites cannot be easily grown under laboratory conditions. Moreover, many Striga species are obligate outcrossers that are not transformable, hence not amenable to genetic analysis. By combining phenotypic screening with ShHTL structural information and hybrid drug discovery methods, we discovered a potent SL perception inhibitor for Striga, dormirazine (DOZ). Structural analysis of this piperazine-based antagonist reveals a novel binding mechanism, distinct from that of known SLs, blocking access of the hormone to its receptor. Furthermore, DOZ reduces the flexibility of protein–protein interaction domains important for receptor signaling to downstream partners. In planta, we show, via temporal additions of DOZ, that SL receptors are required at a specific time during seed conditioning. This conditioning is essential to prime seed germination at the right time; thus, this SL-sensitive stage appears to be critical for adequate receptor signaling. Aside from uncovering a function for ShHTL during seed conditioning, these results suggest that future Ag-Biotech Solutions to Striga infestations will need to carefully time the application of antagonists to exploit receptor availability and outcompete natural SLs, critical elements for successful parasitic plant invasions.

Surface Glycans Regulate Salmonella Infection-Dependent Directional Switch in Macrophage Galvanotaxis Independent of NanH

Salmonella invades and disrupts gut epithelium integrity, creating an infection-generated electric field that can drive directional migration of macrophages, a process called galvanotaxis. Phagocytosis of bacteria reverses the direction of macrophage galvanotaxis, implicating a bioelectrical mechanism to initiate life-threatening disseminations. The force that drives direction reversal of macrophage galvanotaxis is not understood. One hypothesis is that Salmonella can alter the electrical properties of the macrophages by modifying host cell surface glycan composition, which is supported by the fact that cleavage of surface-exposed sialic acids with a bacterial neuraminidase severely impairs macrophage galvanotaxis, as well as phagocytosis. Here, we utilize N-glycan profiling by nanoLC-chip QTOF mass cytometry to characterize the bacterial neuraminidase-associated compositional shift of the macrophage glycocalyx, which revealed a decrease in sialylated and an increase in fucosylated and high mannose structures. The Salmonella nanH gene, encoding a putative neuraminidase, is required for invasion and internalization in a human colonic epithelial cell infection model. To determine whether NanH is required for the Salmonella infection-dependent direction reversal, we constructed and characterized a nanH deletion mutant and found that NanH is partially required for Salmonella infection in primary murine macrophages. However, compared to wild type Salmonella, infection with the nanH mutant only marginally reduced the cathode-oriented macrophage galvonotaxis, without canceling direction reversal. Together, these findings strongly suggest that while neuraminidase-mediated N-glycan modification impaired both macrophage phagocytosis and galvanotaxis, yet to be defined mechanisms other than NanH may play a more important role in bioelectrical control of macrophage trafficking, which potentially triggers dissemination.

Parameter Reliability and Understanding Enzyme Function

Knowledge of the Michaelis-Menten parameters and their meaning in different circumstances is an essential prerequisite to understanding enzyme function and behaviour. The published literature contains an abundance of values reported for many enzymes. The problem concerns assessing the appropriateness and validity of such material for the purpose to which it is to be applied. This review considers the evaluation of such data with particular emphasis on the assessment of its fitness for purpose.

EPSP Synthase-Depleted Cells Are Aromatic Amino Acid Auxotrophs in Mycobacterium smegmatis

The epidemiological importance of mycobacterial species is indisputable, and the necessity to find new molecules that can inhibit their growth is urgent. The shikimate pathway, required for the synthesis of important bacterial metabolites, represents a set of targets for inhibitors of Mycobacterium tuberculosis growth. The aroA-encoded 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) enzyme catalyzes the sixth step of the shikimate pathway. In this study, we combined gene disruption, gene knockdown, point mutations (D61W, R134A, E321N), and kinetic analysis to evaluate aroA gene essentiality and vulnerability of its protein product, EPSPS, from Mycolicibacterium (Mycobacterium) smegmatis (MsEPSPS). We demonstrate that aroA-deficient cells are auxotrophic for aromatic amino acids (AroAAs) and that the growth impairment observed for aroA-knockdown cells grown on defined medium can be rescued by AroAA supplementation. We also evaluated the essentiality of selected MsEPSPS residues in bacterial cells grown without AroAA supplementation. We found that the catalytic residues R134 and E321 are essential, while D61, presumably important for protein dynamics and suggested to have an indirect role in catalysis, is not essential under the growth conditions evaluated. We have also determined the catalytic efficiencies (K_cat/K_m) of recombinant wild-type (WT) and mutated versions of MsEPSPS (D61W, R134A, E321N). Our results suggest that drug development efforts toward EPSPS inhibition may be ineffective if bacilli have access to external sources of AroAAs in the context of infection, which should be evaluated further. In the absence of AroAA supplementation, aroA from M. smegmatis is essential, its essentiality is dependent on MsEPSPS activity, and MsEPSPS is vulnerable.

IMPORTANCE We found that cells from Mycobacterium smegmatis, a model organism safer and easier to study than the disease-causing mycobacterial species, when depleted of an enzyme from the shikimate pathway, are auxotrophic for the three aromatic amino acids (AroAAs) that serve as building blocks of cellular proteins: l-tryptophan, l-phenylalanine, and l-tyrosine. That supplementation with only AroAAs is sufficient to rescue viable cells with the shikimate pathway inactivated was unexpected, since this pathway produces an end product, chorismate, that is the starting compound of essential pathways other than the ones that produce AroAAs. The depleted enzyme, the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), catalyzes the sixth step of shikimate pathway. Depletion of this enzyme inside cells was performed by disrupting or silencing the EPSPS-encoding aroA gene. Finally, we evaluated the essentiality of specific residues from EPSPS that are important for its catalytic activity, determined with experiments of enzyme kinetics using recombinant EPSPS mutants.

Pyruvate Production by Escherichia coli by Use of Pyruvate Dehydrogenase Variants

Altering metabolic flux at a key branch point in metabolism has commonly been accomplished through gene knockouts or by modulating gene expression. An alternative approach to direct metabolic flux preferentially toward a product is decreasing the activity of a key enzyme through protein engineering. In Escherichia coli, pyruvate can accumulate from glucose when carbon flux through the pyruvate dehydrogenase complex is suppressed. Based on this principle, 16 chromosomally expressed AceE variants were constructed in E. coli C and compared for growth rate and pyruvate accumulation using glucose as the sole carbon source. To prevent conversion of pyruvate to other products, the strains also contained deletions in two nonessential pathways: lactate dehydrogenase (ldhA) and pyruvate oxidase (poxB). The effect of deleting phosphoenolpyruvate synthase (ppsA) on pyruvate assimilation was also examined. The best pyruvate-accumulating strains were examined in controlled batch and continuous processes. In a nitrogen-limited chemostat process at steady-state growth rates of 0.15 to 0.28 h-1, an engineered strain expressing the AceE[H106V] variant accumulated pyruvate at a yield of 0.59 to 0.66 g pyruvate/g glucose with a specific productivity of 0.78 to 0.92 g pyruvate/g cells·h. These results provide proof of concept that pyruvate dehydrogenase complex variants can effectively shift carbon flux away from central carbon metabolism to allow pyruvate accumulation. This approach can potentially be applied to other key enzymes in metabolism to direct carbon toward a biochemical product. IMPORTANCE Microbial production of biochemicals from renewable resources has become an efficient and cost-effective alternative to traditional chemical synthesis methods. Metabolic engineering tools are important for optimizing a process to perform at an economically feasible level. This study describes an additional tool to modify central metabolism and direct metabolic flux to a product. We have shown that variants of the pyruvate dehydrogenase complex can direct metabolic flux away from cell growth to increase pyruvate production in Escherichia coli. This approach could be paired with existing strategies to optimize metabolism and create industrially relevant and economically feasible processes.

Metabolic response to point mutations reveals principles of modulation of in vivo enzyme activity and phenotype

The relationship between sequence variation and phenotype is poorly understood. Here, we use metabolomic analysis to elucidate the molecular mechanism underlying the filamentous phenotype of E. coli strains that carry destabilizing mutations in dihydrofolate reductase (DHFR). We find that partial loss of DHFR activity causes reversible filamentation despite SOS response indicative of DNA damage, in contrast to thymineless death (TLD) achieved by complete inhibition of DHFR activity by high concentrations of antibiotic trimethoprim. This phenotype is triggered by a disproportionate drop in intracellular dTTP, which could not be explained by drop in dTMP based on the Michaelis-Menten-like in vitro activity curve of thymidylate kinase (Tmk), a downstream enzyme that phosphorylates dTMP to dTDP. Instead, we show that a highly cooperative (Hill coefficient 2.5) in vivo activity of Tmk is the cause of suboptimal dTTP levels. dTMP supplementation rescues filamentation and restores in vivo Tmk kinetics to Michaelis-Menten. Overall, this study highlights the important role of cellular environment in sculpting enzymatic kinetics with system-level implications for bacterial phenotype.

Towards rational glyco-engineering in CHO: from data to predictive models

Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.

Characterization of different biocatalyst formats for BVMO-catalyzed cyclohexanone oxidation

Cyclohexanone monooxygenase (CHMO), a member of the Baeyer-Villiger monooxygenase family, is a versatile biocatalyst that efficiently catalyzes the conversion of cyclic ketones to lactones. In this study, an Acidovorax-derived CHMO gene was expressed in Pseudomonas taiwanensis VLB120. Upon purification, the enzyme was characterized in vitro and shown to feature a broad substrate spectrum and up to 100% conversion in 6 h. Furthermore, we determined and compared the cyclohexanone conversion kinetics for different CHMO-biocatalyst formats, that is, isolated enzyme, suspended whole cells, and biofilms, the latter two based on recombinant CHMO-containing P. taiwanensis VLB120. Biofilms showed less favorable values for K_S (9.3-fold higher) and k_cat (4.8-fold lower) compared with corresponding K_M and k_cat values of isolated CHMO, but a favorable K_I for cyclohexanone (5.3-fold higher). The unfavorable K_S and k_cat values are related to mass transfer- and possibly heterogeneity issues and deserve further investigation and engineering, to exploit the high potential of biofilms regarding process stability. Suspended cells showed only 1.8-fold higher K_S , but 1.3- and 4.2-fold higher k_cat and K_I values than isolated CHMO. This together with the efficient NADPH regeneration via glucose metabolism makes this format highly promising from a kinetics perspective.

The Protein Interactome of Glycolysis in Escherichia coli

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

The nucleocytosolic O-fucosyltransferase SPINDLY affects protein expression and virulence in Toxoplasma gondii

Once considered unusual, nucleocytoplasmic glycosylation is now recognized as a conserved feature of eukaryotes. While in animals, O-GlcNAc transferase (OGT) modifies thousands of intracellular proteins, the human pathogen Toxoplasma gondii transfers a different sugar, fucose, to proteins involved in transcription, mRNA processing, and signaling. Knockout experiments showed that TgSPY, an ortholog of plant SPINDLY and paralog of host OGT, is required for nuclear O-fucosylation. Here we verify that TgSPY is the nucleocytoplasmic O-fucosyltransferase (OFT) by 1) complementation with TgSPY-MYC3, 2) its functional dependence on amino acids critical for OGT activity, and 3) its ability to O-fucosylate itself and a model substrate and to specifically hydrolyze GDP-Fuc. While many of the endogenous proteins modified by O-Fuc are important for tachyzoite fitness, O-fucosylation by TgSPY is not essential. Growth of Δspy tachyzoites in fibroblasts is modestly affected, despite marked reductions in the levels of ectopically expressed proteins normally modified with O-fucose. Intact TgSPY-MYC3 localizes to the nucleus and cytoplasm, whereas catalytic mutants often displayed reduced abundance. Δspy tachyzoites of a luciferase-expressing type II strain exhibited infection kinetics in mice similar to wild-type but increased persistence in the chronic brain phase, potentially due to an imbalance of regulatory protein levels. The modest changes in parasite fitness in vitro and in mice, despite profound effects on reporter protein accumulation, and the characteristic punctate localization of O-fucosylated proteins suggest that TgSPY controls the levels of proteins to be held in reserve for response to novel stresses.

Thermodynamics and Kinetics of Glycolytic Reactions. Part II: Influence of Cytosolic Conditions on Thermodynamic State Variables and Kinetic Parameters

For systems biology, it is important to describe the kinetic and thermodynamic properties of enzyme-catalyzed reactions and reaction cascades quantitatively under conditions prevailing in the cytoplasm. While in part I kinetic models based on irreversible thermodynamics were tested, here in part II, the influence of the presumably most important cytosolic factors was investigated using two glycolytic reactions (i.e., the phosphoglucose isomerase reaction (PGI) with a uni-uni-mechanism and the enolase reaction with an uni-bi-mechanism) as examples. Crowding by macromolecules was simulated using polyethylene glycol (PEG) and bovine serum albumin (BSA). The reactions were monitored calorimetrically and the equilibrium concentrations were evaluated using the equation of state ePC-SAFT. The pH and the crowding agents had the greatest influence on the reaction enthalpy change. Two kinetic models based on irreversible thermodynamics (i.e., single parameter flux-force and two-parameter Noor model) were applied to investigate the influence of cytosolic conditions. The flux-force model describes the influence of cytosolic conditions on reaction kinetics best. Concentrations of magnesium ions and crowding agents had the greatest influence, while temperature and pH-value had a medium influence on the kinetic parameters. With this contribution, we show that the interplay of thermodynamic modeling and calorimetric process monitoring allows a fast and reliable quantification of the influence of cytosolic conditions on kinetic and thermodynamic parameters.

Harnessing the potential of artificial neural networks for predicting protein glycosylation

Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.

Exploring a New Natural Treating Agent for Primary Hypertension: Recent Findings and Forthcoming Perspectives

Primary hypertension describes abnormally-high systolic/diastolic blood pressure in a resting condition caused by various genetic or environmental risk factors. Remarkably, severe complications, such as ischemic cardiovascular disease, stroke, and chronic renal disease have led to primary hypertension becoming a huge burden for almost one-third of the total population. Medication is the major regimen for treating primary hypertension; however, recent medications may have adverse effects that attenuate energy levels. Hence, the search for new hypotensive agents from folk or traditional medicine may be fruitful in the discovery and development of new drugs. This review assembles recent findings for natural antihypertensive agents, extracts, or decoctions published in PubMed, and provides insights into the search for new hypotensive compounds based on blood-pressure regulating mechanisms, including the renin-angiotensin-aldosterone system and the sympathetic/adrenergic receptor/calcium channel system.

Biological Relativity Requires Circular Causality but Not Symmetry of Causation: So, Where, What and When Are the Boundaries?

Since the Principle of Biological Relativity was formulated and developed there have been many implementations in a wide range of biological fields. The purpose of this article is to assess the status of the applications of the principle and to clarify some misunderstandings. The principle requires circular causality between levels of organization. But the forms of causality are also necessarily different. They contribute in asymmetric ways. Upward causation can be represented by the differential or similar equations describing the mechanics of lower level processes. Downward causation is then best represented as determining initial and boundary conditions. The questions tackled in this article are: (1) where and when do these boundaries exist? and (2) how do they convey the influences between levels? We show that not all boundary conditions arise from higher-level organization. It is important to distinguish those that do from those that don't. Both forms play functional roles in organisms, particularly in their responses to novel challenges. The forms of causation also change according to the levels concerned. These principles are illustrated with specific examples.

Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

Quantifying enzyme activity in living cells

For over a century, enzymatic activity has been studied in vitro, assuming similar activity in the crowded cellular milieu. Here, we determined in real time the catalytic activity of TEM1-β-lactamase inside living cells and compared the values to those obtained in vitro We found the apparent in vivo catalytic efficiency, k_cat/K_m , to be lower than in vitro, with significant cell-to-cell variability. Surprisingly, the results show that inside the cell the apparent catalytic efficiency decreases, and K_m increases with increasing enzyme concentration. To rationalize these findings, we measured enzyme and substrate diffusion rates in the cell and found the latter to be slower than expected. Simulations showed that for attenuated diffusion the substrate flux becomes rate-limiting, explaining why reaction rates in vivo can be independent on enzyme concentrations. The octanol/water partition of the substrate is 4.5, which is in the range of Food and Drug Administration-approved drugs. This suggests substrate-limited reaction rates to be common. These findings indicate that in vitro data cannot be simply extrapolated to the crowded in vivo environment.

Measurement of In Vivo Protein Binding Affinities in a Signaling Network with Mass Spectrometry

Protein interaction networks play a key role in signal processing. Despite the progress in identifying the interactions, the quantification of their strengths lags behind. Here we present an approach to quantify the in vivo binding of proteins to their binding partners in signaling-transcriptional networks, by the pairwise genetic isolation of each interaction and by varying the concentration of the interacting components over time. The absolute quantification of the protein concentrations was performed with targeted mass spectrometry. The strengths of the interactions, as defined by the apparent dissociation constants, ranged from subnanomolar to micromolar values in the yeast galactose signaling network. The weak homodimerization of the Gal4 activator amplifies the signal elicited by glucose. Furthermore, combining the binding constants in a feedback loop correctly predicted cellular memory, a characteristic network behavior. Thus, this genetic-proteomic binding assay can be used to faithfully quantify how strongly proteins interact with proteins, DNA and metabolites.

The advantage of channeling nucleotides for very processive functions

Nucleoside triphosphate (NTP)s, like ATP (adenosine 5'-triphosphate) and GTP (guanosine 5'-triphosphate), have long been considered sufficiently concentrated and diffusible to fuel all cellular ATPases (adenosine triphosphatases) and GTPases (guanosine triphosphatases) in an energetically healthy cell without becoming limiting for function. However, increasing evidence for the importance of local ATP and GTP pools, synthesised in close proximity to ATP- or GTP-consuming reactions, has fundamentally challenged our view of energy metabolism. It has become evident that cellular energy metabolism occurs in many specialised 'microcompartments', where energy in the form of NTPs is transferred preferentially from NTP-generating modules directly to NTP-consuming modules. Such energy channeling occurs when diffusion through the cytosol is limited, where these modules are physically close and, in particular, if the NTP-consuming reaction has a very high turnover, i.e. is very processive. Here, we summarise the evidence for these conclusions and describe new insights into the physiological importance and molecular mechanisms of energy channeling gained from recent studies. In particular, we describe the role of glycolytic enzymes for axonal vesicle transport and nucleoside diphosphate kinases for the functions of dynamins and dynamin-related GTPases.

The advantage of channeling nucleotides for very processive functions

Nucleoside triphosphate (NTP)s, like ATP (adenosine 5'-triphosphate) and GTP (guanosine 5'-triphosphate), have long been considered sufficiently concentrated and diffusible to fuel all cellular ATPases (adenosine triphosphatases) and GTPases (guanosine triphosphatases) in an energetically healthy cell without becoming limiting for function. However, increasing evidence for the importance of local ATP and GTP pools, synthesised in close proximity to ATP- or GTP-consuming reactions, has fundamentally challenged our view of energy metabolism. It has become evident that cellular energy metabolism occurs in many specialised 'microcompartments', where energy in the form of NTPs is transferred preferentially from NTP-generating modules directly to NTP-consuming modules. Such energy channeling occurs when diffusion through the cytosol is limited, where these modules are physically close and, in particular, if the NTP-consuming reaction has a very high turnover, i.e. is very processive. Here, we summarise the evidence for these conclusions and describe new insights into the physiological importance and molecular mechanisms of energy channeling gained from recent studies. In particular, we describe the role of glycolytic enzymes for axonal vesicle transport and nucleoside diphosphate kinases for the functions of dynamins and dynamin-related GTPases.

Contribution of Bistability and Noise to Cell Fate Transitions Determined by Feedback Opening

Alternative cell fates represent a form of non-genetic diversity, which can promote adaptation and functional specialization. It is difficult to predict the rate of the transition between two cell fates due to the strong effect of noise on feedback loops and missing parameters. We opened synthetic positive feedback loops experimentally to obtain open-loop functions. These functions allowed us to identify a deterministic model of bistability by bypassing noise and the requirement to resolve individual processes in the loop. Combining the open-loop function with kinetic measurements and reintroducing the measured noise, we were able to predict the transition rates for the feedback systems without parameter tuning. Noise in gene expression was the key determinant of the transition rates inside the bistable range. Transitions between two cell fates were also observed outside of the bistable range, evidenced by bimodality and hysteresis. In this case, a slow transient process was the rate-limiting step in the transitions. Thus, feedback opening is an effective approach to identify the determinants of cell fate transitions and to predict their rates.

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements

Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.62 in log scale (p < 10(-10)), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between kmax(vivo) and kcat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a high-throughput method for extracting enzyme kinetic constants from omics data.

Computing the functional proteome: recent progress and future prospects for genome-scale models

Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M-Models). Recent expansions in network content to encompass proteome synthesis have resulted in models of metabolism and protein expression (ME-Models). ME-Models advance the predictions possible with constraint-based models from network flux states to the spatially resolved molecular composition of a cell. Specifically, ME-Models enable the prediction of transcriptome and proteome allocation and limitations, and basal expression states and regulatory needs. Continued expansion in reconstruction content and constraints will result in an increasingly refined representation of cellular composition and behavior.

Understanding carbon catabolite repression in Escherichia coli using quantitative models

Carbon catabolite repression (CCR) controls the order in which different carbon sources are metabolized. Although this system is one of the paradigms of the regulation of gene expression in bacteria, the underlying mechanisms remain controversial. CCR involves the coordination of different subsystems of the cell that are responsible for the uptake of carbon sources, their breakdown for the production of energy and precursors, and the conversion of the latter to biomass. The complexity of this integrated system, with regulatory mechanisms cutting across metabolism, gene expression, and signaling, and that are subject to global physical and physiological constraints, has motivated important modeling efforts over the past four decades, especially in the enterobacterium Escherichia coli. Different hypotheses concerning the dynamic functioning of the system have been explored by a variety of modeling approaches. We review these studies and summarize their contributions to the quantitative understanding of CCR, focusing on diauxic growth in E. coli. Moreover, we propose a highly simplified representation of diauxic growth that makes it possible to bring out the salient features of the models proposed in the literature and confront and compare the explanations they provide.

Predictive sulfur metabolism - a field in flux

The key role of sulfur metabolites in response to biotic and abiotic stress in plants, as well as their importance in diet and health has led to a significant interest and effort in trying to understand and manipulate the production of relevant compounds. Metabolic engineering utilizes a set of theoretical tools to help rationally design modifications that enhance the production of a desired metabolite. Such approaches have proven their value in bacterial systems, however, the paucity of success stories to date in plants, suggests that challenges remain. Here, we review the most commonly used methods for understanding metabolic flux, focusing on the sulfur assimilatory pathway. We highlight known issues with both experimental and theoretical approaches, as well as presenting recent methods for integrating different modeling strategies, and progress toward an understanding of flux at the whole plant level.

A kinetic-based approach to understanding heterologous mevalonate pathway function in E. coli

To aid in debugging efforts to increase yield, titer, and productivity of engineered metabolic pathways, computational models are increasingly needed to predict how changes in experimentally manipulable variables such as enzyme expression map to changes in pathway flux. Here, an ordinary differential equation model is developed for a heterologous mevalonate pathway in E. coli using kinetic parameters culled from literature and enzyme concentrations derived from Selective Reaction Monitoring Mass Spectrometry (SRM-MS). To identify parameters most important to further experimental investigation, a global sensitivity analysis was performed, which pointed to amorphadiene synthase activity as the main limiting factor for amorphadiene production. Furthermore, the model predicted that in this local enzyme expression regime, the overall pathway flux is insensitive to farnesyl pyrophosphate (FPP)-mediated inhibition of mevalonate kinase, not supporting a hypothesis that had previously been posited to be limiting amorphadiene production. To test these predictions experimentally, two strains were constructed: (1) a strain containing a homologous mevalonate kinase with weaker feedback inhibition, and (2) a strain with greater amorphadiene synthase expression. The experimental results validate the qualitative model hypotheses and accurately match the predicted productivities for the two strains, particularly when an in vivo-derived kcat for amorphadiene synthase was substituted for the literature value. These results demonstrate the utility of using kinetic representations of engineered metabolic pathways parameterized with experimentally derived protein concentrations and enzyme kinetic constants to predict productivities and test hypotheses about engineering strategies.

Advancing metabolic models with kinetic information

Kinetic models are crucial to quantitatively understand and predict how functional behavior emerges from dynamic concentration changes of cellular components. The current challenge is on resolving uncertainties about parameter values of reaction kinetics. Additionally, there are also major structural uncertainties due to unknown molecular interactions and only putatively assigned regulatory functions. What if one or few key regulators of biochemical reactions are missing in a metabolic model? By reviewing current advances in building kinetic models of metabolism, we found that such models experience a paradigm shift away from fitting parameters towards identifying key regulatory interactions.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Mass spectrometry-based workflow for accurate quantification of Escherichia coli enzymes: how proteomics can play a key role in metabolic engineering

Metabolic engineering aims to design high performance microbial strains producing compounds of interest. This requires systems-level understanding; genome-scale models have therefore been developed to predict metabolic fluxes. However, multi-omics data including genomics, transcriptomics, fluxomics, and proteomics may be required to model the metabolism of potential cell factories. Recent technological advances to quantitative proteomics have made mass spectrometry-based quantitative assays an interesting alternative to more traditional immuno-affinity based approaches. This has improved specificity and multiplexing capabilities. In this study, we developed a quantification workflow to analyze enzymes involved in central metabolism in Escherichia coli (E. coli). This workflow combined full-length isotopically labeled standards with selected reaction monitoring analysis. First, full-length (15)N labeled standards were produced and calibrated to ensure accurate measurements. Liquid chromatography conditions were then optimized for reproducibility and multiplexing capabilities over a single 30-min liquid chromatography-MS analysis. This workflow was used to accurately quantify 22 enzymes involved in E. coli central metabolism in a wild-type reference strain and two derived strains, optimized for higher NADPH production. In combination with measurements of metabolic fluxes, proteomics data can be used to assess different levels of regulation, in particular enzyme abundance and catalytic rate. This provides information that can be used to design specific strains used in biotechnology. In addition, accurate measurement of absolute enzyme concentrations is key to the development of predictive kinetic models in the context of metabolic engineering.