Acid Metabolic Network Research Articles

Bile salt hydrolase catalyses formation of amine-conjugated bile acids.

Bacteria in the gastrointestinal tract produce amino acid bile acid amidates that can affect host-mediated metabolic processes1-6; however, the bacterial gene(s) responsible for their production remain unknown. Herein, we report that bile salt hydrolase (BSH) possesses dual functions in bile acid metabolism. Specifically, we identified a previously unknown role for BSH as an amine N-acyltransferase that conjugates amines to bile acids, thus forming bacterial bile acid amidates (BBAAs). To characterize this amine N-acyltransferase BSH activity, we used pharmacological inhibition of BSH, heterologous expression of bsh and mutants in Escherichia coli and bsh knockout and complementation in Bacteroides fragilis to demonstrate that BSH generates BBAAs. We further show in a human infant cohort that BBAA production is positively correlated with the colonization of bsh-expressing bacteria. Lastly, we report that in cell culture models, BBAAs activate host ligand-activated transcription factors including the pregnane X receptor and the aryl hydrocarbon receptor. These findings enhance our understanding of how gut bacteria, through the promiscuous actions of BSH, have a significant role in regulating the bile acid metabolic network.

The importance of taste on swallowing function.

The world's population is aging. Pneumonia is the leading cause of death among the older adults, with aspiration pneumonia being particularly common. Aspiration pneumonia is caused by a decline in swallowing function. Causes can include age-related sarcopenia of swallowing muscles, cognitive decline, cerebrovascular and other diseases or even changes in individual taste preference. Currently, the main treatment approach for dysphagia is resistance training of swallowing-related muscles. This approach has not been effective and establishment of novel methods are required. In this review, we introduce and discuss the relationship between taste, taste preference, carbonation and swallowing function. Taste and preference improve swallowing function. Recently, it has been shown that a carbonated beverage that combines the functionality of a thickening agent, the appeal of taste, and the stimulation of carbonation improves swallowing function. This may be very useful in the recovery of swallowing function. It is important to note that deliciousness is based not only on taste and preference, but also on visual information such as food form. Umami taste receptors are expressed not only in taste buds but also in skeletal muscle and small intestine. These receptors may be involved in homeostasis of the amino acid metabolic network, i.e., the process of amino acid ingestion, intestine absorption, and storage in skeletal muscle. Proper stimulation of umami receptors in organs other than taste buds may help maintain nutritional status and muscle mass. Umami receptors are therefore a potential therapeutic target for dysphagia.

Comprehensive insight into an amino acid metabolic network in postharvest horticultural products: a review.

Amino acid (AA) metabolism plays a vital role in the central metabolism of plants. In addition to protein biosynthesis, AAs are involved in secondary metabolite biosynthesis, signal transduction, stress response, defense against pathogens, flavor formation, and so on. Besides these functions, AAs can be degraded into precursors or intermediates of the tricarboxylic acid cycle to substitute respiratory substrates and restore energy homeostasis, as well as directly acting as signal molecules or be involved in the regulation of plant signals to delay senescence of postharvest horticultural products (PHPs). AA metabolism and its role in plants growth have been clarified; however, only a few studies about their roles exist concerning the postharvest preservation of fruit and vegetables. This study reviews the potential functions of various AAs by comparing the difference in AA metabolism at the postharvest stage and then discusses the crosstalk of AA metabolism and energy metabolism, the target of rapamycin/sucrose nonfermenting-related kinase 1 signaling and secondary metabolism. Finally, the roles and effect mechanism of several exogenous AAs in the preservation of PHPs are highlighted. This review provides a comprehensive insight into the AA metabolism network in PHPs. © 2023 Society of Chemical Industry.

Stereoselective metabolomic and lipidomic responses of lettuce (Lactuca sativa L.) exposing to chiral triazole fungicide tebuconazole

In this study, non-targeted and targeted metabolomics/lipidomics studies based on UPLC-QTOF-MS and UPLC-MS/MS were carried out to clarify the effects of tebuconazole and its different enantiomers on lettuce metabolites and lipids. Slight enantioselective degradation of tebuconazole was observed and six degradation metabolites were tentatively identified. The endogenous metabolites involved in carbohydrate metabolism, amino acid metabolism, nucleic acid metabolism, phenylpropanoid and flavonoid metabolism, vitamins, and lipid metabolism were significantly affected with enantioselectivity by tebuconazole exposure. Nucleotide metabolism and nicotinic acid metabolic network were significantly activated by the stimulation of tebuconazole. Rac- and (−)-R-tebuconazole caused the down-regulation of soluble sugars and subsequent amino acids and organic acids. Overall, lettuce exposed to tebuconazole was shown to have a significant impact on plant metabolism and lipid metabolism, with notable stereoselectivity. The results showed stereoselective toxicity of tebuconazole and provided a better understanding of its metabolomic and lipidomic effects on lettuce.

Predicting multi-enzyme inhibition in the arachidonic acid metabolic network by Heritiera fomes extracts

Heritiera fomes is a mangrove plant with a rich history of ethnomedicinal usage against chronic inflammation. Biochemical analyses of H. fomes have exposed a plethora of bioactive phytochemicals that contribute to this therapeutic effect by perturbing enzymes of a complex inflammatory network mediated by arachidonic acid (AA) metabolism. This study is the first instance of utilizing cheminformatic approaches to elucidate a molecular linkage between these phytochemical interventions and the multi-enzyme AA metabolic network regulation. Analysis of the simulations reflects H. fomes as a functional reservoir of multiple safe and potent natural anti-inflammatory compounds. The investigation suggests two phytocompounds extracted from the plant: a sesquiterpene lactone and a flavone glycoside, as candidate inhibitors of multiple catalytic checkpoints of the inflammatory network. The outcomes of this research act as a primary guideline for future laboratory and clinical testing of anti-inflammatory potentials of H. fomes as an exploitable source of safe and potent drug-like molecules. Communicated by Ramaswamy H. Sarma

Screening drug target combinations in disease-related molecular networks

BackgroundFor treating a complex disease such as cancer, some effective means are needed to control biological networks that underlies the disease. The one-target one-drug paradigm has been the dominating drug discovery approach in the past decades. Compared to single target-based drugs, combination drug targets may overcome many limitations of single drug target and achieve a more effective and safer control of the disease. Most of existing combination drug targets are developed based on clinical experience or text-and-trial strategy, which cannot provide theoretical guidelines for designing and screening effective drug combinations. Therefore, systematic identification of multiple drug targets and optimal intervention strategy needs to be developed.ResultsWe developed a strategy to screen the synergistic combinations of two drug targets in disease networks based on the classification of single drug targets. The method tried to identify the sensitivity of single intervention and then the combination of multiple interventions that can restore the disease network to a desired normal state. In our strategy of screening drug target combinations, we first classified all drug targets into sensitive and insensitive single drug targets. Then, we identified the synergistic and antagonistic of drug target combinations, including the combinations of sensitive drug targets, the combinations of insensitive drug target and the combination of sensitive and insensitive targets. Finally, we applied our strategy to Arachidonic Acid (AA) metabolic network and found 18 pairs of synergistic drug target combinations, five of which have been proven to be viable by biological or medical experiments.ConclusionsDifferent from traditional methods for judging drug synergy and antagonism, we propose the framework of how to enhance the efficiency by perturbing two sensitive targets in a combinatorial way, how to decrease the drug dose and therefore its side effect and cost by perturbing combinatorially a main sensitive target and an auxiliary insensitive target, and how to perturb two insensitive targets to realize the transition from a disease state to a healthy one which cannot be realized by perturbing each insensitive target alone. Although the idea is mainly applied to an AA metabolic network, the strategy holds for more general molecular networks such as combinatorial regulation in gene regulatory networks.

Abnormal arachidonic acid metabolic network may reduce sperm motility via P38 MAPK.

Asthenozoospermia is a common cause of male infertility, the aetiology of which remains unclear in 50–60% of cases. The current study aimed to characterize metabolic alterations in asthenozoospermic seminal plasma and to explore the signalling pathways involved in sperm motility regulation. At first, high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry was used to detect the targeted metabolic network of arachidonic acid (AA). Metabolomic multivariate data analysis showed significant distinction of AA metabolites between asthenozoospermic and healthy seminal plasma. AA as well as its lipoxygenase (LOX) and cytochrome P450 metabolites were found to be abnormally increased, while cyclooxygenase (COX) metabolites were complicatedly disturbed in asthenozoospermic volunteers compared with those in healthy ones. In vitro experiments and western blot analysis of sperm cells revealed a decrease in sperm motility and upregulation of sperm phosphor-P38 induced by AA. P38 inhibitor could increase AA-reduced sperm motility. Also, all the inhibitors of the three metabolic pathways of AA could block AA-induced P38 mitogen-activated protein kinase (MAPK) activation and further improve sperm motility. We report here for the first time that an abnormal AA metabolic network could reduce sperm motility via P38 MAPK activation through the LOX, cytochrome P450 and COX metabolic pathways, which might be an underlying pathomechanism of asthenozoospermia.

Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network

BackgroundArachidonic acid (AA) metabolic network is activated in the most inflammatory related diseases, and small‐molecular drugs targeting AA network are increasingly available. However, side effects of above mentioned drugs have always been the biggest obstacle. (+)‐2‐(1‐hydroxyl‐4‐oxocyclohexyl) ethyl caffeate (HOEC), a natural product acted as an inhibitor of 5‐lipoxygenase (5‐LOX) and 15‐LOX in vitro, exhibited weaker therapeutic effect in high dose than that in low dose to collagen induced arthritis (CIA) rats. In this study, we tried to elucidate the potential regulatory mechanism by using quantitative pharmacology.MethodsFirst, we generated an experimental data set by monitoring the dynamics of AA metabolites’ concentration in A23187 stimulated and different doses of HOEC co‐incubated RAW264.7. Then we constructed a dynamic model of A23187‐stimulated AA metabolic model to evaluate how a model‐based simulation of AA metabolic data assists to find the most suitable treatment dose by predicting the pharmacodynamics of HOEC.ResultsCompared to the experimental data, the model could simulate the inhibitory effect of HOEC on 5‐LOX and 15‐LOX, and reproduced the increase of the metabolic flux in the cyclooxygenase (COX) pathway. However, a concomitant, early‐stage of stimulation‐related decrease of prostaglandins (PGs) production in HOEC incubated RAW264.7 cells was not simulated in the model.ConclusionUsing the model, we predict that higher dose of HOEC disrupts the flux balance in COX and LOX of the AA network, and increased COX flux can interfere the curative effects of LOX inhibitor on resolution of inflammation which is crucial for the efficient and safe drug design.

Cyclooxygenase-1 (COX-1) and COX-1 Inhibitors in Cancer: A Review of Oncology and Medicinal Chemistry Literature.

Prostaglandins and thromboxane are lipid signaling molecules deriving from arachidonic acid by the action of the cyclooxygenase isoenzymes COX-1 and COX-2. The role of cyclooxygenases (particularly COX-2) and prostaglandins (particularly PGE2) in cancer-related inflammation has been extensively investigated. In contrast, COX-1 has received less attention, although its expression increases in several human cancers and a pathogenetic role emerges from experimental models. COX-1 and COX-2 isoforms seem to operate in a coordinate manner in cancer pathophysiology, especially in the tumorigenesis process. However, in some cases, exemplified by the serous ovarian carcinoma, COX-1 plays a pivotal role, suggesting that other histopathological and molecular subtypes of cancer disease could share this feature. Importantly, the analysis of functional implications of COX-1-signaling, as well as of pharmacological action of COX-1-selective inhibitors, should not be restricted to the COX pathway and to the effects of prostaglandins already known for their ability of affecting the tumor phenotype. A knowledge-based choice of the most appropriate tumor cell models, and a major effort in investigating the COX-1 issue in the more general context of arachidonic acid metabolic network by using the systems biology approaches, should be strongly encouraged.

Activation of Glutathione Peroxidase 4 as a Novel Anti-inflammatory Strategy.

The anti-oxidative enzyme, glutathione peroxidase 4 (GPX4), helps to promote inflammation resolution by eliminating oxidative species produced by the arachidonic acid (AA) metabolic network. Up-regulating its activity has been proposed as a promising strategy for inflammation intervention. In the present study, we aimed to study the effect of GPX4 activator on the AA metabolic network and inflammation related pathways. Using combined computational and experimental screen, we identified a novel compound that can activate the enzyme activity of GPX4 by more than two folds. We further assessed its potential in a series of cellular assays where GPX4 was demonstrated to play a regulatory role. We are able to show that GPX4 activation suppressed inflammatory conditions such as oxidation of AA and NF-κB pathway activation. We further demonstrated that this GPX4 activator can decrease the intracellular ROS level and suppress ferroptosis. Our study suggests that GPX4 activators can be developed as anti-inflammatory or cyto-protective agent in lipid-peroxidation-mediated diseases.

A Regulatory Hierarchy of the Arabidopsis Branched-Chain Amino Acid Metabolic Network.

The branched-chain amino acids (BCAAs) Ile, Val, and Leu are essential nutrients that humans and other animals obtain from plants. However, total and relative amounts of plant BCAAs rarely match animal nutritional needs, and improvement requires a better understanding of the mechanistic basis for BCAA homeostasis. We present an in vivo regulatory model of BCAA homeostasis derived from analysis of feedback-resistant Arabidopsis thaliana mutants for the three allosteric committed enzymes in the biosynthetic network: threonine deaminase (also named l-O-methylthreonine resistant 1 [OMR1]), acetohydroxyacid synthase small subunit 2 (AHASS2), and isopropylmalate synthase 1 (IPMS1). In this model, OMR1 exerts primary control on Ile accumulation and functions independently of AHAS and IPMS AHAS and IPMS regulate Val and Leu homeostasis, where AHAS affects total Val+Leu and IPMS controls partitioning between these amino acids. In addition, analysis of feedback-resistant and loss-of-function single and double mutants revealed that each AHAS and IPMS isoenzyme contributes to homeostasis rather than being functionally redundant. The characterized feedback resistance mutations caused increased free BCAA levels in both seedlings and seeds. These results add to our understanding of the basis of in vivo BCAA homeostasis and inform approaches to improve the amount and balance of these essential nutrients in crops.

Enhanced production of fatty acids in three strains of microalgae using a combination of nitrogen starvation and chemical inhibitors of carbohydrate synthesis

Microalgae are attracting much attention as superior biodiesel producers. In particular, under stressful conditions, they accumulate organic compounds consisting entirely of carbon and hydrogen. The aim of this work was to increase intracellular fatty acid content in Dunaliella tertiolecta (Chlorophyceae), Nannochloropsis oculata (Eustigmatophyceae), and Porphyridium cruentum (Rhodophyceae) using a combination of nitrogen starvation and chemical inhibitors of carbohydrate biosynthesis. These microalgae were subjected to nitrogen starvation and their physiological changes were then observed over time. In D. tertiolecta, no significant change in total fatty acid content was detected on day 3.5 relative to the initial total fatty acid content (day 0), while total carbohydrate content dramatically increased as the nitrogen starvation period was extended. In N. oculata, total fatty acid content rapidly increased, reaching up to nearly 40% of the DCW at day 3.5. However, total carbohydrate content exhibited a gradual reduction throughout the experiment. In P. cruentum, total carbohydrate content increased up to 43% of DCW on day 3.5 and total fatty acid content increased slightly under nitrogen depletion. These data suggest that different eukaryotic microalgae use different storage products under stressful conditions. Among the three strains, D. tertiolecta showed decreased total carbohydrate content and enhanced total fatty acid content following inhibition of carbohydrate synthesis by dichlorophenyl dimethylurea and cyclohexane diamine tetra acetic acid. The results demonstrate the possibility of furthering our understanding of the fatty acid and carbohydrate biosynthesis metabolic network that responds to environmental changes in microalgae.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective.

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Motions of Allosteric and Orthosteric Ligand-Binding Sites in Proteins are Highly Correlated.

Allostery is the phenomenon in which a ligand binding at one site affects other sites in the same macromolecule. Allostery has important roles in many biological processes. Theoretically, all nonfibrous proteins are potentially allosteric. However, few allosteric proteins have been validated, and the identification of novel allosteric sites remains a challenge. The motion of residues and subunits underlies protein function; therefore, we hypothesized that the motions of allosteric and orthosteric sites are correlated. We utilized a data set of 24 known allosteric sites from 23 monomer proteins to calculate the correlations between potential ligand-binding sites and corresponding orthosteric sites using a Gaussian network model (GNM). Most of the known allosteric site motions showed high correlations with corresponding orthosteric site motions, whereas other surface cavities did not. These high correlations were robust when using different structural data for the same protein, such as structures for the apo state and the orthosteric effector-binding state, whereas the contributions of different frequency modes to motion correlations depend on the given protein. The high correlations between allosteric and orthosteric site motions were also observed in oligomeric allosteric proteins. We applied motion correlation analysis to predict potential allosteric sites in the 23 monomer proteins, and some of these predictions were in good agreement with published experimental data. We also performed motion correlation analysis to identify a novel allosteric site in 15-lipoxygenase (an enzyme in the arachidonic acid metabolic network) using recently reported activating compounds. Our analysis correctly identified this novel allosteric site along with two other sites that are currently under experimental investigation. Our study demonstrates that the motions of allosteric sites are highly correlated with the motions of orthosteric sites. Our correlation analysis method provides new tools for predicting potential allosteric sites.

Coelimination and Survival in Gene Network Evolution: Dismantling the RA-Signaling in a Chordate.

The bloom of genomics is revealing gene loss as a pervasive evolutionary force generating genetic diversity that shapes the evolution of species. Outside bacteria and yeast, however, the understanding of the process of gene loss remains elusive, especially in the evolution of animal species. Here, using the dismantling of the retinoic acid metabolic gene network (RA-MGN) in the chordate Oikopleura dioica as a case study, we combine approaches of comparative genomics, phylogenetics, biochemistry, and developmental biology to investigate the mutational robustness associated to biased patterns of gene loss. We demonstrate the absence of alternative pathways for RA-synthesis in O. dioica, which suggests that gene losses of RA-MGN were not compensated by mutational robustness, but occurred in a scenario of regressive evolution. In addition, the lack of drastic phenotypic changes associated to the loss of RA-signaling provides an example of the inverse paradox of Evo-Devo. This work illustrates how the identification of patterns of gene coelimination-in our case five losses (Rdh10, Rdh16, Bco1, Aldh1a, and Cyp26)-is a useful strategy to recognize gene network modules associated to distinct functions. Our work also illustrates how the identification of survival genes helps to recognize neofunctionalization events and ancestral functions. Thus, the survival and extensive duplication of Cco and RdhE2 in O. dioica correlated with the acquisition of complex compartmentalization of expression domains in the digestive system and a process of enzymatic neofunctionalization of the Cco, while the surviving Aldh8 could be related to its ancestral housekeeping role against toxic aldehydes.

Integrated bioinformatics to decipher the ascorbic acid metabolic network in tomato.

Ascorbic acid is involved in a plethora of reactions in both plant and animal metabolism. It plays an essential role neutralizing free radicals and acting as enzyme co-factor in several reaction. Since humans are ascorbate auxotrophs, enhancing the nutritional quality of a widely consumed vegetable like tomato is a desirable goal. Although the main reactions of the ascorbate biosynthesis, recycling and translocation pathways have been characterized, the assignment of tomato genes to each enzymatic step of the entire network has never been reported to date. By integrating bioinformatics approaches, omics resources and transcriptome collections today available for tomato, this study provides an overview on the architecture of the ascorbate pathway. In particular, 237 tomato loci were associated with the different enzymatic steps of the network, establishing the first comprehensive reference collection of candidate genes based on the recently released tomato gene annotation. The co-expression analyses performed by using RNA-Seq data supported the functional investigation of main expression patterns for the candidate genes and highlighted a coordinated spatial-temporal regulation of genes of the different pathways across tissues and developmental stages. Taken together these results provide evidence of a complex interplaying mechanism and highlight the pivotal role of functional related genes. The definition of genes contributing to alternative pathways and their expression profiles corroborates previous hypothesis on mechanisms of accumulation of ascorbate in the later stages of fruit ripening. Results and evidences here provided may facilitate the development of novel strategies for biofortification of tomato fruit with Vitamin C and offer an example framework for similar studies concerning other metabolic pathways and species.

Amino acid composition and amino acid-metabolic network in supragingival plaque.

Dental plaque metabolizes both carbohydrates and amino acids. The former can be degraded to acids mainly, while the latter can be degraded to various metabolites, including ammonia, acids and amines, and associated with acid-neutralization, oral malodor and tissue inflammation. However, amino acid metabolism in dental plaque is still unclear. This study aimed to elucidate what kinds of amino acids are available as metabolic substrates and how the amino acids are metabolized in supragingival plaque, by a metabolome analysis. Amino acids and the related metabolites in supragingival plaque were extracted and quantified comprehensively by CE-TOFMS. Plaque samples were also incubated with amino acids, and the amounts of ammonia and amino acid-related metabolites were measured. The concentration of glutamate was the highest in supragingival plaque, while the ammonia-production was the highest from glutamine. The obtained metabolome profile revealed that amino acids are degraded through various metabolic pathways, including deamination, decarboxylation and transamination and that these metabolic systems may link each other, as well as with carbohydrate metabolic pathways in dental plaque ecosystem. Moreover, glutamine and glutamate might be the main source of ammonia production, as well as arginine, and contribute to pH-homeostasis and counteraction to acid-induced demineralization in supragingival plaque.

Kriging-Based Parameter Estimation Algorithm for Metabolic Networks Combined with Single-Dimensional Optimization and Dynamic Coordinate Perturbation.

The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

De novo amino acid biosynthesis contributes to salmonella enterica growth in Alfalfa seedling exudates.

Salmonella enterica is a member of the plant microbiome. Growth of S. enterica in sprouting-seed exudates is rapid; however, the active metabolic networks essential in this environment are unknown. To examine the metabolic requirements of S. enterica during growth in sprouting-seed exudates, we inoculated alfalfa seeds and identified 305 S. enterica proteins extracted 24 h postinoculation from planktonic cells. Over half the proteins had known metabolic functions, and they are involved in over one-quarter of the known metabolic reactions. Ion and metabolite transport accounted for the majority of detected reactions. Proteins involved in amino acid transport and metabolism were highly represented, suggesting that amino acid metabolic networks may be important for S. enterica growth in association with roots. Amino acid auxotroph growth phenotypes agreed with the proteomic data; auxotrophs in amino acid-biosynthetic pathways that were detected in our screen developed growth defects by 48 h. When the perceived sufficiency of each amino acid was expressed as a ratio of the calculated biomass requirement to the available concentration and compared to growth of each amino acid auxotroph, a correlation between nutrient availability and bacterial growth was found. Furthermore, glutamate transport acted as a fitness factor during S. enterica growth in association with roots. Collectively, these data suggest that S. enterica metabolism is robust in the germinating-alfalfa environment; that single-amino-acid metabolic pathways are important but not essential; and that targeting central metabolic networks, rather than dedicated pathways, may be necessary to achieve dramatic impacts on bacterial growth.