MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.     

Computational Analysis of Reciprocal Association of Metabolism and Epigenetics in the Budding Yeast: A Genome-Scale Metabolic Model (GSMM) Approach

Metaboloepigenetics is a newly coined term in biological sciences that investigates the crosstalk between epigenetic modifications and metabolism. The reciprocal relation between biochemical transformations and gene expression regulation has been experimentally demonstrated in cancers and metabolic syndromes. In this study, we explored the metabolism-histone modifications crosstalk by topological analysis and constraint-based modeling approaches in the budding yeast. We constructed nine models through the integration of gene expression data of four mutated histone tails into a genome-scale metabolic model of yeast. Accordingly, we defined the centrality indices of the lowly expressed enzymes in the undirected enzyme-centric network of yeast by CytoHubba plug-in in Cytoscape. To determine the global effects of histone modifications on the yeast metabolism, the growth rate and the range of possible flux values of reactions, we used constraint-based modeling approach. Centrality analysis shows that the lowly expressed enzymes could affect and control the yeast metabolic network. Besides, constraint-based modeling results are in a good agreement with the experimental findings, confirming that the mutations in histone tails lead to non-lethal alterations in the yeast, but have diverse effects on the growth rate and reveal the functional redundancy.     

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.     

A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks

Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.     

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.     

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties.     

Responses to Light Intensity in a Genome-Scale Model of Rice Metabolism

We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms.Rice (Oryza sativa) makes up nearly 20% of the total caloric intake for the human population as a whole; the income of more than 100 million households in developing countries depends on rice cultivation. Although rice yield has increased, though gradually more slowly, during the last four decades, the world population continues to grow, while the land and water resources for cultivation are declining, leading to a need for high-yielding, stress-tolerant, nutrient-rich rice cultivars (Nguyen and Ferrero, 2006).Researchers are trying to meet the challenges of improving production in different ways. Some of the efforts include (1) identifying the stress-tolerant rice varieties and stress-responsive genes (Xiang et al., 2007), (2) producing a “Green Super Rice” combining, in a single plant, many different favorable characteristics from the large number of available strains and cultivars, guided by molecular marker-based selection (Zhang, 2007), (3) introducing the genes of C₄ plant to change the leaf anatomy of rice and hence improving the photosynthesis (Kajala et al., 2011), and (4) producing vitamin A-enriched golden rice (Al-Babili and Beyer, 2005). In addition, current research on the genetic basis of signaling between nitrogen-fixing soil bacteria and legumes (Xie et al., 2012) has the potential to allow the engineering of nodule formation in cereal crops such as rice and wheat (Triticum aestivum).Here, we present a genome-scale model of rice metabolism and examine its responses to changing light availability. Because rice is also a model organism for other cereal crops, such as wheat, this effort should help researchers to understand the biochemistry of a photosynthetic crop plant as well as to compare it with other plants. In addition, the metabolic model of rice, which is the second metabolic model of a crop plant, can be used as a template for comparing the metabolism of different varieties of rice that are pathogen tolerant, drought tolerant, lower or higher yield, etc., and thus, it may also help in identifying characteristics of individual varieties that may assist rice biotechnologists to breed the desired rice crop.     

A Variational Bayes Approach to the Analysis of Occupancy Models

Detection-nondetection data are often used to investigate species range dynamics using Bayesian occupancy models which rely on the use of Markov chain Monte Carlo (MCMC) methods to sample from the posterior distribution of the parameters of the model. In this article we develop two Variational Bayes (VB) approximations to the posterior distribution of the parameters of a single-season site occupancy model which uses logistic link functions to model the probability of species occurrence at sites and of species detection probabilities. This task is accomplished through the development of iterative algorithms that do not use MCMC methods. Simulations and small practical examples demonstrate the effectiveness of the proposed technique. We specifically show that (under certain circumstances) the variational distributions can provide accurate approximations to the true posterior distributions of the parameters of the model when the number of visits per site (K) are as low as three and that the accuracy of the approximations improves as K increases. We also show that the methodology can be used to obtain the posterior distribution of the predictive distribution of the proportion of sites occupied (PAO).     

A New Approach to Bacterial Vaccines

Immunizing antigens against only 10 bacterial diseases—cholera, diphtheria, paratyphoid, pertussis, plague, scarlet fever, staphylococcal disease, tetanus, tuberculosis and typhoid—have been licensed for sale in Canada and the United States. Convincing evidence of efficacy is available for only four of these—diphtheria and tetanus toxoids, and pertussis and typhoid vaccines.The principles which determine the efficacy of different immunizing antigens are not always the same. Toxoids, for example, stimulate the formation of antitoxin-producing mechanisms which can neutralize toxins produced by invading organisms, thereby rendering them harmless. Conversely, vaccines stimulate the formation of antibacterial mechanisms which stop the growth of organisms before they can produce disease.Use of enzyme-lysed vaccines for prevention of staphylococcal disease represents a new approach in vaccine research. Animal tests have shown lysed vaccines to be 10 to 100 times less toxic, and about eight times more effective, than whole bacterial vaccines. Studies with lysed vaccines for other diseases are now in progress.     

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-K_i was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-K_i server is freely available via: http://sdd.whu.edu.cn/dpiki.     

Bacterial Metabolism of Mevalonic Acid

Soluble cell-free extracts of actinomycete S4 grown on media containing mevalonate catalyze acetoacetate formation from mevalonate, mevaldate, and β-hydroxy-β-methylglutaryl-coenzyme A (CoA). Conversion of mevalonate to acetoacetate involves formation of free β-hydroxy-β-methylglutaryl-CoA, but not free mevaldate. The reaction favors mevalonate oxidation, and nicotinamide adenine dinucleotide, rather than nicotinamide adenine dinucleotide phosphate, acts as oxidant.     

Bacterial Metabolism of 2,6-Xylenol

Strain DM1, a Mycobacterium sp. that utilizes 2,6-xylenol, 2,3,6-trimethylphenol, and o-cresol as sources of carbon and energy, was isolated. Intact cells of Mycobacterium strain DM1 grown with 2,6-xylenol cooxidized 2,4,6-trimethylphenol to 2,4,6-trimethylresorcinol. 4-Chloro-3,5-dimethylphenol prevents 2,6-xylenol from being totally degraded; it was quantitatively converted to 2,6-dimethylhydroquinone by resting cells. 2,6-Dimethylhydroquinone, citraconate, and an unidentified metabolite were detected as products of 2,6-xylenol oxidation in cells that were partially inactivated by EDTA. Under oxygen limitation, 2,6-dimethylhy-droquinone, citraconate, and an unidentified metabolite were released during 2,6-xylenol turnover by resting cells. Cell extracts of 2,6-xylenol-grown cells contained a 2,6-dimethylhydroquinone-converting enzyme. When supplemented with NADH, cell extracts catalyzed the reduction of 2,6-dimethyl-3-hydroxyquinone to 2,6-dimethyl-3-hydroxyhydroquinone. Since a citraconase was also demonstrated in cell extracts, a new metabolic pathway with 2,6-dimethyl-3-hydroxyhydroquinone as the ring fission substrate is proposed.     

Purine Metabolism in B-Cell Lymphocytic Leukemia: A Microarray Approach

B-cell chronic lymphocytic leukemia (B-CLL) is an adult-onset highly heterogeneous malignancy characterized by a cells resistance to apoptosis rather than to highly proliferative cells. In previous research, we evidenced an imbalance of purine metabolism in B-CLL cells. Since the extracellular adenosine has been proved to induce apoptosis via A2b receptor, enzymes involved in adenosine metabolism could play an important role in apoptosis resistance of B-CLL cells. We prepared a microarray chip for the analysis of 50 selected genes that could be of interest in B-CLL: enzymes of purine de-novo, salvage and catabolic pathway, oxidative stress enzymes, and apoptotis-related proteins. Preliminary results identify many genes of purine metabolism that exhibit low or high expression, while genes involved in signal transduction and apoptosis exhibit lower alterations even if of remarkable interest. This application of microarray technique seems promising and at least a subset of these genes will be valid candidates for further studies.     

Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations.     

Diagnosis of Bacterial Bloodstream Infections: A 16S Metagenomics Approach

Background

Bacterial bloodstream infection (bBSI) is one of the leading causes of death in critically ill patients and accurate diagnosis is therefore crucial. We here report a 16S metagenomics approach for diagnosing and understanding bBSI.

Methodology/Principal Findings

The proof-of-concept was delivered in 75 children (median age 15 months) with severe febrile illness in Burkina Faso. Standard blood culture and malaria testing were conducted at the time of hospital admission. 16S metagenomics testing was done retrospectively and in duplicate on the blood of all patients. Total DNA was extracted from the blood and the V3–V4 regions of the bacterial 16S rRNA genes were amplified by PCR and deep sequenced on an Illumina MiSeq sequencer. Paired reads were curated, taxonomically labeled, and filtered. Blood culture diagnosed bBSI in 12 patients, but this number increased to 22 patients when combining blood culture and 16S metagenomics results. In addition to superior sensitivity compared to standard blood culture, 16S metagenomics revealed important novel insights into the nature of bBSI. Patients with acute malaria or recovering from malaria had a 7-fold higher risk of presenting polymicrobial bloodstream infections compared to patients with no recent malaria diagnosis (p-value = 0.046). Malaria is known to affect epithelial gut function and may thus facilitate bacterial translocation from the intestinal lumen to the blood. Importantly, patients with such polymicrobial blood infections showed a 9-fold higher risk factor for not surviving their febrile illness (p-value = 0.030).

Conclusions/Significance

Our data demonstrate that 16S metagenomics is a powerful approach for the diagnosis and understanding of bBSI. This proof-of-concept study also showed that appropriate control samples are crucial to detect background signals due to environmental contamination.     

拟似然非线性模型中的置信域：几何法

对拟似然非线性模型在欧氏内积实间建立了修改的Ｂａｔｅｓ＆Ｗａｔｔｓ几何结构,基于此几何结构,导出了参数和子集系数的与统计曲率有关的三种近似置信域,进一步推广和发展了Ｈａｍｉｌｔｏｎ ｅｔ ａｌ．（１９８２）。Ｈａｍｉｌｔｏｎ（１９８６）和Ｗｅｉ（１９９４,１９９８）等人的相应结果。