Ali Navid

Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach

BackgroundConstraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network.ResultsWe applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress.ConclusionsComparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.

Do genome-scale models need exact solvers or clearer standards?

Constraint‐based analysis of genome‐scale models (GEMs) arose shortly after the first genome sequences became available. As numerous reviews of the field show, this approach and methodology has proven to be successful in studying a wide range of biological phenomena (McCloskey et al, 2013; Bordbar et al, 2014). However, efforts to expand the user base are impeded by hurdles in correctly formulating these problems to obtain numerical solutions. In particular, in a study entitled “An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models” (Chindelevitch et al, 2014), the authors apply an exact solver to 88 genome‐scale constraint‐based models of metabolism. The authors claim that COBRA calculations (Orth et al, 2010) are inconsistent with their results and that many published and actively used (Lee et al, 2007; McCloskey et al, 2013) genome‐scale models do support cellular growth in existing studies only because of numerical errors. They base these broad claims on two observations: (i) three reconstructions (iAF1260, iIT341, and iNJ661) compute feasibly in COBRA, but are infeasible when exact numerical algorithms are used by their software (entitled MONGOOSE); (ii) linear programs generated by MONGOOSE for iIT341 were submitted to the NEOS Server (a Web site that runs linear programs through various solvers) and gave inconsistent results. They further claim that a large percentage of these COBRA models are actually unable to produce biomass flux. Here, we demonstrate that the claims made by Chindelevitch et al (2014) stem from an incorrect parsing of models from files rather than actual problems with numerical error or COBRA computations.

D-Lactate production as a function of glucose metabolism in Saccharomyces cerevisiae

Methylglyoxal, a reactive, toxic dicarbonyl, is generated by the spontaneous degradation of glycolytic intermediates. Methylglyoxal can form covalent adducts with cellular macromolecules, potentially disrupting cellular function. We performed experiments using the model organism Saccharomyces cerevisiae, grown in media containing low, moderate and high glucose concentrations, to determine the relationship between glucose consumption and methylglyoxal metabolism. Normal growth experiments and glutathione depletion experiments showed that metabolism of methylglyoxal by log‐phase yeast cultured aerobically occurred primarily through the glyoxalase pathway. Growth in high‐glucose media resulted in increased generation of the methylglyoxal metabolite d‐lactate and overall lower efficiency of glucose utilization as measured by growth rates. Cells grown in high‐glucose media maintained higher glucose uptake flux than cells grown in moderate‐glucose or low‐glucose media. Computational modelling showed that increased glucose consumption may impair catabolism of triose phosphates as a result of an altered NAD+:NADH ratio. Copyright

Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

YEAST DYNAMIC METABOLIC FLUX MEASUREMENT IN NUTRIENT-RICH MEDIA BY HPLC AND ACCELERATOR MASS SPECTROMETRY

Metabolic flux, the flow of metabolites through networks of enzymes, represents the dynamic productive output of cells. Improved understanding of intracellular metabolic fluxes will enable targeted manipulation of metabolic pathways of medical and industrial importance to a greater degree than is currently possible. Flux balance analysis (FBA) is a constraint-based approach to modeling metabolic fluxes, but its utility is limited by a lack of experimental measurements. Incorporation of experimentally measured fluxes as system constraints will significantly improve the overall accuracy of FBA. We applied a novel, two-tiered approach in the yeast Saccharomyces cerevisiae to measure nutrient consumption rates (extracellular fluxes) and a targeted intracellular flux using a (14)C-labeled precursor with HPLC separation and flux quantitation by accelerator mass spectrometry (AMS). The use of AMS to trace the intracellular fate of (14)C-glutamine allowed the calculation of intracellular metabolic flux through this pathway, with glutathione as the metabolic end point. Measured flux values provided global constraints for the yeast FBA model which reduced model uncertainty by more than 20%, proving the importance of additional constraints in improving the accuracy of model predictions and demonstrating the use of AMS to measure intracellular metabolic fluxes. Our results highlight the need to use intracellular fluxes to constrain the models. We show that inclusion of just one such measurement alone can reduce the average variability of model predicted fluxes by 10%.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

Systems biology of Microbial Communities

Microbes exist naturally in a wide range of environments in communities where their interactions are significant, spanning the extremes of high acidity and high temperature environments to soil and the ocean. We present a practical discussion of three different approaches for modeling microbial communities: rate equations, individual-based modeling, and population dynamics. We illustrate the approaches with detailed examples. Each approach is best fit to different levels of system representation, and they have different needs for detailed biological input. Thus, this set of approaches is able to address the operation and function of microbial communities on a wide range of organizational levels.

Application of a Physiologically Based Pharmacokinetic Model to Study Theophylline Metabolism and Its Interactions With Ciprofloxacin and Caffeine

Theophylline is a commonly used bronchodilator. However, due to its narrow therapeutic range, moderate elevation of serum concentration can result in adverse drug reactions (ADRs). ADRs occur because of interhuman pharmacokinetic variability and interactions with coprescribed medicines. We developed a physiologically based pharmacokinetic (PBPK) model of theophylline, caffeine, and ciprofloxacin metabolisms to: examine theophylline pharmacokinetic variability, and predict population‐level outcomes of drug–drug interactions (DDIs). A simulation‐based equation for personalized dosing of theophylline was derived. Simulations of DDI show that calculated personalized doses are safe even after cotreatment with large doses of strong inhibitors. Simulations of adult populations indicate that the elderly are most susceptible to ADRs stemming from theophylline–ciprofloxacin and theophylline–caffeine interactions. Females, especially Asians, due to their smaller average size, are more susceptible to DDI‐induced ADRs following typical dosing practices. Our simulations also show that the higher adipose and lower muscle fractions in females significantly alter the pharmacokinetics of theophylline or ciprofloxacin.

Quantitative In Silico analysis of transient metabolism of acetaminophen and associated causes of hepatotoxicity in humans

PurposeAlthough safe at therapeutic levels, excess intake of acetaminophen can lead to hepatic injury or acute liver failure (ALF). A number of different factors influence metabolism and hepatotoxicity of acetaminophen in patients. Three of the most important are a patient’s physiological response to fasting, alcohol consumption, and chronic acetaminophen consumption. The molecular and enzymatic underpinnings for these processes have been extensively studied. The purpose of this study is to examine and quantify the effects of the noted conditions, provide possible reasons for conflicting clinical observations, and examine dangers associated with uptake of therapeutic doses of acetaminophen.MethodsIn order to gain a better understanding of the transient hepatic changes associated with each physiological and nutritional process, examine risks of ALF associated with individuals based on their unique lifestyle and health issues, and predict improved dosing strategies, a multi-compartmented physiologically-based pharmacokinetic (PBPK) model of acetaminophen metabolism in adult humans was developed. By varying the parameters of this model, changes in metabolism of acetaminophen and its toxic byproducts for a variety of medically relevant conditions were assessed.ResultsSimulated results indicate that in case of chronic ingestion of acetaminophen, the increased rate of glucuronidation plays a significant role in protecting patients from liver damage following uptake of excessive quantities. Analysis of metabolism of acetaminophen in persons who have imbibed excessive amounts of alcohol show that the primary reason for hepatotoxicity in such individuals is decreased availability of glutathione in the liver and not the observed increased production of toxic byproducts. When the glutathione depleting effects of alcohol consumption are combined with those associated with chronic acetaminophen use, intake of slightly higher quantities than the recommended therapeutic doses of acetaminophen can result in initiation of hepatotoxicity.ConclusionsThe results of simulations show that, in healthy and well-fed individuals, chronic uptake of acetaminophen doses even five times the therapeutic recommendations should be safe. However, in persons who have diminished hepatic glutathione regeneration capacities, depending on the magnitude of this deleterious shortcoming, minor overdoses can result in hepatotoxicity. Hence, it can be concluded that for such persons, acetaminophen is just as toxic as any other compound that would generate reactive oxidative species.

System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris

Background Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system’s critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. Results To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris, a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbons sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris. Our results revealed that phototrophic metabolism in R. palustris is a light-limited growth mode under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. Palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO2 fixation, even in presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. Conclusions Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon’s secondary role as a regulating factor, R. palustris’ metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.