Flux balance analysis of photoautotrophic metabolism

Photosynthesis is the principal process responsible for fixation of inorganic carbon dioxide into organic molecules with sunlight as the energy source. Potentially, many chemicals could be inexpensively produced by photosynthetic organisms. Mathematical modeling of photoautotrophic metabolism is therefore important to evaluate maximum theoretical product yields and to deeply understand the interactions between biochemical energy, carbon fixation, and assimilation pathways. Flux balance analysis based on linear programming is applied to photoautotrophic metabolism. The stoichiometric network of a model photosynthetic prokaryote, Synechocystis sp. PCC 6803, has been reconstructed from genomic data and biochemical literature and coupled with a model of the photophosphorylation processes. Flux map topologies for the hetero-, auto-, and mixotrophic modes of metabolism under conditions of optimal growth were determined and compared. The roles of important metabolic reactions such as the glyoxylate shunt and the transhydrogenase reaction were analyzed. We also theoretically evaluated the effect of gene deletions or additions on biomass yield and metabolic flux distributions.     

Subcellular analysis of starch metabolism in developing barley seeds using a non-aqueous fractionation method

Compartmentation of metabolism in developing seeds is poorly understood due to the lack of data on metabolite distributions at the subcellular level. In this report, a non-aqueous fractionation method is described that allows subcellular concentrations of metabolites in developing barley endosperm to be calculated. (i) Analysis of subcellular volumes in developing endosperm using micrographs shows that plastids and cytosol occupy 50.5% and 49.9% of the total cell volume, respectively, while vacuoles and mitochondria can be neglected. (ii) By using non-aqueous fractionation, subcellular distribution between the cytosol and plastid of the levels of metabolites involved in sucrose degradation, starch synthesis, and respiration were determined. With the exception of ADP and AMP which were mainly located in the plastid, most other metabolites of carbon and energy metabolism were mainly located outside the plastid in the cytosolic compartment. (iii) In developing barley endosperm, the ultimate precursor of starch, ADPglucose (ADPGlc), was mainly located in the cytosol (80-90%), which was opposite to the situation in growing potato tubers where ADPGlc was almost exclusively located in the plastid (98%). This reflects the different subcellular distribution of ADPGlc pyrophosphorylase (AGPase) in these tissues. (iv) Cytosolic concentrations of ADPGlc were found to be close to the published K(m) values of AGPase and the ADPGlc/ADP transporter at the plastid envelope. Also the concentrations of the reaction partners glucose-1-phosphate, ATP, and inorganic pyrophosphate were close to the respective K(m) values of AGPase. (v) Knock-out of cytosolic AGPase in Riso16 mutants led to a strong decrease in ADPGlc level, in both the cytosol and plastid, whereas knock-down of the ADPGlc/ADP transporter led to a large shift in the intracellular distribution of ADPGlc. (v) The thermodynamic structure of the pathway of sucrose to starch was determined by calculating the mass-action ratios of all the steps in the pathway. The data show that AGPase is close to equilibrium, in both the cytosol and plastid, whereas the ADPGlc/ADP transporter is strongly displaced from equilibrium in vivo. This is in contrast to most other tissues, including leaves and potato tubers. (vi) Results indicate transport rather than synthesis of ADPGlc to be the major regulatory site of starch synthesis in barley endosperm. The reversibility of AGPase in the plastid has important implications for the regulation of carbon partitioning between different biosynthetic pathways.     

Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii

Background  

Photosynthetic organisms convert atmospheric carbon dioxide into numerous metabolites along the pathways to make new biomass. Aquatic photosynthetic organisms, which fix almost half of global inorganic carbon, have great potential: as a carbon dioxide fixation method, for the economical production of chemicals, or as a source for lipids and starch which can then be converted to biofuels. To harness this potential through metabolic engineering and to maximize production, a more thorough understanding of photosynthetic metabolism must first be achieved. A model algal species, C. reinhardtii, was chosen and the metabolic network reconstructed. Intracellular fluxes were then calculated using flux balance analysis (FBA).     

Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum

Diatoms (Bacillarophyceae) are photosynthetic unicellular microalgae that have risen to ecological prominence in oceans over the past 30 million years. They are of interest as potential feedstocks for sustainable biofuels. Maximizing production of these feedstocks will require genetic modifications and an understanding of algal metabolism. These processes may benefit from genome‐scale models, which predict intracellular fluxes and theoretical yields, as well as the viability of knockout and knock‐in transformants. Here we present a genome‐scale metabolic model of a fully sequenced and transformable diatom: Phaeodactylum tricornutum. The metabolic network was constructed using the P. tricornutum genome, biochemical literature, and online bioinformatic databases. Intracellular fluxes in P. tricornutum were calculated for autotrophic, mixotrophic and heterotrophic growth conditions, as well as knockout conditions that explore the in silico role of glycolytic enzymes in the mitochondrion. The flux distribution for lower glycolysis in the mitochondrion depended on which transporters for TCA cycle metabolites were included in the model. The growth rate predictions were validated against experimental data obtained using chemostats. Two published studies on this organism were used to validate model predictions for cyclic electron flow under autotrophic conditions, and fluxes through the phosphoketolase, glycine and serine synthesis pathways under mixotrophic conditions. Several gaps in annotation were also identified. The model also explored unusual features of diatom metabolism, such as the presence of lower glycolysis pathways in the mitochondrion, as well as differences between P. tricornutum and other photosynthetic organisms.     

Flux balance analysis: A geometric perspective

Advances in the field of bioinformatics have led to reconstruction of genome-scale networks for a number of key organisms. The application of physicochemical constraints to these stoichiometric networks allows researchers, through methods such as flux balance analysis, to highlight key sets of reactions necessary to achieve particular objectives. The key benefits of constraint-based analysis lie in the minimal knowledge required to infer systemic properties. However, network degeneracy leads to a large number of flux distributions that satisfy any objective; moreover, these distributions may be dominated by biologically irrelevant internal cycles. By examining the geometry underlying the problem, we define two methods for finding a unique solution within the space of all possible flux distributions; such a solution contains no internal cycles, and is representative of the space as a whole. The first method draws on typical geometric knowledge, but cannot be applied to large networks because of the high computational complexity of the problem. Thus a second method, an iteration of linear programs which scales easily to the genome scale, is defined. The algorithm is run on four recent genome-scale models, and unique flux solutions are found. The algorithm set out here will allow researchers in flux balance analysis to exchange typical solutions to their models in a reproducible format. Moreover, having found a single solution, statistical analyses such as correlations may be performed.     

Purification and properties of two ribonucleases and a nuclease from barley seeds

Three enzymes possessing RNAase activity were isolated from barley seeds. These enzymes were further purified by ammonium sulphate precipitation DEAE-cellulose chromatography, gel filtration on Sephadex G-75 and DEAE-Sephadex A-50 chromatography. These enzymes have been characterized and classified as: 1. Plant RNAase I (EC 3.1.27.1). It has a pH optimum at 5.7 and molecular weight of 19 000. 2. Plant RNAase II (EC 3.1.27.1). It has a pH optimum at 6.35 and molecular weight of 19 000. 3. Plant nuclease I (EC 3.1.30.2). It has a pH optimum at 6.8 and molecular weight of 37 000. Two RNAases were purified to homogeneity by means of affinity chromatography on poly(G)-Sepharose 4B, as shown by polyacrylamide gel electrophoresis in the presence of sodium dodecyl sulfate.     

An integrated approach to assessing nitroso-redox balance in systemic inflammation

Most studies examining the metabolic fate of NO during systemic inflammation have focused on measuring the quantitatively predominating, stable anions nitrite and nitrate within the circulation. However, these are not necessarily the NO-related products that govern NO metabolism and signaling in tissues. We assessed all major NO derivatives temporally in blood and vital organs during inflammation and explored their relationship to insult severity and redox status. Male rats receiving intraperitoneal endotoxin or vehicle were sacrificed for organ and blood sampling between 0 and 24 h. Endotoxin induced transient and organ-specific changes in a variety of NO metabolites. Nitrite and nitrate increased, peaking at 8 and 12 h, respectively. S- and N-nitrosation and heme-nitrosylation products also peaked at 8 h; these posttranslational protein modifications were associated with decreased myocardial function (echocardiography). Evidence of oxidative stress and systemic inflammation was also obtained. The rise in most NO derivatives was proportional to insult severity. All metabolite levels normalized within 24 h, despite evidence of persisting myocardial dysfunction and clinical unwellness. Our findings point to a complex interplay between NO production, antioxidant defense, and redox status. Although the precise (patho)physiologic roles of specific NO derivatives and their diagnostic/prognostic utility await further investigation, nitroso species in erythrocytes are the most sensitive markers of NO in systemic inflammation, detectable before clinical symptoms manifest.     

Purification of a non-histone protein with properties of antizyme to ornithine decarboxylase from germinated barley seeds

The purification of a chromatin-bound antizyme to ornithine decarboxylase from germinated barley seeds is described. This antizyme was extracted from chromatin by 2 M NaCl and purified to homogeneity. Its molecular weight was found to be 9000 with an isoelectric point of 4.1. It reacts with both cytosolic and chromatinbound ornithine decarboxylase from germinated barley seeds and E. coli, but it does not inhibit ornithine decarboxylase of Tetrahymena pyriformis or rat liver.     

Polyamine metabolism and its relation to response of the aleurone layers of barley seeds to gibberellic Acid

Polyamine metabolism and its relation to the induction of α-amylase formation in the aleurone layers of barley seeds (Hordeum vulgare cv Himalaya) in response to gibberellic acid (GA₃) has been investigated. A high-performance liquid chromatographic system has been employed for qualitative and quantitative analyses of putrescine (Put), cadaverine (Cad), spermidine (Spd), spermine (Spm), and agmatine (Agm).

Active polyamine metabolism occurs in the aleurone cells of deembryonate barley half seeds during imbibition. The aleurone layers isolated from fully imbibed half seeds contain about 880 nanomoles of Put, 920 nanomoles of Spd, and 610 nanomoles of Spm as free form per gram tissue dry weight while the levels of Cad and Agm are relatively low. The polyamine levels do not change significantly in the aleurone layers in response to added GA₃ (1.5 micromolar) during the 8-hour lag period of the growth substance-induced formation of α-amylase. Also, the polyamine levels are not altered by the presence of abscisic acid (3 micromolar) which inhibits the enzyme induction by GA₃. Kinetic studies show that both applied [U-¹⁴C]ornithine and [U-¹⁴C]arginine are primarily incorporated into Put during 2 hours of incubation, but the incorporation is not significantly affected by added GA₃. Additionally, added GA₃ does not affect the uptake and turnover of [1,4-¹⁴C]Put, nor does it affect the conversion of Put → Spd or Spd → Spm. Treatment of the aleurone layers with GA₃ for 2 hours results in no significant changes in the total activities or the specific activities of ornithine decarboxylase and arginine decarboxylase.

Experiments with polyamine synthesis inhibitors demonstrate that the level of Spd in the aleurone layers could be substantially reduced by the presence of methylglyoxal-bis(guanylhydrazone) (MGBG) during imbibition. MGBG treatment does not affect in vivo incorporation of [8-¹⁴C] adenosine into ATP. The lower the level of Spd the less α-amylase formation is induced by added GA₃. The reduction of GA₃-induced α-amylase formation by MGBG treatment can be either completely or partially overcome by added Spd, depending upon the concentration of MGBG used in the imbibition medium. The results indicate that the early action of GA₃, with respect to induction of α-amylase formation in barley aleurone layers, appears to be not on polyamine metabolism. However, polyamines, particularly Spd, may be involved in regulation of the growth substance-dependent enzyme induction.

     

Initial proteome analysis of mature barley seeds and malt

Several barley (Hordeum vulgare) cultivars are used in the production of malt for brewing. The malt quality depends on the cultivar, its growth and storage conditions, and the industrial process. To enhance studies on malt quality, we embarked on a proteome analysis approach for barley seeds and malt. The proteome analysis includes two-dimensional (2-D) gel electrophoresis, mass spectrometry, and bioinformatics for identification of selected proteins. This project initially focused on proteins in major spots in the neutral isoelectric point range (pI 4-7) including selected spots that differ between four barley cultivars. The excellent malting barley cultivar Barke was used as reference. Cultivar differences in the 2-D gel spot patterns are observed both at the seed and the malt level. In seed extracts one of the proteins causing variations has been identified as an alpha-amylase/trypsin inhibitor. In malt extracts multiple forms of the alpha-amylase isozyme 2 have been identified in varying cultivar characteristic spot patterns. The present identification of proteins in major spots from 2-D gels includes 27 different proteins from 42 spots from mature seed extract, while only three specific proteins were identified by analysing 13 different spots from the corresponding malt extract. It is suggested that post-translational processing causes the same protein to occur in different spots.     

A heterogeneity-aware approach to load balancing of computational tasks: a theoretical and simulation study

One of the distinct characteristics of computing platforms shared by multiple users such as a cluster and a computational grid is heterogeneity on each computer and/or among computers. Temporal heterogeneity refers to variation, along the time dimension, of computing power available for a task on a computer, and spatial heterogeneity represents the variation among computers. In minimizing the average parallel execution time of a target task on a spatially heterogeneous computing system, it is not optimal to distribute the target task linearly proportional to the average computing powers available on computers. In this paper, effects of the temporal and spatial heterogeneity on performance of a target task have been analyzed in terms of the mean and standard deviation of parallel execution time. Based on the analysis results, an approach to load balancing for minimizing the average parallel execution time of a target task is described. The proposed approach whose validity has been verified through simulation considers temporal and spatial heterogeneities in addition to the average computing power on each computer.

Glucose metabolism of embryos and endosperms from deteriorating barley and wheat seeds

Changes in glucose utilization into CO₂ and ethanol-insoluble material were followed in whole seeds, embryos, and endosperms of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) which had reached different levels of deterioration through accelerated aging treatments. Excised embryos from deteriorated wheat seeds had reduced respiration and glucose utilization into ethanol-insoluble material but not into CO₂. These treatments had no effect on respiration of excised endosperms, although they reduced utilization of glucose into ethanol-insoluble material and CO₂. Changes in metabolic activity of whole seeds in response to deterioration treatments are difficult to interpret because they represent the sum of the changes that take place in the embryos and endosperms. Changes in respiration and glucose utilization in these two tissues neither proceed at the same rate nor go in the same direction during deterioration.     

Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification

In the following work we discuss the application of image processing and pattern recognition to the field of quantitative phycology. We overview the area of image processing and review previously published literature pertaining to the image analysis of phycological images and, in particular, cyanobacterial image processing. We then discuss the main operations used to process images and quantify data contained within them. To demonstrate the utility of image processing to cyanobacteria classification, we present details of an image analysis system for automatically detecting and classifying several cyanobacterial taxa of Lake Biwa, Japan. Specifically, we initially target the genus Microcystis for detection and classification from among several species of Anabaena. We subsequently extend the system to classify a total of six cyanobacteria species. High-resolution microscope images containing a mix of the above species and other nontargeted objects are analyzed, and any detected objects are removed from the image for further analysis. Following image enhancement, we measure object properties and compare them to a previously compiled database of species characteristics. Classification of an object as belonging to a particular class membership (e.g., “Microcystis,”“A. smithii,”“Other,” etc.) is performed using parametric statistical methods. Leave-one-out classification results suggest a system error rate of approximately 3%. Received: September 6, 1999 / Accepted: February 6, 2000     

Spectroscopic properties of the nonplanar amide group: a computational study

Experimental studies suggest that amide bond may significantly deviate from planar arrangement even in linear peptides and proteins. In order to find out the extent to which such deviation may influence principal amide spectroscopic properties, we conducted a computational study of nonplanar N-methylacetamide (NMA) conformers. Vibrational absorption, Raman, and electronic spectra including optical activity were simulated with ab initio and density functional theory (DFT) methods. According to the results, small nonplanarity deviations may be detectable by nonpolarized spectroscopic techniques, albeit as subtle spectral changes. The optical activity methods, such as the vibrational circular dichroism (VCD), Raman optical activity (ROA), and electronic circular dichroism (CD, ECD), provide enhanced information about the amide nonplanarity, because planar amide is not optically active (chiral). For VCD, however, the inherently chiral contribution in most peptides and proteins most probably provides very weak signal in comparison with other contributions, such as the dipolar coupling. For the electronic CD, the nonplanarity contribution is relatively big and causes a strong CD couplet in the n-pi* absorption region accompanied by a red frequency shift. The pi-pi* CD region is relatively unaffected. The ROA spectroscopy appears most promising for the nonplanarity detection and the inherent chiral signal may dominate entire spectral parts. The amide I and III vibrational ROA bands are most challenging experimentally because of their relatively weak coupling to other peptide vibrations.     

Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs

Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing.     

Photosensitivity in Smith-Lemli-Opitz syndrome: a flux balance analysis of altered metabolism

Ultraviolet A photosensitivity is a debilitating symptom associated with the metabolic disorder Smith-Lemli-Opitz syndrome (SLOS). SLOS is a manifestation of the deficiency of 7-dehydrocholesterol reductase, an enzyme involved in the cholesterol biosynthesis. As a result several abnormal intermediary compounds are formed among which Cholesta 5, 7, 9(11)-trien-3beta-ol is the most likely cause of photosensitivity. The effect of various drugs acting on cholesterol biosynthetic pathway on SLOS is not clear as clinical trials are not available for this rare disorder. A Flux Balance Analysis (FBA) has been carried out using the software CellNetAnalyzer or FluxAnalyzer to gain insight into the probable effects of various drugs acting on cholesterol biosynthetic pathway on photosensitivity in SLOS. The model consisted of 44 metabolites and 40 reactions. The formation flux of Cholesta 5, 7, 9(11)-trien-3beta-ol increased in SLOS and remained unchanged on simulation of the effect of miconazole and SR31747. However zaragozic acid can potentially reduce the flux through the entire pathway. FBA predicts zaragozic acid along with cholesterol supplementation as an effective treatment for photosensitivity in SLOS.     

Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers

Biospectroscopy is employed to derive absorbance spectra representative of biomolecules present in biological samples. The mid-infrared region (λ = 2.5 μm-25 μm) is absorbed to give a biochemical-cell fingerprint (v = 1800-900 cm(-1)). Cellular material produces complex spectra due to the variety of chemical bonds present. The complexity and size of spectral data sets warrant multivariate analysis for data reduction, interpretation, and classification. Various multivariate analyses are available including principal component analysis (PCA), partial least-squares (PLS), linear discriminant analysis (LDA), and evolving fuzzy rule-based classifier (eClass). Interpretation of both visual and numerical results facilitates biomarker identification, cell-type discrimination, and predictive and mechanistic understanding of cellular behavior. Biospectroscopy is a high-throughput nondestructive technology. A comparison of biomarkers/mechanistic knowledge determined from conventional approaches to biospectroscopy coupled with multivariate analysis often provides complementary answers and a novel approach for diagnosis of disease and cell biology.     

EXAMINE: a computational approach to reconstructing gene regulatory networks

Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously.