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).     

Genome-scale metabolic analysis of <Emphasis Type="Italic">Clostridium thermocellum</Emphasis> for bioethanol production

Background  

Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous.     

Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA

Malonyl-coenzyme A is an important precursor metabolite for the biosynthesis of polyketides, flavonoids and biofuels. However, malonyl-CoA naturally synthesized in microorganisms is consumed for the production of fatty acids and phospholipids leaving only a small amount available for the production of other metabolic targets in recombinant biosynthesis. Here we present an integrated computational and experimental approach aimed at improving the intracellular availability of malonyl-CoA in Escherichia coli. We used a customized version of the recently developed OptForce methodology to predict a minimal set of genetic interventions that guarantee a prespecified yield of malonyl-CoA in E. coli strain BL21 Star™. In order to validate the model predictions, we have successfully constructed an E. coli recombinant strain that exhibits a 4-fold increase in the levels of intracellular malonyl-CoA compared to the wild type strain. Furthermore, we demonstrate the potential of this E. coli strain for the production of plant-specific secondary metabolites naringenin (474 mg/L) with the highest yield ever achieved in a lab-scale fermentation process. Combined effect of the genetic interventions was found to be synergistic based on a developed analysis method that correlates genetic modification to cell phenotype, specifically the identified knockout targets (ΔfumC and ΔsucC) and overexpression targets (ACC, PGK, GAPD and PDH) can cooperatively force carbon flux towards malonyl-CoA. The presented strategy can also be readily expanded for the production of other malonyl-CoA-derived compounds like polyketides and biofuels.     

Lactate production yield from engineered yeasts is dependent from the host background, the lactate dehydrogenase source and the lactate export

Background  

Metabolic pathway manipulation for improving the properties and the productivity of microorganisms is becoming a well established concept. For the production of important metabolites, but also for a better understanding of the fundamentals of cell biology, detailed studies are required. In this work we analysed the lactate production from metabolic engineered Saccharomyces cerevisiae cells expressing a heterologous lactate dehydrogenase (LDH) gene. The LDH gene expression in a budding yeast cell introduces a novel and alternative pathway for the NAD⁺ regeneration, allowing a direct reduction of the intracellular pyruvate to lactate, leading to a simultaneous accumulation of lactate and ethanol.     

Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

Background  

Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.     

Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum

Background  

Quantification of the metabolic network of an organism offers insights into possible ways of developing mutant strain for better productivity of an extracellular metabolite. The first step in this quantification is the enumeration of stoichiometries of all reactions occurring in a metabolic network. The structural details of the network in combination with experimentally observed accumulation rates of external metabolites can yield flux distribution at steady state. One such methodology for quantification is the use of elementary modes, which are minimal set of enzymes connecting external metabolites. Here, we have used a linear objective function subject to elementary modes as constraint to determine the fluxes in the metabolic network of Corynebacterium glutamicum. The feasible phenotypic space was evaluated at various combinations of oxygen and ammonia uptake rates.     

Leakage-free rapid quenching technique for yeast metabolomics

Accurate determination of intracellular metabolite levels requires reliable, reproducible techniques for sampling and sample treatment. Quenching in 60% (v/v) methanol at −40°C is currently the standard method for sub-second arrest of metabolic activity in microbial metabolomics but there have been contradictory reports in the literature on whether leakage of metabolites from the cells occurs. We have re-evaluated this method in S. cerevisiae using a comprehensive, strictly quantitative approach. By determining the levels of a large range of metabolites in different sample fractions and establishing mass balances we could trace their fate during the quenching procedure and confirm that leakage of metabolites from yeast cells does occur during conventional cold methanol quenching, to such an extent that the levels of most metabolites have been previously underestimated by at least twofold. In addition, we found that the extent of leakage depends on the time of exposure, the temperature and the properties of the methanol solutions. Using the mass balance approach we could study the effect of different quenching conditions and demonstrate that leakage can be entirely prevented by quenching in pure methanol at ≤−40°C, which we propose as a new improved method. Making use of improved data on intracellular metabolite levels we also re-evaluated the need of sub-second quenching of metabolic activity and of removing the extracellular medium. Our findings have serious implications for quantitative metabolomics-based fields such as non-stationary ¹³C flux analysis, in vivo kinetic modeling and thermodynamic network analysis.

Effect of <Emphasis Type="Italic">HXT</Emphasis> 1 and <Emphasis Type="Italic">HXT</Emphasis> 7 hexose transporter overexpression on wild-type and lactic acid producing <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> cells

Background  

Since about three decades, Saccharomyces cerevisiae can be engineered to efficiently produce proteins and metabolites. Even recognizing that in baker's yeast one determining step for the glucose consumption rate is the sugar uptake, this fact has never been conceived to improve the metabolite(s) productivity.     

Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test

Introduction

Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies.

Materials and Methods

The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow.

Conclusions

Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.

     

Characterization of the iron-regulated <Emphasis Type="Italic">desA</Emphasis> promoter of <Emphasis Type="Italic">Streptomyces pilosus</Emphasis> as a system for controlled gene expression in actinomycetes

Background  

The bioavailability of iron is quite low since it is usually present as insoluble complexes. To solve the bioavailability problem microorganisms have developed highly efficient iron-scavenging systems based on the synthesis of siderophores that have high iron affinity. The systems of iron assimilation in microorganisms are strictly regulated to control the intracellular iron levels since at high concentrations iron is toxic for cells. Streptomyces pilosus synthesizes the siderofore desferrioxamine B. The first step in desferrioxamine biosynthesis is decarboxylation of L-lysine to form cadaverine, a desferrioxamine B precursor. This reaction is catalyzed by the lysine decarboxylase, an enzyme encoded by the desA gene that is repressed by iron.     

Taking into account water use impacts in the LCA of biofuels: an Argentinean case study

Purpose  

The assessment of biofuels has until now mainly focused on energy demand and greenhouse gas emissions. Only little attention has been given to other impacts, although the general importance of water use for the life cycle assessment (LCA) of agricultural products has been recognized in recent publications. The aim of this work is to assess in detail the water consumption along a biofuel production chain taking into account irrigation efficiencies, levels of water scarcity, and type of feedstock, and to integrate those results in a full LCA. Furthermore, we compare the results for biofuels from various feedstocks and regions with conventional petrol.     

Origin and distribution of epipolythiodioxopiperazine (ETP) gene clusters in filamentous ascomycetes

Background  

Genes responsible for biosynthesis of fungal secondary metabolites are usually tightly clustered in the genome and co-regulated with metabolite production. Epipolythiodioxopiperazines (ETPs) are a class of secondary metabolite toxins produced by disparate ascomycete fungi and implicated in several animal and plant diseases. Gene clusters responsible for their production have previously been defined in only two fungi. Fungal genome sequence data have been surveyed for the presence of putative ETP clusters and cluster data have been generated from several fungal taxa where genome sequences are not available. Phylogenetic analysis of cluster genes has been used to investigate the assembly and heredity of these gene clusters.     

Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period

Background  

This study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype. Changes in metabolism, and in metabolite profile, occur as a part of, and in response to, developmental events. These changes are influenced by the developmental program, as well as external factors impinging on it. Many samples are needed, however, to characterize quantitative aspects of developmental variation. A biomarker metabolite set could benefit screening of quantitative plant developmental variation by providing some of the advantages of both comprehensive metabolomic studies and focused studies of particular metabolites or pathways.     

Identification and characterization of rhizosphere fungal strain MF‐91 antagonistic to rice blast and sheath blight pathogens

Aim

To examine the inhibition effects of rhizosphere fungal strain MF‐91 on the rice blast pathogen Magnaporthe grisea and sheath blight pathogen Rhizoctonia solani.

Methods and Results

Rhizosphere fungal strain MF‐91 and its metabolites suppressed the in vitro mycelial growth of R. solani. The inhibitory effect of the metabolites was affected by incubation temperature, lighting time, initial pH and incubation time of rhizosphere fungal strain MF‐91. The in vitro mycelial growth of M. grisea was insignificantly inhibited by rhizosphere fungal strain MF‐91 and its metabolites. The metabolites of rhizosphere fungal strain MF‐91 significantly inhibited the conidial germination and appressorium formation of M. grisea. Moreover, the metabolites reduced the disease index of rice sheath blight by 35·02% in a greenhouse and 57·81% in a field as well as reduced the disease index of rice blast by 66·07% in a field. Rhizosphere fungal strain MF‐91 was identified as Chaetomium aureum based on the morphological observation, the analysis of 18S ribosomal DNA internal transcribed spacer sequence and its physiological characteristics, such as the optimal medium, temperature and initial pH for mycelial growth and sporulation production.

Conclusions

Rhizosphere fungus C. aureum is effective in the biocontrolling of rice blast pathogen M. grisea and sheath blight pathogen R. solani both in in vitro and in vivo conditions.

Significance and Impact of the Study

This study is the first to show that rhizosphere fungus C. aureum is a potential fungicide against rice blast and sheath blight pathogens.     

Combined inactivation of the <Emphasis Type="Italic">Clostridium cellulolyticum</Emphasis> lactate and malate dehydrogenase genes substantially increases ethanol yield from cellulose and switchgrass fermentations

Background  

The model bacterium Clostridium cellulolyticum efficiently degrades crystalline cellulose and hemicellulose, using cellulosomes to degrade lignocellulosic biomass. Although it imports and ferments both pentose and hexose sugars to produce a mixture of ethanol, acetate, lactate, H₂ and CO₂, the proportion of ethanol is low, which impedes its use in consolidated bioprocessing for biofuels production. Therefore genetic engineering will likely be required to improve the ethanol yield. Plasmid transformation, random mutagenesis and heterologous expression systems have previously been developed for C. cellulolyticum, but targeted mutagenesis has not been reported for this organism, hindering genetic engineering.     

Proteome analysis of Aspergillus niger: Lactate added in starch-containing medium can increase production of the mycotoxin fumonisin B2 by modifying acetyl-CoA metabolism

Background  

Aspergillus niger is a filamentous fungus found in the environment, on foods and feeds and is used as host for production of organic acids, enzymes and proteins. The mycotoxin fumonisin B₂ was recently found to be produced by A. niger and hence very little is known about production and regulation of this metabolite. Proteome analysis was used with the purpose to reveal how fumonisin B₂ production by A. niger is influenced by starch and lactate in the medium.     

Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach

Background  

Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways.     

Granulocyte colony-stimulating factor alters the systemic metabolomic profile in healthy donors

Introduction

Peripheral blood stem cells mobilized by granulocyte colony-stimulating factor (G-CSF) from healthy donors are commonly used for allogeneic stem cell transplantation. The effect of G-CSF administration on global serum metabolite profiles has not been investigated before.

Objectives

This study aims to examine the systemic metabolomic profiles prior to and following administration of G-CSF in healthy adults.

Methods

Blood samples were collected from 15 healthy stem cell donors prior to and after administration of G-CSF 10 µg/kg/day for 4 days. Using a non-targeted metabolomics approach, metabolite levels in serum were determined using ultrahigh performance liquid chromatography-tandem mass spectrometry and gas chromatography/mass spectrometry.

Results

Comparison of the metabolite profiles of donors before and after G-CSF treatment revealed 239 metabolites that were significantly altered. The major changes of the metabolite profiles following G-CSF administration included alteration of several fatty acids, including increased levels of several medium and long-chain fatty acids, as well as polyunsaturated fatty acids; while there were lower levels of other lipid metabolites such as phospholipids, lysolipids, sphingolipids. Furthermore, there were significantly lower levels of several amino acids and/or their metabolites, including several amino acids with known immunoregulatory functions (methionine, tryptophan, valine). Lastly, the levels of several nucleotides and nucleotide metabolites (guanosine, adenosine, inosine) were also decreased after G-CSF administration, while methylated products were increased. Some of these altered products/metabolites may potentially have angioregulatory effects whereas others may suggest altered intracellular epigenetic regulation.

Conclusion

Our results show that G-CSF treatment alters biochemical serum profiles, in particular amino acid, lipid and nucleotide metabolism. Additional studies are needed to further evaluate the relevance of these changes in healthy donors.