Biosynthesis of glyoxylate from glycine in

Evaluation of amino acid profile by targeted metabolomics in the eukaryotic model under exposure of benzo[a]pyrene as the exclusive stressor

Amino acids (AAs) are a class of important metabolites in metabolomics methodology that investigates metabolite changes in a cell, tissue, or organism for early diagnosis of diseases. Benzo[a]pyrene (BaP) is considered a priority contaminant by different environmental control agencies because it is a proven carcinogenic compound for humans. Therefore, it is important to evaluate the BaP interference in the metabolism of amino acids. In this work, a new amino acid extraction procedure (derivatized with propyl chloroformate/propanol) using functionalized magnetic carbon nanotubes was developed and optimized. A hybrid nanotube was used followed by desorption without heating, and excellent extraction of analytes was obtained. After exposure of Saccharomyces cerevisiae, the BaP concentration of 25.0 μmol L-1 caused changes in cell viability, indicating metabolic changes. A fast and efficient GC/MS method using a Phenomenex ZB-AAA column was optimized, enabling the determination of 16 AAs in yeasts exposed or not to BaP. A comparison of AA concentrations obtained in the two experimental groups showed that glycine (Gly), serine (Ser), phenylalanine (Phe), proline (Pro), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), tyrosine (Tyr), and leucine (Leu) statistically differentiated, after subsequent application of ANOVA with Bonferroni post-hoc test, with a confidence level of 95%. This amino acid pathway analysis confirmed previous studies that revealed the potential of these AAs as toxicity biomarker candidates.

Genome Sequence and Analysis of the Flavinogenic Yeast Candida membranifaciens IST 626

The ascomycetous yeast Candida membranifaciens has been isolated from diverse habitats, including humans, insects, and environmental sources, exhibiting a remarkable ability to use different carbon sources that include pentoses, melibiose, and inulin. In this study, we isolated four C. membranifaciens strains from soil and investigated their potential to overproduce riboflavin. C. membranifaciens IST 626 was found to produce the highest concentrations of riboflavin. The volumetric production of this vitamin was higher when C. membranifaciens IST 626 cells were cultured in a commercial medium without iron and when xylose was the available carbon source compared to the same basal medium with glucose. Supplementation of the growth medium with 2 g/L glycine favored the metabolization of xylose, leading to biomass increase and consequent enhancement of riboflavin volumetric production that reached 120 mg/L after 216 h of cultivation. To gain new insights into the molecular basis of riboflavin production and carbon source utilization in this species, the first annotated genome sequence of C. membranifaciens is reported in this article, as well as the result of a comparative genomic analysis with other relevant yeast species. A total of 5619 genes were predicted to be present in C. membranifaciens IST 626 genome sequence (11.5 Mbp). Among them are genes involved in riboflavin biosynthesis, iron homeostasis, and sugar uptake and metabolism. This work put forward C. membranifaciens IST 626 as a riboflavin overproducer and provides valuable molecular data for future development of superior producing strains capable of using the wide range of carbon sources, which is a characteristic trait of the species.

Modulating Wine Aromatic Amino Acid Catabolites by Using Torulaspora delbrueckii in Sequentially Inoculated Fermentations or Saccharomyces cerevisiae Alone

Yeasts are the key microorganisms that transform grape juice into wine, and nitrogen is an essential nutrient able to affect yeast cell growth, fermentation kinetics and wine quality. In this work, we focused on the intra- and extracellular metabolomic changes of three aromatic amino acids (tryptophan, tyrosine, and phenylalanine) during alcoholic fermentation of two grape musts by two Saccharomyces cerevisiae strains and the sequential inoculation of Torulaspora delbrueckii with Saccharomyces cerevisiae. An UPLC-MS/MS method was used to monitor 33 metabolites, and 26 of them were detected in the extracellular samples and 8 were detected in the intracellular ones. The results indicate that the most intensive metabolomic changes occurred during the logarithm cellular growth phase and that pure S. cerevisiae fermentations produced higher amounts of N-acetyl derivatives of tryptophan and tyrosine and the off-odour molecule 2-aminoacetophenone. The sequentially inoculated fermentations showed a slower evolution and a higher production of metabolites linked to the well-known plant hormone indole acetic acid (auxin). Finally, the production of sulfonated tryptophol during must fermentation was confirmed, which also may explain the bitter taste of wines produced by Torulaspora delbrueckii co-fermentations, while sulfonated indole carboxylic acid was detected for the first time in such an experimental design.

Butanol production by Saccharomyces cerevisiae: perspectives, strategies and challenges

The search for gasoline substitutes has grown in recent decades, leading to the increased production of ethanol as viable alternative. However, research in recent years has shown that butanol exhibits various advantages over ethanol as a biofuel. Furthermore, butanol can also be used as a chemical platform, serving as an intermediate product and as a solvent in industrial reactions. This alcohol is naturally produced by some Clostridium species; however, Clostridial fermentation processes still have inherent problems, which focuses the interest on Saccharomyces cerevisiae for butanol production, as an alternative organism for the production of this alcohol. S. cerevisiae exhibits great adaptability to industrial conditions and can be modified with a wide range of genetic tools. Although S. cerevisiae is known to naturally produce isobutanol, the n-butanol synthesis pathway has not been well established in wild S. cerevisiae strains. Two strategies are most commonly used for of S. cerevisiae butanol production: the heterologous expression of the Clostridium pathway or the amino acid uptake pathways. However, butanol yields produced from S. cerevisiae are lower than ethanol yield. Thus, there are still many challenges needed to be overcome, which can be minimized through genetic and evolutive engineering, for butanol production by yeast to become a reality.

Alternative fate of glyoxylate during acetate and hexadecane metabolism in Acinetobacter oleivorans DR1

The glyoxylate shunt (GS), involving isocitrate lyase (encoded by aceA) and malate synthase G (encoded by glcB), is known to play important roles under several conditions including oxidative stress, antibiotic defense, or certain carbon source metabolism (acetate and fatty acids). Comparative growth analyses of wild type (WT), aceA, and glcB null-strains revealed that aceA, but not glcB, is essential for cells to grow on either acetate (1%) or hexadecane (1%) in Acinetobacter oleivorans DR1. Interestingly. the aceA knockout strain was able to grow slower in 0.1% acetate than the parent strain. Northern Blot analysis showed that the expression of aceA was dependent on the concentration of acetate or H₂O₂, while glcB was constitutively expressed. Up-regulation of stress response-related genes and down-regulation of main carbon metabolism-participating genes in a ΔaceA mutant, compared to that in the parent strain, suggested that an ΔaceA mutant is susceptible to acetate toxicity, but grows slowly in 0.1% acetate. However, a ΔglcB mutant showed no growth defect in acetate or hexadecane and no susceptibility to H₂O₂, suggesting the presence of an alternative pathway to eliminate glyoxylate toxicity. A lactate dehydrogenase (LDH, encoded by a ldh) could possibly mediate the conversion from glyoxylate to oxalate based on our RNA-seq profiles. Oxalate production during hexadecane degradation and impaired growth of a ΔldhΔglcB double mutant in both acetate and hexadecane-supplemented media suggested that LDH is a potential detoxifying enzyme for glyoxylate. Our constructed LDH-overexpressing Escherichia coli strain also showed an important role of LDH under lactate, acetate, and glyoxylate metabolisms. The LDH-overexpressing E. coli strain, but not wild type strain, produced oxalate under glyoxylate condition. In conclusion, the GS is a main player, but alternative glyoxylate pathways exist during acetate and hexadecane metabolism in A. oleivorans DR1.

n-Butanol production in S. cerevisiae: co-ordinate use of endogenous and exogenous pathways

n-Butanol represents a key commodity chemical and holds significant potential as a biofuel. It can be produced naturally by Clostridia species via the ABE pathway. However, butanol production via such systems can be associated with significant drawbacks. Therefore, substantial efforts have been made toward engineering a suitable industrial host for butanol production. For instance, we previously generated a metabolically engineered Saccharomyces cerevisiae strain that produces ~300 mg/L butanol from combined endogenous and exogenous pathways. In this current study, the endogenous and exogenous pathways of butanol production were further characterised, and their relative contribution to the overall butanol titre was assessed. Deletion of any single component of the exogenous ABE pathway was sufficient to significantly reduce butanol production. Further evidence for a major contribution from the ABE pathway came with the discovery that specific yeast deletion mutants only affected butanol production from this pathway and had a significant impact on butanol levels. In previous studies, the threonine-based ketoacid (TBK) pathway has been proposed to explain endogenous butanol synthesis in ADH1 mutants. However, we find that key mutants in this pathway have little impact on endogenous butanol production; hence, this pathway does not explain endogenous butanol production in our strains. Instead, endogenous butanol production appears to rely on glycine metabolism via an α-ketovalerate intermediate. Indeed, yeast cells can utilise α-ketovalerate as a supplement to generate high butanol titres (> 2 g/L). The future characterisation and optimisation of the enzymatic activities required for this pathway provides an exciting area in the generation of robust butanol production strategies.

Metabolome- and genome-scale model analyses for engineering of Aureobasidium pullulans to enhance polymalic acid and malic acid production from sugarcane molasses

BACKGROUND

Polymalic acid (PMA) is a water-soluble biopolymer with many attractive properties for food and pharmaceutical applications mainly produced by the yeast-like fungus Aureobasidium pullulans. Acid hydrolysis of PMA, resulting in release of the monomer l-malic acid (MA), which is widely used in the food and chemical industry, is a competitive process for producing bio-based platform chemicals.

RESULTS

In this study, the production of PMA and MA from sucrose and sugarcane molasses by A. pullulans was studied in shake flasks and bioreactors. Comparative metabolome analysis of sucrose- and glucose-based fermentation identified 81 intracellular metabolites and demonstrated that pyruvate from the glycolysis pathway may be a key metabolite affecting PMA synthesis. In silico simulation of a genome-scale metabolic model (iZX637) further verified that pyruvate carboxylase (pyc) via the reductive tricarboxylic acid cycle strengthened carbon flux for PMA synthesis. Therefore, an engineered strain, FJ-PYC, was constructed by overexpressing the pyc gene, which increased the PMA titer by 15.1% compared with that from the wild-type strain in a 5-L stirred-tank fermentor. Sugarcane molasses can be used as an economical substrate without any pretreatment or nutrient supplementation. Using fed-batch fermentation of FJ-PYC, we obtained the highest PMA titers (81.5, 94.2 g/L of MA after hydrolysis) in 140 h with a corresponding MA yield of 0.62 g/g and productivity of 0.67 g/L h.

CONCLUSIONS

We showed that integrated metabolome- and genome-scale model analyses were an effective approach for engineering the metabolic node for PMA synthesis, and also developed an economical and green process for PMA and MA production from renewable biomass feedstocks.

Genetic resources for advanced biofuel production described with the Gene Ontology

Dramatic increases in research in the area of microbial biofuel production coupled with high-throughput data generation on bioenergy-related microbes has led to a deluge of information in the scientific literature and in databases. Consolidating this information and making it easily accessible requires a unified vocabulary. The Gene Ontology (GO) fulfills that requirement, as it is a well-developed structured vocabulary that describes the activities and locations of gene products in a consistent manner across all kingdoms of life. The Microbial ENergy processes Gene Ontology () project is extending the GO to include new terms to describe microbial processes of interest to bioenergy production. Our effort has added over 600 bioenergy related terms to the Gene Ontology. These terms will aid in the comprehensive annotation of gene products from diverse energy-related microbial genomes. An area of microbial energy research that has received a lot of attention is microbial production of advanced biofuels. These include alcohols such as butanol, isopropanol, isobutanol, and fuels derived from fatty acids, isoprenoids, and polyhydroxyalkanoates. These fuels are superior to first generation biofuels (ethanol and biodiesel esterified from vegetable oil or animal fat), can be generated from non-food feedstock sources, can be used as supplements or substitutes for gasoline, diesel and jet fuels, and can be stored and distributed using existing infrastructure. Here we review the roles of genes associated with synthesis of advanced biofuels, and at the same time introduce the use of the GO to describe the functions of these genes in a standardized way.

Quantitative determination of glycine in aqueous solution using glutamate dehydrogenase-immobilized glyoxal agarose beads

In this study, an enzymatic procedure for the determination of glycine (Gly) was developed by using a column containing immobilized glutamate dehydrogenase (GDH) on glyoxal agarose beads. Ammonia is produced from the enzymatic reactions between Gly and GDH with NAD(+) in phosphate buffer medium. The indophenol blue method was used for ammonia detection based on the spectrophotometric measurements of blue-colored product absorbing at 640 nm. The calibration graph is linear in the range of 0.1-10 mM of Gly concentrations. The effect of pH, temperature, and time interval was studied to find column stability, and also the interference effects of other amino acids was investigated. The interaction between GDH and glyoxal agarose beads was analyzed by Fourier transform infrared (FTIR) spectroscopy. The morphology of the immobilized and non-immobilized agarose beads were characterized by atomic force microscopy (AFM).

CMO1 encodes a putative choline monooxygenase and is required for the utilization of choline as the sole nitrogen source in the yeast Scheffersomyces stipitis (syn. Pichia stipitis)

Sixteen yeasts with sequenced genomes belonging to the ascomycete subphyla Saccharomycotina and Taphrinomycotina were assayed for their ability to utilize a variety of primary, secondary, tertiary and quartenary aliphatic amines as nitrogen sources. The results support a previously proposed pathway of quaternary amine catabolism whereby glycine betaine is first converted into choline, which is then cleaved to release trimethylamine, followed by stepwise demethylation of trimethylamine to release free ammonia. There were only a few instances of utilization of N-methylated glycine species (sarcosine and N,N-dimethylglycine), which suggests that this pathway is not intact in any of the species tested. The ability to utilize choline as a sole nitrogen source correlated strongly with the presence of a putative Rieske non-haem iron protein homologous to bacterial ring-hydroxylating oxygenases and plant choline monooxygenases. Deletion of the gene encoding the Rieske non-haem iron protein in the yeast Scheffersomyces stipitis abolished its ability to utilize choline as the sole nitrogen source, but did not affect its ability to use methylamine, dimethylamine, trimethylamine, ethylamine, diethylamine, ethanolamine or glycine as nitrogen sources. The gene was named CMO1 for putative choline monooxygenase 1. A bioinformatic survey of eukaryotic genomes showed that CMO1 homologues are found throughout the eukaryotic domain.

GABA transaminases from Saccharomyces cerevisiae and Arabidopsis thaliana complement function in cytosol and mitochondria

GABA transaminase (GABA-T) catalyses the conversion of GABA to succinate semialdehyde (SSA) in the GABA shunt pathway. The GABA-T from Saccharomyces cerevisiae (ScGABA-TKG) is an α-ketoglutarate-dependent enzyme encoded by the UGA1 gene, while higher plant GABA-T is a pyruvate/glyoxylate-dependent enzyme encoded by POP2 in Arabidopsis thaliana (AtGABA-T). The GABA-T from A. thaliana is localized in mitochondria and mediated by an 18-amino acid N-terminal mitochondrial targeting peptide predicated by both web-based utilities TargetP 1.1 and PSORT. Yeast UGA1 appears to lack a mitochondrial targeting peptide and is localized in the cytosol. To verify this bioinformatic analysis and examine the significance of ScGABA-TKG and AtGABA-T compartmentation and substrate specificity on physiological function, expression vectors were constructed to modify both ScGABA-TKG and AtGABA-T, so that they express in yeast mitochondria and cytosol. Physiological function was evaluated by complementing yeast ScGABA-TKG deletion mutant Δuga1 with AtGABA-T or ScGABA-TKG targeted to the cytosol or mitochondria for the phenotypes of GABA growth defect, thermosensitivity and heat-induced production of reactive oxygen species (ROS). This study demonstrates that AtGABA-T is functionally interchangeable with ScGABA-TKG for GABA growth, thermotolerance and limiting production of ROS, regardless of location in mitochondria or cytosol of yeast cells, but AtGABA-T is about half as efficient in doing so as ScGABA-TKG. These results are consistent with the hypothesis that pyruvate/glyoxylate-limited production of NADPH mediates the effect of the GABA shunt in moderating heat stress in Saccharomyces.

A novel pathway to produce butanol and isobutanol in Saccharomyces cerevisiae

BACKGROUND

The sustainable production of biofuels remains one of the major issues of the upcoming years. Among the number of most desirable molecules to be produced, butanol and isobutanol deserve a prominent place. They have superior liquid-fuel features in respect to ethanol. Particularly, butanol has similar properties to gasoline and thus it has the potential to be used as a substitute for gasoline in currently running engines. Clostridia are recognized as natural and good butanol producers and are employed in the industrial-scale production of solvents. Due to their complex metabolic characteristics and to the difficulty of performing genetic manipulations, in recent years the Clostridia butanol pathway was expressed in other microorganisms such as Escherichia coli and Saccharomyces cerevisiae, but in yeast the obtained results were not so promising. An alternative way for producing fusel alcohol is to exploit the degradation pathway of aminoacids released from protein hydrolysis, where proteins derive from exhausted microbial biomasses at the end of the fermentation processes.

RESULTS

It is known that wine yeasts can, at the end of the fermentation process, accumulate fusel alcohols, and butanol is among them. Despite it was quite obvious to correlate said production with aminoacid degradation, a putative native pathway was never proposed. Starting from literature data and combining information about different organisms, here we demonstrate how glycine can be the substrate for butanol and isobutanol production, individuating at least one gene encoding for the necessary activities leading to butanol accumulation. During a kinetic of growth using glycine as substrate, butanol and isobutanol accumulate in the medium up to 92 and 58 mg/L, respectively.

CONCLUSIONS

Here for the first time we demonstrate an alternative metabolic pathway for butanol and isobutanol production in the yeast S. cerevisiae, using glycine as a substrate. Doors are now opened for a number of optimizations, also considering that starting from an aminoacid mixture as a side stream process, a fusel alcohol blend can be generated.

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

Systematic applications of metabolomics in metabolic engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity

MOTIVATION

Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples. It has evolved very quickly during the last decade. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes.

RESULTS

Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. However, PAPi is time consuming to perform manually. Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, the PAPi package calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. PAPi also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity.

AVAILABILITY

These datasets are available in http://www.4shared.com/file/hTWyndYU/extra.html and http://www.4shared.com/file/VbQIIDeu/intra.html. PAPi package is available in: http://www.4shared.com/file/s0uIYWIg/PAPi_10.html

CONTACT

s.villas-boas@auckland.ac.nz

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry

This protocol describes an analytical platform for the analysis of intra- and extracellular metabolites of microbial cells (yeast, filamentous fungi and bacteria) using gas chromatography-mass spectrometry (GC-MS). The protocol is subdivided into sampling, sample preparation, chemical derivatization of metabolites, GC-MS analysis and data processing and analysis. This protocol uses two robust quenching methods for microbial cultures, the first of which, cold glycerol-saline quenching, causes reduced leakage of intracellular metabolites, thus allowing a more reliable separation of intra- and extracellular metabolites with simultaneous stopping of cell metabolism. The second, fast filtration, is specifically designed for quenching filamentous micro-organisms. These sampling techniques are combined with an easy sample-preparation procedure and a fast chemical derivatization reaction using methyl chloroformate. This reaction takes place at room temperature, in aqueous medium, and is less prone to matrix effect compared with other derivatizations. This protocol takes an average of 10 d to complete and enables the simultaneous analysis of hundreds of metabolites from the central carbon metabolism (amino and nonamino organic acids, phosphorylated organic acids and fatty acid intermediates) using an in-house MS library and a data analysis pipeline consisting of two free software programs (Automated Mass Deconvolution and Identification System (AMDIS) and R).

Systems biology of industrial microorganisms

The field of industrial biotechnology is expanding rapidly as the chemical industry is looking towards more sustainable production of chemicals that can be used as fuels or building blocks for production of solvents and materials. In connection with the development of sustainable bioprocesses, it is a major challenge to design and develop efficient cell factories that can ensure cost efficient conversion of the raw material into the chemical of interest. This is achieved through metabolic engineering, where the metabolism of the cell factory is engineered such that there is an efficient conversion of sugars, the typical raw materials in the fermentation industry, into the desired product. However, engineering of cellular metabolism is often challenging due to the complex regulation that has evolved in connection with adaptation of the different microorganisms to their ecological niches. In order to map these regulatory structures and further de-regulate them, as well as identify ingenious metabolic engineering strategies that full-fill mass balance constraints, tools from systems biology can be applied. This involves both high-throughput analysis tools like transcriptome, proteome and metabolome analysis, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies. It is in fact expected that systems biology may substantially improve the process of cell factory development, and we therefore propose the term Industrial Systems Biology for how systems biology will enhance the development of industrial biotechnology for sustainable chemical production.

Fueling industrial biotechnology growth with bioethanol

Industrial biotechnology is the conversion of biomass via biocatalysis, microbial fermentation, or cell culture to produce chemicals, materials, and/or energy. Industrial biotechnology processes aim to be cost-competitive, environmentally favorable, and self-sustaining compared to their petrochemical equivalents. Common to all processes for the production of energy, commodity, added value, or fine chemicals is that raw materials comprise the most significant cost fraction, particularly as operating efficiencies increase through practice and improving technologies. Today, crude petroleum represents the dominant raw material for the energy and chemical sectors worldwide. Within the last 5 years petroleum prices, stability, and supply have increased, decreased, and been threatened, respectively, driving a renewed interest across academic, government, and corporate centers to utilize biomass as an alternative raw material. Specifically, bio-based ethanol as an alternative biofuel has emerged as the single largest biotechnology commodity, with close to 46 billion L produced worldwide in 2005. Bioethanol is a leading example of how systems biology tools have significantly enhanced metabolic engineering, inverse metabolic engineering, and protein and enzyme engineering strategies. This enhancement stems from method development for measurement, analysis, and data integration of functional genomics, including the transcriptome, proteome, metabolome, and fluxome. This review will show that future industrial biotechnology process development will benefit tremendously from the precedent set by bioethanol - that enabling technologies (e.g., systems biology tools) coupled with favorable economic and socio-political driving forces do yield profitable, sustainable, and environmentally responsible processes. Biofuel will continue to be the keystone of any industrial biotechnology-based economy whereby biorefineries leverage common raw materials and unit operations to integrate diverse processes to produce demand-driven product portfolios.

Over-expression of a hydroxypyruvate reductase in Methylobacterium sp. MB200 enhances glyoxylate accumulation

Methylobacterium sp. MB200 capable of producing glyoxylate from methanol was obtained by enrichment culture using a medium containing methanol as the sole carbon source. A hpr gene that encodes a hydroxypyruvate reductase (HPR) was cloned from this strain and was ligated into the vector pLAFR3 to obtain the recombinant plasmid pLAFRh, which was transferred into M. sp. MB200 to generate an recombinant strain MB201. Homologous expression of hpr under the control of the lacZ promoter led to the enhanced glyoxylate accumulation in cultures of Methylobacterium sp MB201. The yield of glyoxylate reached 14.38 mg/mL, representing nearly a twofold increase when compared with the wild-type strain.

The ORF YNL274c (GOR1) codes for glyoxylate reductase in Saccharomyces cerevisiae

The enzyme glyoxylate reductase reversibly reduces glyoxylate to glycolate, or alternatively hydroxypyruvate to D-glycerate, using either NADPH or NADH as a co-factor. The enzyme has multiple metabolic roles in different organisms. In this paper we show that GOR1 (ORF YNL274c) encodes a glyoxylate reductase and not a hydroxyisocaproate dehydrogenase in Saccharomyces cerevisiae, even though it also has minor activity on alpha-ketoisocaproate. In addition, we show that deletion of the glyoxylate reductase-encoding gene leads to higher biomass concentration after diauxic shift.

Integration of metabolome data with metabolic networks reveals reporter reactions

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.