Purpose

Whether addition of cilostazol is superior to increasing dose of clopidogrel in patients with hyporesponsiveness to chronic clopidogrel therapy is unknown.

Materials and Methods

We studied 73 patients with hyporesponsiveness to clopidogrel on standard dual antiplatelet therapy for more than 2 weeks. Clopidogrel hyporesponsiveness was defined as percent inhibition of P2Y12 reaction units (PRU) <30% on VerifyNow P2Y12 assay. Patients were randomly assigned to increased dose of clopidogrel (aspirin 100 mg+clopidogrel 150 mg daily: group A, n=38) or to receiving additional cilostazol (aspirin 100 mg+clopidogrel 75 mg+cilostazol 100 mg bid daily: group B, n=35).

Results

Baseline percent inhibition of PRU and PRU was similar between 2 groups (13.0±10.2% versus 11.8±9.7%, p=0.61, and 286.3±54.7 versus 295.7±53.7, p=0.44, respectively). At follow-up, percent inhibition of PRU was higher and PRU was lower significantly in group B than in group A (38.5±17.9% versus 28.3±16.6%, p=0.02, and 207.3±68.2 versus 241.3±76.7, p=0.050, respectively). Among those still showing hyporesponsiveness to clopidogrel at follow-up (21 patients in group A, 10 patients in group B), 12 patients completed further crossover study. Compared to the baseline, magnitude of change in percent inhibition of PRU and PRU showed an improved tendency after the crossover (from 2.7±8.7% to 15.8±18.4%, p=0.08, and from -18.6±58.0 to -61.9±84.3, p=0.08).

Conclusion

Adjunctive cilostazol improved clopidogrel responsiveness better than the higher maintenance dose of clopidogrel in hyporesponsive patients with chronic clopidogrel therapy.

ABSTRACT

Butanol is an important industrial solvent and advanced biofuel that can be produced by biphasic fermentation by Clostridium acetobutylicum. It has been known that acetate and butyrate first formed during the acidogenic phase are reassimilated to form acetone-butanol-ethanol (cold channel). Butanol can also be formed directly from acetyl-coenzyme A (CoA) through butyryl-CoA (hot channel). However, little is known about the relative contributions of the two butanol-forming pathways. Here we report that the direct butanol-forming pathway is a better channel to optimize for butanol production through metabolic flux and mass balance analyses. Butanol production through the hot channel was maximized by simultaneous disruption of the pta and buk genes, encoding phosphotransacetylase and butyrate kinase, while the adhE1D485G gene, encoding a mutated aldehyde/alcohol dehydrogenase, was overexpressed. The ratio of butanol produced through the hot channel to that produced through the cold channel increased from 2.0 in the wild type to 18.8 in the engineered BEKW(pPthlAAD**) strain. By reinforcing the direct butanol-forming flux in C. acetobutylicum, 18.9 g/liter of butanol was produced, with a yield of 0.71 mol butanol/mol glucose by batch fermentation, levels which are 160% and 245% higher than those obtained with the wild type. By fed-batch culture of this engineered strain with in situ recovery, 585.3 g of butanol was produced from 1,861.9 g of glucose, with the yield of 0.76 mol butanol/mol glucose and productivity of 1.32 g/liter/h. Studies of two butanol-forming routes and their effects on butanol production in C. acetobutylicum described here will serve as a basis for further metabolic engineering of clostridia aimed toward developing a superior butanol producer.

IMPORTANCE

Renewable biofuel is one of the answers to solving the energy crisis and climate change problems. Butanol produced naturally by clostridia has superior liquid fuel characteristics and thus has the potential to replace gasoline. Due to the lack of efficient genetic manipulation tools, however, strain improvement has been rather slow. Furthermore, complex metabolic characteristics of acidogenesis followed by solventogenesis in this strain have hampered development of engineered clostridia having highly efficient and selective butanol production capability. Here we report for the first time the results of systems metabolic engineering studies of two butanol-forming routes and their relative importances in butanol production. Based on these findings, a metabolically engineered Clostridium acetobutylicum strain capable of producing butanol to a high titer with high yield and selectivity could be developed by reinforcing the direct butanol-forming flux.

Capacitances of five types of viruses, adenovirus type 5 (AV 5) herpes simplex virus type 1 (HSV1), simian virus 40 (SV40), vaccinia (MVA), and cowpea mosaic virus (CPMV) were compared by AC capacitance scanning probe microscopy. This technique, using a Pt-coated AFM tip as an electrode to probe capacitance of materials between the tip and a bottom electrode, has been applied to study surface structures of semiconductors and polymers with nanometer spacial resolution, however biological samples at the nanoscale have not been explored by this technique yet. Because most biological cells are poor conductors, this approach to probe electric properties of cells by capacitance is logical. This scanning probe technique (SPM) showed that all of these viruses have distinguishable and characteristic capacitances, respectively. Series of control experiments were carried out using mutant viruses in order to validate the origin of the characteristic capacitance responses for different viruses. A mutation on the capsid in HSV1 virus with green fluorescence proteins (GFP) increased capacitance from 9×10−6 F/cm2 to 1×10−5 F/cm2 at the frequency of 104 Hz. HSV2 virus decreased capacitance when its envelope and glycoproteins were chemically extracted. These control experiments indicate that dielectric properties of capsid proteins and envelope glycoproteins significantly influence overall dielectric constants of viruses. Because those capsid proteins and glycoproteins are characteristic to the virus strain, this technique could be applied to detect and identify viruses at the single viron level using their distinct capacitance spectra as fingerprints without labeling.

Clostridium acetobutylicum naturally produces acetone as well as butanol and ethanol. Since acetone cannot be used as a biofuel, its production needs to be minimized or suppressed by cell or bioreactor engineering. Thus, there have been attempts to disrupt or inactivate the acetone formation pathway. Here we present another approach, namely, converting acetone to isopropanol by metabolic engineering. Since isopropanol can be used as a fuel additive, the mixture of isopropanol, butanol, and ethanol (IBE) produced by engineered C. acetobutylicum can be directly used as a biofuel. IBE production is achieved by the expression of a primary/secondary alcohol dehydrogenase gene from Clostridium beijerinckii NRRL B-593 (i.e., adhB-593) in C. acetobutylicum ATCC 824. To increase the total alcohol titer, a synthetic acetone operon (act operon; adc-ctfA-ctfB) was constructed and expressed to increase the flux toward isopropanol formation. When this engineering strategy was applied to the PJC4BK strain lacking in the buk gene (encoding butyrate kinase), a significantly higher titer and yield of IBE could be achieved. The resulting PJC4BK(pIPA3-Cm2) strain produced 20.4 g/liter of total alcohol. Fermentation could be prolonged by in situ removal of solvents by gas stripping, and 35.6 g/liter of the IBE mixture could be produced in 45 h.

Background

In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model’s prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes.

Results

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via “GR constraints”. This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.

Conclusions

FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.

Background

Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.

Results

Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).

Conclusion

This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data.

Mannheimia succiniciproducens, a rumen bacterium belonging to the family Pasteurellaceae, has two putative β-galactosidase genes, bgaA and bgaB, encoding polypeptides whose deduced amino acid sequences share 56% identity with each other and show approximately 30% identity to the Escherichia coli gene for LacZ. The M. succiniciproducens bgaA (MsbgaA) gene-deletion mutant was not able to grow on lactose as the sole carbon source, suggesting its essential role in lactose metabolism, whereas the MsbgaB gene-deletion mutant did not show any growth defect on a lactose medium. Furthermore, the expression of the MsbgaA gene was induced by the addition of lactose in the growth medium, whereas the MsbgaB gene was constitutively expressed independently of a carbon source. Biochemical characterization of the recombinant proteins revealed that MsBgaA is more efficient than MsBgaB in hydrolyzing o-nitrophenyl-β-d-galactopyranoside and p-nitrophenyl-β-d-galactopyranoside. MsBgaA was highly specific for the hydrolysis of lactose, with a catalytic efficiency of 46.9 s−1 mM−1. However, MsBgaB was more efficient for the hydrolysis of lactulose than lactose, and the catalytic efficiency was 10.0 s−1 mM−1. Taken together, our results suggest that the β-galactosidase paralogues of M. succiniciproducens BgaA and BgaB play a critical role in lactose metabolism and in an unknown but likely specific function for rumen bacteria, respectively.

We have developed a simple electrochemical biosensing strategy for the label-free diagnosis of hepatitis B virus (HBV) on a gold electrode surface. Gold-binding polypeptide (GBP) fused with single-chain antibody (ScFv) against HBV surface antigen (HBsAg), in forms of genetically engineered protein, was utilized. This GBP-ScFv fusion protein can directly bind onto the gold substrate with the strong binding affinity between the GBP and the gold surface, while the recognition site orients toward the sample for target binding at the same time. Furthermore, this one-step immobilization strategy greatly simplifies a fabrication process without any chemical modification as well as maintaining activity of biological recognition elements. This system allows specific immobilization of proteins and sensitive detection of targets, which were verified by surface plasmon resonance analysis and successfully applied to electrochemical cyclic voltammetry and impedance spectroscopy upto 0.14 ng/mL HBsAg.

Background

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology.

Results

We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model.

Conclusions

After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Variovorax paradoxus is a microorganism of special interest due to its diverse metabolic capabilities, including the biodegradation of both biogenic compounds and anthropogenic contaminants. V. paradoxus also engages in mutually beneficial interactions with both bacteria and plants. The complete genome sequence of V. paradoxus S110 is composed of 6,754,997 bp with 6,279 predicted protein-coding sequences within two circular chromosomes. Genomic analysis has revealed multiple metabolic features for autotrophic and heterotrophic lifestyles. These metabolic diversities enable independent survival, as well as a symbiotic lifestyle. Consequently, S110 appears to have evolved into a superbly adaptable microorganism that is able to survive in ever-changing environmental conditions. Based on our findings, we suggest V. paradoxus S110 as a potential candidate for agrobiotechnological applications, such as biofertilizer and biopesticide. Because it has many associations with other biota, it is also suited to serve as an additional model system for studies of microbe-plant and microbe-microbe interactions.

Background

Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO2 and H2 as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs) from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level.

Results

We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(-)-3hydroxybutyrate] (PHB) under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16.

Conclusion

The genome-scale metabolic model, RehMBEL1391, successfully represented metabolic characteristics of R. eutropha H16 at systems level. The reconstructed genome-scale metabolic model can be employed as an useful tool for understanding its metabolic capabilities, predicting its physiological consequences in response to various environmental and genetic changes, and developing strategies for systems metabolic engineering to improve its metabolic performance.

Chromosome 1 of Vibrio vulnificus tends to contain larger portion of essential or housekeeping genes on the basis of the genomic analysis and gene knockout experiments performed in this study, while its chromosome 2 seems to have originated and evolved from a plasmid.The genome-scale metabolic network model of V. vulnificus was reconstructed based on databases and literature, and was used to identify 193 essential metabolites.Five essential metabolites finally selected after the filtering process are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA), which were predicted to be essential in V. vulnificus, absent in human, and are consumed by multiple reactions.Chemical analogs of the five essential metabolites were screened and a hit compound showing the minimal inhibitory concentration (MIC) of 2 μg/ml and the minimal bactericidal concentration (MBC) of 4 μg/ml against V. vulnificus was identified.

Discovering new antimicrobial targets and consequently new antimicrobials is important as drug resistance of pathogenic microorganisms is becoming an increasingly serious problem in human healthcare management (Fischbach and Walsh, 2009). There clearly exists a gap between genomic studies and drug discovery as the accumulation of knowledge on pathogens at genome level has not successfully transformed into the development of effective drugs (Mills, 2006; Payne et al, 2007). In this study, we dissected the genome of a microbial pathogen in detail, and subsequently developed a systems biological strategy of employing genome-scale metabolic modeling and simulation together with metabolite essentiality analysis for effective drug targeting and discovery. This strategy was used for identifying new drug targets in an opportunistic pathogen Vibrio vulnificus CMCP6 as a model.

V. vulnificus is a Gram-negative halophilic bacterium that is found in estuarine waters, brackish ponds, or coastal areas, and its Biotype 1 is an opportunistic human pathogen that can attack immune-compromised patients, and causes primary septicemia, necrotized wound infections, and gastroenteritis. We previously found that many metabolic genes were specifically induced in vivo, suggesting that specific metabolic pathways are essential for in vivo survival and virulence of this pathogen (Kim et al, 2003; Lee et al, 2007). These results motivated us to carry out systems biological analysis of the genome and the metabolic network for new drug target discovery.

V. vulnificus CMCP6 has two chromosomes. We first re-sequenced genomic regions assembled in low quality and low depth, and subsequently re-annotated the whole genome of V. vulnificus. Horizontal gene transfer was suspected to be responsible for the diversification of each chromosome of V. vulnificus, and the presence of metabolic genes was more biased to chromosome 1 than chromosome 2. Further studies on V. vulnificus genome revealed that chromosome 2 is more prone to diversification for better adaptation to the environment than its chromosome 1, while chromosome 1 tends to expand their genetic repertoire while maintaining the core genes at a constant level.

Next, a genome-scale metabolic network VvuMBEL943 was reconstructed based on literature, databases and experiments for systematic studies on the metabolism of this pathogen and prediction of drug targets. The VvuMBEL943 model is composed of 943 reactions and 765 metabolites, and covers 673 genes. The model was validated by comparing its simulated cell growth phenotype obtained by constraints-based flux analysis with the V. vulnificus-specific experimental data previously reported in the literature. In this study, constraints-based flux analysis is an optimization-based simulation method that calculates intracellular fluxes under the specific genetic and environmental condition (Kim et al, 2008). As a result, 17 growth phenotypes were correctly predicted out of 18 cases, which demonstrate the validity of VvuMBEL943.

The main objective of constructing VvuMBEL943 in this study is to predict potential drug targets by system-wide analysis of the metabolic network for the effective treatment of V. vulnificus. To achieve this goal, a set of drug target candidates was predicted by taking a metabolite-centric approach. Metabolite essentiality analysis is a concept recently introduced for the study of cellular robustness to complement conventional reaction or gene-centric approach (Kim et al, 2007b). Metabolite essentiality analysis observes changes in flux distribution by removing each metabolite from the in silico metabolic network. Hence, metabolite essentiality predicts essential metabolites whose absence causes cell death. By selecting essential metabolites, it is possible to directly screen only their structural analogs, which substantially reduces the number of chemical compounds to screen from the chemical compound library. As a result of implementing this approach, 193 metabolites were initially identified to be essential to the cell. These essential metabolites were then further filtered based on the predetermined criteria, mainly organism specificity and multiple connectivity associated with each metabolite, in order to reduce the number of initial target candidates towards identifying the most effective ones.

Five essential metabolites finally selected are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA). Enzymes that consume these essential metabolites were experimentally verified to be essential, which indeed demonstrates the essentiality of these five metabolites. On the basis of the structural information of these five essential metabolites, whole-cell screening assay was performed using their analogs for possible antibacterial discovery. We screened 352 chemical analogs of the essential metabolites selected from the chemical compound library, and found a hit compound 24837, which shows the minimal inhibitory concentration (MIC) of 2 μg/ml and minimal bactericidal concentration (MBC) of 4 μg/ml, showing good antibacterial activity without further structural modification. Although this study demonstrates a proof-of-concept, the approaches and their rationale taken here should serve as a general strategy for discovering novel antibiotics and drugs based on systems-level analysis of metabolic networks.

Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.

Background

Escherichia coli is a model prokaryote, an important pathogen, and a key organism for industrial biotechnology. E. coli W (ATCC 9637), one of four strains designated as safe for laboratory purposes, has not been sequenced. E. coli W is a fast-growing strain and is the only safe strain that can utilize sucrose as a carbon source. Lifecycle analysis has demonstrated that sucrose from sugarcane is a preferred carbon source for industrial bioprocesses.

Results

We have sequenced and annotated the genome of E. coli W. The chromosome is 4,900,968 bp and encodes 4,764 ORFs. Two plasmids, pRK1 (102,536 bp) and pRK2 (5,360 bp), are also present. W has unique features relative to other sequenced laboratory strains (K-12, B and Crooks): it has a larger genome and belongs to phylogroup B1 rather than A. W also grows on a much broader range of carbon sources than does K-12. A genome-scale reconstruction was developed and validated in order to interrogate metabolic properties.

Conclusions

The genome of W is more similar to commensal and pathogenic B1 strains than phylogroup A strains, and therefore has greater utility for comparative analyses with these strains. W should therefore be the strain of choice, or 'type strain' for group B1 comparative analyses. The genome annotation and tools created here are expected to allow further utilization and development of E. coli W as an industrial organism for sucrose-based bioprocesses. Refinements in our E. coli metabolic reconstruction allow it to more accurately define E. coli metabolism relative to previous models.

Background

Zymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.

Results

The genome-scale metabolic model of Z. mobilis ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of Z. mobilis were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of Z. mobilis as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum in silico yields of ethanol for Z. mobilis and Escherichia coli were compared and analyzed. Furthermore, the substrate utilization range of Z. mobilis was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow Z. mobilis to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of Z. mobilis.

Conclusion

The genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of Z. mobilis under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of Z. mobilis metabolism and consequently designing metabolic engineering strategies for various biotechnological applications.

The identification of genes to be deleted or amplified is an essential step in metabolic engineering for strain improvement toward the enhanced production of desired bioproducts. In the past, several methods based on flux analysis of genome-scale metabolic models have been developed for identifying gene targets for deletion. Genome-wide identification of gene targets for amplification, on the other hand, has been rather difficult. Here, we report a strategy called flux scanning based on enforced objective flux (FSEOF) to identify gene amplification targets. FSEOF scans all the metabolic fluxes in the metabolic model and selects fluxes that increase when the flux toward product formation is enforced as an additional constraint during flux analysis. This strategy was successfully employed for the identification of gene amplification targets for the enhanced production of the red-colored antioxidant lycopene. Additional metabolic engineering based on gene knockout simulation resulted in further synergistic enhancement of lycopene production. Thus, FSEOF can be used as a general strategy for selecting genome-wide gene amplification targets in silico.

The succinic acid producer Mannheimia succiniciproducens can efficiently utilize sucrose as a carbon source, but its metabolism has not been understood. This study revealed that M. succiniciproducens uses a sucrose phosphotransferase system (PTS), sucrose 6-phosphate hydrolase, and a fructose PTS for the transport and utilization of sucrose.

A cell surface display system was developed using Escherichia coli OmpC as an anchoring motif. The fused Pseudomonas fluorescens SIK W1 lipase was successfully displayed on the surface of E. coli cells, and the lipase activity could be enhanced by the coexpression of the gadBC genes identified by transcriptome analysis.

Aim

To develop a diagnostic DNA chip to detect mutations in the βigh3 gene causing the most common corneal dystrophies (CDs).

Methods

Samples from 98 people, including patients with βigh3‐associated CDs (β‐aCDs), were examined. Specific primer and probe sets were designed to examine exons 4 and 12 of the βigh3 gene, in order to identify mutant and wild‐type alleles. Mutations were then identified by hybridisation signals of sequence‐specific probes immobilised on the slide glass.

Results

Direct sequencing of exons 4 and 12 of the βigh3 gene in the patients' genome showed that β‐aCDs could be mainly classified into five types: homozygotic Avellino corneal dystrophy (ACD), heterozygotic ACD, heterozygotic lattice CD I, heterozygotic Reis–Bucklers CD and heterozygotic granular CD. Blind tests were performed by applying the target DNA amplified from the genomic DNA isolated from the peripheral blood of the participants onto a DNA chip. The results obtained by DNA chip hybridisation matched well with the direct DNA sequencing results.

Conclusions

The DNA chip developed in this study allowed successful detection of β‐aCDs with a sensitivity of 100%. Mutational analysis of exons 4 and 12 of the βigh3 gene, which are the mutational hot spots causing β‐aCDs, can be successfully performed with the DNA chip. Thus, this DNA chip‐based method should allow a convenient, yet highly accurate, diagnosis of β‐aCDs, and can be further applied to diagnose other types of CDs.

Background

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism.

Results

In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates.

Conclusions

It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

Background

The Ada-dependent adaptive response system in Escherichia coli is important for increasing resistance to alkylation damage. However, the global transcriptional and translational changes during this response have not been reported. Here we present time-dependent global gene and protein expression profiles following treatment with methyl methanesulfonate (MMS) in E. coli W3110 and its ada mutant strains.

Results

Transcriptome profiling showed that 1138 and 2177 genes were differentially expressed in response to MMS treatment in the wild-type and mutant strains, respectively. A total of 81 protein spots representing 76 nonredundant proteins differentially expressed were identified using 2-DE and LC-MS/MS. In the wild-type strain, many genes were differentially expressed upon long-exposure to MMS, due to both adaptive responses and stationary phase responses. In the ada mutant strain, the genes involved in DNA replication, recombination, modification and repair were up-regulated 0.5 h after MMS treatment, indicating its connection to the SOS and other DNA repair systems. Interestingly, expression of the genes involved in flagellar biosynthesis, chemotaxis, and two-component regulatory systems related to drug or antibiotic resistance, was found to be controlled by Ada.

Conclusion

These results show in detail the regulatory components and pathways controlling adaptive response and how the related genes including the Ada regulon are expressed with this response.

The room temperature synthesis of β-Ga2O3 nanocrystal was examined by coupling two biomimetic crystallization techniques, the enzymatic peptide nano-assembly templating and the aggregation-driven crystallization. The catalytic template of peptide assembly nucleated and mineralized primary β-Ga2O3 crystals, and then fused them to grow single-crystalline and monodisperse nanoparticles in the cavity of the peptide assembly at room temperature. In this work, the peptide assembly was exploited as a nano-reactor with an enzymatic functionality catalyzing the hydrolysis of gallium precursors. In addition, the characteristic ring-structure of peptide assembly is expected to provide an efficient dehydration pathway and the crystallization control over the surface tension, which are advantageous for the β-Ga2O3 crystal growth. This multifunctional peptide assembly could be applied for syntheses of a variety of nanomaterials that are kinetically difficult to grow at room temperature.

Background

Quasi-steady state approximation (QSSA) based on time-scale analysis is known to be an effective method for simplifying metabolic reaction system, but the conventional analysis becomes time-consuming and tedious when the system is large. Although there are automatic methods, they are based on eigenvalue calculations of the Jacobian matrix and on linear transformations, which have a high computation cost. A more efficient estimation approach is necessary for complex systems.

Results

This work derived new time-scale factor by focusing on the problem structure. By mathematically reasoning the balancing behavior of fast species, new time-scale criteria were derived with a simple expression that uses the Jacobian matrix directly. The algorithm requires no linear transformation or decomposition of the Jacobian matrix, which has been an essential part for previous automatic time-scaling methods. Furthermore, the proposed scale factor is estimated locally. Therefore, an iterative procedure was also developed to find the possible multiple boundary layers and to derive an appropriate reduced model.

Conclusion

By successive calculation of the newly derived time-scale criteria, it was possible to detect multiple boundary layers of full ordinary differential equation (ODE) models. Besides, the iterative procedure could derive the appropriate reduced differential algebraic equation (DAE) model with consistent initial values, which was tested with simple examples and a practical example.

The genomewide gene expression profiling of the methylotrophic yeast Hansenula polymorpha exposed to cadmium (Cd) allowed us to identify novel genes responsive to Cd treatment. To select genes whose promoters can be useful for construction of a cellular Cd biosensor, we further analyzed a set of H. polymorpha genes that exhibited >6-fold induction upon treatment with 300 μM Cd for 2 h. The putative promoters, about 1,000-bp upstream fragments, of these genes were fused with the yeast-enhanced green fluorescence protein (GFP) gene. The resultant reporter cassettes were introduced into H. polymorpha to evaluate promoter strength and specificity. The promoter derived from the H. polymorpha SEO1 gene (HpSEO1) was shown to drive most strongly the expression of GFP upon Cd treatment among the tested promoters. The Cd-inducible activity was retained in the 500-bp deletion fragment of the HpSEO1 promoter but was abolished in the further truncated 250-bp fragment. The 500-bp HpSEO1 promoter directed specific expression of GFP upon exposure to Cd in a dose-dependent manner, with Cd detection ranging from 1 to 900 μM. Comparative analysis of the Saccharomyces cerevisiae SEO1 (ScSEO1) promoter revealed that the ScSEO1 promoter has a broader specificity for heavy metals and is responsive to arsenic and mercury in addition to Cd. Our data demonstrate the potential use of the HpSEO1 promoter as a bioelement in whole-cell biosensors to monitor heavy metal contamination, particularly Cd.

In Escherichia coli, a long-chain acyl-CoA is a regulatory signal that modulates gene expression through its binding to a transcription factor FadR. In this study, comparative proteomic analysis of E. coli in the presence of glucose and oleic acid was performed to understand cell physiology in response to oleic acid. Among total of 52 proteins showing altered expression levels with oleic acid presence, 9 proteins including AldA, Cdd, FadA, FadB, FadL, MalE, RbsB, Udp, and YccU were newly synthesized. Among the genes that were induced by oleic acid, the promoter of the aldA gene was used for the production of a green fluorescent protein (GFP). Analysis of fluorescence intensities and confocal microscopic images revealed that soluble GFP was highly expressed under the control of the aldA promoter. These results suggest that proteomics is playing an important role not only in biological research but also in various biotechnological applications.