Improving the <Emphasis Type="Italic">i</Emphasis>MM904 <Emphasis Type="Italic">S. cerevisiae</Emphasis> metabolic model using essentiality and synthetic lethality data

Background  

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.     

Modeling <Emphasis Type="Italic">Lactococcus lactis</Emphasis> using a genome-scale flux model

Background  

Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks.     

Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction

Background  

Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction.     

Genome-scale reconstruction and <Emphasis Type="Italic">in silico</Emphasis> analysis of the <Emphasis Type="Italic">Clostridium acetobutylicum</Emphasis> ATCC 824 metabolic network

To understand the metabolic characteristics of Clostridium acetobutylicum and to examine the potential for enhanced butanol production, we reconstructed the genome-scale metabolic network from its annotated genomic sequence and analyzed strategies to improve its butanol production. The generated reconstructed network consists of 502 reactions and 479 metabolites and was used as the basis for an in silico model that could compute metabolic and growth performance for comparison with fermentation data. The in silico model successfully predicted metabolic fluxes during the acidogenic phase using classical flux balance analysis. Nonlinear programming was used to predict metabolic fluxes during the solventogenic phase. In addition, essential genes were predicted via single gene deletion studies. This genome-scale in silico metabolic model of C. acetobutylicum should be useful for genome-wide metabolic analysis as well as strain development for improving production of biochemicals, including butanol. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. J. L. and H. Y. equally contributed to this work.     

Hybrid metabolic flux analysis: combining stoichiometric and statistical constraints to model the formation of complex recombinant products

Background  

Stoichiometric models constitute the basic framework for fluxome quantification in the realm of metabolic engineering. A recurrent bottleneck, however, is the establishment of consistent stoichiometric models for the synthesis of recombinant proteins or viruses. Although optimization algorithms for in silico metabolic redesign have been developed in the context of genome-scale stoichiometric models for small molecule production, still rudimentary knowledge of how different cellular levels are regulated and phenotypically expressed prevents their full applicability for complex product optimization.     

iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations

Background  

Bacillus subtilis is an organism of interest because of its extensive industrial applications, its similarity to pathogenic organisms, and its role as the model organism for Gram-positive, sporulating bacteria. In this work, we introduce a new genome-scale metabolic model of B. subtilis 168 called iBsu1103. This new model is based on the annotated B. subtilis 168 genome generated by the SEED, one of the most up-to-date and accurate annotations of B. subtilis 168 available.     

Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation

Background  

Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties.     

An engineered non-oxidative glycolysis pathway for acetone production in <Emphasis Type="Italic">Escherichia coli</Emphasis>

Objectives

To find new metabolic engineering strategies to improve the yield of acetone in Escherichia coli.

Results

Results of flux balance analysis from a modified Escherichia coli genome-scale metabolic network suggested that the introduction of a non-oxidative glycolysis (NOG) pathway would improve the theoretical acetone yield from 1 to 1.5 mol acetone/mol glucose. By inserting the fxpk gene encoding phosphoketolase from Bifidobacterium adolescentis into the genome, we constructed a NOG pathway in E.coli. The resulting strain produced 47 mM acetone from glucose under aerobic conditions in shake-flasks. The yield of acetone was improved from 0.38 to 0.47 mol acetone/mol glucose which is a significant over the parent strain.

Conclusions

Guided by computational analysis of metabolic networks, we introduced a NOG pathway into E. coli and increased the yield of acetone, which demonstrates the importance of modeling analysis for the novel metabolic engineering strategies.

     

Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

Background  

In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.     

High cell density cultivation of <Emphasis Type="Italic">Escherichia coli</Emphasis> K4 in a microfiltration bioreactor: a step towards improvement of chondroitin precursor production

Background  

The bacteria Escherichia coli K4 produces a capsular polysaccharide (K4 CPS) whose backbone is similar to the non sulphated chondroitin chain. The chondroitin sulphate is one of the major components of the extra-cellular matrix of the vertebrate connective tissues and a high value molecule, widely employed as active principle in the treatment of osteoarthritis. It is usually obtained by extraction from animal tissues, but the risk of virus contaminations, as well as the scarceness of raw material, makes this productive process unsafe and unable to satisfy the growing market demand. In previous studies a new biotechnological process to produce chondroitin from Escherichia coli K4 capsular polysaccharide was investigated and a 1.4 g·L^-1 K4 CPS concentration was reached using fed-batch fermentation techniques. In this work, on the trail of these results, we exploited new fermentation strategies to further improve the capsular polysaccharide production.     

Construction of non-polar mutants in <Emphasis Type="Italic">Haemophilus influenzae</Emphasis> using FLP recombinase technology

Background  

Nontypeable Haemophilus influenzae (NTHi) is a gram-negative bacterium that causes otitis media in children as well as other infections of the upper and lower respiratory tract in children and adults. We are employing genetic strategies to identify and characterize virulence determinants in NTHi. NTHi is naturally competent for transformation and thus construction of most mutants by common methodologies is relatively straightforward. However, new methodology was required in order to construct unmarked non-polar mutations in poorly expressed genes whose products are required for transformation. We have adapted the lambda red/FLP-recombinase-mediated strategy used in E. coli for use in NTHi.     

Extracellular <Emphasis Type="Italic">Paracoccidioides brasiliensis</Emphasis> phospholipase B involvement in alveolar macrophage interaction

Background  

Phospholipase B (PLB) has been reported to be one of the virulence factors for human pathogenic fungi and has also been described as necessary for the early events in infection. Based on these data, we investigated the role of PLB in virulence and modulation of the alveolar pulmonary immune response during infection using an in-vitro model of host-pathogen interaction, i.e. Paracoccidioides brasiliensis yeast cells infecting alveolar macrophage (MH-S) cells.     

Two distinct regions in the model protein Peb1 are critical for its heterologous transport out of <Emphasis Type="Italic">Escherichia coli</Emphasis>

Background  

Escherichia coli is frequently the first-choice host organism in expression of heterologous recombinant proteins in basic research as well as in production of commercial, therapeutic polypeptides. Especially the secretion of proteins into the culture medium of E. coli is advantageous compared to intracellular production due to the ease in recovery of the recombinant protein. Since E. coli naturally is a poor secretor of proteins, a few strategies for optimization of extracellular secretion have been described. We have previously reported efficient secretion of the diagnostically interesting model protein Peb1 of Campylobacter jejuni into the growth medium of Escherichia coli strain MKS12 (ΔfliCfliD). To generate a more detailed understanding of the molecular mechanisms behind this interesting heterologous secretion system with biotechnological implications, we here analyzed further the transport of Peb1 in the E. coli host.     

<Emphasis Type="Italic">Acanthamoeba</Emphasis> produces disseminated infection in locusts and traverses the locust blood-brain barrier to invade the central nervous system

Background  

Many aspects of Acanthamoeba granulomatous encephalitis remain poorly understood, including host susceptibility and chronic colonization which represent important features of the spectrum of host-pathogen interactions. Previous studies have suggested locusts as a tractable model in which to study Acanthamoeba pathogenesis. Here we determined the mode of parasite invasion of the central nervous system (CNS).     

Assessment of poly‐l‐lysine dendrigrafts for virus concentration in water: use of MS2 bacteriophage as proof of concept

Aims

Virus detection has often been difficult due to a low concentration in water. In this study, we developed a new procedure based on concentration of virus particles on an innovative support: poly‐l ‐lysine dendrigrafts (DGL), coupled with directed nucleic acid extraction and real‐time PCR quantification.

Methods and Results

This method was evaluated using the bacteriophage MS2 as a model virus. This virus exhibited the size and structural properties of human pathogenic enteric viruses and has often been used to assess new supports of concentration. Moreover, this bacteriophage is also a faecal contamination indicator. In this study, many water filtration conditions were tested (volume of water, concentration, etc.), and more than 80% of bacteriophage were recovered after filtration on polymer, in most conditions. We demonstrated that the method was linear (slope = 0·99 ± 0·04 and Y intercept when x = ?0·02 ± 0·28), valid (as manipulators, tested concentrations, volumes of sample and batch of polymer did not have any influence on concentration) and sensitive (allowing to concentrate up to 16 600‐fold 1 l of sample and to detect and quantify down to 750 GC l^?1 and 7500 GC l^?1, respectively).

Conclusions

To conclude, this support exhibits high interest to retain viruses and to allow to detect low concentration of virus in water.

Significance and Impact of the Study

This study gives valuable advance in the methods of concentration and diagnosis of virus in water.     

Genome-scale metabolic models as platforms for strain design and biological discovery

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems’ metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.     

In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis,degradation of aromatics and anaerobic survival

Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli.