Optimization of γ-PGA biosynthesis supported by synthetic biology and metabolic engineering strategies

Poly-γ-glutamate (γ-PGA) is a natural polymer composed by glutamic acid residues, synthesized by the pgs operon of Bacillus subtilis. γ-PGA has a wide range of applications as food, cosmetic and pharmaceutical additive. However, to increase its industrial attractiveness, it is necessary to cut production costs utilizing cost-competitive feedstocks for fermentation. A low-cost by-product that can be used as feedstock is raw glycerol, that accounts for 10% (w/w) of the total biodiesel production. To achieve cost-competitive γ-PGA production from glycerol a multifaceted approach has been set up that includes: 1) improvement of pgs expression; 2) accumulation of γ-PGA precursors by metabolic engineering; 3) enhancement of glycerol metabolism. 1) The strength of the pgs operon regulatory elements has been analysed both by a synthetic biology approach, exploiting the well-characterized expression operating unit (EOU) inserted in amyE, and by a classical in-locus transcriptional fusion. Results from the two settings will be compared. These data will be then used to finely tune pgs expression and optimize γ-PGA yield. To this end, an inducible pgs operon has been constructed. 2) A genome-scale metabolic model was used to identify suitable targets for enhancing central carbon pathway flux toward γ-PGA synthesis. The first two B. subtilis strains, engineered following this analysis, showed enhanced polymer production. Other target genes are currently under investigation. 3) B. subtilis tolerance to raw glycerol obtained from a biodiesel plant (from both vegetable and animal origin) was verified. Further investigations are underway to improve glycerol uptake and consumption

Integration of enzymatic data in <i>Bacillus subtilis</i> genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains

Abstract Background Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design. Results Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain. Conclusions This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis

Computational and experimental methods for metabolic engineering: applications in Escherichia coli and Bacillus subtilis

Metabolic engineering was defined more than 25 years ago as the directed modulation of metabolic pathways, using methods of recombinant DNA technology, for the purpose of overproducing high-value compounds, such as pharmaceutical products, food additives and fuels. Given the increasing need of more sustainable processes for the production of value-added chemicals and materials from renewable resources, metabolic engineering became a powerful tool for the development of highly efficient microbial cell factories. The main innovation introduced from metabolic engineering, compared to traditional trial-and-error approaches, is the use of predictive modelling methods to study the behaviour of cellular metabolism and to guide the rational strain design. In this context, the cellular metabolism is described by the complete set of biochemical reactions that occur in the target microorganism, known as genome-scale metabolic model, and can be analyzed in terms of flux distributions, namely the reaction rates. Differently from gene expression levels or protein and metabolite concentration, the metabolic flux profiles are able to reflect the consequences of cellular component interactions. Despite a variety of in-silico modelling approaches have been developed for the study of cellular metabolism, only those requiring a limited number of readily available parameters can be successfully applied to genome-scale models. Currently, constraint-based modelling approach is the best methodology by which genome-scale models are constructed and analyzed. This approach identifies a set of allowable solutions, by the assumption of steady-state conditions and limiting the fluxes, and then finds an unique flux distribution, by an optimization problem that maximizes or minimizes a biological objective function. Several methods based on different objective functions, and therefore appropriate for specific study goals, were developed. Flux Balance Analysis is the most popular method, which determines the flux through the metabolic network that maximizes growth rate. However, in some contexts the reliability of such models in the quantitative prediction of cellular phenotypes and fluxes through biochemical reactions can be low. The integration of additional biological information in the model, e.g., genome-scale transcriptomic or proteomic profiles, has been recently proposed as an attempt to improve prediction accuracy. The last and crucial step for strain improvement is the application of genetic manipulations for the control of metabolic fluxes through recombinant DNA technologies. The perturbations, identified by the in-silico design phase, are implemented through the synthetic biology techniques for the tight control of gene expression levels, namely over-, down-expression and deletion. Synthetic biology is an emerging discipline, closely coupled with metabolic engineering field, that promotes the optimization of microorganisms using toolkits of pre-characterized regulatory elements. In particular, regulatory parts, such as promoters or ribosome binding sites, are commonly used for the over- or down-regulation of transcriptional and translational processes of target genes, respectively, whereas gene knockouts are implemented using homologous recombination or silencing the gene via the new proposed techniques. This thesis work includes both in-silico and in-vivo investigations on different metabolic engineering tools on Escherichia coli and Bacillus subtilis

Synthetic and systems biology for cost-competitive γ-PGA production

γ-PGA is a natural polymer secreted by Bacillus subtilis and other bacteria with huge potential in various biotechnological fields. Yet its commercial development is lagging behind due to the still high price. To increase the industrial attractiveness of this product it is therefore necessary to focus research efforts on reducing production costs. This objective can be achieved through different approaches which can be synergistically combined: 1. The expression of the pgs operon for biopolymer synthesis can be improved; 2. The bacterial metabolism can be modified a. so that cost-competitive substrates, such as industrial or crop waste products, can be used as feedstock; b. to accumulate the metabolic precursors required for product synthesis. Our aim is to act at all these levels with a common strategy, i.e. exploiting tools and data made available by synthetic & systems biology. For the first approach we are quantitatively evaluating the endogenous expression of the pgs operon, in order to tune it using pre-characterized regulatory "parts" from recently described libraries. For the second approach glycerol by-produced in biodiesel plants was chosen as low cost feedstock. We assessed the propensity of the γ-PGA producer and other B. subtilis strains to grow on glycerol analyzing its consumption flux compared to that of glucose, used until now. For optimization, the glycerol uptake and catabolic genes will be up-regulated with synthetic biology tools. Finally, a systems biology approach, exploiting genome-scale metabolic models, was chosen to identify genes whose deletion /over-expression should allow accumulation of the metabolic precursors necessary for γ-PGA production. The first results of such a multilevel strategy will be presented and discussed

Quantification of the gene silencing performances of rationally-designed synthetic small RNAs

Small RNAs (sRNAs) are genetic tools for the efficient and specific tuning of target genes expression in bacteria. Inspired by naturally occurring sRNAs, recent works proposed the use of artificial sRNAs in synthetic biology for predictable repression of the desired genes. Their potential was demonstrated in several application fields, such as metabolic engineering and bacterial physiology studies. Guidelines for the rational design of novel sRNAs have been recently proposed. According to these guidelines, in this work synthetic sRNAs were designed, constructed and quantitatively characterized in Escherichia coli. An sRNA targeting the reporter gene RFP was tested by measuring the specific gene silencing when RFP was expressed at different transcription levels, under the control of different promoters, in different strains, and in single-gene or operon architecture. The sRNA level was tuned by using plasmids maintained at different copy numbers. Results demonstrated that RFP silencing worked as expected in an sRNA and mRNA expression-dependent fashion. A mathematical model was used to support sRNA characterization and to estimate an efficiency-related parameter that can be used to compare the performance of the designed sRNA. Gene silencing was also successful when RFP was placed in a two-gene synthetic operon, while the non-target gene (GFP) in the operon was not considerably affected. Finally, silencing was evaluated for another designed sRNA targeting the endogenous lactate dehydrogenase gene. The quantitative study performed in this work elucidated interesting performance-related and context-dependent features of synthetic sRNAs that will strongly support predictable gene silencing in disparate basic or applied research studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11693-015-9177-7) contains supplementary material, which is available to authorized users

Red Flags, Prognostic Impact, and Management of Patients With Cardiac Amyloidosis and Aortic Valve Stenosis: A Systematic Review and Meta-Analysis

BACKGROUND: Cardiac amyloidosis (CA) has been recently recognized as a condition frequently associated with aortic stenosis (AS). The aim of this study was to evaluate: the main characteristics of patients with AS with and without CA, the impact of CA on patients with AS mortality, and the effect of different treatment strategies on outcomes of patients with AS with concomitant CA. MATERIALS AND METHODS: A detailed search related to CA in patients with AS and outcomes was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seventeen studies enrolling 1,988 subjects (1,658 AS alone and 330 AS with CA) were included in the qualitative and quantitative analysis of main patients with AS characteristics with and without CA, difference in mortality, and treatment strategy. RESULTS: The prevalence of CA resulted in a mean of 15.4% and it was even higher in patients with AS over 80 years old (18.2%). Patients with the dual diagnosis were more often males, had lower body mass index (BMI), were more prone to have low flow, low gradient with reduced left ventricular ejection fraction AS phenotype, had higher E/A and E/e', and greater interventricular septum hypertrophy. Lower Sokolow–Lyon index, higher QRS duration, higher prevalence of right bundle branch block, higher levels of N-terminal pro-brain natriuretic peptide, and high-sensitivity troponin T were significantly associated with CA in patients with AS. Higher overall mortality in the 178 patients with AS + CA in comparison to 1,220 patients with AS alone was observed [odds ratio (OR) 2.25, p = 0.004]. Meta-regression analysis showed that younger age and diabetes were associated with overall mortality in patients with CS with CA (Z-value −3.0, p = 0.003 and Z-value 2.5, p = 0.013, respectively). Finally, patients who underwent surgical aortic valve replacement (SAVR) or transcatheter aortic valve implantation (TAVI) had a similar overall mortality risk, but lower than medication-treated only patients. CONCLUSION: Results from our meta-analysis suggest that several specific clinical, electrocardiographic, and echocardiographic features can be considered “red flags” of CA in patients with AS. CA negatively affects the outcome of patients with AS. Patients with concomitant CA and AS benefit from SAVR or TAVI

Purinergic Receptor P2Y2 Stimulation Averts Aortic Valve Interstitial Cell Calcification and Myofibroblastic Activation

Rationale&mdash;Calcific aortic valve stenosis (CAVS) is a pathological condition of the aortic valve with a prevalence of 3% in the general population. It is characterized by massive rearrangement of the extracellular matrix, mostly due to the accumulation of fibro-calcific deposits driven by valve interstitial cells (VIC), and no pharmacological treatment is currently available. The aim of this study was to evaluate the effects of P2Y2 receptor (P2RY2) activation on fibro-calcific remodeling of CAVS. Methods&mdash;We employed human primary VICs isolated from CAVS leaflets treated with 2-thiouridine-5&prime;-triphosphate (2ThioUTP, 10 &micro;M), an agonist of P2RY2. The calcification was induced by inorganic phosphate (2 mM) and ascorbic acid (50 &micro;g/mL) for 7 or 14 days, while the 2ThioUTP was administered starting from the seventh day. 2ThioUTP was chronically administered for 5 days to evaluate myofibroblastic activation. Results&mdash;P2RY2 activation, under continuous or interrupted pro-calcific stimuli, led to a significant inhibition of VIC calcification potential (p &lt; 0.01). Moreover, 2ThioUTP treatment was able to significantly reduce pro-fibrotic gene expression (p &lt; 0.05), as well as that of protein &alpha;-smooth muscle actin (p = 0.004). Conclusions&mdash;Our data suggest that P2RY2 activation should be further investigated as a pharmacological target for the prevention of CAVS progression, acting on both calcification and myofibroblastic activation