Distribution of the most prevalent spa types among clinical isolates of methicillin-resistant and -susceptible Staphylococcus aureus around the world: A review

Background: Staphylococcus aureus, a leading cause of community-acquired and nosocomial infections, remains a major health problem worldwide. Molecular typing methods, such as spa typing, are vital for the control and, when typing can be made more timely, prevention of S. aureus spread around healthcare settings. The current study aims to review the literature to report the most common clinical spa types around the world, which is important for epidemiological surveys and nosocomial infection control policies. Methods: A search via PubMed, Google Scholar, Web of Science, Embase, the Cochrane library, and Scopus was conducted for original articles reporting the most prevalent spa types among S. aureus isolates. The search terms were "Staphylococcus aureus, spa typing." Results: The most prevalent spa types were t032, t008 and t002 in Europe; t037 and t002 in Asia; t008, t002, and t242 in America; t037, t084, and t064 in Africa; and t020 in Australia. In Europe, all the isolates related to spa type t032 were MRSA. In addition, spa type t037 in Africa and t037and t437 in Australia also consisted exclusively of MRSA isolates. Given the fact that more than 95 of the papers we studied originated in the past decade there was no option to study the dynamics of regional clone emergence. Conclusion: This review documents the presence of the most prevalent spa types in countries, continents and worldwide and shows big local differences in clonal distribution. © 2018 Asadollahi, Farahani, Mirzaii, Khoramrooz, van Belkum, Asadollahi, Dadashi and Darban-Sarokhalil

Phenotypic and genotypic evaluation of fluoroquinolone resistance in clinical isolates of Staphylococcus aureus in Tehran

Background: Fluoroquinolones are broad-spectrum antibiotics widely used in the treatment of bacterial infections such as Staphylococcus aureus isolates. Resistance to these antibiotics is increasing. Material/Methods: The occurrence of mutations in the grlA and gyrA loci were evaluated in 69 fluoroquinolone-resistant S. aureus isolates from 2 teaching hospitals of Tehran University of Medical Sciences. Results: Out of the 165 S. aureus isolates, 87 (52.7) were resistant to methicillin and 69 (41.8) were resistant to fluoroquinolone. Fluoroquinolone-resistant S. atoms isolates had a mutation at codon 80 in the grlA gene and different mutational combinations in the gyrA gene. These mutational combinations included 45 isolates at codons 84 and 86,23 isolates at codons 84,86 and 106 and 1 isolate at codons 84, 86 and 90. Fluoroquinolone-resistant S. aureus isolates were clustered into 33 PFGE types. Conclusions: The findings of this study show that the fluoroquinolone-resistant S. aureus strains isolated in the teaching hospitals in Tehran had multiple mutations in the QRDRs region of both grlA and gyrA genes

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

<div><p>Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in <em>Saccharomyces cerevisiae</em>.</p> </div

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models

Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism’s metabolic network. Principal Findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not. Conclusions: Inferences about a microorganism’s nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism’s alteration of gene expression is adaptive to its nutrient environment.National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 2722008000059C)National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 26620040000IC)Bill & Melinda Gates Foundation (grant 18651010-37352-A

Production of mannosylglycerate in Saccharomyces cerevisiae by metabolic engineering and bioprocess optimization

Mannosylglycerate (MG) is one of the most widespread compatible solutes among marine microorganisms adapted to hot environments. This ionic solute holds excellent ability to protect proteins against thermal denaturation, hence a large number of biotechnological and clinical applications have been put forward. However, the current prohibitive production costs impose severe constraints towards large-scale applications. All known microbial producers synthesize MG from GDP-mannose and 3-phosphoglycerate via a two-step pathway in which mannosyl-3-phosphoglycerate is the intermediate metabolite. In an early work, this pathway was expressed in Saccharomyces cerevisiae with the goal to confirm gene function (Empadinhas et al. in J Bacteriol 186:4075--4084, 2004), but the level of MG accumulation was low. Therefore, in view of the potential biotechnological value of this compound, we decided to invest further effort to convert S. cerevisiae into an efficient cell factory for MG production.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684), BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte and also by project LISBOA-01-0145-FEDER-007660 (Microbiologia Molecular, Estrutural e Celular) funded by FEDER through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI). Cristiana Faria was supported by a Ph.D. Grant from FCT (Ref. SFRH/ BD/79552/2011).info:eu-repo/semantics/publishedVersio