First sequence-confirmed case of infection with the new influenza A(H1N1) strain in Germany

Here, we report on the first sequence-confirmed case of infection with the new influenza A(H1N1) virus in Germany. Two direct contacts of the patient were laboratory-confirmed as cases and demonstrate a chain of direct human-to-human transmission

Calcium-dependent release of adenosine and uridine nucleotides from A549 cells

Extracellular nucleotides play an important role in lung defense, but the release mechanism and relative abundance of different nucleotide species secreted by lung epithelia are not well defined. In this study, to minimize cell surface hydrolysis, we used a low-volume, flow-through chamber and examined adenosine and uridine nucleotide concentrations in perfusate aliquots of human lung A549 cells challenged by 50% hypotonic shock. Adenosine triphosphate (ATP), adenosine diphosphate (ADP), adenosine monophosphate (AMP), and adenosine (Ado) were quantified in high-performance liquid chromatography (HPLC) analysis of fluorescent etheno derivatives, and uridine triphosphate (UTP) and uridine diphosphate (UDP) were measured using HPLC-coupled radioenzymatic assays. After the onset of hypotonic shock, ATP, ADP, UTP, and UDP in the perfusates increased markedly and peaked at approximately 2.5 min, followed by a gradual decay in the next 15–20 min; peak changes in Ado and AMP were relatively minor. The peak concentrations and fold increment (in parentheses) were: 34 ± 13 nM ATP (5.6), 11 ± 5 nM ADP (3.7), 3.3 ± 1.2 nM AMP (1.4), 23 ± 7 nM Ado (2.1), 21 nM UTP (>7), and 11 nM UDP (27). Nucleotide release was almost completely abolished from cells loaded with the calcium chelator 1,2-bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid (BAPTA). Under isotonic conditions, elevation of intracellular calcium with the calcium ionophore ionomycin (5 μM, 3 min) also released nucleotides with kinetics and relative abundance as above, albeit less robust. ADP:ATP (1:3) and UDP:UTP (1:2) ratios in perfusates from stimulated cells were markedly higher than the cytosolic ratios of these species, suggesting that a nucleotide diphosphate (NDP)-rich compartment, e.g., the secretory pathway, contributed to nucleotide release. Laser confocal microscopy experiments illustrated increased FM1-43 uptake into the plasma membrane upon hypotonic shock or ionomycin treatment, consistent with enhanced vesicular exocytosis under these conditions. In summary, our results strongly suggest that calcium-dependent exocytosis is responsible, at least in most part, for adenosine and uridine nucleotide release from A549 cells

Structural Insights into the Inhibition of Cytosolic 5′-Nucleotidase II (cN-II) by Ribonucleoside 5′-Monophosphate Analogues

Cytosolic 5′-nucleotidase II (cN-II) regulates the intracellular nucleotide pools within the cell by catalyzing the dephosphorylation of 6-hydroxypurine nucleoside 5′-monophosphates. Beside this physiological function, high level of cN-II expression is correlated with abnormal patient outcome when treated with cytotoxic nucleoside analogues. To identify its specific role in the resistance phenomenon observed during cancer therapy, we screened a particular class of chemical compounds, namely ribonucleoside phosphonates to predict them as potential cN-II inhibitors. These compounds incorporate a chemically and enzymatically stable phosphorus-carbon linkage instead of a regular phosphoester bond. Amongst them, six compounds were predicted as better ligands than the natural substrate of cN-II, inosine 5′-monophosphate (IMP). The study of purine and pyrimidine containing analogues and the introduction of chemical modifications within the phosphonate chain has allowed us to define general rules governing the theoretical affinity of such ligands. The binding strength of these compounds was scrutinized in silico and explained by an impressive number of van der Waals contacts, highlighting the decisive role of three cN-II residues that are Phe 157, His 209 and Tyr 210. Docking predictions were confirmed by experimental measurements of the nucleotidase activity in the presence of the three best available phosphonate analogues. These compounds were shown to induce a total inhibition of the cN-II activity at 2 mM. Altogether, this study emphasizes the importance of the non-hydrolysable phosphonate bond in the design of new competitive cN-II inhibitors and the crucial hydrophobic stacking promoted by three protein residues

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production

Expression of Melk, a new protein kinase, during early mouse development.

A new gene named maternal embryonic leucine zipper kinase, Melk, has been recently identified (Heyer et al. [1997] Mol. Reprod. Dev.47:148-156). As a basis for further study of the function of the gene, we have examined the expression of Melk across a wide range of embryonic stages, from the ovulated egg and 2-cell embryo through the gastrulation and early organogenesis stages, by in situ hybridization and immunohistochemistry. Melk is expressed in a spatially and temporally specific pattern during mammalian embryogenesis. The strongest expression was detected during maturation of oocytes and preimplantation development. Given its expression pattern, Melk may play an important role during preimplantation embryonic development. Dev Dyn 1999;215:344-351. Copyright 1999 Wiley-Liss, Inc

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells' ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer