Further insights into the tRNA modification process controlled by proteins MnmE and GidA of Escherichia coli

In Escherichia coli, proteins GidA and MnmE are involved in the addition of the carboxymethylaminomethyl (cmnm) group onto uridine 34 (U34) of tRNAs decoding two-family box triplets. However, their precise role in the modification reaction remains undetermined. Here, we show that GidA is an FAD-binding protein and that mutagenesis of the N-terminal dinucleotide-binding motif of GidA, impairs capability of this protein to bind FAD and modify tRNA, resulting in defective cell growth. Thus, GidA may catalyse an FAD-dependent reaction that is required for production of cmnmU34. We also show that GidA and MnmE have identical cell location and that both proteins physically interact. Gel filtration and native PAGE experiments indicate that GidA, like MnmE, dimerizes and that GidA and MnmE directly assemble in an α2β2 heterotetrameric complex. Interestingly, high-performance liquid chromatography (HPLC) analysis shows that identical levels of the same undermodified form of U34 are present in tRNA hydrolysates from loss-of-function gidA and mnmE mutants. Moreover, these mutants exhibit similar phenotypic traits. Altogether, these results do not support previous proposals that activity of MnmE precedes that of GidA; rather, our data suggest that MnmE and GidA form a functional complex in which both proteins are interdependent

Behavior of a Metabolic Cycling Population at the Single Cell Level as Visualized by Fluorescent Gene Expression Reporters

BACKGROUND: During continuous growth in specific chemostat cultures, budding yeast undergo robust oscillations in oxygen consumption that are accompanied by highly periodic changes in transcript abundance of a majority of genes, in a phenomenon called the Yeast Metabolic Cycle (YMC). This study uses fluorescent reporters of genes specific to different YMC phases in order to visualize this phenomenon and understand the temporal regulation of gene expression at the level of individual cells within the cycling population. METHODOLOGY: Fluorescent gene expression reporters for different phases of the YMC were constructed and stably integrated into the yeast genome. Subsequently, these reporter-expressing yeast were used to visualize YMC dynamics at the individual cell level in cultures grown in a chemostat or in a microfluidics platform under varying glucose concentrations, using fluorescence microscopy and quantitative Western blots. CONCLUSIONS: The behavior of single cells within a metabolic cycling population was visualized using phase-specific fluorescent reporters. The reporters largely recapitulated genome-specified mRNA expression profiles. A significant fraction of the cell population appeared to exhibit basal expression of the reporters, supporting the hypothesis that there are at least two distinct subpopulations of cells within the cycling population. Although approximately half of the cycling population initiated cell division in each permissive window of the YMC, metabolic synchrony of the population was maintained. Using a microfluidics platform we observed that low glucose concentrations appear to be necessary for metabolic cycling. Lastly, we propose that there is a temporal window in the oxidative growth phase of the YMC where the cycling population segregates into at least two subpopulations, one which will enter the cell cycle and one which does not

Free-Space distribution of entanglement and single photons over 144 km

Quantum Entanglement is the essence of quantum physics and inspires fundamental questions about the principles of nature. Moreover it is also the basis for emerging technologies of quantum information processing such as quantum cryptography, quantum teleportation and quantum computation. Bell's discovery, that correlations measured on entangled quantum systems are at variance with a local realistic picture led to a flurry of experiments confirming the quantum predictions. However, it is still experimentally undecided whether quantum entanglement can survive global distances, as predicted by quantum theory. Here we report the violation of the Clauser-Horne-Shimony-Holt (CHSH) inequality measured by two observers separated by 144 km between the Canary Islands of La Palma and Tenerife via an optical free-space link using the Optical Ground Station (OGS) of the European Space Agency (ESA). Furthermore we used the entangled pairs to generate a quantum cryptographic key under experimental conditions and constraints characteristic for a Space-to-ground experiment. The distance in our experiment exceeds all previous free-space experiments by more than one order of magnitude and exploits the limit for ground-based free-space communication; significantly longer distances can only be reached using air- or space-based platforms. The range achieved thereby demonstrates the feasibility of quantum communication in space, involving satellites or the International Space Station (ISS).Comment: 10 pages including 2 figures and 1 table, Corrected typo

Leptin Does Not Directly Affect CNS Serotonin Neurons to Influence Appetite

Serotonin (5-HT) and leptin play important roles in the modulation of energy balance. Here we investigated mechanisms by which leptin might interact with CNS 5-HT pathways to influence appetite. Although some leptin receptor (LepRb) neurons lie close to 5-HT neurons in the dorsal raphe (DR), 5-HT neurons do not express LepRb. Indeed, while leptin hyperpolarizes some non-5-HT DR neurons, leptin does not alter the activity of DR 5-HT neurons. Furthermore, 5-HT depletion does not impair the anorectic effects of leptin. The serotonin transporter-cre allele (Sert(cre)) is expressed in 5-HT (and developmentally in some non-5-HT) neurons. While Sert(cre) promotes LepRb excision in a few LepRb neurons in the hypothalamus, it is not active in DR LepRb neurons, and neuron-specific Sert(cre)-mediated LepRb inactivation in mice does not alter body weight or adiposity. Thus, leptin does not directly influence 5-HT neurons and does not meaningfully modulate important appetite-related determinants via 5-HT neuron function

Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential