Fast sampling method for mammalian cell metabolic analyses using liquid chromatography-mass spectrometry.

Metabolomics has emerged as a powerful tool for addressing biological questions. Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used for metabolic characterization, including targeted and untargeted approaches. Despite recent innovations, a crucial aspect of this technique is the sample preparation for accurate data analyses. In this protocol, we present a robust and adaptable workflow for metabolic analyses of mammalian cells from adherent cell cultures, which is particularly suited for qualitative and quantitative central metabolite characterization by LC-MS. Each sample consists of 600,000 mammalian cells grown on cover glasses, allowing for fast and complete transfer of the cells for metabolite extraction or medium exchange, e.g., for labeling experiments. The sampling procedure includes a fast and efficient washing step in liquid flow in water, which reduces cross-contamination and matrix effects while minimizing perturbation of the metabolic steady state of the cells; it is followed by quenching cell metabolism. The latter is achieved by using a -20 °C cold methanol acetonitrile mixture acidified with formic acid, followed by freeze drying, metabolite extraction and LC-MS. The protocol requires 2 s for cell sampling until quenching, and the entire protocol takes a total of 1.5 h per sample when the provided nanoscale LC-MS method is applied.

Shigella reroutes host cell central metabolism to obtain high-flux nutrient supply for vigorous intracellular growth.

Shigella flexneri proliferate in infected human epithelial cells at exceptionally high rates. This vigorous growth has important consequences for rapid progression to life-threatening bloody diarrhea, but the underlying metabolic mechanisms remain poorly understood. Here, we used metabolomics, proteomics, and genetic experiments to determine host and Shigella metabolism during infection in a cell culture model. The data suggest that infected host cells maintain largely normal fluxes through glycolytic pathways, but the entire output of these pathways is captured by Shigella, most likely in the form of pyruvate. This striking strategy provides Shigella with an abundant favorable energy source, while preserving host cell ATP generation, energy charge maintenance, and survival, despite ongoing vigorous exploitation. Shigella uses a simple three-step pathway to metabolize pyruvate at high rates with acetate as an excreted waste product. The crucial role of this pathway for Shigella intracellular growth suggests targets for antimicrobial chemotherapy of this devastating disease.

Development of a BAC vector for integration-independent and tight regulation of transgenes in rodents via the Tet system.

The establishment of functional transgenic mouse lines is often limited by problems caused by integration site effects on the expression construct. Similarly, tetracycline (Tet) controlled transcription units most commonly used for conditional transgene expression in mice are strongly influenced by their genomic surrounding. Using bacterial artificial chromosome (BAC) technology in constitutive expression systems, it has been shown that integration site effects resulting in unwanted expression patterns can be largely eliminated. Here we describe a strategy to minimize unfavourable integration effects on conditional expression constructs based on a 75 kb genomic BAC fragment. This fragment was derived from a transgenic mouse line, termed LC-1, which carries the Tet-inducible genes luciferase and cre (Schönig et al. 2002). Animals of this mouse line have previously been shown to exhibit optimal expression properties in terms of tightness in the off state and the absolute level of induction, when mated to appropriate transactivator expressing mice. Here we report the cloning and identification of the transgenic LC-1 integration site which was subsequently inserted into a bacterial artificial chromosome. We demonstrate that this vector facilitates the efficient generation of transgenic mouse and rat lines, where the Tet-controlled expression unit is shielded from perturbations caused by the integration site.

Use of fluorescence microscopy to study intracellular signaling in bacteria.

Following the introduction of fluorescent protein tags, the application of fluorescence microscopy in microbial cell biology has advanced the field dramatically. We now understand that bacterial cells are not simple bags of enzymes but have complex internal structures, and that specific intracellular organization plays an important role in a number of processes, including signal transduction. The quantitative nature and high temporal resolution of fluorescence microscopy make it particularly useful for studies of intracellular dynamic systems, such as signaling networks. Applications of fluorescence microscopy in signaling are not limited to studying localization. Several techniques allow researchers to follow real-time dynamics of protein interactions, at steady state or upon stimulation, and therefore to investigate signal propagation, amplification, and integration in the cell. Moreover, microscopy enables investigation of single-cell gene expression kinetics, bringing such concepts as cell individuality and robustness against stochasticity of gene expression to the forefront of signaling studies.

Inducible expression of coding and inhibitory RNAs from retargetable genomic loci.

Conditional gene expression systems have developed into essential tools for the study of gene functions. However, their utility is often limited by the difficulty of identifying clonal cell lines, in which transgene control can be realized to its full potential. Here, we describe HeLa cell lines, in which we have identified-by functional analysis-genomic loci, from which the expression of transgenes can be tightly controlled via tetracycline-regulated expression. These loci can be re-targeted by recombinase-mediated cassette exchange. Upon exchange of the gene of interest, the resulting cell line exhibits the qualitative and quantitative properties of controlled transgene expression characteristic for the parent cell line. Moreover, by using an appropriate promoter, these cell lines express the tetracycline controlled transcription activator rtTA2-M2 uniformly throughout the entire cell population. The potential of this approach for functional genomics is highlighted by utilizing one of our master cell lines for the efficient microRNA-mediated knockdown of the endogenous human lamin A/C gene.

Dynamic map of protein interactions in the Escherichia coli chemotaxis pathway.

Protein-protein interactions play key roles in virtually all cellular processes, often forming complex regulatory networks. A powerful tool to study interactions in vivo is fluorescence resonance energy transfer (FRET), which is based on the distance-dependent energy transfer from an excited donor to an acceptor fluorophore. Here, we used FRET to systematically map all protein interactions in the chemotaxis signaling pathway in Escherichia coli, one of the most studied models of signal transduction, and to determine stimulation-induced changes in the pathway. Our FRET analysis identified 19 positive FRET pairs out of the 28 possible protein combinations, with 9 pairs being responsive to chemotactic stimulation. Six stimulation-dependent and five stimulation-independent interactions were direct, whereas other interactions were apparently mediated by scaffolding proteins. Characterization of stimulation-induced responses revealed an additional regulation through activity dependence of interactions involving the adaptation enzyme CheB, and showed complex rearrangement of chemosensory receptors. Our study illustrates how FRET can be efficiently employed to study dynamic protein networks in vivo.

Protein exchange dynamics at chemoreceptor clusters in Escherichia coli.

Signal processing in bacterial chemotaxis relies on large sensory complexes consisting of thousands of protein molecules. These clusters create a scaffold that increases the efficiency of pathway reactions and amplifies and integrates chemotactic signals. The cluster core in Escherichia coli comprises a ternary complex composed of receptors, kinase CheA, and adaptor protein CheW. All other chemotaxis proteins localize to clusters by binding either directly to receptors or to CheA. Here, we used fluorescence recovery after photobleaching (FRAP) to investigate the turnover of chemotaxis proteins at the cluster and their mobility in the cytoplasm. We found that cluster exchange kinetics were protein-specific and took place on several characteristic time scales that correspond to excitation, adaptation, and cell division, respectively. We further applied analytical and numerical data fitting to analyze intracellular protein diffusion and to estimate the rate constants of cluster equilibration in vivo. Our results indicate that the rates of protein turnover at the cluster have evolved to ensure optimal performance of the chemotaxis pathway.

Positioning of chemosensory clusters in E. coli and its relation to cell division.

Chemotaxis receptors and associated signalling proteins in Escherichia coli form clusters that consist of thousands of molecules and are the largest native protein complexes described to date in bacteria. Clusters are located at the cell poles and laterally along the cell body, and play an important role in signal transduction. Much work has been done to study the structure and function of receptor clusters, but the significance of their positioning and the underlying mechanisms are not understood. Here, we used fluorescence imaging to study cluster distribution and follow cluster dynamics during cell growth. Our data show that lateral clusters localise to specific periodic positions along the cell body, which mark future division sites and are involved in the localisation of the replication machinery. The chemoreceptor cluster positioning is thus intricately related to the overall structure and division of an E. coli cell.

Spatial organization of the bacterial chemotaxis system.

Sensory complexes in bacterial chemotaxis are organized in large clusters, building complex signal-processing machinery. Interactions among chemoreceptors are the main determinant of cluster formation and create an allosteric network that is able to integrate and amplify stimuli, before transmitting the signal to downstream proteins. Association of the other proteins with the receptor cluster creates a signalling scaffold, which enhances the efficiency and specificity of the pathway. Clusters localize to specific locations inside the cell, perhaps to ensure their proper distribution during cell division. Clustering is conserved among all studied prokaryotic chemotaxis systems and exemplifies a growing number of bacterial pathways with a reported sub-cellular spatial organization. Moreover, because allostery provides a simple mechanism to achieve very high response sensitivity, it is probable that clustering-based signal amplification is not limited to bacterial chemotaxis but also exists in other prokaryotic and eukaryotic pathways.

Determinants of chemoreceptor cluster formation in Escherichia coli.

Chemotactic stimuli in bacteria are sensed by large sensory complexes, or receptor clusters, that consist of tens of thousands of proteins. Receptor clusters appear to play a key role in signal processing, but their structure remains poorly understood. Here we used fluorescent protein fusions to study in vivo formation of the cluster core, which consists of receptors, a kinase CheA and an assisting protein CheW. We show that receptors aggregate through their cytoplasmic domains even in the absence of other chemotaxis proteins. Clustering is further enhanced by the binding of CheW. Surprisingly, we observed that some fragments of CheA bind receptor clusters well in the absence of CheW, although the latter does assist the binding of full-length CheA. The resulting mode of receptor cluster formation is consistent with an experimentally observed flexible stoichiometry of chemosensory complexes and with assumptions of recently proposed computer models of signal processing in chemotaxis.