Flow cytometry sorting of viable bacteria and yeasts according to beta-galactosidase activity.

We describe a novel method for quantitative measurement of beta-galactosidase (beta-gal) levels in bacteria and yeasts by using flow cytometry, a method which allows viable microbial cells to be sorted on the basis of the expressed activity and to be recultivated. The method is based on encapsulating single cells in agarose microbeads 20 to 30 microns in diameter and analyzing the beta-gal activity of the colonies that develop (containing several hundred cells) by using the fluorogenic substrate fluorescein-di-beta-D-galactopyranoside (FDG). Three strains of Escherichia coli, containing different levels of beta-gal, served as a model system. A high degree of correlation was found between the average fluorescence measured per bead and the level of the enzyme in extracts of the respective strain. Although the use of FDG necessitates cell permeabilization, conditions were found under which a small part of each colony remained viable, yet most of the enzyme was exposed to the substrate. This allowed sorting of microcolonies and plating with close to 100% efficiency. The potential of the technique was demonstrated by selecting beta-gal-positive cells from an artificial mixture of beta-gal-positive and beta-gal-negative E. coli strains.     

Network-Free Inference of Knockout Effects in Yeast

Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels. We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data, and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating. Differing from previous approaches, our prediction approach does not depend on physical network information; the latter is used only for the annotation task. Consequently, it is both more efficient and of wider applicability than previous methods. We evaluate our approach using a large scale collection of gene knockout experiments in yeast, comparing it to the state-of-the-art SPINE algorithm. In cross validation tests, our algorithm exhibits superior prediction accuracy, while at the same time increasing the coverage by over 25-fold. Significant coverage gains are obtained also in the annotation of the physical network.     

Flow cytometry sorting of viable bacteria and yeasts according to beta-galactosidase activity

We describe a novel method for quantitative measurement of beta-galactosidase (beta-gal) levels in bacteria and yeasts by using flow cytometry, a method which allows viable microbial cells to be sorted on the basis of the expressed activity and to be recultivated. The method is based on encapsulating single cells in agarose microbeads 20 to 30 microns in diameter and analyzing the beta-gal activity of the colonies that develop (containing several hundred cells) by using the fluorogenic substrate fluorescein-di-beta-D-galactopyranoside (FDG). Three strains of Escherichia coli, containing different levels of beta-gal, served as a model system. A high degree of correlation was found between the average fluorescence measured per bead and the level of the enzyme in extracts of the respective strain. Although the use of FDG necessitates cell permeabilization, conditions were found under which a small part of each colony remained viable, yet most of the enzyme was exposed to the substrate. This allowed sorting of microcolonies and plating with close to 100% efficiency. The potential of the technique was demonstrated by selecting beta-gal-positive cells from an artificial mixture of beta-gal-positive and beta-gal-negative E. coli strains.     

Promotion and inhibition of vesicle fusion by polylysine

Polylysine induced rapid aggregation of large unilamellar vesicles composed of phosphatidylcholine-cardiolipin (1:1 molar ratio) but not their fusion. Application of the terbium-dipicolinic acid fusion assay showed that addition of polylysine at nanomolar concentrations enabled a significant lowering of the Ca2+ threshold concentration for vesicle fusion from 9 to 1 mM. Analysis of the kinetics of fusion with a mass-action kinetic model showed that polylysine enhanced significantly the rate of aggregation but affected only slightly the rate of fusion per se. Maximal enhancement of overall fusion rates occurred at a charge ratio (polylysine/cardiolipin) of about 0.5. At larger polylysine concentrations, e.g., at charge ratios greater than 3, polylysine inhibited vesicle fusion.     

Identification of Tumor Antigens in the HLA Peptidome of Patient-derived Xenograft Tumors in Mouse

1. Download : Download high-res image (147KB)
2. Download : Download full-size image

Highlights

• •Sufficient tumor tissues are often unavailable large HLA peptidome discovery.
• •Using patient derived xenograft (PDX) tumors can overcome this limitation.
• •The large PDX HLA peptidomes expand significantly those of the original biopsies.
• •The HLA peptidomes of the PDX tumors included many tumor antigens.

     

Clostridium botulinum Strain Af84 Contains Three Neurotoxin Gene Clusters: Bont/A2, bont/F4 and bont/F5

Sanger and shotgun sequencing of Clostridium botulinum strain Af84 type Af and its botulinum neurotoxin gene (bont) clusters identified the presence of three bont gene clusters rather than the expected two. The three toxin gene clusters consisted of bont subtypes A2, F4 and F5. The bont/A2 and bont/F4 gene clusters were located within the chromosome (the latter in a novel location), while the bont/F5 toxin gene cluster was located within a large 246 kb plasmid. These findings are the first identification of a C. botulinum strain that contains three botulinum neurotoxin gene clusters.     

Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH4R Antagonism

The human histamine H₄ receptor (hH₄R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH₄R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH₄R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH₄R bioactivity. An application of the prediction model on external test set composed of more than 160 hH₄R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H₄R antagonists'' candidates. Next, a series of 3D models of hH₄R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH₄R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.