Predatory activity of Myxococcus xanthus outer-membrane vesicles and properties of their hydrolase cargo.

The deltaproteobacterium Myxococcus xanthus predates upon members of the soil microbial community by secreting digestive factors and lysing prey cells. Like other Gram-negative bacteria, M. xanthus produces outer membrane vesicles (OMVs), and we show here that M. xanthus OMVs are able to kill Escherichia coli cells. The OMVs of M. xanthus were found to contain active proteases, phosphatases, other hydrolases and secondary metabolites. Alkaline phosphatase activity was found to be almost exclusively associated with OMVs, implying that there is active targeting of phosphatases into OMVs, while other OMV components appear to be packaged passively. The kinetic properties of OMV alkaline phosphatase suggest that there may have been evolutionary adaptation of OMV enzymes to a relatively indiscriminate mode of action, consistent with a role in predation. In addition, the observed regulation of production, and fragility of OMV activity, may protect OMV-producing cells from exploitation by M. xanthus cheating genotypes and/or other competitors. Killing of E. coli by M. xanthus OMVs was enhanced by the addition of a fusogenic enzyme (glyceraldehyde-3-phosphate dehydrogenase; GAPDH), which triggers fusion of vesicles with target membranes within eukaryotic cells. This suggests that the mechanism of prey killing involves OMV fusion with the E. coli outer membrane. M. xanthus secretes GAPDH, which could potentially modulate the fusion of co-secreted OMVs with prey organisms in nature, enhancing their predatory activity.

Genome-wide analysis of longevity in nutrient-deprived Saccharomyces cerevisiae reveals importance of recycling in maintaining cell viability.

Although typically cosseted in the laboratory with constant temperatures and plentiful nutrients, microbes are frequently exposed to much more stressful conditions in their natural environments where survival and competitive fitness depend upon both growth rate when conditions are favourable and on persistence in a viable and recoverable state when they are not. In order to determine the role of genetic heterogeneity in environmental fitness we present a novel approach that combines the power of fluorescence-activated cell sorting with barcode microarray analysis and apply this to determining the importance of every gene in the Saccharomyces cerevisiae genome in a high-throughput, genome-wide fitness screen. We have grown > 6000 heterozygous mutants together and exposed them to a starvation stress before using fluorescence-activated cell sorting to identify and isolate those individual cells that have not survived the stress applied. Barcode array analysis of the sorted and total populations reveals the importance of cellular recycling mechanisms (autophagy, pexophagy and ribosome breakdown) in maintaining cell viability during starvation and provides compelling evidence for an important role for fatty acid degradation in maintaining viability. In addition, we have developed a semi-batch fermentor system that is a more realistic model of environmental fitness than either batch or chemostat culture. Barcode array analysis revealed that arginine biosynthesis was important for fitness in semi-batch culture and modelling of this regime showed that rapid emergence from lag phase led to greatly increased fitness. One hundred and twenty-five strains with deletions in unclassified proteins were identified as being over-represented in the sorted fraction, while 27 unclassified proteins caused a haploinsufficient phenotype in semi-batch culture. These methods thus provide a screen to identifying other genes and pathways that have a role in maintaining cell viability.

Life, death, and in-between: meanings and methods in microbiology.

Determination of microbial viability by the plate count method is routine in microbiology laboratories worldwide. However, limitations of the technique, particularly with respect to environmental microorganisms, are widely recognized. Many alternatives based upon viability staining have been proposed, and these are often combined with techniques such as image analysis and flow cytometry. The plethora of choices, however, adds to confusion when selecting a method. Commercial staining kits aim to simplify the performance of microbial viability determination but often still need adaptation to the specific organism of interest and/or the instruments available to the researcher. This review explores the meaning of microbial viability and offers guidance in the selection and interpretation of viability testing methods.

Multivariate data analysis methods for the interpretation of microbial flow cytometric data.

Flow cytometry is an important technique in cell biology and immunology and has been applied by many groups to the analysis of microorganisms. This has been made possible by developments in hardware that is now sensitive enough to be used routinely for analysis of microbes. However, in contrast to advances in the technology that underpin flow cytometry, there has not been concomitant progress in the software tools required to analyse, display and disseminate the data and manual analysis, of individual samples remains a limiting aspect of the technology. We present two new data sets that illustrate common applications of flow cytometry in microbiology and demonstrate the application of manual data analysis, automated visualisation (including the first description of a new piece of software we are developing to facilitate this), genetic programming, principal components analysis and artificial neural nets to these data. The data analysis methods described here are equally applicable to flow cytometric applications with other cell types.

Identification and characterization of high-flux-control genes of yeast through competition analyses in continuous cultures.

Using competition experiments in continuous cultures grown in different nutrient environments (glucose limited, ammonium limited, phosphate limited and white grape juice), we identified genes that show haploinsufficiency phenotypes (reduced growth rate when hemizygous) or haploproficiency phenotypes (increased growth rate when hemizygous). Haploproficient genes (815, 1,194, 733 and 654 in glucose-limited, ammonium-limited, phosphate-limited and white grape juice environments, respectively) frequently show that phenotype in a specific environmental context. For instance, genes encoding components of the ubiquitination pathway or the proteasome show haploproficiency in nitrogen-limited conditions where protein conservation may be beneficial. Haploinsufficiency is more likely to be observed in all environments, as is the case with genes determining polar growth of the cell. Haploproficient genes seem randomly distributed in the genome, whereas haploinsufficient genes (685, 765, 1,277 and 217 in glucose-limited, ammonium-limited, phosphate-limited and white grape juice environments, respectively) are over-represented on chromosome III. This chromosome determines a yeast's mating type, and the concentration of haploinsufficient genes there may be a mechanism to prevent its loss.

Detection of mycoplasmas in goat milk by flow cytometry.

The detection of mycoplasma in milk can be performed by either culture techniques or polymerase chain reaction (PCR) based methods. Although PCR can reduce the average diagnostic time to 5 h in comparison with the several days for the isolation of the agent, there is still a need to develop methods, which could give earlier results. For this purpose, we tested the ability of flow cytometry (FC) to detect mycoplasmas in milk samples. Milk samples inoculated with four different mycoplasmas, Mycoplasma agalactiae, Mycoplasma putrefaciens, Mycoplasma capricolum subsp. Capricolum, or Mycoplasma mycoides subsp. mycoides large-colony type, known to cause contagious agalactia in goats, were stained with the DNA stain SYBR Green I and analyzed by FC. Three goat milk samples, from which mycoplasmas have been isolated in broth medium were also analyzed. All mycoplasmas were easily distinguished from debris of milk samples, but it was not possible to distinguish between the different mycoplasma species. In our conditions, the detection limit of the technique was of the order of 10(3)-10(4) cells ml(-1). Furthermore, mycoplasmas were also distinguished from Staphylococcus aureus. FC together with SYBR Green I was able to distinguish between mycoplasma cells and debris present in milk samples and gave results in 20-30 min. This is an important first step in developing a robust, routine flow cytometric method for the detection of mycoplasmas in milk samples.

Flow cytometric method for the assessment of the minimal inhibitory concentrations of antibacterial agents to Mycoplasma agalactiae.

In this study, flow cytometry was evaluated for the determination of the minimal inhibitory concentrations (MIC) of seven antibacterial agents (enrofloxacin, ciprofloxacin, gentamicin, streptomycin, chloramphenicol, oxytetracycline, and tylosin) on Mycoplasma (M.) agalactiae. Flow cytometry was able to detect M. agalactiae inhibition from 6 h postincubation, although it seems that definitive MIC values determined by flow cytometry were only possible at 12-h postincubation. However, the results obtained by the traditional method were only obtained at 24 h, when a visible change in the medium had occurred. At 24 h, both methods gave the same result for six antibacterial agents (enrofloxacin, ciprofloxacin, gentamicin, streptomycin, chloramphenicol, and oxytetracycline); whereas flow cytometry gave slightly higher MIC for tylosin. This was attributed to the fact that the M. agalactiae growth that had occurred in the tubes containing tylosin was not enough to visibly change the color of the medium. Futhermore, flow cytometry detected that inhibitory concentrations of oxytetracycline, chloramphenicol, and tylosin as judged at 24 h were not able to inhibit the M. agalactiae growth after 48 h. MIC values of enrofloxacin and ciprofloxacin were sufficient only to maintain the total counts per milliliter throughout the time matched samples, whereas higher concentrations of theses antibacterial agents reduced the total counts per milliliter over the course of the experiment. The main advantage of the flow cytometric method is that MIC results for M. agalactiae can be obtained in a shorter time than is possible with the traditional method. The method presented makes identification of resistant populations of M. agalactiae possible and, unlike the traditional method, allows the effect of each antibacterial agent to be determined in real-time at the single-cell level.

Flow cytometric determination of the effects of antibacterial agents on Mycoplasma agalactiae, Mycoplasma putrefaciens, Mycoplasma capricolum subsp. capricolum, and Mycoplasma mycoides subsp. mycoides large colony type.

Flow cytometry together with SYBR green I and propidium iodide was used to study the effects of enrofloxacin, ciprofloxacin, gentamicin, chloramphenicol, oxytetracycline, and tylosin on four mycoplasma species. Inhibition of mycoplasma growth could be detected by as early as 3 h after the start of treatment. The strongest effect was observed with enrofloxacin- and ciprofloxacin-treated cells.

Metabolic footprinting and systems biology: the medium is the message.

One element of classical systems analysis treats a system as a black or grey box, the inner structure and behaviour of which can be analysed and modelled by varying an internal or external condition, probing it from outside and studying the effect of the variation on the external observables. The result is an understanding of the inner make-up and workings of the system. The equivalent of this in biology is to observe what a cell or system excretes under controlled conditions - the 'metabolic footprint' or exometabolome - as this is readily and accurately measurable. Here, we review the principles, experimental approaches and scientific outcomes that have been obtained with this useful and convenient strategy.

Discrimination of modes of action of antifungal substances by use of metabolic footprinting.

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.

A directed evolution strategy for optimized export of recombinant proteins reveals critical determinants for preprotein discharge.

A directed evolutionary approach is described that searches short, random peptide sequences for appendage at the secretory signal peptide-mature protein junction to seek ideal algorithms for both efficient and hyper export of recombinant proteins to the periplasm of Escherichia coli. The strategy employs simple, visual detection of positive clones using a PINK expression system that faithfully reports on export status of a mammalian hemoprotein in E. coli. With-in "sequence spaces" ranging from 1 to 13 residues, a significant but highly variable secretory fitness was scored such that the rate of secretion reciprocally correlated with the membrane-associated precursor pool of the evolved exportable hemoproteins. Three clusters of hyper, median, and hypo exporters were isolated. These had corresponding net charges of -1, 0, and +1 within the evolved sequence space, which in turn clearly correlated with the prevailing magnitude and polarity of the membrane energization states. The findings suggest that both the nature of the charged residue and the proximal sequence in the early mature region are the crucial determinants of the protonophore-dependent electrophoretic discharge of the precursor across the inner membrane of E. coli. We conclude that the directed evolutionary approach will find ready application in engineering recombinant proteins for their efficient secretion via the sec export pathway in E. coli.

Estimation of microbial viability using flow cytometry.

For microorganisms in particular, viability is a term that is difficult to define and a state consequently difficult to measure. The traditional (and gold-standard) usage equates viability and culturability (i.e., the ability to multiply), but the process of determining culturability is often too slow. Flow cytometry provides the opportunity to make rapid and quantitative measurements of dye uptake in large numbers of cells, and we can therefore exploit the flow cytometric approach to evaluate so-called viability stains and to develop protocols for more routine assessments of microbial viability. This unit is primarily commentary, but several basic protocols have been included to ensure that users have a firm basis for attempting these reasonably difficult assays on traditional flow cytometer instruments. What is clear is that each assay must be carefully validated with the particular microorganism of interest before being applied in any research, clinical, or service form.

High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.

Using flow cytometry to quantify microbial heterogeneity.

Flow cytometry is a powerful technique for the study of single cells, and thus it is of particular utility in the study of heterogeneity in microbial populations. This review seeks to highlight the role of flow cytometric analyses in studies of microbial heterogeneity, drawing wherever possible on recently published research articles. Whilst microbial heterogeneity is well documented in both natural and laboratory environments, the underlying causes are less well understood. Possible sources for the heterogeneity that is observed in microbial systems are discussed, together with the flow cytometric tools that aid its study. The role of flow cytometry in molecular biology is discussed with reference to gene reporter systems, which enable heterogeneity of gene expression to be monitored. With the recent sequencing of a variety of microbial genomes, it is anticipated that flow cytometry will have an increasing role to play in studying the effects of gene expression and mutation on heterogeneity, and in resolving the interactions of genetics and physiology.

Flow cytometric techniques for the detection of microorganisms.

Flow cytometry (FCM) is a technique, which allows one to analyse cells rapidly and individually, and permits the quantitative analysis of distributions of a property or properties in a population. It therefore offers many advantages over conventional measurements for the analysis of biological cells. Historically the technique has been widely applied for the study of mammalian cells, but its use in microbiology has been more limited; this is mainly a consequence of the smaller size of microbes, which results in the smaller optical signals that can be obtained from them. Developments in light sources and optics, together with brighter, spectrally-diverse dyes have reduced this barrier over recent years and the flow cytometer is now an essential tool in many microbiological research establishments. FCM has an increasing role to play in the detection of microbes in both industrial and clinical settings. Environmental monitoring to prevent outbreaks of human diseases such as cryptosporidiosis and Legionnaires' disease and to detect acts of biowarfare or bioterrorism are all amenable to flow cytometric study. This review seeks to highlight the role of the flow cytometer in the detection of microbial cells.