Bacterial dormancy and culturability: the role of autocrine growth factors.
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Bacteria↗
Biomedical subjects
D B Kell
Publications and source records attributed to D B Kell.
On-line, real-time measurements of cellular biomass using dielectric spectroscopy.
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Animals↗
Culturability of Mycobacterium tuberculosis cells isolated from murine macrophages: a bacterial growth factor promotes recovery.
Very little is known about the culturability and viability of mycobacteria following their phagocytosis by macrophages. We therefore studied populations of the avirulent 'Academia' strain of Mycobacterium tuberculosis isolated from murine peritoneal macrophage lysates several days post-infection in vivo. The resulting bacterial suspensions contained a range of morphological types including rods, ovoid forms and coccoid forms. Bacterial viability measured using the MPN method (dilution to extinction in liquid medium) was often much higher than that measured by CFU (plating on solid medium). Viability in the MPN assay was further enhanced when the Micrococcus luteus protein, Rpf, was incorporated into the liquid culture medium at picomolar concentrations. Rpf is an example of a family of autocrine growth factors found throughout the high G+C cohort of Gram-positive bacteria including M. tuberculosis. M. tuberculosis cells obtained from macrophages had altered surface properties, as compared with bacteria grown in vitro. This was indicated by loss of the ability to adsorb bacteriophage DS6A, a reduced tendency to form clumps, acquisition of ethidium bromide stainability following heat treatment, and loss of Rpf-mediated resuscitation following freezing and thawing. These results indicate that a proportion of 'unculturable' M. tuberculosis cells obtained from macrophages is either injured or dormant and that these cells may be recovered or resuscitated using Rpf in liquid medium.
Animals↗
Efficient improvement of silage additives by using genetic algorithms.
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e. , no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a "fitness" value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a "cost" element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.
Algorithms↗
Rapid analysis of the expression of heterologous proteins in Escherichia coli using pyrolysis mass spectrometry and Fourier transform infrared spectroscopy with chemometrics: application to alpha 2-interferon production.
Cell pastes and supernatant Escherichia coli samples, taken from an industrial bioprocess overproducing recombinant alpha 2 IFN were analysed using pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR). PyMS and FT-IR are physico-chemical methods which measure predominantly the bond strengths of molecules and the vibrations of bonds within functional groups, respectively. They therefore give quantitative information about the total biochemical composition of the bioprocess sample. The interpretation of these hyperspectral data, in terms of the quantity of alpha 2 IFN in the cell pastes and supernatant samples was possible only after the application of the 'supervised learning' methods of artificial neural networks (ANNs) and partial least squares (PLS) regression. Both PyMS and FT-IR are novel, rapid and economical methods for the screening and the quantitative analysis of complex biological bioprocess over producing recombinant proteins. Models established using either spectral data set had a similarly satisfactory predictive ability. This shows that whole-reaction mixture spectral methods, which measure all molecules simultaneously, do contain enough information to allow their quantification when the entire spectra are used as the inputs to methods based on supervised learning. Moreover, this is the first study where FT-IR in the mid-IR range has been used to quantify the expression of a heterologous protein directly from fermentation broths and the first study to compare the abilities of PyMS and FT-IR for the quantitative analyses of an industrial bioprocess.
Chemistry Techniques, Analytical↗
Revolutionary ideas come round again.
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Biochemistry↗
Variable selection and multivariate methods for the identification of microorganisms by flow cytometry.
BACKGROUND: When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles. MATERIALS AND METHODS: We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification. RESULTS: We show that while all cocktails and all methods gave good accuracy of predictions (>94%), careful selection of both the stains and the analysis method could improve this figure (to > 99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model. CONCLUSIONS: Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis.
Bacillus subtilis↗
Stimulation of the multiplication of Micrococcus luteus by an autocrine growth factor.
Viable cells of Micrococcus luteus secrete a proteineous growth factor (Rpf) which promotes the resuscitation of dormant, nongrowing cells to yield normal, colony-forming bacteria. When washed M. luteus cells were used as an inoculum, there was a pronounced influence of Rpf on the true lag phase and cell growth on lactate minimal medium. In the absence of Rpf, there was no increase in colony-forming units for up to 10 days. When the inoculum contained less than 10(5) cells ml-1, macroscopically observable M. luteus growth was not obtained in succinate minimal medium unless Rpf was added. Incubation of M. luteus in the stationary phase for 100 h resulted in a failure of the cells to grow in lactate minimal medium from inocula of small size although the viability of these cells was close to 100% as estimated using agar plates made from lactate minimal medium or rich medium. The underestimation of viable cells by the most-probable-number (MPN) method in comparison with colony-forming units was equivalent to the requirement that at least 10(5) cells grown on succinate medium, 10(3) cells from old stationary phase, or approximately 10-500 washed cells are required per millilitre of inoculum for growth to lead to visible turbidity. The addition of Rpf in the MPN dilutions led to an increase of the viable cell numbers estimated to approximately the same levels as those determined by colony-forming units. Thus, a basic principle of microbiology - "one cell-one culture" - may not be applicable in some circumstances in which the metabolic activity of "starter" cells is not sufficient to produce enough autocrine growth factor to support cell multiplication.
Bacterial Proteins↗
Genetic programming as an analytical tool for non-linear dielectric spectroscopy.
By modelling the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field using supervised multivariate analysis methods, Non-Linear Dielectric Spectroscopy (NLDS) has previously been shown to produce quantitative information that is indicative of the metabolic state of various organisms. The use of Genetic Programming (GP) for the multivariate analysis of NLDS data recorded from yeast fermentations is discussed, and GPs are compared with previous results using Partial Least Squares (PLS) and Artificial Neural Nets (NN). GP considerably outperforms these methods, both in terms of the precision of the predictions and their interpretability.
Computational Biology↗
A bacterial cytokine.
Viable cells of Micrococcus luteus secrete a factor, which promotes the resuscitation and growth of dormant, nongrowing cells of the same organism. The resuscitation-promoting factor (Rpf) is a protein, which has been purified to homogeneity. In picomolar concentrations, it increases the viable cell count of dormant M. luteus cultures at least 100-fold and can also stimulate the growth of viable cells. Rpf also stimulates the growth of several other high G+C Gram-positive organisms, including Mycobacterium avium, Mycobacterium bovis (BCG), Mycobacterium kansasii, Mycobacterium smegmatis, and Mycobacterium tuberculosis. Similar genes are widely distributed among high G+C Gram-positive bacteria; genome sequencing has uncovered examples in Mycobacterium leprae and Mb. tuberculosis and others have been detected by hybridization in Mb. smegmatis, Corynebacterium glutamicum, and Streptomyces spp. The mycobacterial gene products may provide different targets for the detection and control of these important pathogens. This report is thus a description of a proteinaceous autocrine or paracrine bacterial growth factor or cytokine.
Amino Acid Sequence↗
Quantification of microbial productivity via multi-angle light scattering and supervised learning.
This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such suspensions.
Biotechnology↗
The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species.
Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called 'black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces 'rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.
Artificial Intelligence↗
Systematic functional analysis of the yeast genome.
The genome sequence of the yeast Saccharomyces cerevisiae has provided the first complete inventory of the working parts of a eukaryotic cell. The challenge is now to discover what each of the gene products does and how they interact in a living yeast cell. Systematic and comprehensive approaches to the elucidation of yeast gene function are discussed and the prospects for the functional genomics of eukaryotic organisms evaluated.
Genetic Techniques↗
Viability and activity in readily culturable bacteria: a review and discussion of the practical issues.
In microbiology the terms 'viability' and 'culturability' are often equated. However, in recent years the apparently self-contradictory expression 'viable-but-nonculturable' ('VBNC') has been applied to cells with various and often poorly defined physiological attributes but which, nonetheless, could not be cultured by methods normally appropriate to the organism concerned. These attributes include apparent cell integrity, the possession of some form of measurable cellular activity and the apparent capacity to regain culturability. We review the evidence relating to putative VBNC cells and stress our view that most of the reports claiming a return to culturability have failed to exclude the regrowth of a limited number of cells which had never lost culturability. We argue that failure to differentiate clearly between use of the terms 'viability' and 'culturability' in an operational versus a conceptual sense is fuelling the current debate, and conclude with a number of proposals that are designed to help clarify the major issues involved. In particular, we suggest an alternative operational terminology that replaces 'VBNC' with expressions that are internally consistent.
Animals↗
On resuscitation from the dormant state of Micrococcus luteus.
It has been found previously that a significant number of Micrococcus luteus cells starved in a prolonged stationary phase (up to 2 months) and then held on the bench at room temperature without agitation for periods of up to a further 2-7 months can be resuscitated in liquid media which contained (statistically) no initially-viable (colony-forming) cells but which were fortified with sterile supernatant from the late logarithmic phase of batch growth. Here it was found that such resuscitation can be done only within a defined time period after taking the first sample from such cultures, necessarily involving agitation of the cells. The duration of this period depends on the age of the starved culture: cells kept on the bench for 3 months possess a 2 month period of resuscitability while cells starved for 6 months can be resuscitated only within 10 days after the beginning of sampling. It is suggested that the input of oxygen to the starved cultures while they are agitated may exert a negative influence on the cells, since cultures stored in anaerobic conditions (under nitrogen) had a more prolonged 'survival' time. The cells which experienced between 10 and 60 days of starvation on the bench could be resuscitated, although the number of resuscitable cells depended strongly on the concentration of yeast extract in the resuscitation medium. This concentration for cells stored on the bench for more than 2 months was 0.05% while '1-month-old' cells displayed a maximum resuscitability in the presence of 0.01% of yeast extract. Application of the fluorescent probe propidium iodide revealed the formation of cells with a damaged permeability barrier if resuscitation was performed by using concentrations of yeast extract of 0.1% and above. Thus the successful resuscitation of bacterial cultures under laboratory conditions may need rather strictly defined parameters if it is to be successfully performed for the majority of cells in a population.
Adaptation, Physiological↗
Discrimination between methicillin-resistant and methicillin-susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neural networks.
Curie-point pyrolysis mass spectra were obtained from 15 methicillin-resistant and 22 methicillin-susceptible Staphylococcus aureus strains. Cluster analysis showed that the major source of variation between the pyrolysis mass spectra resulted from the phage group of the bacteria, not their resistance or susceptibility to methicillin. By contrast, artificial neural networks could be trained to recognize those aspects of the pyrolysis mass spectra that differentiated methicillin-resistant from methicillin-sensitive strains. The trained neural network could then use pyrolysis mass spectral data to assess whether an unknown strain was resistant to methicillin. These results give the first demonstration that the combination of pyrolysis mass spectrometry with neural networks can provide a very rapid and accurate antibiotic susceptibility testing technique.
Cluster Analysis↗
Fluorescent brighteners: novel stains for the flow cytometric analysis of microorganisms.
Flow cytometry is a rapid method for measuring the optical properties of individual cells. The technique has found great utility in the study of mammalian cells, but microbiological applications have been more limited. We here show that UV-excited fluorescent whitening agents, in particular Tinopal CBS-X, are effective stains for both vegetative microbial cells and for spores of Gram-positive bacteria. Pretreatment of samples with ethanol speeds the staining process. Under favourable conditions, Tinopal CBS-X may be used to discriminate among organisms, a fact that may be useful when screening for a target microorganism against a high biological background.
Bacillus subtilis↗
If you've got it, flaunt it--rapid screening for microbial biocatalysts.
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Automation↗