Estimation of Staphylococcus aureus Growth Parameters from Turbidity Data: Characterization of Strain Variation and Comparison of Methods

Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17°C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.     

Estimation of temperature dependent growth rate and lag time of Listeria monocytogenes by optical density measurements.

An automated turbidimetric system, Bioscreen C, was used to monitor growth of ten strains of Listeria monocytogenes at different temperatures. Several methods for estimation of maximum specific growth rate (mu(max)) and lag time (lag) from turbidimetric data were compared to values estimated from viable count data. By using a calibration factor, reliable estimations of mu(max) could be obtained from turbidimetric measurements. On the other hand, accurate estimations of lag required some viable count data.     

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.     

Estimation of Mycobacterium avium subsp. paratuberculosis growth parameters: strain characterization and comparison of methods

The growth rate of Mycobacterium avium subsp. paratuberculosis was assessed by different methods in 7H9 medium supplemented with OADC (oleic acid, albumin, dextrose, catalase), Tween 80, and mycobactin J. Generation times and maximum specific growth rates were determined by wet weight, turbidometric measurement, viable count, and quantitative PCR (ParaTB-Kuanti; F57 gene) for 8 M. avium subsp. paratuberculosis strains (K10, 2E, 316F, 81, 445, 764, 22G, and OVICAP 49). Strain-to-strain differences were observed in growth curves and calculated parameters. The quantification methods gave different results for each strain at specific time points. Generation times ranged from an average of 1.4 days for viable count and qPCR to approximately 10 days for wet weight and turbidometry. The wet-weight, turbidometry, and ParaTB-Kuanti qPCR methods correlated best with each other. Generally, viability has been assessed by viable count as a reference method; however, due to M. avium subsp. paratuberculosis clumping problems and the presence of noncultivable M. avium subsp. paratuberculosis cells, we conclude that qPCR of a single-copy gene may be used reliably for rapid estimation of M. avium subsp. paratuberculosis bacterial numbers in a sample.     

Variation within and between cyanobacterial species and strains affects competition: Implications for phytoplankton modelling

Cyanobacteria Microcystis aeruginosa and Cylindrospermopsis raciborskii are two harmful species which co-occur and successively dominate in freshwaters globally. Within-species strain variability affects cyanobacterial population responses to environmental conditions, and it is unclear which species/strain would dominate under different environmental conditions. This study applied a Monte Carlo approach to a phytoplankton dynamic growth model to identify how growth variability of multiple strains of these two species affects their competition.Pairwise competition between four M. aeruginosa and eight C. raciborskii strains was simulated using a deterministic model, parameterized with laboratory measurements of growth and light attenuation for all strains, and run at two temperatures and light intensities. 17 000 runs were simulated for each pair using a statistical distribution with Monte Carlo approach.The model results showed that cyanobacterial competition was highly variable, depending on strains present, light and temperature conditions. There was no absolute ‘winner’ under all conditions as there were always strains predicted to coexist with the dominant strains, which were M. aeruginosa strains at 20 °C and C. raciborskii strains at 28 °C. The uncertainty in prediction of species competition outcomes was due to the substantial variability of growth responses within and between strains. Overall, this study demonstrates that within-species strain variability has a potentially large effect on cyanobacterial population dynamics, and therefore this variability may substantially reduce confidence in predicting outcomes of phytoplankton competition in deterministic models, that are based on only one set of parameters for each species or strain.     

Photooxidation of cyanobacteria in natural conditions.

Photodynamic effects were demonstrated and assayed under field conditions in a number of different laboratory strains and pond isolates of cyanobacteria; parameters assayed for resistance to photooxidation were viable count, turbidity of the cyanobacterial suspension, and protein and pigment contents. The effects of density, colonial structure, and internal gas vacuoles on the lethal outcome were investigated. The stability and formation of superoxide dismutase under photooxidative conditions in the field and laboratory were studied in the different strains. An isolate of Microcystis from blooms in ponds exhibited extremely high resistance to photooxidation, which was abolished by exposure to chloramphenicol.     

A model for analyzing growth kinetics of a slowly growing Mycobacterium sp.

This report describes a simple method for quantifying viable mycobacteria and for determining generation time. We used statistical models and computer analysis of growth curves generated for the slowly growing mycobacterium Mycobacterium paratuberculosis under controlled conditions to derive a mathematical formula relating the dependent variable, growth, to the independent variables, log10 number of organisms in the inoculum (inoculum size) and incubation time. Growth was measured by a radiometric method which detects 14CO2 release during metabolism of a 14C-labeled substrate. The radiometric method allowed for early detection of growth and detected as few as three viable bacteria. The coefficient of variation between culture vials inoculated with the same number of M. paratuberculosis was 0.083. Radiometric measurements were highly correlated to spectrophotometric and plate count methods for measuring growth (r = 0.962 and 0.992, respectively). The proportion of the total variability explained by the model in a goodness of fit test was 0.9994. Application of the model to broth cultures provided accurate estimates of the number of M. paratuberculosis (standard error = 0.21, log10 scale) and the growth rate (coefficient of variation, 0.03). Generation time was observed to be dependent upon the number of organisms in the inoculum. The model accurately described all phases of growth of M. paratuberculosis and can likely be applied to other slowly growing microorganisms.     

A model for analyzing growth kinetics of a slowly growing Mycobacterium sp

This report describes a simple method for quantifying viable mycobacteria and for determining generation time. We used statistical models and computer analysis of growth curves generated for the slowly growing mycobacterium Mycobacterium paratuberculosis under controlled conditions to derive a mathematical formula relating the dependent variable, growth, to the independent variables, log10 number of organisms in the inoculum (inoculum size) and incubation time. Growth was measured by a radiometric method which detects 14CO2 release during metabolism of a 14C-labeled substrate. The radiometric method allowed for early detection of growth and detected as few as three viable bacteria. The coefficient of variation between culture vials inoculated with the same number of M. paratuberculosis was 0.083. Radiometric measurements were highly correlated to spectrophotometric and plate count methods for measuring growth (r = 0.962 and 0.992, respectively). The proportion of the total variability explained by the model in a goodness of fit test was 0.9994. Application of the model to broth cultures provided accurate estimates of the number of M. paratuberculosis (standard error = 0.21, log10 scale) and the growth rate (coefficient of variation, 0.03). Generation time was observed to be dependent upon the number of organisms in the inoculum. The model accurately described all phases of growth of M. paratuberculosis and can likely be applied to other slowly growing microorganisms.     

A Markov model approach shows a large variation in the length of S phase in MCF-7 breast cancer cells.

BACKGROUND: The potential doubling time of a tumor has been suggested to be a measurement of tumor aggressiveness; therefore, it is of interest to find reliable methods to estimate this time. Because of variability in length of the various cell cycle phases, stochastic modeling of the cell cycle might be a suitable approach. METHODS: The relative movement curve and the DNA synthesis time were estimated by using local polynomial regression methods. Further, the rate of nucleotide incorporation was estimated by using a Markov pure birth process with one absorbing state to model the progression of the DNA distribution through S phase. RESULTS: An estimate of the DNA synthesis time, with confidence intervals, was obtained from the relative movement curve. The Markov approach provided an estimate of the distribution of the time to complete S phase given the initial distribution. Using the Markov approach we also made an estimate of the mean number of active replicons during S phase. CONCLUSIONS: A Markov pure birth process has shown to be useful to model the progression of cells through S phase and to increase knowledge about the variability in the length of S phase and a large variation is shown.     

Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.     

食源性致病菌菌株变异性在微生物风险评估中的研究进展

菌株变异性是影响食源性致病菌风险评估结果准确性的一个重要因素,它普遍存在于单核细胞增生李斯特氏菌、沙门氏菌等各种食源性致病菌中。菌株变异性是菌株之间的固有差异,不能通过改变试验方法或改善试验方案消除。本文针对近年来菌株变异性的研究内容,基于菌株变异性对风险评估结果的影响,从食品链中变异性的来源、食源性致病菌表型变异性以及整合菌株生长、失活变异性到预测微生物模型中的方法三个方面进行综述,并指出目前菌株变异性研究中的不足,提出深入研究菌株变异性机制,扩展不同来源变异性的比较,进一步整合基因表达、蛋白质和细胞代谢等菌株变异性于预测模型中的建议。     

Estimating negative binomial parameters from occurrence data with detection times

The negative binomial distribution is a common model for the analysis of count data in biology and ecology. In many applications, we may not observe the complete frequency count in a quadrat but only that a species occurred in the quadrat. If only occurrence data are available then the two parameters of the negative binomial distribution, the aggregation index and the mean, are not identifiable. This can be overcome by data augmentation or through modeling the dependence between quadrat occupancies. Here, we propose to record the (first) detection time while collecting occurrence data in a quadrat. We show that under what we call proportionate sampling, where the time to survey a region is proportional to the area of the region, that both negative binomial parameters are estimable. When the mean parameter is larger than two, our proposed approach is more efficient than the data augmentation method developed by Solow and Smith ( 2010 , Am. Nat. 176 , 96–98), and in general is cheaper to conduct. We also investigate the effect of misidentification when collecting negative binomially distributed data, and conclude that, in general, the effect can be simply adjusted for provided that the mean and variance of misidentification probabilities are known. The results are demonstrated in a simulation study and illustrated in several real examples.     

Development of a dynamic continuous-discrete-continuous model describing the lag phase of individual bacterial cells

AIMS: A previous model for adaptation and growth of individual bacterial cells was not dynamic in the lag phase, and could not be used to perform simulations of growth under non-isothermal conditions. The aim of the present study was to advance this model by adding a continuous adaptation step, prior to the discrete step, to form a continuous-discrete-continuous (CDC) model. METHODS AND RESULTS: The revised model uses four parameters: N(0), initial population; N(max), maximum population; p0, mean initial individual cell physiological state; SD(p0), standard deviation of the distribution of individual physiological states. A truncated normal distribution was used to generate tables of distributions to allow fitting of the CDC model to viable count data for Listeria monocytogenes grown at 5 degrees C to 35 degrees C. The p0 values increased with increasing SD(p0) and were, on average, greater than the corresponding population physiological states (h0); p0 and h0 were equivalent for individual cells. CONCLUSION: The CDC model has improved the ability to simulate the behaviour of individual bacterial cells by using a physiological state parameter and a distribution function to handle inter-cell variability. The stages of development of this model indicate the importance of physiological state parameters over the population lag concept, and provide a potential approach for making growth models more mechanistic by incorporating actual physiological events. SIGNIFICANCE AND IMPACT OF THE STUDY: Individual cell behaviour is important in modelling bacterial growth in foods. The CDC model provides a means of improving existing growth models, and increases the value of mathematical modelling to the food industry.     

Analysis of PCB-degrading bacteria : physiological aspects

Several aerobic co-cultures capable of co-metabolizing polychlorinated biphenyls (PCBs) were acquired by cultivation on biphenyls (BP). The source of micro-organisms was PCB-contaminated soil taken from various sites in the Czech Republic. Several bacterial strains (Gram-negative rods) were isolated, and their capacity to degrade Delor 103 (a PCB mixture containing di- to hexachlorobiphenyls) was analysed. This study was focused on co-culture 319 and isolate 2. The growth parameters of both those cultures were studied on BP ; for isolate number 2 the specific growth rate μ = 0·122 (h⁻¹) was calculated. The degradation of the individual congeners was estimated and resulted in more than 50% of the degradation of nearly all congeners during a 2-week experiment. Toxicity of Delor 103 on the vitality of the cells was followed by using viable plate count. The viability of the tested strain was preserved in the 100 times higher Delor 103 concentration compared with conditions in degradation experiments.     

Storage of brewing yeasts by liquid nitrogen refrigeration

Many yeast strains are difficult to maintain in culture in a stable state, and long-term preservation by lyophilization, which has proved useful for other fungi, has given poor results with brewing yeasts. As an alternative to continuous subculture, which maximizes strain variability, various methods of cryogenic storage were investigated. Yeast strains were frozen with or without cryoprotectants (such as glycerol or inositol) and stored at -196 C. Recovery after warming was estimated from plate counts, and survivors were screened to detect changes in the frequency of morphological types, respiratory-deficient mutants, and glycerol-sensitive mutants. Strains varied in their sensitivity to freezing, and survival was modified by the growth medium, the freezing munstrua, and the freezing conditions. Suspension of cells in 10% (vol/vol) glycerol, cooled at 1 C/min, warmed rapidly and plated on malt-yeast extract-glucose-peptone agar produced the highest percentage of viable colonies with a minimal change in metabolic characteristics. In two of the strains tested, no significant increase in mutation rate was detected under any of the treatments; the strains were maintained in a stable state and were metabolically comparable to unfrozen strains. In one strain of Saccharomyces uvarum after some freezing treatments, the percentage of respiratory-deficient mutants increased markedly, the fermentation rate declined, and a loss of flocculation occurred. The freezing parameters which increased the level of respiratory-deficient cells should be avoided in maintaining this strain. Maintenance of cultures of brewing yeasts by cryogenic storage has several advantages over other preservation techniques: the method is simple and reproducible, the cultures have remained stable over a 3-year test period, and the viability is high.     

The study of Salmonellaenteritidis growth kinetics using Rapid Automated Bacterial Impedance Technique

In order to study strain-specific differences in their growth behaviour at different, and particularly lower, temperatures, generation times for 45 strains of Salmonella enteritidis isolated from food were determined impedimetrically over a temperature range from 7 to 42 °C. In practical terms, 7 °C is the minimum requirement for Salm. enteritidis growth, and generation time variability increases markedly as this temperature is reached. Reports in the literature describing psychrotrophic behaviour and multiplication at lower temperatures cannot be confirmed. Generation time variability increased as temperature moved away from the optimal range with variation coefficients tending to rise as temperature fell. The great variability of multiplication parameters near the growth limit found in Salm. enteritidis may also be a characteristic of other bacterial species. It is therefore imperative to commence studies on larger numbers of strains to allow prediction of their behaviour at lower temperatures.     

Mutant of Escherichia coli with Anomalous Cell Division and Ability to Decrease Episomally and Chromosomally Mediated Resistance to Ampicillin and Several Other Antibiotics

In a mutation experiment with a rough, ampicillin-resistant strain, we isolated two smooth mutants which were both sensitive to ampicillin and carried defects in the cell envelope. One of the strains (with the envA gene) is hindered in its completion of septa and forms chains of cells. The envA gene has been mapped to a position between leu and proB, at 2 to 4 min. The envA gene decreased the resistance mediated by both episomal and chromosomal genes for resistance to several antibiotics. During growth the envA mutant was characterized by abnormal ratios between viable count or cell count and optical density. The ratio between viable count and optical density was affected during shift-up and shift-down experiments. When compared to the parent strain, the envA mutant was found to be more resistant to ultraviolet irradiation on plates. Prestarvation for tryptophan had a protective effect against irradiation both on the parent strain and the envA mutant.     

Effect of pre-incubation pH on the growth characteristics of Aeromonas hydrophila at 5°C, as assessed by two methods

Two strains of Aeromonas hydrophila (the type strain ATCC 7966 and a food-derived strain JAH4) were pre-incubated at 5°C in Brain Heart Infusion (BHI) broth with pH adjusted to 6.0 or 7.0, and then incubated at the same temperature in BHI broth with pH adjusted to 6.0, 6.5, 7.0 and 7.5. Growth kinetics during incubation were determined by two methods: viable count (VC) and measurement of optical density (O.D.). Pre-incubation at different pH values did not significantly affect the maximum specific growth rates of the strains during incubation, but the lag phases were shorter after pre-incubation at pH 6.0 than at pH 7.0. The VC method was more sensitive than O.D. measurements for assessing lag phase.     

Variability in Growth Characteristics of Different E. coli O157:H7 Isolates, and its Implications for Predictive Microbiology

In growth experiments 75 clinical isolates of Escherichia coli O157:H7 were studied for the variability in seven growth characteristics: minimum, optimum and maximum growth temperature, minimum and optimum pH, minimum water activity and optimum specific growth rate. With these characteristics, growth can be predicted for any given set of conditions (temperature, acidity and water activity), when the gamma model is used as predictive microbiology model. The optimum specific growth rate of the 75 strains, as conceptually defined by the model, had a mean value of 4.71 (ln[emsp4 ](count)/h), with a standard deviation of 0.39. It could not be shown that the mean optimum specific growth rate differs significantly per strain, so the variability found will predominantly be the result of other sources of variation. In contrast, the experimental results suggest that the differences in minimum temperature of growth may be partially strain specific. As variability in growth is crucial for quantitative risk assessment, the results were implemented in the gamma model. Predictions at three sets of growth conditions were compared with predictions of the Pathogen Modeling Program (PMP) (USDA) and some published experimental results. This comparison showed that growth rates higher than those published and outside the 95% confidence interval predicted by the PMP are feasible. Although it needs further development and additional tests, our approach offers a promising strategy to predict the variability in growth.     

Suppression of plant wilt diseases by nonpathogenic Fusarium oxysporum Fo47 combined with actinomycete strains

In order to build integrated strains with superior growth-promoting and disease-suppression effects, the biological control efficacy of Fo47 solid agents combined with actinomycetes strains toward Fusarium oxysporum and Verticillium dahliae were investigated in experiments on watermelon, cotton and eggplant. Five actinomycetes strains were prepared by solid fermentation. The count of viable solid agents, initially with propagules at 10⁷–10¹¹ CFU/g, slowly decreased after being stored one year at room temperature. After being inoculated into sterile soil for 50 days, the viable count of strain Fo47 remained at a stable level. The suppressive effects of Fo47 combined with strain QLP12 on Fusarium wilt on watermelon and cotton, and Verticillium wilt on eggplant, reaching 58.47%, 50.73% and 58.82%, respectively. This was significantly better than the single strain Fo47 alone, and growth of these treated plants and the colonisation rate of Fo47 were increased substantially as well. These results indicate that solid integrated agents with a high viability count and superior stability in soil could increase disease suppression and promote plant growth by synergy with different strains. The increased suppression obtained by Fo47 combined with actinomycete strains was not due to a simple addition of different mechanisms of biocontrol agents. By being intelligently integrated, these combinations increase disease suppression and provide the best biocontrol effect.