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Edited by: Rosanna Tofalo, University of Teramo, Italy

Reviewed by: Antonio Valero, University of Cordoba, Spain; Giorgia Perpetuini, University of Teramo, Italy

*Correspondence: Yong Zhao

This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

†These authors have contributed equally to this study.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Microbial growth variability plays an important role on food safety risk assessment. In this study, the growth kinetic characteristics corresponding to maximum specific growth rate (μ_{max}) of 50 _{max} for temperature was larger than that for salinity, indicating that the impact of temperature on strain growth variability was greater than that of salinity. The strains isolated from freshwater aquatic products had more conspicuous growth variations than those from seawater. Moreover, the strains with ^{+}^{+}^{−} exhibited higher growth variability than ^{+}^{−}^{−} or ^{+}^{−}^{+}, revealing that gene heterogeneity might have possible relations with the growth variability. This research illustrates that the growth environments, strain sources as well as genotypes have impacts on strain growth variability of

It was announced that the strain variability gave the importance as well as the difficulty in controlling

In previous studies of

As the growth variability can introduce the food safety risk, the quantification of the growth variability can better service to the QMRA in microbiology. Aiming at furthering the development of precautionary food safety against

Fifty strains of ^{+}^{+}^{−}, ^{+}^{−}^{+}, and ^{+}^{−}^{−} genes were used for distinguishing the genotype of the isolates (Bej et al., ^{+}^{+}^{−} genotype, eleven ^{+}^{−}^{+} genotype, one strain (42) was ^{+}^{+}^{+} genotype and others were ^{+}^{−}^{−} genotype in Table ^{9} CFU/ml after incubation. The automated turbidimetric system Bioscreen C (Oy Growth Curves Ab Ltd., Raisio, Finland) was used for testing the corresponding Optical density (OD) values. OD measurements were taken at regular time intervals using the wideband filter (420–580 nm) of the instrument, for a total time period such that a considerable OD change was observed.

1 | + | − | + | Freshwater | 26 | + | − | − | Freshwater |

2 | + | − | − | Seawater | 27 | + | − | − | Freshwater |

3 | + | − | + | Freshwater | 28 | + | + | − | Freshwater |

4 | + | − | − | Freshwater | 29 | + | − | + | Freshwater |

5 | + | − | − | Seawater | 30 | + | − | + | Seawater |

6 | + | − | − | Seawater | 31 | + | + | − | Freshwater |

7 | + | + | − | Seawater | 32 | + | − | − | Seawater |

8 | + | − | + | Freshwater | 33 | + | − | − | Seawater |

9 | + | + | − | Seawater | 34 | + | − | − | Seawater |

10 | + | + | − | Seawater | 35 | + | − | + | Freshwater |

11 | + | − | − | Seawater | 36 | + | − | + | Freshwater |

12 | + | − | + | Freshwater | 37 | + | + | − | Seawater |

13 | + | + | − | Seawater | 38 | + | − | − | Freshwater |

14 | + | − | − | Seawater | 39 | + | − | − | Seawater |

15 | + | + | − | Seawater | 40 | + | − | + | Freshwater |

16 | + | − | − | Seawater | 41 | + | − | − | Freshwater |

17 | + | + | − | Seawater | 42 | + | + | + | Human |

18 | + | − | + | Freshwater | 43 | + | + | − | Human |

19 | + | − | − | Freshwater | 44 | + | − | − | Freshwater |

20 | + | − | + | Seawater | 45 | + | − | − | Seawater |

21 | + | − | − | Freshwater | 46 | + | − | − | Freshwater |

22 | + | − | − | Seawater | 47 | + | − | − | Freshwater |

23 | + | − | − | Freshwater | 48 | + | − | − | Freshwater |

24 | + | − | − | Seawater | 49 | + | − | − | Seawater |

25 | + | − | − | Seawater | 50 | + | − | − | Freshwater |

To evaluate the single effect of the T value or NaCl concentration on the growth variability in terms of the two environmental factors, a total of 20 different growth conditions were assessed with 4-levels (10, 20, 30, and 37°C) of temperature and 5-levels (0.5, 3, 5, 7, and 9%) of NaCl concentrations so as to cover the most probable growth region of the ^{4} CFU/ml, the inoculated TSB were transferred into 100-well microtiter plates, which were then placed in the automated turbidimetric system Bioscreen C for 4 levels of temperatures, respectively. Totally three OD measurement replicates were tested in this process. Additionally, three independent experiments were conducted at each growth condition and therefore there were three samples per strain altogether for testing. In such a way, the total number of the described OD curves would amount to 9000 patterns (3 replicates × 3 independent experiments × 20 growth conditions × 50 types of

The maximum specific growth rate (μ_{max}) (Dalgaard and Koutsoumanis, ^{*}h^{−1} can be formulated in the model of Modified Gompertz (Gibson et al., _{m} represents maximum specific growth rate and λ is the lag time of the strain growth.

To calculate the maximum growth rate, the obtained data with both OD values and cultured times were taken into the above equation in the place of _{m}.

The statistical indicators were used to compare the performance of the models: correlation coefficients (^{2}), the p values from the Fisher _{f}), and bias factor (B_{f}), whose mathematical expressions are as follows:
_{f} provides the accuracy of the model, which reflects how close the predicted values are to the observed values, while B_{f} indicates the mean difference between observed and predicted value. Ideally, predictive models would have A_{f} = B_{f} = 1 (Wang et al.,

The coefficient of variation (CV) of μ_{max} in different conditions were calculated within the formula as

The estimated maximum specific growth rate μ_{max} vs. 50 strains in various growth environments were calculated are presented in Supplementary Material, and almost all of the values were fitted in the equation given above. By statistical analysis, all the correlation coefficients achieved above 93%, and all RMSE values approached zero. Both accuracy factors and bias factors got close to 1. The results showed a satisfactory goodness-of-fit in this study. A fraction of the maximal growth rate values could not yet be evaluated by Modified Gompertz model (Lammerding,

Based on the μ_{max} in Supplementary Material, the tendency chats in various growth environments are shown in Figure _{max} (OD^{*}h^{−1}) ranged from 0.03 to 0.24 in the condition of 0.5% NaCl, from 0.02 to 0.44 at 3% NaCl, from 0.01 to 0.26 at 5% NaCl, from 0 to 0.15 at 7% NaCl, and from 0 to 0.12 at 9% NaCl among the 50 strains. While with the same NaCl concentration of 3% in the TSB, the mean μ_{max} (OD^{*}h^{−1}) ranged from 0.02 to 0.44 at 37°C, from 0.005 to 0.065 at 30 °C, from 0.007 to 0.031 at 20°C, and from 0.001 to 0.014 at 10°C. Obviously, the average growth rate in the condition of 37°C and 3% NaCl concentration was found to be the largest (Figure

_{max}) of 50

The optimal growth condition at 37°C with 3% NaCl concentration was used as the reference in Figure _{max}. While in other conditions, the strains from No. 1 to No. 50 seemed to grow randomly with no fixed growth trend as compared with that of the optimal growth condition. For example, the strains No. 50 and No. 1 at 37°C with 3% NaCl salinity had the highest growth rate and the lowest growth rate respectively, but in the condition at 20°C with 3% NaCl salinity, the No. 50 and No. 1 both located in the intermediate range of μ_{max} in all 50 strains, nearly 0.02 OD^{*}h^{−1}. Similar situations also appeared in other strains like No. 2, No. 13, No. 28 strains at 37°C with 5% NaCl salinity compared with those at 10°C with 3% NaCl concentration.

The curves related to the coefficient of variation (CV) of μ_{max} in different conditions were drawn in Figure _{max} of approximately 0.16 OD^{*}h^{−1} was 12.7% for 37°C-3% NaCl concentration, while corresponding to a mean μ_{max} of approximately 0.03 OD^{*}h^{−1}, the CV value was 16.3% for 30°C-3% NaCl concentration in Figure ^{*}h^{−1}, the CV values of μ_{max} would similarly drop down, with less variance of growth variability in

_{max}) and coefficient of variation curve of μ_{max} among strains (CV-Strain) in different (A) NaCl concentrations and (B)

From the different types of environmental sources in Table _{max} in freshwater and seawater accordingly. In addition, the significant differences were calculated by

_{max} most consistent), ^{*}.

Further investigation of the growth variability of ^{+}^{−}^{−}, ^{+}/^{+}^{−}, ^{+}/^{−}^{+}, and ^{+}/^{+}^{+}, were introduced in this research in order to explore the internal causes of the growth variability of ^{+}/^{+}^{−} (colored in red) embodied the largest strain growth variability. The associated CV values were set at a high level compared with 3 other genotypes. In contrast, the ^{+}/^{+}^{+} genotype had the lowest CV values overall. Similar circumstances appeared in Figure ^{+}^{−}^{+} (colored in green) and ^{+}^{−}^{−} (colored in blue) performed moderate, overtopping the CV values only in the condition at 30°C and 3% NaCl concentration.

It has been reported that the

It seemed that the inter-species growth variability of the 50 strains occurred at different environmental conditions. The trend of maximum growth rate in various conditions indicates that the external environment, such as the temperature and salinity, can affect the growth variability among

Moreover, because of incomplete knowledge of the effects of environmental conditions on model parameters in current microbiological studies (Nauta, _{max} value of about 0.06; In another extreme condition with the most non-optimal temperature case while the optimal salinity: 10°C-3% NaCl concentration, although there were few inactive strains, the mean value of the growth rate μ_{max} could just achieve 0.01 or below, and μ_{max} ranged at a smaller scale from 0.015 to 0. The difference indicates that although the temperature and salinity have the same net effect on strain variability, meaning that the μ_{max} variability among the strains increases as the _{max} appears to be greater than that of high NaCl concentration in Figure

The analysis in Figure _{max} value of 0.12 for strain No. 7, 30, and 31, it seemed that the strain growth rate was not suppressed. To avoid the growth of most pathogens, the other impact parameter, the temperature, plays an important role in the suppression of μ_{max}. As it was revealed above in the data from 20°C or even 10°C conditions, the

As reviewed by Nauta (_{max} values increased as the growth conditions became more stressful both in terms of NaCl (Figure

In Figure

In Figure _{max} was not significant. Obviously, this environment condition is the common state found in nature, especially in the subtropical and temperate coastal areas, which means that the

Another interesting point is that there was an extremely significant difference in the growth variability of the freshwater and seawater _{max} than an increasing NaCl concentration does.

According to collected data from Table ^{+}^{+}^{+}, it should have no typical representativeness for the properties of this genotype, while the curve in red gave some reference for the tendency of different genotypes in Figure ^{+}/^{+}^{−} resulted in the largest variation degree in the growth variability of the ^{+}/^{+}^{+} illustrated the least obvious variation degree from among those cultured in the environment condition with temperature and NaCl concentration, which verified that gene heterogeneity also affected the growth inter-specific variability for ^{+}/^{+}^{−} modeled the most non-optimal case in evaluating QMRA, due to there being a large risk of growth variability in reality. In addition, it is suggested that in the food safety control of clinical ^{+}/^{+}^{−}, which is associated with serious virulence (Miyamoto et al.,

In the present study, the growth kinetics characteristics of 50

BL performed the data analyses and wrote the manuscript; contributed significantly to analysis and manuscript preparation; HL helped perform the analysis with constructive discussions; Substantial contributions to the design of the work and analysis the results. YP, JX drafted the work or revising it critically for important intellectual content. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. YZ contributed to the conception of the study. Drafting the work or revising it critically for important intellectual content. Final approval of the version to be published.

This research was supported by the National Natural Science Foundation of China (31271870, 31571917), the project of Science and Technology Commission of Shanghai Municipality (14DZ1205100, 14320502100), Key Project of Shanghai Agriculture Prosperity through Science and Technology (2014, 3-5 and 2015, 4-8), Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation (11DZ2280300), and the “Dawn” Program of Shanghai Education Commission (15SG48).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The Supplementary Material for this article can be found online at:

^{+}—induced secretion

_{w}) on the individual cell lag phase and generation time of