e. coli in coastal environment : oualidia lagoon - ijcmas et al.pdfmodeled the survival of...

Int.J.Curr.Microbiol.App.Sci (2014) 3(1): 127-139

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Original Research Article

Adjusting a primary survival model of Enteric Bacteria "E. coli " in coastal environment : Oualidia lagoon

M.Hennani1*, N.Hassou1, A. Aajjane2 and O.Assobhei1

1Laboratory of marine biotechnology and environment, department of biology, Faculty of Sciences, University Chouaib Doukkali, El Jadida, Morocco.

2Laboratory of Marine Geosciences and Soil Sciences, Unit associated CNRST (URAC 45) Team of Soil Science and Heritage, department of geology, University Chouaib Doukkali, El

Jadida, Morocco. *Corresponding author

A B S T R A C T

Introduction

The enteric bacteria dispersed in coastal areas are completely depending on their environment. It has been observed that bacterial concentration decreases over time once introduced in the coastal environment (Chraibi, 1996; Chedad et Assobhei 2007). Because into coastal or marine environments, bacteria are exposed to numerous factors which cause stress (Rozen and Belkin, 2001; Chedad et

Assobhei, 2007; Rippy et al., 2013). Based on a large number of laboratory studies, various physicochemical factors such as visible light, temperature, pH, Salinity, turbidity, toxicity of heavy metals, are believed to have a significant impact on bacterial mortality (Sinton et al., 2002; Noble et al, 2004; Richard et al, 2004; Chedad et Assobhei, 2007; Semenov et al., 2007). A bacterial decrease can present

ISSN: 2319-7706 Volume 3 Number 1 (2014) pp. 127-139 http://www.ijcmas.com

K e y w o r d s

Survival Bacteria; Escherichia coli; Oualidia; linear model; non-linear model; AIC criterion.

Enteric Bacteria especially Escherichia coli in natural environment present an important health risk. Adequate modeling of the bacteria survival in such systems can provide a valuable tool in assessing water quality. This study quantified and modeled the survival of Escherichia coli in coastal environment; Oualidia lagoon. The survival experimental data used in this research were obtained from diffusion culture of Escherichia coli along the Oualidia lagoon at different parts and time of the year. Two primary models, linear and non-linear regression were analyses and compared. The statistical analyses test using the RSS, R²adj et AIC criterion was applied to compare the survival curves of two models fitted to experimental data. The results of this study revealed that the survival model of Escherichia coli in Oualidia found by non-linear regression. Although the AIC values for the two distributions in question were close to each other, the non-linear model which had the lowest AIC value was determined at the most suitable model.

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different shapes of survival curves. Linear curves, frequently concave curves may become convex or sigmoidal when the intensity of the stress varies (Whiting et al, 1996; Koutsoumanis et al., 1999; Samelis et al., 2001; Virto et al, 2005; Hajmeer et al., 2006). An understanding of the survival of fecal indicator organisms and the enteric bacteria in coastal water is basic to the meaningful interpretation of sanitary environment quality. In almost all areas of water quality, the preventive mathematical models are currently used to predict and estimate the pollution risk and defining the spatial-temporal factors dispersion of bacterial pollution introduced in natural environments to provide useful management information for minimizing fecal pollution in these areas.

In order to model survival bacteria curve, numbers of primary models were proposed. Among these models, we can find the models based on a log-linear decrease such as the vitalistic models (Cole et al., 1993; Little et al., 1994; Skandamis et al., 2002), the exponential models (Membre et al., 1997), log- linear with latency time (Buchanan et al., 1994)...ect, and the models based on a sigmoidal inactivation such as Weibull model (Mafart et al, 2002). The purpose of this research is to determine the primary survival model of Escherichia coli introduced in the Moroccan oyster area; Oualidia lagoon, on three different season (winter, spring and summer), where the climatic condition were almost constant for each season. Two primary models (log-linear model and Weibull model) of survival bacteria; were compared based on an experimental data.

Materials and Methods

Study area and sampling experiments

Lagoon Oualidia is located on the Atlantic coast of Morocco (Fig. 1). Three distinct parts can be characterized (Rharbi et al, 2001):

The lower zone which ranges from 2.5 km at low tide to 4 km at high tide seems to be strongly influenced by the sea (low variations of temperature and salinity);

The upper zone which ranges from 1.5 km at high tide to 3 km at low tide looks much more confined and presents a pool for the nitrogen elements, suspended particles and organic matter as well;

The intermediary zone appears to be under both marine and continental influences.

The lagoon watershed area is not well recognized; the area is characterized by diffuse pollution. The main sources of pollution are due to tourism, shellfishes, agriculture or related to sewages infiltration from septic tanks (Hennani et al, 2012). There are no other industrial activities (Gonenc et al, 2006).

They are two main reasons for choosing Oualidia lagoon in this study:

The area is located in nutrient rich coastal waters which are sheltered from currents and waves, they are often vulnerable to bacterial pollution;

The economic impact of shellfishing and the high cost of purification are two reasons why more comprehensive and predictive tools should he developed.


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Fig.1 Map of Oualidia lagoon (Google Maps, 2013)

Fig.2 Survival experiments schema


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The experiments were carried out to establish the survival of enteric bacteria "Escherichia coli" in the lagoon for three season of the year 2012: winter, spring and summer. Along the lagoon, three zones characterized by different hydrodynamical and environmental conditions were investigated (Fig. 2).

Inoculum preparation and survival experiments

The strain used in this study was Escherichia coli isolated from sewages, by solid media; Eosin Methylene Blue agar (EMB, Biokar Diagnostic). Vegetative cells were recovered in 250 ml of Nutrient Broth (NB, Biokar Diagnostic) incubated at 37°C. After 24 h of incubation, cultivable bacteria were recovered in 100 ml of Ringer solution after centrifugation (4°C - 15 min). Bacterial suspension was prepared and was inoculated into sterilized diffusion chamber (1ml per chamber). The diffusion chamber is a Plexiglas box (250 ml) with a capped opening midway to allow sampling of the chamber. Each chamber was closed at both ends with a Nuclepore membrane (0.47 µm), held in place between two Plexiglas retainers. Te on O rings ensured water tightness. Leaks were checked by slowly immersing each empty chamber in lagoon water and allowing them to gradually ll via the Nuclepore membranes at each end. The Prepared diffusion chambers were introduced in the lagoon by describe experiments points into Fig. 2, that were been exposed in the environment conditions for various lengths of time.

After the exposed time of bacteria in the lagoon, the viable cells are defined based on the assessment technique using a standard method. Briefly, 20 µl of bacterial suspension was mixed with 100

µl of Trypan blue (TB, 0.4%). The sample was loaded into a sterilized Neubauer hemocytometer, and the cells lying within a 1 mm² area, were counted using a light microscope. Blue stained cells were counted non-viable and non stained cell as viable.

Tested primary models

Based on a shape of decrease curve, we have chosen in this study to evaluate two primary survival models of bacteria. The firstly is the classical primary model based on a Log-linear decrease (Chick, 1910). The secondly is the Weibull model based on a sigmoidal decrease (Mafart et al, 2002).

Log-linear model

Based on a simple linear regression, the model equation is derived from the model proposed by (Chick, 1910), confirmed by (Esty et al., 1922; Esty and Williams, 1924) et transformed on the familiar form by (Katzin et al, 1943; Ball and Olson, 1957) (Eq. 1).

(Eq.1)

where

is the initial number of Bacteria, the number of surviving bacteria after a

duration of heat exposed stress,

is the time, and the classical

value presents a simple biological significance: time that leads to a tenfold reduction of surviving population.

Non Log-linear model

Based on a nonlinear regression, the Weibull model has been widely used to describe resistance to thermal stress during the past few decades in heat treatment studies but also in non-thermal treatment


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studies (Peleg and Cole, 1998; Van Boekel, 2002). Different forms of this model were present, but the familiar form is that described by (Mafart et al., 2002; Van Boekel, 2002), (Eq. 2).

(Eq.2)

where

is the initial number of Bacteria, the number of surviving bacteria after a duration of heat exposed stress,

is the time,

is to the first reduction time that leads a tenfold reduction of survival population, and is a shape parameter.

For the traditional case where the survival curve, originated from a first order, is linear p equal 1 and the parameter correspond to the classical value.

Adjusting and Model evaluation

Generally, the evolution function of survival curves during time can be expressed as follows:

(Eq.3)

where

is the decimal logarithm of

and f is the regression function.

is the vectors of parameters of models, were estimated by minimization of the sum of square of the residual values

defined by :

(Eq.4)

The primary models were adjusted by the polyfit function (Polyfit, MATLAB 6.1) for linear regression (Eq. 1), and by nonlinear fitting module (NLINFIT, MATLAB 6.1, Optimization Toolbox; The Math-Works) for nonlinear regression (Eq (2). The fit of the two models was compared using the statistical analysis, MATLAB 6.1. Two parameters were

computed: The Akaike Information Criterion (AIC) (Akaike, 1977) and the adjusting Correlation coefficient (R²adj).

Results and Discussion

The results of models analysis were fitted to experimental data are shown in the Fig. 3, 4, 5. Based on a curves of experimental survival data of Escherichia coli, were showed that bacterial concentration decreases according time, when they were exposed to natural stress. Several aspects of the effects of environmental condition were considered, including water temperature, salinity, turbidity, solar radiation.... etc. The incident of unfavorably environmental condition increases from winter to summer and from surface water to depth. Visually, the non-linear model (Weibull model) fitted better than linear model. The fitted line plot of non-linear regression shows that the raw data follow a nice tight function which looks pretty good. However, the linear regression systematically adjusting at an experimental data in the curve indicates a bad fit.

Generally, the estimates from non-linear model are near to experimental data than the estimates from the linear model (Table 1). Indeed, the experimental concentration of bacteria introduced at

was 8.60 , however, this value is

estimated in an interval of 8.7 to , and 6 to 8.4

, respectively for non-linear and linear model.

These parameters provide valuable information to model bacterial survival in natural environment but we cannot use for the selection of a reliable model. It was interesting to perform a statistical analysis for choosing the better of these two models tested.


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Table.1 Estimate parameter for linear model (1) and non-linear model (2)

Estimate parameter Model 1 Model 2

Area Period Log 10 N0 (FCU/ml)

D (h) Log 10 N0 (FCU/ml)

(h)

p

Surface water 7.93 -7.81 8.9 1.52 0.51 Winter Depth 7.48 -10.34 8.92 2.32 0.54 Surface water 7.47 -7.81 8.83 1.52 0.51 Spring Depth 8.17 -7.99 8.87 3.34 0.69 Surface water 6.81 -7.45 8.85 0.41 0.43

Inlet

Summer Depth 7.17 -8.16 8.86 0.7 0.45 Surface water 8.38 -12.27 8.85 6.09 0.68 Winter Depth 8.4 -15.2 8.85 11.18 0.8 Surface water 8.46 -9.48 8.91 4.79 0.72 Spring Depth 8.64 -16.5 8.78 13.39 0.84 Surface water 8.44 -8.67 8.83 5.29 0.79

Oyster farm

Summer Depth 8.46 -12 8.9 6.2 0.7 Surface water 8.58 -11.6 8.93 7.02 0.76 Winter Depth 8.68 -17.18 8.79 14.71 0.92 Surface water 8.48 -11.96 8.83 7.23 0.75 Spring Depth 8.65 -15.72 8.82 12.21 0.83 Surface water 8.45 -10.15 8.89 5.57 0.74

Agricultural Area

Summer Depth 8.55 -12.79 8.85 8.22 0.77

Table.2 Statistical parameter for testing Linear Model (Model1) and non-linear model (Model2)

Model 1 Model 2 RSS AIC R²adj RSS AIC R²adj

Surface water

4.2982 -4.65 0.7568 2.5059

-7.51 0.8654 Winter Depth 5.3106 -2.75 0.8169 2.0202

-9.45 0.8667 Surface water 5.333 -2.71 0.8165 1.5723

-11.70 0.9368 spring Depth 2.2255 -10.58 0.9119 1.0984

-14.93 0.9493 Surface water 11.3327 4.07 0.6917 3.1281

-5.51 0.9007

Inlet

Summer

Depth 8.459 1.44 0.7152 2.5879

-7.22 0.8984 Surface water 1.7203 -12.89 0.8489 1.1773

-14.31 0.8791 Winter Depth 1.076 -17.12 0.854 0.9511

-16.23 0.8492 Surface water 1.8223 -12.37 0.8997 1.1394

-14.60 0.9266 spring Depth 0.3824 -26.43 0.9332 0.3251

-25.89 0.9337 Surface water 1.8245 -12.36 0.9148 1.4355

-12.52 0.9216

Oyster farm

Summer

Depth 1.927 -11.87 0.8395 1.3796

-12.88 0.8658 Surface water 1.955 -11.74 0.8468 1.6028

-11.53 0.8533 Winter Depth 0.4821 -24.34 0.9116 0.3717

-24.68 0.9275 Surface water 0.9991 -17.78 0.9113 0.6878

-19.14 0.9291 spring Depth 0.6982 -21.01 0.8947 0.6179

-20.11 0.8912 Surface water 2.439 -9.75 0.8468 1.9282

-9.87 0.8644

Agricultural area

Summer

Depth 0.9876 -17.89 0.9009 0.748 -18.39 0.9126


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Fig.3 Shape of the survival curves fitting for Escherichia coli in lower Zone (Inlet) of the Oualidia Lagoon. (a): surface water, (b): Depth.


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Fig.4 Shape of the survival curves fitting for Escherichia coli in Intermediary Zone (oyster farm) of the Oualidia Lagoon.

(a): surface water, (b): Depth


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Fig.5 Shape of the survival curves fitting for Escherichia coli in upper Zone (agricultural area) of the Oualidia Lagoon.

(a): surface water, (b): Depth


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In this study, the non-linear model gives the lowest RSS for each analysis point (100%), while the traditional linear model never gave the lowest RSS value (Table 2). According to the R²adjusting test, non-linear model showed significantly correlation to experimental data than linear model in 16 out of 18 cases (88.89%). The results of the AIC (Table 2) model comparison procedures indicate for majority experimentation that the non-linear model (Model 2) present the lowest AIC value than linear model (Model 1).

The environmental conditions may have a predictable effect on bacteria survival in coastal area. That is important as this information could be used for estimating and modeled the survival bacteria at different time of the year. Generally, for Escherichia Coli, diffusion culture may be a more useful tool for establishing factors-survival relationships [Lessard and Sieburth, 1983; Salomon and Pommepuy, 1990; Pommepuy and al., 1992; Chraibi, 1996; Chedad et Assobhei 2007; Chandran et al., 2010]. In membrane diffusion chamber, the bacteria under investigation come in contact with continuous exchanges of and solutes and the membrane prevent the entry of autochthonous bacteria and pathogens into diffusion culture. Numerous studies have demonstrated the negative impacts of natural environmental condition on allochthonous bacteria (Chraibi, 1996; Chedad and Assobhei 2007, Chandran et al , 2010). The intensity of decrease change according the climate condition. Linear regression model is the most common way of estimating a survival of bacteria exposed to environmental stress used in majority research. Until now, Escherichia coli survival has been quantified and modeled by either a decay rate constant (Rhodes and Kator, 1988,

1990, Hellweger and al., 2009; Green et al., 2011, Fioravanti et al, 2011, Blaustein et al, 2012) or an estimate of the lag phase of decay (Gameson, 1984; Barcina et al, 1986, 1991; Baranyi, 2010). However, our results (Fig.3, 4, 5) confirmed by some research show that enteric bacterial (E.coli) survival curve follow a sigmoidal curve when the logarithm of cell number is plotted against time (Gonzales, 1995; Mafaret et al., 2002; Van Boekel, 2002; Buzrul and alpas, 2005; Coroller, 2006; Bialka et al. 2008; Izquier and Gómez-López, 2011; Keklik et al. 2012). Two models; linear and non-linear function have been analyzed against experimental data to select the best approach for modeling and quantifying Escherichia Coli survival in coastal environment; Oualidia lagoon. Linear model may be considered acceptable since they give significant linear regression, when we compared to non-linear model, we find that the latter model fitted the experimental data better than linear model. This confirmed with many research suggested that the Weibull model was more successful than the log-linear model in estimating the inactivation for all poultry products evaluated (Coroller, 2006; Keklik et al., 2012). So the classical decay rate constant D for bacterial decrease is not suitable for quantifying and modeling the survival of enteric bacteria. The environmental factor and lagoon conditions present a clear and regular influence on the shape the parameters and p . For each area study in this work, the shapes of p and were estimated (table 1) suggests that the environmental conditions of Oualidia lagoon along year influences their values. This observation is in agreement with the works of Couvert et al., 2005 and Coroller, 2006. Constant p value means that the Weibull probability density function curves presents the same


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convex shape. These parameters models were easily conducted by maximum likelihood estimators.

Statistical analysis conducted to compare the log linear and non-log linear models for each experiments confirmed that the non-log linear model is the best. The residual same of square, associate at correlation coefficient R2 adj and AIC criterion of the non-linear model were smaller, showing that was the better estimation of parameters and better goodness of fit. This agrees with previous studies reporting sigmoid survival plots for the total number of enteric bacteria in aquatic systems (Gonzales, 1995; Blaustein, 2012, Keklik et al., 2012).

In summary, results reported in this paper have shown that survival of Escherichia coli in costal lagoon Oualidia can be adequately modeled by using a non-linear model (Weibull model) (Mafart et al., 2002). It might also lead to more reliable estimates of environment quality, which certainly has important consequences for human health.

Further research is required to allow the use of non-linear model in an environmental stress studies. The evolution of p and according the environmental parameter requires a special attention. The advantage of the Weibull model (non-linear regression) is that it has great flexibility because of a strong correlation between the scale and the shape p parameter (Coroller, 2006). Indeed, these parameters might change according to the intensity of stress.

Acknowledgement

This work was supported by many organizations and people. We would like to thanks the REMER (the National of

Marines Sciences Network in Morocco), Ministry of scientific research, faculty of science of Chouaib Doukkali university, BIOMAR laboratory and Geosciences Marine and Soil Sciences laboratory. Special thanks to Louis COROLLER for her help of uses Matlab software. The authors are grateful to the reviewers of the journal for helpful comments.

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