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Growth modeling of Uropathogenic Escherichia coli in ground chicken meat
Food Control 86 (2018) 397e402
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Food Control journal homepage: www.elsevier.com/locate/foodcont
Growth modeling of Uropathogenic Escherichia coli in ground chicken meat* Christopher Sommers a, *, Chi-Yun Huang b, Lee-Yan Sheen b, Shiowshuh Sheen a, Lihan Huang a a b
U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center, 600 East Mermaid Lane, Wyndmoor, PA 19038, USA Institute of Food Science and Technology, The National Taiwan University, Taipei, Taiwan, ROC
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 September 2017 Received in revised form 3 December 2017 Accepted 4 December 2017 Available online 6 December 2017
Extraintestinal Pathogenic Escherichia coli (ExPEC), including Uropathogenic E. coli (UPEC), are common contaminants in poultry meat, and are a major pathogen associated with inflammatory bowel disease, ulcerative colitis, sepsis, and urinary tract infections. The purpose of this study was to determine the growth potential of UPEC in ground chicken meat. A multi-isolate cocktail of UPEC was inoculated into ground chicken meat 1034 log CFU/g and incubated at 4, 10, 15, 22, and 30 C. The USDA Integrated Pathogen Modeling Program (IPMP) was used to conduct mathematical modeling and validation of UPEC growth using the Huang Primary Model and the Huang Square Root Secondary Model. No growth occurred at 4 C, while the lag phases were ca. 23.6, 11.5, 5.2, and 0.36 h at 10, 15, 22, and 30 C. According to the model, the Tmin, the minimum temperature for UPEC growth in ground chicken, was 5.1 C. The growth rates (mmax, ln CFU/g h1) were ca. 0.06, 0.27, 0.48, and 0.90. Approximately 83.9% of the residual errors are between ±0.5 log CFU/g, suggesting that the predictive models and the associated kinetic parameters are sufficiently accurate in predicting the growth of UPEC in ground chicken. These models have been validated and can be used in risk assessment of ExPEC in poultry meat. © 2017 Published by Elsevier Ltd.
Keywords: Uropathogenic E. coli Extraintestinal pathogenic E.coli Growth model Ground chicken
1. Introduction Extraintestinal pathogenic E. coli (ExPEC) are common contaminants in poultry meat, red meat, unpasteurized cheeses, fish and seafood, as well as fresh produce. (Johnson, Kuskowski, Smith, O'Bryan, & Tatini, 2005; Mitchell, Johnson, Johnston, Curtiss, & Mellata, 2015; Muller, Stephan, & Nuesch-Inderbinen, 2016; Vincent et al., 2010). It has only recently been determined the ExPEC, including Uropathogenic E. coli (UPEC) are associated with inflammatory bowel disease, ulcerative colitis and Crohn's Disease (Mirsepasi-Lauridsen et al., 2016). Once in the gastrointestinal (GI) tract the ExPEC are able to colonize ulcerative lesions, escape, and eventually cause sepsis. After colonization of the distal colon by
* Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. * Corresponding author. Eastern Regional Research Center, USDA-ARS, 600 East Mermaid Lane, Wyndmoor, PA 19038. E-mail address: [email protected] (C. Sommers).
https://doi.org/10.1016/j.foodcont.2017.12.007 0956-7135/© 2017 Published by Elsevier Ltd.
ExPEC contaminated feces can accidentally transferred from the anus to the vagina and urethra where they cause urinary tract infections, cystitis, and pyelonephritis (Flores-Mireles, Walker, Caparon, & Hultgren, 2015; Nordstom, Liu, & Price, 2013; Plavsic, Stimac, & Hauser, 2013). ExPEC isolated directly from food which contain the appropriate virulence factors have been shown to cause disease in animal models (Stromberg et al., 2017; Vincent et al., 2010). Thus, there is a continuum of ExPEC/UPEC-associated disease from the upper GI tract to the urinary tract, which could be of food origin. Between sepsis mediated deaths, ulcerative colitis, and UTI over 11 million people are affected by the ExPEC annually including ca. 750,000 cases of UC and sepsis, plus 10 million cases of UTI, at a cost of ca. $20 billion per year (Epstein, Magill, & Fiore, 2016; Jensen et al., 2015; Torio and Andrews, 2016; Vejborg, Hancock, Petersen, Krogfelt, & Klemm, 2011). Six percent of all deaths in the US (1999e2014) were attributed to sepsis originating from either the GI tract or urinary tract (Epstein et al., 2016). In contrast, Shiga toxin-producing Escherichia coli (STEC) are responsible for approximately 176,000 illnesses, 3700 hospitalizations, and 30 deaths in the US annually (Scallan et al., 2011). Unlike the STEC,
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there are currently no testing requirements to determine the presence of ExPEC in red meat and poultry, and ExPEC/UPEC is not considered to be an adulterant as are the STEC (USDA FSIS, 2014). In recent years public health policy and consumer groups have requested additional information regarding the characterization, detection, and control of ExPEC in foods given the their high public health impact and the increasing frequency of antibiotic resistant ExPEC/UPEC-associated infections (Bennington-Castro, 2016; PCAST, 2014; PEW 2016; Ranjan et al., 2017). One of the questions we have been attempting to answer is: “whether food processing and preservation technologies used to control the STEC able to control the UPEC”? While there have been a large number of reports which model the growth potential of STEC in red meat, there is little if any regarding growth kinetics of UPEC. Growth modeling and predictive microbiology is a critical component of food safety risk assessments (USDA FSIS, 2012). In this research we developed and validated a growth model for UPEC in ground chicken breast meat using the Huang Primary and Secondary Models (Huang, 2014, 2013, 2008). 2. Materials and methods 2.1. Ground chicken Ground chicken (95% lean), freshly prepared from skinless breasts was purchased from a local wholesaler (Lansdale, PA). Multiple lots of ground chicken were tested and lots with low E. coli levels were (<1 log CFU/g) was selected. The chicken were divided into 5 ± 0.1 g portions and packaged into filter bags (Whirl-Pak R, 7 oz, 95 mm 180 mm 0.08 mm, NASCOdFort Atkinson, Fort Atkinson, Wis., U.S.A.). With the openings sealed, the filter bags containing chicken samples were frozen at 70 C and used within 30 days. Freezing inactivates ca. an additional one log CFU/g of E. coli. No E. coli was detected in the chicken following frozen storage. No E. coli was detected in the chicken meat following thawing of the incubation for 18 h at 37 C storage. 2.2. Uropathogenic E. coli (UPEC) The E. coli isolates were obtained from the American Type Culture Collection (Manassas, VA). These include 700414, 700415, 700416, 700336 (Accession number ALIN 02000000), 700928 (Accession Number AR014026), and BAA-1161 (Accession number CU928163) (http://www.atcc.org), which was isolated from women with UTI and sepsis. The phylogroup and presence of ExPEC virulence factors of TA 700414 700416 were verified by polymerase chain reaction (Clermont, Christenson, Denamur, & Gordon, 2013; Johnson & Stell, 2000). ATCC 700414 - 700416 were subjected to genomic DNA sequencing and submissions to NCBI/GenBank are being prepared. Multi-isolate cocktails of the pathogens were used as recommended for appropriate validation of nonthermal processing technologies (NACMCF, 2006). The isolates were maintained on tryptic soy agar plates (4 C) prior to experimentation. 2.3. Inoculation preparation Each bacterial strain isolate was cultured individually in 5-ml of Tryptic Soy Broth (BD-Difco Laboratories, Sparks, MD) in a sterile 50-mL polypropylene tube at 37 C for 18e24 h (New Brunswick, Model G34, Edison, NJ). The cocktail was made by combining 5 ml of each strain and centrifuged at 3600 g for 10 min (1200g, Hermle Model Z206A, Hermle Labortechnik, Germany). The pellet was re-suspended in sterile peptone water (BD-Difco Laboratories, Sparks, MD) to the original cocktail volume.
2.4. Inoculation of ground chicken One night before the experiment, the frozen 5 g samples were retrieved from the freezer and thawed overnight in a refrigerator (4e5 C). The thawed samples were inoculated with 0.1 mL of the bacterial cocktail, which was diluted before inoculation. The final concentration of UPEC in the ground chicken was ca. 1034 log CFU/ g. The inoculated samples were pulsated for 1 min in a mechanical stomacher (Model BagMixer R-100W, Interscience Co., France) at maximum speed. Immediately after inoculation, the samples were incubated at 4, 10, 15, 22, and 30 C. The incubating samples were periodically retrieved to enumerate UPEC. The sampling frequencies were determined by the incubation temperature, and ranged from every 0.5 h to every 24 h. Growth experiments were replicated at least 3 times at each temperature. No UPEC was detected from the control raw ground chicken samples. 2.5. UPEC enumeration The bag containing the 5 g inoculated chicken samples was aseptically opened and 45 ml of 0.1% sterile peptone water added to obtain a 1:10 dilution. The samples were stomached for 2 min and serial diluted with 0.1% sterile peptone water before being placed (1 ml) on duplicate E. coli Petrifilms™ (3M, St. Paul, MN) to determine survivor counts. Use of E. coli petrifilms versus of nonselective media has been previously validated in our laboratory and is used by USDA FSIS for enumeration of E. coli (FSIS, 2014; Khosravi, Silva, Sommers, & Sheen, 2013; Sommers, Scullen, & Sheen, 2016; USDA). The Petrifilms were incubated and scored at ca. 24 h using a calibrated Petrifilm reader. 2.6. Kinetic analysis and mathematical modeling In this study, three growth curves, representing three replicates, were obtained under each incubation temperature (10, 15, 22, and 30 C). Two replicates from each temperature were combined and analyzed to determine kinetic parameters, including specific growth rates (mmax, ln CFU/g h1) and lag time (l, h). The other replication was set aside for validation of the models. The growth curves at 10, 15, and 22 C exhibited lag and stationary phases, while the growth curves at 30 C showed lag, exponential, and stationary phases. Therefore, the growth curves obtained at 10, 15, and 22 C were analyzed to fit to the reduced Huang model (Eq. (1)), and the growth curves obtained at 30 C were fit to the full Huang model (Eq. (2)) during primary model analysis (Huang, 2008, 2013). The effect of temperature on bacterial growth rate was described by the sub-optimal Huang Square-Root Model (Eq. (3), Huang et al., 2012). Temperature also affected the lag time of bacterial growth. Its effect was described by an empirical relationship (Eq. (4)). In Eq. (1) and Eq. (2), Y0 and Y are the initial and real-time bacterial concentrations in ln CFU/g. Ymax is the maximum cell concentration in the sample. mmax is the specific growth rate (ln CFU/g h1), and l is the lag time at a constant temperature (h). In Eqs. (3) and (4), a, a, and b are regression coefficients.
" #) 1 1 þ e4ðtlÞ t þ ln 4 1 þ e4l
( Y ¼ Y 0 þ mmax
i o n h Y ¼ Y 0 þ Y max ln eY 0 þ eY max eY 0 emmax BðtÞ ! 1 1 þ e4ðtlÞ BðtÞ ¼ t þ ln 4 1 þ e4 l
(1)
(2)
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Fig. 1. ExPEC did not exhibit grow at 4 C.
pffiffiffiffiffiffiffiffiffiffiffi
mmax ¼ aðT T min Þ0:75
l ¼ a þ b lnðmmax Þ
(3) (4)
2.7. Data analysis, regression, and model validation The USDA Integrated Pathogen Modeling Program (USDA-IPMP, 2014; Huang, 2014) was used in analyzing the isothermal growth curves (primary model) at each incubation temperature between
10 and 30 C to estimate the specific growth rates and lag times. The secondary model for the specific growth rate was also analyzed using the USDA-IPMP. The linear regression for the secondary model of lag time was performed in Excel®. Once the regression analysis was completed, the kinetic models were validated using the data set aside for validation. Each temperature, the kinetic parameters were calculated using the secondary models and used in the primary model to predict the bacterial growth, which was compared with the validation data. The difference (ε) between the predictions and observations (observation - model) was calculated and analyzed using @Risk (Version 6.3.1, Professional Edition, Palisade Corp., Ithaca, NY) to
Fig. 2. Growth curves of ExPEC in ground chicken at 10, 15, 22, and 30 C. Empty diamonds (REP1) and squares (REP2) are raw data. Solid curves are model values.
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determine the distribution of the residual errors (ε). The mean and standard deviation of the residual errors were calculated. 3. Results and discussion 3.1. Control of UPEC using food safety intervention technologies Extraintestinal pathogenic E. coli have only recently been determined to be a potential foodborne pathogen, and while they are a much more serious public health problem than other foodborne pathogens (e.g. Shigatoxin-producing E. coli, Salmonella spp., and Campylobacter spp.) very little research has been devoted to their characterization, detection, and control. The percentage of human illnesses (e.g. ulcerative colitis, sepsis, urinary tract infections) which could be attributed to food is still unknown due to the relatively slow disease process as compares to typical diarrheal pathogens (Moreno et al., 2008). In recent research we determined the radiation resistance, high pressure processing resistance, ultraviolet light resistance, 405-nm light resistance, and resistance to natural antimicrobials for UPEC in ground chicken or beef, and chicken purge (Sommers et al., 2016, 2017; Chien et al., 2016). Those studies, based on historical research, indicated the parameters needed to control STEC in chicken meat would also control the UPEC. To our knowledge this is the first validated growth model for UPEC in a meat and poultry product. 3.2. Kinetic analysis and model development When the inoculated samples were incubated at 4 C, no growth was observed for up to 120 h (5 days) (Fig. 1). The bacterial population in ground chicken samples slightly declined at a rate of 0.04 log CFU/g per day. However, when the inoculated samples were incubated at 10, 15, 22, and 30 C, growth occurred at each temperature (Fig. 2). All growth curves exhibited lag phases and exponential phases, although the lag phase is very short for the growth curves observed at 30 C. While the growth rate increased with temperature, the lag time noticeably decreased at the incubation temperature was increased. The growth curves (two replicates combined) were successfully analyzed with the USDA-IPMP, with the model (smooth curves) matching well with the raw data (Fig. 2). The specific growth rates (mmax) were also successfully analyzed with the USDA-IPMP (Fig. 3A). Table 1 lists the estimated kinetic parameters for the specific growth rate (Eq. (3)). According to Table 1, the minimum growth temperature (Tmin) of ExPEC in chicken is 5.1 C, which corroborates well with the data shown in Fig. 4, in which no growth was observed at 4 C. The lag time can be expressed as a log-linear function of specific growth rate (Fig. 3B, R2 ¼ 0.98). Table 1 also lists the estimated values of a and b. Heo et al. (2010) obtained growth rates 0.09, 0.10 and 0.24 log CFU/g h1 at 5, 15, and 25 C, respectively for generic E. coli on whole chicken breast pieces, while the growth rates estimated from the growth models obtained in this study are 0.03, 0.1, and 0.29 log CFU/g h1, suggesting that the growth rate of ExPEC is similar to generic E. coli. Lag times of generic E. coli were ca. 49, 4.2, and 0.47 h at 5, 15, and 25 C, respectively, in ground chicken (Heo et al.,
Fig. 3. Secondary models for kinetic parameters for growth of ExPEC in ground chicken meat. Empty diamonds are the raw data. Solid curve is model values.
2010). The estimated lag times of ExPEC are 28.2, 13.2, and 1.4 h at 5, 15, and 25 C, respectively, suggesting that the ExPEX may have shorter lag time at lower temperatures, but longer lag time at higher temperature than the generic E. coli.
3.3. Model validation In this study, the data from one replicate was set aside for validating the predictive models and kinetic parameters. After the kinetic parameters were estimated, they were used in conjunction with the primary model to predict the microbial growth. Fig. 4 shows that the predictions of the models agree well with the raw data. Overall, the residual errors of validation, the difference between the observations and the predictions, follow a normal distribution (Fig. 5). The mean and standard deviation of the residual errors are 0.06 log CFU/g and 0.32 log CFU/g, respectively. Overall, about 83.9% of the residual errors is between ±0.5 log CFU/g, suggesting that the predictive models and the associated kinetic parameters are sufficiently accurate in predicting the growth of ExPEC (UPEC) in ground chicken.
Table 1 Estimated kinetic parameters for the secondary models. Parameter
Estimated value
Standard error
t-value
p-value
Lower 95% confidence interval
Upper 95% confidence interval
A Tmin ( C)
0.086 5.10 3.73 5.02
0.004 0.97 0.28 0.52
19.1 5.25 13.5 9.67
2.73E-03 3.45e-02 5.45E-03 1.05E-02
0.066 0.916 2.54 7.25
0.48 9.287 4.92 2.79
a b
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References
Fig. 4. Validation of predictive models. Empty diamonds, triangles, circles, and squares are raw data for validation for each temperature. Smooth curves are model values.
Fig. 5. Distribution of residual errors of validation.
4. Conclusions In this study we modeled, and validated the model, for growth of UPEC in ground chicken meat. The duration of lag phase was inversely proportional to the logarithm of specific growth rate as determined by the secondary model. As expected, the growth rate increased with increasing temperature. No growth occurred at 4 C, while the lag phases were ca. 23.6, 11.5, 5.2, and 0.36 h at 10, 15, 22, and 30 C. According to the model, the estimated Tmin, the minimum temperature for UPEC growth in ground chicken, was 5.1 C. The growth rates (mmax, ln CFU/g h1) were ca. 0.06, 0.27, 0.48, and 0.90. Our research used ground chicken, as opposed to whole muscle breast meat, as the food matrix. This research will help in the development of risk assessments for growth of ExPEC (UPEC) in foods.
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Contents lists available at ScienceDirect
Food Control journal homepage: www.elsevier.com/locate/foodcont
Growth modeling of Uropathogenic Escherichia coli in ground chicken meat* Christopher Sommers a, *, Chi-Yun Huang b, Lee-Yan Sheen b, Shiowshuh Sheen a, Lihan Huang a a b
U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center, 600 East Mermaid Lane, Wyndmoor, PA 19038, USA Institute of Food Science and Technology, The National Taiwan University, Taipei, Taiwan, ROC
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 September 2017 Received in revised form 3 December 2017 Accepted 4 December 2017 Available online 6 December 2017
Extraintestinal Pathogenic Escherichia coli (ExPEC), including Uropathogenic E. coli (UPEC), are common contaminants in poultry meat, and are a major pathogen associated with inflammatory bowel disease, ulcerative colitis, sepsis, and urinary tract infections. The purpose of this study was to determine the growth potential of UPEC in ground chicken meat. A multi-isolate cocktail of UPEC was inoculated into ground chicken meat 1034 log CFU/g and incubated at 4, 10, 15, 22, and 30 C. The USDA Integrated Pathogen Modeling Program (IPMP) was used to conduct mathematical modeling and validation of UPEC growth using the Huang Primary Model and the Huang Square Root Secondary Model. No growth occurred at 4 C, while the lag phases were ca. 23.6, 11.5, 5.2, and 0.36 h at 10, 15, 22, and 30 C. According to the model, the Tmin, the minimum temperature for UPEC growth in ground chicken, was 5.1 C. The growth rates (mmax, ln CFU/g h1) were ca. 0.06, 0.27, 0.48, and 0.90. Approximately 83.9% of the residual errors are between ±0.5 log CFU/g, suggesting that the predictive models and the associated kinetic parameters are sufficiently accurate in predicting the growth of UPEC in ground chicken. These models have been validated and can be used in risk assessment of ExPEC in poultry meat. © 2017 Published by Elsevier Ltd.
Keywords: Uropathogenic E. coli Extraintestinal pathogenic E.coli Growth model Ground chicken
1. Introduction Extraintestinal pathogenic E. coli (ExPEC) are common contaminants in poultry meat, red meat, unpasteurized cheeses, fish and seafood, as well as fresh produce. (Johnson, Kuskowski, Smith, O'Bryan, & Tatini, 2005; Mitchell, Johnson, Johnston, Curtiss, & Mellata, 2015; Muller, Stephan, & Nuesch-Inderbinen, 2016; Vincent et al., 2010). It has only recently been determined the ExPEC, including Uropathogenic E. coli (UPEC) are associated with inflammatory bowel disease, ulcerative colitis and Crohn's Disease (Mirsepasi-Lauridsen et al., 2016). Once in the gastrointestinal (GI) tract the ExPEC are able to colonize ulcerative lesions, escape, and eventually cause sepsis. After colonization of the distal colon by
* Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. * Corresponding author. Eastern Regional Research Center, USDA-ARS, 600 East Mermaid Lane, Wyndmoor, PA 19038. E-mail address: [email protected] (C. Sommers).
https://doi.org/10.1016/j.foodcont.2017.12.007 0956-7135/© 2017 Published by Elsevier Ltd.
ExPEC contaminated feces can accidentally transferred from the anus to the vagina and urethra where they cause urinary tract infections, cystitis, and pyelonephritis (Flores-Mireles, Walker, Caparon, & Hultgren, 2015; Nordstom, Liu, & Price, 2013; Plavsic, Stimac, & Hauser, 2013). ExPEC isolated directly from food which contain the appropriate virulence factors have been shown to cause disease in animal models (Stromberg et al., 2017; Vincent et al., 2010). Thus, there is a continuum of ExPEC/UPEC-associated disease from the upper GI tract to the urinary tract, which could be of food origin. Between sepsis mediated deaths, ulcerative colitis, and UTI over 11 million people are affected by the ExPEC annually including ca. 750,000 cases of UC and sepsis, plus 10 million cases of UTI, at a cost of ca. $20 billion per year (Epstein, Magill, & Fiore, 2016; Jensen et al., 2015; Torio and Andrews, 2016; Vejborg, Hancock, Petersen, Krogfelt, & Klemm, 2011). Six percent of all deaths in the US (1999e2014) were attributed to sepsis originating from either the GI tract or urinary tract (Epstein et al., 2016). In contrast, Shiga toxin-producing Escherichia coli (STEC) are responsible for approximately 176,000 illnesses, 3700 hospitalizations, and 30 deaths in the US annually (Scallan et al., 2011). Unlike the STEC,
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there are currently no testing requirements to determine the presence of ExPEC in red meat and poultry, and ExPEC/UPEC is not considered to be an adulterant as are the STEC (USDA FSIS, 2014). In recent years public health policy and consumer groups have requested additional information regarding the characterization, detection, and control of ExPEC in foods given the their high public health impact and the increasing frequency of antibiotic resistant ExPEC/UPEC-associated infections (Bennington-Castro, 2016; PCAST, 2014; PEW 2016; Ranjan et al., 2017). One of the questions we have been attempting to answer is: “whether food processing and preservation technologies used to control the STEC able to control the UPEC”? While there have been a large number of reports which model the growth potential of STEC in red meat, there is little if any regarding growth kinetics of UPEC. Growth modeling and predictive microbiology is a critical component of food safety risk assessments (USDA FSIS, 2012). In this research we developed and validated a growth model for UPEC in ground chicken breast meat using the Huang Primary and Secondary Models (Huang, 2014, 2013, 2008). 2. Materials and methods 2.1. Ground chicken Ground chicken (95% lean), freshly prepared from skinless breasts was purchased from a local wholesaler (Lansdale, PA). Multiple lots of ground chicken were tested and lots with low E. coli levels were (<1 log CFU/g) was selected. The chicken were divided into 5 ± 0.1 g portions and packaged into filter bags (Whirl-Pak R, 7 oz, 95 mm 180 mm 0.08 mm, NASCOdFort Atkinson, Fort Atkinson, Wis., U.S.A.). With the openings sealed, the filter bags containing chicken samples were frozen at 70 C and used within 30 days. Freezing inactivates ca. an additional one log CFU/g of E. coli. No E. coli was detected in the chicken following frozen storage. No E. coli was detected in the chicken meat following thawing of the incubation for 18 h at 37 C storage. 2.2. Uropathogenic E. coli (UPEC) The E. coli isolates were obtained from the American Type Culture Collection (Manassas, VA). These include 700414, 700415, 700416, 700336 (Accession number ALIN 02000000), 700928 (Accession Number AR014026), and BAA-1161 (Accession number CU928163) (http://www.atcc.org), which was isolated from women with UTI and sepsis. The phylogroup and presence of ExPEC virulence factors of TA 700414 700416 were verified by polymerase chain reaction (Clermont, Christenson, Denamur, & Gordon, 2013; Johnson & Stell, 2000). ATCC 700414 - 700416 were subjected to genomic DNA sequencing and submissions to NCBI/GenBank are being prepared. Multi-isolate cocktails of the pathogens were used as recommended for appropriate validation of nonthermal processing technologies (NACMCF, 2006). The isolates were maintained on tryptic soy agar plates (4 C) prior to experimentation. 2.3. Inoculation preparation Each bacterial strain isolate was cultured individually in 5-ml of Tryptic Soy Broth (BD-Difco Laboratories, Sparks, MD) in a sterile 50-mL polypropylene tube at 37 C for 18e24 h (New Brunswick, Model G34, Edison, NJ). The cocktail was made by combining 5 ml of each strain and centrifuged at 3600 g for 10 min (1200g, Hermle Model Z206A, Hermle Labortechnik, Germany). The pellet was re-suspended in sterile peptone water (BD-Difco Laboratories, Sparks, MD) to the original cocktail volume.
2.4. Inoculation of ground chicken One night before the experiment, the frozen 5 g samples were retrieved from the freezer and thawed overnight in a refrigerator (4e5 C). The thawed samples were inoculated with 0.1 mL of the bacterial cocktail, which was diluted before inoculation. The final concentration of UPEC in the ground chicken was ca. 1034 log CFU/ g. The inoculated samples were pulsated for 1 min in a mechanical stomacher (Model BagMixer R-100W, Interscience Co., France) at maximum speed. Immediately after inoculation, the samples were incubated at 4, 10, 15, 22, and 30 C. The incubating samples were periodically retrieved to enumerate UPEC. The sampling frequencies were determined by the incubation temperature, and ranged from every 0.5 h to every 24 h. Growth experiments were replicated at least 3 times at each temperature. No UPEC was detected from the control raw ground chicken samples. 2.5. UPEC enumeration The bag containing the 5 g inoculated chicken samples was aseptically opened and 45 ml of 0.1% sterile peptone water added to obtain a 1:10 dilution. The samples were stomached for 2 min and serial diluted with 0.1% sterile peptone water before being placed (1 ml) on duplicate E. coli Petrifilms™ (3M, St. Paul, MN) to determine survivor counts. Use of E. coli petrifilms versus of nonselective media has been previously validated in our laboratory and is used by USDA FSIS for enumeration of E. coli (FSIS, 2014; Khosravi, Silva, Sommers, & Sheen, 2013; Sommers, Scullen, & Sheen, 2016; USDA). The Petrifilms were incubated and scored at ca. 24 h using a calibrated Petrifilm reader. 2.6. Kinetic analysis and mathematical modeling In this study, three growth curves, representing three replicates, were obtained under each incubation temperature (10, 15, 22, and 30 C). Two replicates from each temperature were combined and analyzed to determine kinetic parameters, including specific growth rates (mmax, ln CFU/g h1) and lag time (l, h). The other replication was set aside for validation of the models. The growth curves at 10, 15, and 22 C exhibited lag and stationary phases, while the growth curves at 30 C showed lag, exponential, and stationary phases. Therefore, the growth curves obtained at 10, 15, and 22 C were analyzed to fit to the reduced Huang model (Eq. (1)), and the growth curves obtained at 30 C were fit to the full Huang model (Eq. (2)) during primary model analysis (Huang, 2008, 2013). The effect of temperature on bacterial growth rate was described by the sub-optimal Huang Square-Root Model (Eq. (3), Huang et al., 2012). Temperature also affected the lag time of bacterial growth. Its effect was described by an empirical relationship (Eq. (4)). In Eq. (1) and Eq. (2), Y0 and Y are the initial and real-time bacterial concentrations in ln CFU/g. Ymax is the maximum cell concentration in the sample. mmax is the specific growth rate (ln CFU/g h1), and l is the lag time at a constant temperature (h). In Eqs. (3) and (4), a, a, and b are regression coefficients.
" #) 1 1 þ e4ðtlÞ t þ ln 4 1 þ e4l
( Y ¼ Y 0 þ mmax
i o n h Y ¼ Y 0 þ Y max ln eY 0 þ eY max eY 0 emmax BðtÞ ! 1 1 þ e4ðtlÞ BðtÞ ¼ t þ ln 4 1 þ e4 l
(1)
(2)
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Fig. 1. ExPEC did not exhibit grow at 4 C.
pffiffiffiffiffiffiffiffiffiffiffi
mmax ¼ aðT T min Þ0:75
l ¼ a þ b lnðmmax Þ
(3) (4)
2.7. Data analysis, regression, and model validation The USDA Integrated Pathogen Modeling Program (USDA-IPMP, 2014; Huang, 2014) was used in analyzing the isothermal growth curves (primary model) at each incubation temperature between
10 and 30 C to estimate the specific growth rates and lag times. The secondary model for the specific growth rate was also analyzed using the USDA-IPMP. The linear regression for the secondary model of lag time was performed in Excel®. Once the regression analysis was completed, the kinetic models were validated using the data set aside for validation. Each temperature, the kinetic parameters were calculated using the secondary models and used in the primary model to predict the bacterial growth, which was compared with the validation data. The difference (ε) between the predictions and observations (observation - model) was calculated and analyzed using @Risk (Version 6.3.1, Professional Edition, Palisade Corp., Ithaca, NY) to
Fig. 2. Growth curves of ExPEC in ground chicken at 10, 15, 22, and 30 C. Empty diamonds (REP1) and squares (REP2) are raw data. Solid curves are model values.
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determine the distribution of the residual errors (ε). The mean and standard deviation of the residual errors were calculated. 3. Results and discussion 3.1. Control of UPEC using food safety intervention technologies Extraintestinal pathogenic E. coli have only recently been determined to be a potential foodborne pathogen, and while they are a much more serious public health problem than other foodborne pathogens (e.g. Shigatoxin-producing E. coli, Salmonella spp., and Campylobacter spp.) very little research has been devoted to their characterization, detection, and control. The percentage of human illnesses (e.g. ulcerative colitis, sepsis, urinary tract infections) which could be attributed to food is still unknown due to the relatively slow disease process as compares to typical diarrheal pathogens (Moreno et al., 2008). In recent research we determined the radiation resistance, high pressure processing resistance, ultraviolet light resistance, 405-nm light resistance, and resistance to natural antimicrobials for UPEC in ground chicken or beef, and chicken purge (Sommers et al., 2016, 2017; Chien et al., 2016). Those studies, based on historical research, indicated the parameters needed to control STEC in chicken meat would also control the UPEC. To our knowledge this is the first validated growth model for UPEC in a meat and poultry product. 3.2. Kinetic analysis and model development When the inoculated samples were incubated at 4 C, no growth was observed for up to 120 h (5 days) (Fig. 1). The bacterial population in ground chicken samples slightly declined at a rate of 0.04 log CFU/g per day. However, when the inoculated samples were incubated at 10, 15, 22, and 30 C, growth occurred at each temperature (Fig. 2). All growth curves exhibited lag phases and exponential phases, although the lag phase is very short for the growth curves observed at 30 C. While the growth rate increased with temperature, the lag time noticeably decreased at the incubation temperature was increased. The growth curves (two replicates combined) were successfully analyzed with the USDA-IPMP, with the model (smooth curves) matching well with the raw data (Fig. 2). The specific growth rates (mmax) were also successfully analyzed with the USDA-IPMP (Fig. 3A). Table 1 lists the estimated kinetic parameters for the specific growth rate (Eq. (3)). According to Table 1, the minimum growth temperature (Tmin) of ExPEC in chicken is 5.1 C, which corroborates well with the data shown in Fig. 4, in which no growth was observed at 4 C. The lag time can be expressed as a log-linear function of specific growth rate (Fig. 3B, R2 ¼ 0.98). Table 1 also lists the estimated values of a and b. Heo et al. (2010) obtained growth rates 0.09, 0.10 and 0.24 log CFU/g h1 at 5, 15, and 25 C, respectively for generic E. coli on whole chicken breast pieces, while the growth rates estimated from the growth models obtained in this study are 0.03, 0.1, and 0.29 log CFU/g h1, suggesting that the growth rate of ExPEC is similar to generic E. coli. Lag times of generic E. coli were ca. 49, 4.2, and 0.47 h at 5, 15, and 25 C, respectively, in ground chicken (Heo et al.,
Fig. 3. Secondary models for kinetic parameters for growth of ExPEC in ground chicken meat. Empty diamonds are the raw data. Solid curve is model values.
2010). The estimated lag times of ExPEC are 28.2, 13.2, and 1.4 h at 5, 15, and 25 C, respectively, suggesting that the ExPEX may have shorter lag time at lower temperatures, but longer lag time at higher temperature than the generic E. coli.
3.3. Model validation In this study, the data from one replicate was set aside for validating the predictive models and kinetic parameters. After the kinetic parameters were estimated, they were used in conjunction with the primary model to predict the microbial growth. Fig. 4 shows that the predictions of the models agree well with the raw data. Overall, the residual errors of validation, the difference between the observations and the predictions, follow a normal distribution (Fig. 5). The mean and standard deviation of the residual errors are 0.06 log CFU/g and 0.32 log CFU/g, respectively. Overall, about 83.9% of the residual errors is between ±0.5 log CFU/g, suggesting that the predictive models and the associated kinetic parameters are sufficiently accurate in predicting the growth of ExPEC (UPEC) in ground chicken.
Table 1 Estimated kinetic parameters for the secondary models. Parameter
Estimated value
Standard error
t-value
p-value
Lower 95% confidence interval
Upper 95% confidence interval
A Tmin ( C)
0.086 5.10 3.73 5.02
0.004 0.97 0.28 0.52
19.1 5.25 13.5 9.67
2.73E-03 3.45e-02 5.45E-03 1.05E-02
0.066 0.916 2.54 7.25
0.48 9.287 4.92 2.79
a b
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References
Fig. 4. Validation of predictive models. Empty diamonds, triangles, circles, and squares are raw data for validation for each temperature. Smooth curves are model values.
Fig. 5. Distribution of residual errors of validation.
4. Conclusions In this study we modeled, and validated the model, for growth of UPEC in ground chicken meat. The duration of lag phase was inversely proportional to the logarithm of specific growth rate as determined by the secondary model. As expected, the growth rate increased with increasing temperature. No growth occurred at 4 C, while the lag phases were ca. 23.6, 11.5, 5.2, and 0.36 h at 10, 15, 22, and 30 C. According to the model, the estimated Tmin, the minimum temperature for UPEC growth in ground chicken, was 5.1 C. The growth rates (mmax, ln CFU/g h1) were ca. 0.06, 0.27, 0.48, and 0.90. Our research used ground chicken, as opposed to whole muscle breast meat, as the food matrix. This research will help in the development of risk assessments for growth of ExPEC (UPEC) in foods.
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