Detection and Enumeration of Food Pathogens with the BAX® PCR System

Thomas P. Oscar, Ph.D.Thomas P. Oscar, Ph.D.

Research Food TechnologistResearch Food Technologist

Welcome… …thank you for coming!

Detection and Enumeration of Food Pathogens with the BAX® PCR System

Thomas P. Oscar, Ph.D.Thomas P. Oscar, Ph.D.

Research Food TechnologistResearch Food Technologist

Welcome… …thank you for coming!

University of Delaware (1978-1982)

Undergraduate Research AssistantUndergraduate Research Assistant B.S. in Animal ScienceB.S. in Animal Science

Pre-Veterinary MedicinePre-Veterinary Medicine

“Interaction of Tiamulin and Monensin in Chickens”

Pennsylvania State University (1982-1984)

Graduate Research AssistantGraduate Research Assistant M.S. in Animal NutritionM.S. in Animal Nutrition

Minor in BiochemistryMinor in Biochemistry

“Characterization of the Bovine Mammary Insulin Receptor”

North Carolina State University (1984-1987)

Graduate Research & Teaching AssistantGraduate Research & Teaching Assistant Ph.D. in Animal SciencePh.D. in Animal Science

Ruminant NutritionRuminant Nutrition

“Role of Nickel in Methane Production”

University of Tennessee, Memphis (1987-1988)

NIH Post-Doctoral Research AssociateNIH Post-Doctoral Research Associate Type II Diabetes Type II Diabetes

Rat Fat Cell ModelRat Fat Cell Model

West Virginia University (1988-1992)

Assistant Professor of Animal ScienceAssistant Professor of Animal Science Growth & DevelopmentGrowth & Development Meat TechnologyMeat Technology

“Hormonal Regulation of Lipolysis in Chicken Fat Cells”

ARS, Poultry Research LaboratoryGeorgetown, DE (1992-1994)

Research Physiologist (Poultry)Research Physiologist (Poultry) Growth & DevelopmentGrowth & Development

DelmarvaPoultryIndustry

“Improve the Lean-to-Fat Ratio of Broiler Chickens”

UMES

ARS, Nutrient Conservation & Metabolism LabBeltsville, MD (1994-1995)

Research Dairy ScientistResearch Dairy Scientist Ruminant NutritionRuminant Nutrition

Beltsville Agricultural Research Center

ARS, Microbial Food Safety Research Unit UMES, Princess Anne, MD (1995-present)

Research Food TechnologistResearch Food Technologist Predictive MicrobiologyPredictive Microbiology OutreachOutreach

Feature Presentation

Current Food Safety ApproachJack-in-the-Box

HACCPHACCP No testingNo testing

Performance StandardsPerformance Standards DetectionDetection EnumerationEnumeration

C. jejuniC. jejuni

To test or not to test,that is the question

Traditional Culture MethodDetection and Enumeration

Pre-enrichmentPre-enrichment

Selective enrichmentSelective enrichment

Selective platingSelective plating

ConfirmationConfirmation

5 to 7Days

Rapid Detection Method

BAX® PCR system

24 to 30 h

102 103 104 105 106 107101100

Bailey, J.S. 1998. J. Food Prot. 61:792-795.

Sample IncubationImportant Factors

Target pathogen (< 1/ml)

•Food Factors

•Inhibitors

•Competition

•Pathogen Factors

•Injury

•Strain

•PCR Sensitivity•104 cells/mlPCR

DetectionTime

Sample SizeChicken carcass rinse

SalmonellaSalmonella Incidence Incidence 4.9% for 10 ml4.9% for 10 ml 20.5% for 270 ml20.5% for 270 ml

Surkiewicz et al., 1969. Food Tech.23:80-85.

Monte Carlo SimulationExtrapolation to other sample sizes

Pathogen Incidence = 10/100 or

10%

100, 10 g Samples

Monte Carlo SimulationExtrapolation to other sample sizes

Pathogen Incidence = 6/10 or 60%

10, 100 g Samples

Objectives

To develop a standard curve for enumerating food To develop a standard curve for enumerating food pathogens as a function of PCR detection time.pathogens as a function of PCR detection time.

To determine the effects of strain variation, meat type and To determine the effects of strain variation, meat type and microbial competition on the shape of the standard curve.microbial competition on the shape of the standard curve.

To develop a Monte Carlo simulation model for To develop a Monte Carlo simulation model for enumeration of food pathogens as a function of sample enumeration of food pathogens as a function of sample size.size.

Materials and Methods

SalmonellaSalmonella Typhimurium 14028Typhimurium 14028

WorthingtonWorthington

Starter culturesStarter cultures 3737°C for 23 h at 150 opm°C for 23 h at 150 opm

Brain heart infusion brothBrain heart infusion broth

Inoculated Pack Study Pre-enrichment Samples

SampleSample 25 g of chicken + 225 ml of buffered peptone water25 g of chicken + 225 ml of buffered peptone water

InoculumInoculum 10100.70.7 to 10 to 1066 CFU CFU

IncubationIncubation 3737°C without shaking°C without shaking

SamplingSampling 0, 2, 4, 6, 8, 10, 12, 24 h0, 2, 4, 6, 8, 10, 12, 24 h

PCR Detection Time Score

PCR AnalysisPCR Analysis BAXBAX® System® System

One gel per sampleOne gel per sample

Scoring SystemScoring System 0 = no band0 = no band

1 = faint band1 = faint band

2 = < full band2 = < full band

3 = full band3 = full band

0 2 4 6 8 10 12 24 MWSubsample (h)

Score 0 0 1 2 3 3 3 3

Example

TotalScore

15

DatasetSterile breast meat and Typhimurium 14028

Strain Chicken Dilution VolumeLog

CFU/mlLog CFU PCR Score

s2 sBM -8 0.5 9.89 1.65 11s2 sBM -5 1 10.03 5.01 17s2 sBM -6 0.5 10.01 3.70 15s2 sBM -9 0.5 10.03 0.71 11s2 sBM -5 0.5 10.05 4.72 18s2 sBM -7 0.5 10.01 2.70 14s2 sBM -4 1 9.97 5.99 18s2 sBM -9 1 9.97 0.99 11s2 sBM -6 1 9.94 3.97 16s2 sBM -4 0.5 9.97 5.69 21s2 sBM -8 1 9.94 1.97 14s2 sBM -7 1 9.97 2.99 14

0 1 2 3 4 5 6 70

5

10

15

20

25

Thigh

Breast

Breast

Thigh

Salmonella Typhimurium (log number/25 g)

PC

R d

etec

tion

time

scor

e

Type of Chicken MeatSterile cooked (autoclaved) chicken meat

8 12 16 20 24 28 32 36 40 44 480

10

20

30

40

50Breast

Breast

Thigh

Thigh

Temperature ( C)

Lag

Tim

e (h

)

Previous StudySalmonella Typhimurium 14028

Oscar, 2002. Int. J. Food Microbiol. 76:177-190.

0 10 20 30 40 50 60 700

1

2

3

4

5

6

7

8

9

10

11

Time (h)

Salm

onel

la T

yphi

mur

ium

AT

CC

140

28(l

og C

FU/g

)

8 12 16 20 24 28 32 36 40 44 480.0

0.2

0.4

0.6

0.8

1.0Breast

Breast

Thigh

Thigh

Temperature ( C)

Gro

wth

Rat

e(l

og C

FU/h

)

Previous StudySalmonella Typhimurium 14028

Oscar, 2002. Int. J. Food Microbiol. 76:177-190.

Conclusion

Dilution may minimize effects of the food matrix Dilution may minimize effects of the food matrix on PCR detection time score. on PCR detection time score.

Strain Variation117 Salmonella Isolates

Chicken Operations

0 1 2 30

1

2

3

4

5

S1S2S5S7S14S17S20S22

S26S30S31S33S38S44S52S62

Log cycle increase

Tim

e, h

Strain variation at 40°Cin brain heart infusion broth

Oscar, 1998. J. Food Prot. 61:964-968.

TyphimuriumWorthington

Previous Study

0 1 2 3 4 5 6 70

4

8

12

16

20

24

Worthington

Typhimurium

Typhimurium

Worthington

Salmonella spp. (log number/25 g)

PC

R d

etec

tion

time

scor

e

ResultsNaturally contaminated breast skin

Generation Time

Variation among 45 strains of Variation among 45 strains of S.S. Enteritidis was: Enteritidis was: 22% at 922% at 9°C °C 4% at 37°C4% at 37°C

Fehlhaber and Kruger, 1998. J. Appl. Microbiol. 84: 945-949.

Conclusion

Strain variation may not greatly affect PCR Strain variation may not greatly affect PCR detection time score under optimal growth detection time score under optimal growth conditions.conditions.

Microbial Competition

0 1 2 3 4 5 6 70

4

8

12

16

20

24 Naturally contaminated breast skinSterile cooked breast or thigh meat

Salmonella spp. (log number/25 g)

PC

R d

etec

tion

time

scor

e

Microbial CompetitionSalmonella Typhimurium DT104

0 10 20 30 40 504

5

6

7

8

9

10

11

12

DT104; nsBM14028; sBM

Temperature ( C)

MP

D (

log

CF

U/g

)

S. Enteritidis

10 20 30 40 500.0

0.2

0.4

0.6

0.8

GFPParent

Temperature ( C)

SGR

(lo

g/h)

Microbial CompetitionGreen fluorescent protein

Conclusion

Microbial competition affected PCR detection Microbial competition affected PCR detection time score and thus, needs to be incorporated into time score and thus, needs to be incorporated into the standard curve.the standard curve.

Monte Carlo Simulation Modeling

0 1 2 3 4 5 6 70

5

10

15

20

25

Y = 1 + 4.89X - 0.31X2

R2 = 0.9611Most likely

Maximum

Minimum

Salmonella spp. (log number/25 g)

PC

R d

etec

tion

time

scor

e

Final Standard Curve95% Prediction Interval

Pert(1.4, 2.1, 2.9)X <= 1.6578

5.0%X <= 2.5890

95.0%

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

Simulation ModelExcel + @Risk

Sample Type of chicken PCR detection time score1 Thigh skin 02 Thigh skin 03 Thigh skin 14 Breast skin 05 Breast skin 06 Breast skin 07 Breast meat 08 Breast meat 09 Breast meat 010 Breast meat 411 Breast meat 012 Breast meat 0

Naturally Contaminated ChickenNot inoculated with Salmonella

Sample size, g Incidence, % Minimum Median Maximum

25 16.7 1 2 16

50 30.8 1 3 22

100 51.5 1 3 33

200 76.7 1 5 42

400 94.1 1 8 70

500 97.4 1 9 81

Distribution of contamination (number of Salmonella )

Effect of Sample SizeSimulation results

Conclusion

Linear extrapolation of detection and enumeration Linear extrapolation of detection and enumeration results is not appropriate.results is not appropriate.

Future ResearchEnumeration

Automated BAXAutomated BAX® System® System Cycle threshold rather than band width score.Cycle threshold rather than band width score.

Other Pathogens and FoodsOther Pathogens and Foods

The End

Thank you for your attention!

I will be glad to answer your questions

The End

Thank you for your attention!

I will be glad to answer your questions