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研究課題の概要研究課題名予測微生物学的解析手法を用いた微生物学的リスク評価システムの開発主任研究者所属機関小関成樹 ( 独 ) 農業食品産業技術総合研究機構食品総合研究所研究成果の概要食品における微生物学的リスク評価を効率的効果的に行うための支援システムを開発し論文化の上公開した具体的にはリスク評価に必要不可欠な各種の食中毒菌挙動を効率的かつ確実に検索収集可能とする従来にない操作性と検索容易性を備えたデータベース(Microbial Responses Viewer, MRV)を開発したまた食中毒菌を食事とともに摂食した場合の胃内における食中毒菌の死滅を予測するシミュレータを開発したさらに実際の食品 ( 鶏挽肉とマグロすき身 )における食中毒菌の種々の保存温度における詳細な増殖挙動データを蓄積しそれらを元にして数理モデルを構築した開発したモデルによって変動温度環境下における食中毒菌数および食品常在菌数の同時予測と予測菌数の取り得る確率分布の推定を可能としたこれらのデータベースと予測モデルとの連携は効率的かつ適切なリスク評価の遂行に資する評価所見食中毒のリスク評価を行うためのデータベース並びにシュミレーションモデルの開発に成功した胃内の細菌の挙動をシュミレートできるモデルは食中毒の基礎データとして有用である食中毒菌の食品中や体内での挙動の予測に活用できると思われる評価結果目標を達成した

0705 Microbial Resp onses Viewer, MRV 1

( 1921 ) ( 0705) 1 2 3 3 1 1-1 2

7 6 (a) L. monocytogenes 7 6 5 5 4 4 3 3 2 2 1 1 0 0 20 40 60 80 100 120 Time (h) 0 0 20 40 60 80 100 120 Time (h) 1-1 L. monocytogenes ( ) Natural flora() ( ) ---- ph 1-2 3

2 ComBase (http://www.combase.cc/) 2008 8 Microbial Responses Viewer (MRV) (http://cbnfri.dc.affrc.go.jp/mrv-j) MRV 1-3 Microbial Responses Viewer (MRV) ComBase ph ph 4

3 Salmonella 1-4, L. monocytogenes 1-5 1-4 ( )( ) A: B: 1-5 L. monocytogenes natural flora 5 : natural flora 10 2 CFU/g L. monocytogenes : natural flora 10 4 CFU/g L. monocytogenes : natural flora 10 6 CFU/g L. monocytogenes : natural flora 10 2 CFU/g : natural flora 10 4 CFU/g : natural flora 10 6 CFU/g 5

ComBase ComBase ComBase 2-1 ph L. monocytogenes 2-1 1 29 2 ph web Combase UK, Food Research Institute, USDA, Eastern Regional Research Center, Australian Food Safety Centre of Excellence ComBase 1 PCR 6

PCR (Fujikawa et al., J. Food Hyg. Japan, 47,151-156,2006) Salmonella Enteritidis Percoll (EEM) inv A PCR Ct Ct EEM Ct 2-2 30 2-2 2 L. monocytogenes L. monocytogenes L. monocytogenes L. monocytogenes 2-3 L. monocytogenes 20 C L. monocytogenes 20-5-1 7

MRV (Microbial Responses Viewer) ComBase ComBase MRV Microbial Responses Viewer, http://cbnfri.dc.affrc.go.jp/ MRV 16 ph, 2-4 MRV MRV 2-4 E. coli O157:H7 <10 2 2-5 2-5 E. coli O157:H7 1 PCR -20 8

30 12 Salmonella Enteritidis 10 3.3 CFU/g 32 28 24 20 16 (2 ) ( ) DHL ( ) DHL PCR O Salmonella 32 2-6 20 C : : : : 2-6 10 6.8 CFU/g 10 4.8 CFU/g 100 2 Listeria monocytogenes L. monocytogenes Natural flora 2ºC 5 ºC 10 ºC 15 ºC 20ºC 25ºC 30 ºC L. monocytogenes 1.5%NaCl TSA L. monocytogenes PALCAM (MERCK) 30 24 L. monocytogenes 48 5 ºC 2-7 natural flora 6 L. monocytogenes 5 log CFU/g 4 6 log cfu/g 2 7log CFU/g 8log CFU/g natural flora L. 2-7 L. monocytogenes natural flora 5 : natural flora 10 2 CFU/g L. monocytogenes : natural flora 10 4 CFU/g L. monocytogenes : natural flora 10 6 CFU/g L. monocytogenes : natural flora 10 2 CFU/g : natural flora 10 4 CFU/g : natural flora 10 6 CFU/g monocytogenes L. monocytogenes 9

5 ºC L. monocytogenes natural flora 20 C natural flora MRV (Microbial Responses Viewer) ComBase (http://www.combase.cc/) 2008 8 Microbial Responses Viewer (MRV) (http://cbnfri.dc.affrc.go.jp/mrv-j) MRV ComBase (ver. 4) 29 3 5 50 C 3 1 1.0 log Ratkowsky MRV (http://cbnfri.dc.affrc.go.jp/mrv-j/) ph ph 2-8 10

2-8 MRV 2-9 29 18 2-9 L. monocytogenes 3 11

H20 L. monocytogenes 2-10 σ 2 τ 2 2-10 2-10 Markov Chain Monte Carlo (MCMC), APC ~ Poisson (mean.apc) APC Natural flora conc.apc ln.apc mean.apc = exp(ln.apc) / conc.apc ln.apc ln.apc ~ N (xa, τ 2 A ) xa Natural flora 12

(ln CFU/g) τ 2 A. L. monocytogenes LM ~ Poisson (mean.lm) mean.lm = exp(ln.lm) / conc.lm ln.lm ~ N (xl, τ 2 L ) ln.lm xl L. monocytogenes (ln CFU/g) L. monocytogenes (ln CFU/g) xa xl mean.xa mean.xl σ 2 A σ 2 L xa ~ N (mean.xa, σ 2 A ) xl ~ N (mean.xl, σ 2 L ) mean.xa mean.xl (1) (2) t, Temp, xa.max, xl.max, and xl.x0 (h), ( C), natural flora (ln CFU/g), L. monocytogenes (ln CFU/g), L. monocytogenes (ln CFU/g) pa[i] pl[i] MCMC Gibbs R2WinBUGS package (Sturtz et al., 2005) WinBUGS 1.4.3 (Lunn et al., 2000) R 2.8.1 (R Development Core Team, 2008) 13

L. monocytogenes Natural flora 28 584 10 (a) L. monocytogenes 10 (b) Natural flora 8 8 10 6 6 4 4 2 2 0 0 2 4 6 8 10 Prediction (log 10 CFU/g) 0 0 2 4 6 8 10 Prediction (log 10 CFU/g) 2-11 (a) L. monocytogenes (b) Natural flora Baranyi-Ratkowsky model 7 (a) Bayesian modeling 20 7 (b) Conventional modeling 20 6 6 5 15 5 15 4 3 10 4 3 10 2 5 2 5 1 1 0 0 0 20 40 60 80 100 120 Time (h) 0 0 0 20 40 60 80 100 120 Time (h) 2-12 L. monocytogenes ( ) Natural flora ( ) (a) New Bayesian State Space model, (b) Conventional Baranyi model 14

7 (a) L. monocytogenes 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 20 40 60 80 100 120 Time (h) 0 0 20 40 60 80 100 120 Time (h) 2-13 L. monocytogenes ( ) Natural flora ( ) ---- 1) 2-14 The Key Events Dose-Response Framework 15

30 ml 37 ph ph 4.5 3.0 4.0 2.5 3.5 2.0 3.0 1.5 2.5 1.0 2.0 0.5 E. coli O157:H7 Salmonella L. monocytogenes ND ND 0 0 0.5 1.0 1.5 2.0 2.5 3.0 Time (h) 1.5 1.0 Time (h) E. coli - ph Lm -ph Sal- ph 0 0.5 1.0 1.5 2.0 2.5 3.0 2-15 ph 10 2 ~10 3 CFU/g 1 ph ph 2.5 ph ph L. monocytogenes E. coli O157:H7 ph 2-3-16 L. monocytogenes E. coli O157:H7 Salmonella ph (~2.5) 0-0.2-0.4-0.6-0.8-1.0-1.2-1.4 1.0 1.5 2.0 2.5 3.0 3.5 4.0 ph LM L. monocytogenes E. O157 coli O157:H7 Salmonella spp. 2-16 ph 16

ph 1 ph ph USFDA http://www.fda.gov/food/foodsafety/foodborneillness/foodborneillnessfoodbornepathogensnaturaltoxi ns/badbugbook/ucm122561.htm http://www.mext.go.jp/b_menu/shingi/gijyutu/gijyutu3/toushin/05031802/002.htm PDF ph 2 ph ph 1.5, 40 ml ph = - log10 [H+] 3 1 0.1 0.01% 3 ph ph max ph 2 ph min ph 1.5 a -0.01 4 ph ph x[t] <- x[t - 1] + Delta.time[t] * (p[1] * exp(p[2] * ph[t])) t p[1], p[2] MCMC 95 ph 3 ph 17

ph ph a 5 a = 0 a= 1 5 g CFU/g = CFU ph 2-17 27 3 18

Salmonella enterica serovar Enteritidis low-contaminated chicken high-contaminated chicken 30 10 TSB 10 3.5 CFU/g 10g 110ml standard plate counts (SPC) DHL XLD PCR O AM-7002 Fujikawa 2004, 2005 N t k N max N min m n Fujikawa and Kano 2009 k log 1 2-18 19

2-18 24 2 ( 2-19) 2 19 2 20 20

2-20 4 2-21 (A) (B) 2-21 2-22 2-22 21

(log ) 2-1 2-1 (log ) A 0.0303 0.0317 B 0.1560 0.0728 A 0.0626 0.1370 B 0.1650 0.1620 2 22 5 Fujikawa, H., Kai, A., Morozumi S., 2004. A new logistic model for Escherichia coli at constant and dynamic temperatures. Food Microbiol. 21, 501-509. Fujikawa, H., Morozumi, S., 2005. Modeling Surface Growth of Escherichia coli on Agar Plates. Appl. Environ. Microbiol. 71, 7920-7926. Fujikawa, H., Kano, Y., 2009. Development of a program to fit data to a new logistic model for microbial growth. Biocont. Sci. 14, 83-86. 22

in vitro Listeria monocytogenes ph ph 2 23 3 75 g ph L. monocytogenes ( ) Buchanan The Key Events Dose-Response Framework 1) ( ) in vitro L. monocytogenes GAM 0.1%(w/v) 0.4% L. monocytogenes Scott-A CIP 103575 (Lm) (BHI) 30 C, 6-24h 10 9 cfu/ml BHI 100 0.3ml 3ml (n = 3) Lactobacillus acidophilus NBRC 13951 Enterococcus faecalis NBRC 100480 MRS broth 48h OD 660nm 2.0, 10 9 cfu/ml (OD660nm=2) Lm 0.3ml Lm E. faecalis Lb. acidophilus MRS broth Lm 37 C 24h PALCAM Lm 1) Lm 10 6 cfu/ml 37 24h 10 9 cfu/ml 10 4 cfu ( 2-24) Lm 23

2-24. 10 6 cfu/ml 37 24h 2) MRS Lb. acidophilus E. faecalis 10 8 cfu/ml Lm 2-25 Lb. acidophilus Lm Lm E. faecalis Lm Lm ( 6h)( 12h) 2-25 Lactobacillus acidophilus Enterococcus faecalis Cont., ; Cult., ; Sup., ; Cell, 24

Lm Lm Lm Lb. acidophilus Ec. faecalis Lm 1) Buchanan, R.L. et al., 2009. The Key Events Dose-Response Framework: Its Potential for Application to Foodborne Pathogenic Microorganisms',Critical Reviews in Food Science and Nutrition,49:8,718-728. 25

3 1 2 3 26

3 1 2 ComBase (http://www.combase.cc/) 2008 8Microbial Responses Viewer (MRV) (http://cbnfri.dc.affrc.go.jp/mrv-j)mrv 3 Salmonella, L. monocytogenes ComBase ComBase ComBase Combase UK, Food Research Institute, USDA, Eastern Regional Research Center, Australian Food Safety Centre of Excellence 2 26 27

ComBase (ver. 4) 29 3 5 50 C 3 1 1.0 log 2 26 ComBase Ratkowsky MRV (http://cbnfri.dc.affrc.go.jp/mrv-j/) ph ph 2 27 2 27 MRV 28

ComBase ph ph 2 28 2 28 L. monocytogenes 29 18 3 H20 L. monocytogenes 2 29 29

σ 2 τ 2 2 29 2 29 Markov Chain Monte Carlo (MCMC), APC ~ Poisson (mean.apc) APC Natural flora conc.apc ln.apc mean.apc = exp(ln.apc) / conc.apc ln.apc ln.apc ~ N (xa, τ 2 A ) xa Natural flora (ln CFU/g) τ 2 A. L. monocytogenes LM ~ Poisson (mean.lm) mean.lm = exp(ln.lm) / conc.lm ln.lm ~ N (xl, τ 2 L ) ln.lm xl L. monocytogenes (ln CFU/g) L. monocytogenes (ln CFU/g) xa xl mean.xa mean.xl σ 2 A σ 2 L xa ~ N (mean.xa, σ 2 A ) xl ~ N (mean.xl, σ 2 L ) mean.xa mean.xl (1) 30

(2) t, Temp, xa.max, xl.max, and xl.x0 (h), ( C), natural flora (ln CFU/g), L. monocytogenes (ln CFU/g), L. monocytogenes (ln CFU/g) pa[i] pl[i] MCMC Gibbs R2WinBUGS package (Sturtz et al., 2005) WinBUGS 1.4.3 (Lunn et al., 2000) R 2.8.1 (R Development Core Team, 2008) L. monocytogenes Natural flora 28 584 10 (a) L. monocytogenes 10 (b) Natural flora 8 8 10 6 6 4 4 2 2 0 0 2 4 6 8 10 Prediction (log 10 CFU/g) 0 0 2 4 6 8 10 Prediction (log 10 CFU/g) 2 30 (a) L. monocytogenes (b) Natural flora 31

Baranyi-Ratkowsky model 7 (a) Bayesian modeling 20 7 (b) Conventional modeling 20 6 6 5 15 5 15 4 3 10 4 3 10 2 5 2 5 1 1 0 0 0 20 40 60 80 100 120 Time (h) 0 0 0 20 40 60 80 100 120 Time (h) 2-31 L. monocytogenes ( ) Natural flora ( ) (a) New Bayesian State Space model, (b) Conventional Baranyi model 7 (a) L. monocytogenes 7 6 5 4 3 2 1 0 0 20 40 60 80 100 120 Time (h) 6 5 4 3 2 1 0 0 20 40 60 80 100 120 Time (h) 2-32 L. monocytogenes ( ) Natural flora ( ) ---- 32

1) 2-33 The Key Events Dose-Response Framework 30 ml 37 ph ph 4.5 3.0 4.0 2.5 3.5 2.0 3.0 1.5 2.5 1.0 2.0 0.5 E. coli O157:H7 Salmonella L. monocytogenes ND ND 0 0 0.5 1.0 1.5 2.0 2.5 3.0 Time (h) 1.5 1.0 Time (h) E. coli - ph Lm -ph Sal- ph 0 0.5 1.0 1.5 2.0 2.5 3.0 2-34 ph 33

10 2 ~10 3 CFU/g 1 ph ph 2.5 ph ph L. monocytogenes E. coli O157:H7 ph L. monocytogenes E. coli O157:H7 Salmonella ph (~2.5) ph 0-0.2-0.4-0.6-0.8-1.0-1.2-1.4 1.0 1.5 2.0 2.5 3.0 3.5 4.0 ph LM L. monocytogenes E. O157 coli O157:H7 Salmonella spp. 2-35 ph 1 ph ph USFDA http://www.fda.gov/food/foodsafety/foodborneillness/foodborneillnessfoodbornepathogensnaturalt oxins/badbugbook/ucm122561.htm http://www.mext.go.jp/b_menu/shingi/gijyutu/gijyutu3/toushin/05031802/002.htm PDF ph 2 ph ph 1.5, 40 ml ph = - log10 [H+] 3 1 0.1 0.01% 3 ph 34

ph max ph 2 ph min ph 1.5 a -0.01 4 ph ph x[t] <- x[t - 1] + Delta.time[t] * (p[1] * exp(p[2] * ph[t])) t p[1], p[2] MCMC 95 ph 3 ph ph ph a 5 a = 0 a= 1 5 35

g CFU/g = CFU ph 2-36 27 3 36

4 Salmonella enterica serovar Enteritidis low-contaminated chicken high-contaminated chicken 30 10 TSB 10 3.5 CFU/g 10g 110ml standard plate counts (SPC) DHL XLD PCR O AM-7002 Fujikawa 2004, 2005 N t k N max N min m n Fujikawa and Kano 2009 k log 37

( 10 4.7 CFU/g) 2-38A N max k 8 SPC k N max 2-38B A B 2-38 (A) (B) 2 ( 10 6.8 CFU/g) 2-39A N max k 2-39B k N max A B 2-39 (A) (B) 3 2-40 38

2-40 24 2 ( 2-41) 2-41 2-42 39

2-42 4 2-43 (A) (B) 2-43 2-44 2-44 40

(log ) 2 2 (log ) A 0.0303 0.0317 B 0.1560 0.0728 A 0.0626 0.1370 B 0.1650 0.1620 2 44 5 Fujikawa, H., Kai, A., Morozumi S., 2004. A new logistic model for Escherichia coli at constant and dynamic temperatures. Food Microbiol. 21, 501-509. Fujikawa, H., Morozumi, S., 2005. Modeling Surface Growth of Escherichia coli on Agar Plates. Appl. Environ. Microbiol. 71, 7920-7926. Fujikawa, H., Kano, Y., 2009. Development of a program to fit data to a new logistic model for microbial growth. Biocont. Sci. 14, 83-86. 41

5 L. monocytogenes Natural flora 2ºC 5 ºC 10 ºC 15 ºC 20ºC 25ºC 30 ºC L. monocytogenes 1.5%NaCl TSA L. monocytogenes PALCAM (MERCK) 30 24 L. monocytogenes 48 5 ºC 2-45 natural flora 6 L. monocytogenes 5 log CFU/g 4 6 log cfu/g 2 7log CFU/g 8log CFU/g natural flora L. monocytogenes L. monocytogenes 5 ºC L. monocytogenes natural flora 20 C natural flora 2-45 L. monocytogenes natural flora 5 : natural flora 10 2 CFU/g L. monocytogenes : natural flora 10 4 CFU/g L. monocytogenes : natural flora 10 6 CFU/g L. monocytogenes : natural flora 10 2 CFU/g : natural flora 10 4 CFU/g : natural flora 10 6 CFU/g 6 in vitro Listeria monocytogenes ph ph 2-46 3 75 g ph L. monocytogenes ( ) 42

Buchanan The Key Events Dose-Response Framework 1) ( ) in vitro L. monocytogenes GAM 0.1%(w/v) 0.4% L. monocytogenes Scott-A CIP 103575 (Lm) (BHI) 30 C, 6-24h 10 9 cfu/ml BHI 100 0.3ml 3ml (n = 3) Lactobacillus acidophilus NBRC 13951 Enterococcus faecalis NBRC 100480 MRS broth 48h OD 660nm 2.0, 10 9 cfu/ml (OD660nm=2) Lm 0.3ml Lm E. faecalis Lb. acidophilus MRS broth Lm 37 C 24h PALCAM Lm 1 Lm 10 6 cfu/ml 37 24h 10 9 cfu/ml 10 4 cfu ( 2-47) Lm 2-47. 10 6 cfu/ml 37 24h 43

3 MRS Lb. acidophilus E. faecalis 10 8 cfu/ml Lm 2-48 Lb. acidophilus Lm Lm E. faecalis Lm Lm ( 6h)( 12h) Lm Lm Lm Lb. acidophilus Ec. faecalis Lm 2-48 Lactobacillus acidophilus Enterococcus faecalis Cont., ; Cult., ; Sup., ; Cell, Buchanan, R.L. et al., 2009. The Key Events Dose-Response Framework: Its Potential for Application to Foodborne Pathogenic Microorganisms',Critical Reviews in Food Science and Nutrition,49:8,718-728. 44

1) Koseki, S. 2009. Microbial Responses Viewer (MRV): a new ComBase-derived database of microbial responses to food environments. Int. J. Food Microbiol. 134, 75-82. (IF = 2.753) 2) Shige Koseki 1 *, Yoshiko Takizawa 2, Satoko Miya 2, Hajime Takahashi 2, and Bon Kimura. A Novel Approach to Predicting the Bacterial Growth using Bayesian State Space Model: Quantify both the Bacterial Population and its Probability Distribution. (in submission) 3) Shige Koseki 1 *, Yasuko Mizuno. Pathogen can survive during digesting process: Prediction of pathogen survival in si,ulated gastric fluid with various foods. (in submission) 2009 14 MRV http://dc.cbnfri.affrc.go.jp/mrv-j 45

3 Buchanan 46

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