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Master of Science in Food Science and Biotechnology

Influence of Pathogen Contamination

on Beef Microbiota under

Different Storage Temperatures

식중독균 오염 여부와 보관 온도에 따른

소고기 마이크로바이옴 분석

February, 2020

HyeLim Choi

Department of Agricultural Biotechnology

College of Agriculture and Life Sciences

Seoul National University

석사학위논문

Influence of Pathogen Contamination

on Beef Microbiota under

Different Storage Temperatures

지도교수 최 상 호

이 논문을 석사학위논문으로 제출함

2020년 2월

서울대학교 대학원

농생명공학부

최 혜 림

최혜림의 석사학위논문을 인준함

2020년 2월

위원장 강 동 현 (인)

부위원장 최 상 호 (인)

위원 이 도 엽 (인)

I

Abstract

Outbreaks of food poisoning due to the consumption of

contaminated beef from fast-food chains are becoming more

frequent. Pathogen contamination in beef influences its spoilage as

well as the development of foodborne illness. Thus, the influence of

pathogen contamination on beef microbiota should be analyzed to

evaluate food safety. We analyzed the influence of pathogen

contamination on the shift in microbiota and the interactions

between the pathogen and indigenous microbes in beef stored under

different conditions. Sixty beef samples were stored at 25 °C and

4 °C for 24 h, and the shifts in microbiota were analyzed using the

MiSeq system. The influence of pathogen contamination on

microbiota was analyzed by artificial contamination experiments

with Escherichia coli FORC_044, which was isolated from the stool

of a food poisoning patient in Korea. The bacterial amounts and the

proportion of Escherichia were higher when the beef was stored at

25 °C. Artificially introduced Escherichia positively correlated with

the indigenous microbes such as Pseudomonas, Brochothrix,

Staphylococcus, Rahnella, and Rhizobium as determined by co-

occurrence network analyses. Carnobacterium, a potential spoilage

microbe, was negatively correlated with other microbes, including

Escherichia. The predicted functions of altered microbiota showed

that the pathways related to the process of spoilage including

II

biosynthesis of acetic acid and lactic acid increased over time. The

shift in pathways was more pronounced in contaminated beef stored

at 25 °C. Carnobacterium, Lactobacillus, and Escherichia were the

main genera contributing to the shift in the relative abundance of

functional genes involved in the various spoilage pathways. Our

results indicated that pathogen contamination could influence beef

microbiota and mediate spoilage. This study extends our

understanding of the beef microbiota and provides insights into the

role of pathogen and storage conditions in meat spoilage.

Keywords: Metagenomics, Microbiota, Beef, Spoilage

microorganism, Escherichia coli, Contamination, Microbial

interactions, Food safety

Student Number: 2018-22519

III

Contents

Abstract∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅰ

Contents∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅲ

List of Figures∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅴ

List of Tables∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅵ

Ⅰ. INTRODUCTION∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙1

Ⅱ. MATERIALS AND METHODS∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙4

Sample preparation and artificial Escherichia coli contamination∙∙∙∙∙∙4

Metagenomic DNA extraction∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙6

Quantitative real-time polymerase chain reaction (PCR) ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙7

MiSeq sequencing∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙9

Sequence data analysis∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙10

Ⅲ. RESULTS AND DISCUSSION∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙15

Comparison of bacterial amounts between samples∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙15

IV

Comparison of Shannon diversity index between samples∙∙∙∙∙∙∙∙∙∙∙∙∙∙17

Shift in beef microbiota contaminated with E. coli under different

storage conditions∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙25

Comparison of co-occurrence networks∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙31

Shifts in predicted pathways in the microbiota under different

storage conditions∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙37

Shifts in predicted functional genes in the microbiota under different

storage conditions∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙43

OTU contribution to the shift in functional genes∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙50

Validation of OTU contribution using quantitative real-time PCR∙∙56

Ⅳ. CONCLUSIONS∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙61

Ⅴ. REFERENCES∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙63

Ⅵ. 국문초록∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙71

V

List of Figures

Figure 1. Comparison of bacterial cell numbers among samples∙∙∙∙∙16

Figure 2. Comparison of Shannon diversity index among samples∙∙24

Figure 3. Shift in beef microbiota composition∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙29

Figure 4. Co-occurrence network of microbiota in beef samples

following experimental contamination with E. coli and storage under

different conditions∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙34

Figure 5. Shifts in predicted pathways in beef microbiota under

different storage conditions∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙40

Figure 6. Shifts in predicted functional genes of microbiota under

different storage conditions∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙44

Figure 7. OTUs contributing to the shift in functional genes∙∙∙∙∙∙∙∙∙∙∙52

Figure 8. Validation of OTUs contributing to the shift in predicted

functional genes using quantitative real-time PCR∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙59

VI

List of Tables

Table 1. ANI score between E. coli FORC_044 and other EHEC

strains∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙12

Table 2. Primers used in this study∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙13

Table 3. Summary of diversity indices obtained from Illumina

Miseq∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙18

VII

1

Ⅰ. INTRODUCTION

Beef is one of the most popular meats and is consumed in

large quantities around the world. According to the Organization for

Economic Cooperation and Development (OECD) Agriculture

Statistics, the United States consumes over 27 kilograms of beef

per capita, ranking it second in the world, and South Korea

consumes over 10 kilograms of beef per capita, ranking it the 16th

(OECD/FAO, 2018) in the world. Outbreaks of food poisoning in the

US due to the consumption of contaminated beef has been reported

several times since the outbreak of Escherichia coli O157:H7 in

1982 (Rangel, J. M., 2005). E. coli O157:H7, Shiga toxin-producing

E. coli (STEC), has been reported to cause food poisoning (Nataro,

J. P., & Kaper, J. B., 1998). An estimated 265,000 STEC infections

are reported each year in the US (CDC, 2018). Over 51 cases of

pathogenic E. coli outbreak and over 2,600 patients were reported

in South Korea in the past three years (Ministry of Food and Drug

Safety, 2019). Recently, a case of a young child who lost 90

percent of her kidney function due to the consumption of E. coli-

contaminated hamburgers was reported in South Korea. It was

reported that the child suffered from hemolytic uremic syndrome

(HUS), which is commonly caused by an E. coli O157 infection

(Rangel, J. M., 2005).

2

Several studies have reported the relationship between

pathogenic E. coli contamination and spoilage microorganisms in

beef products. Contamination with E. coli O157:H7 in ground beef

has been reported to be related to spoilage (Koutsoumanis, K.,

2009). This study showed a positive correlation between E. coli

O157:H7 and pseudomonads during retail storage of beef using

kinetic modeling of spoilage bacteria and exposure assessment.

They concluded that spoilage could affect the growth of pathogens

and thus should be considered as a risk factor in foods. Another

study showed the negative correlation between E. coli O157:H7 and

lactic acid bacteria after spiking E. coli in beef (Vold, L., 2000).

However, a comprehensive study to evaluate the effect of

pathogenic E. coli contamination on the indigenous beef microbiota

and its role in spoilage has not been attempted.

Ideal storage conditions are essential to prevent pathogen

contamination and spoilage of meat products. Fresh meat, including

beef, poultry, and seafood should be kept at or below 4 °C (39 °F)

according to the HACCP (Hazard Analysis and Critical Control

Points) Plan (USDA, 1999). These products are recommended to

be stored between -2 and 10 °C in Korea (Food, K., & Drug

Association., 2005). The Center for Disease Control and Prevention

(CDC) recommends that raw beef be refrigerated or frozen within 2

h of purchase and be consume within 1-2 days, even if it is stored

in the refrigerator (CDC, 2019). Meat products might be exposed to

3

higher temperatures and could get contaminated with foodborne

pathogens during transportation and delivery (Mercier, S., 2017), in

particular, during loading and unloading. Furthermore, it is difficult

to maintain the inner temperature of the transport vehicle below

4 °C throughout the distribution process. Therefore, a

comprehensive analysis of microbiota and pathogen contamination in

beef stored at different conditions is necessary to reduce the

potential risk of foodborne illnesses.

This study aimed (1) to investigate the influence of

pathogen contamination and storage temperature on the beef

microbiota, (2) to analyze microbial interaction in the beef

microbiota under different storage conditions over time after

contamination, and (3) to understand the effects of contamination

and temperature on the spoilage of meat. To evaluate the influences

and interactions of pathogens with indigenous microbes, artificial

contamination was induced using E. coli FORC_044 isolated from a

food poisoning patient. Results from this study can extend our

understanding of the influence of pathogen contamination on the

indigenous microbiota in beef and its effect on food safety.

4

Ⅱ. MATERIALS AND METHODS

Sample preparation and artificial Escherichia coli contamination

A total of 60 beef samples were collected from the

Livestock Packing Center (LPC) in Um-Seung in October 2018.

The Um-Seung LPC was selected as they are the largest supplier

of livestock (cattle) to wholesale markets, and over 25% of beef

distributed in Korea is from this LPC (Baek Jong-Ho, 2019).

Ground beef was used in this study as it is commonly used for

hamburger patties and is the most frequently consumed raw meat in

Korea. The influence of pathogen contamination on the indigenous

microbiota of beef was analyzed by artificial contamination with E.

coli FORC_044 under different storage conditions. The FORC_044

strain is an Enterohemorrhagic E. coli (EHEC) that was isolated

from a food poisoning patient by the National Culture Collection for

Pathogens (NCCP) in March 2015. The serotype of FORC_044 is

O157:H7, which has been frequently detected in numerous beef-

related food poisoning outbreaks. The sequenced genome of the

FORC_044 strain was similar to other well-known O157:H7 strains

isolated from beef, including the EDL933 strain (Perna, N. T., 2001)

in Average Nucleotide Identity (ANI) analysis (Table S1). The

FORC_044 strain was cultivated at 37 °C in Luria-Bertani (LB)

medium overnight. The number of contaminant cells was adjusted to

5

10 cell/g, which was the infective dose of EHEC reported by the

United States Department of Agriculture (USDA) (Schmid-Hempel,

2007). The FORC_044 strain was then evenly sprayed on ground

beef and homogenized thoroughly. Samples were stored in sterile

containers at 4 °C or 25 °C and collected at five different time

points (0 h, 4 h, 8 h, 12 h, and 24 h). The contaminated samples at

0 h were acquired immediately after introducing the E. coli and

before the storage process. The samples at 0 h were used to

evaluate the shift in each storage conditions with time. Since the

CDC guide recommends the consumption of beef within one day and

beef tended to decompose at 25 °C after 24 h, we analyzed the

microbiota until 24 h. Samples (25 g) collected at different time

points were mixed with 225 mL buffered peptone water (BPW).

Bacterial cells were detached from meat using a spindle

(microorganism homogenizer, Korea patent registration: 10-2010-

0034930). The samples were homogenized by rotation and

vibration using a direct drive motor in a stomach bag in the spindle.

Bacterial cells were stored at -80 °C before metagenomic DNA

extraction.

Carnobacterium divergens KCTC 3675, Lactobacillus sakei

KCTC 3603, Staphylococcus saprophyticus KCTC 3345, and E. coli

K12 W3110 were cultured as controls for quantitative real-time

PCR. C. divergens KCTC 3675 was cultivated at 37 °C in tryptic soy

6

broth (TSB) medium with 3% yeast extract, and L. sakei KCTC

3603 was cultivated at 30 °C in De Man, Rogosa, and Sharpe (MRS)

medium. S. saprophyticus KCTC 3345 was cultivated at 37 °C in

brain heart infusion (BHI) medium, and E. coli K12 W3110 was

cultivated at 37 °C in LB medium. These four strains were grown

until they attained an optical density (OD) of 1.0 at 600 nm. The

bacteria were collected by centrifugation and then stored at -80 °C

before DNA extraction.

Metagenomic DNA extraction

Metagenomic DNA was extracted from the samples using

the phenol DNA extraction method, as described in previous studies

(Lee, Lee, Chung, Choi & Kim, 2016; Naravaneni & Jamil, 2005).

Briefly, bacterial cells in 225 mL BPW were filtered through a

sterilized gauze filter and centrifuged. The pellets were dissolved in

10 mL TES buffer (10 mM Tris-HCl, pH 8.0, 1 mM

ethylenediaminetetraacetic acid (EDTA), 0.1 M NaCl), and then

centrifuged. The pellets were suspended in 400 μL TE buffer (10

mM Tris-HCl, pH 8.0, 1 mM EDTA) and then, were treated with 50

μL lysozyme solution (100 mg/mL) and 200 μL Proteinase K

mixture (140 μL 0.5 M EDTA, 20 μL 20 mg/mL Proteinase K, 40 μL

10% sodium dodecyl sulfate) and incubated for 1 h at 37 °C. After

7

that, 100 μL 5 M NaCl and 80 μL CTAB/NaCl solution were added

to the pellet and incubated for 10 min at 65 °C. One milliliter of

phenol/chloroform/isoamyl alcohol (25:24:1 v/v/v) was added to the

pellet and mixed well followed by centrifugation at 4 °C. The upper

phase was transferred to 3 μL RNase A (100 mg/mL) and 80 μL 3

M sodium acetate solution was added. One milliliter of 100% ethanol

was then added to the mixture. It was washed again with 70%

ethanol, and the DNA pellet was resuspended in 100 μL TE buffer

and incubated at 55 °C for 1 h. The extracted metagenomic DNA

was purified using the PowerClean DNA Clean-up kit (Mo Bio

Laboratories, Carlsbad, CA, USA) and confirmed by 1% agarose gel

electrophoresis. DNA from the cultured control strains was

extracted similarly.

Quantitative real-time polymerase chain reaction (PCR)

The total amount of bacteria in the sample was determined

using quantitative real-time (qRT) PCR of the 16S rRNA genes.

The rRNA gene was amplified using the primers 340F (5′-

TCCTACGGGAGGCAGCAG-3′) and 518R (5′-

ATTACCGCGGCTGCTGG-3′) with the BioRad CFX96 Real-Time

System (Biorad, CA, United States). Triplicate reactions were

8

performed for each sample with a final volume of 20 μL,

compromising 10 μl SYBR Green Supermix (Biorad), 1 μM each

primer, and 1 μL DNA template (ten-fold diluted DNA) or distilled

water (negative control). The conditions for the reaction were as

follows: initial denaturation at 95 °C for 30 s; 40 cycles of

denaturation at 95 °C for 5 s and extension at 60 °C for 30 s; and

dissociation at 72 °C for 15 s, 60 °C for 30 s, and 95 °C for 15 s.

Standard curves were generated from parallel PCRs with serial

log-concentrations (1 × 102–1 × 108) of the copy number of the

16S rRNA from E. coli K12 w3110. Regression coefficients (r2) for

all standard curves were higher than 0.987.

The amount of contaminant E. coli FORC_044 was

determined by the expression level of the stxI gene, which encodes

the most significant virulence factor (Shiga-like toxins I) in EHEC

strains (Watterworth, L., 2005) (Table S2). Triplicate reactions of

each sample were conducted using a BioRad CFX96 Real-Time

System, as described above. Standard curves were generated from

parallel PCRs of serial log-concentrations (1 × 102–1 × 108) of the

E. coli FORC_044 strain. Regression coefficients (r2) for all

standard curves were higher than 0.995.

The expression levels of functional genes, acetate kinase

(ackA), and menaquinone-specific isochorismate synthase (menF),

9

in the samples was calculated using ackA- and menF-targeted

primers for Carnobacterium, Lactobacillus, Staphylococcus, and

Escherichia (Table S2). Triplicate reactions of each sample were

conducted as described above. Standard curves were generated

from parallel PCRs of serial log-concentrations (1 × 102–1 × 108)

for each strain. Regression coefficients (r2) for all standard curves

were higher than 0.980.

MiSeq sequencing

The extracted metagenomic DNA was amplified using

primers (targeting the V1-V3 region of the 16S rRNA gene). PCR

amplification was performed by following the protocol for preparing

a 16S metagenomic sequencing library using the MiSeq system

(Illumina, Inc., San Diego, CA, USA). Briefly, the first amplification

was performed under the following conditions – initial denaturation

at 95 °C for 3 min; 25 cycles of denaturation at 95 °C for 30 s,

annealing at 55 °C for 30 s, and extension at 72 °C for 30 s; and a

final extension at 72 °C for 5 min. The amplicons were verified by

1.5% agarose gel electrophoresis, and purification and size selection

were performed using the Agencourt AMPure XP beads (Beckman

Coulter, Indianapolis, IN, USA). The index PCR was performed

using 5 μL of the initial PCR product in a final volume of 50 μL using

10

the Nextera XT Index Kit (Illumina, Inc.). The index PCR was

performed under the following conditions – initial denaturation at

95 °C for 3 min; 8 cycles of denaturation at 95 °C for 30 s,

annealing at 55 °C for 30 s, and extension at 72 °C for 30 s; and

final extension at 72 °C for 5 min. The amplicons of each sample

were purified again using Agencourt AMPure XP beads (Beckman

Coulter). The library was quantified using a BioRad CFX96 Real-

Time System. Equimolar concentrations of each library from the

different samples were pooled and sequenced using an Illumina

MiSeq system (300 bp-paired ends) according to the

manufacturer's instructions.

Sequence data analysis

Sequences obtained from the Illumina MiSeq sequencer

were sorted by index, and low-quality sequences were removed

using the USEARCH tool (Edgar, RC, 2010). Trimmed sequences

were clustered with 97% identity using the CLC genomic

workbench (ver. 8.5.1) with the Microbial Genomics Module

(Qiagen, Redwood City, CA, USA). The representative sequence in

each cluster was classified based on their taxonomic position using

the EzTaxon-e database (Yoon, S.H. et al., 2017). Various read

numbers in samples were normalized by random sub-sampling, and

the diversity indices were calculated using MOTHUR (Schloss et al.,

11

2009). Spearman coefficient was used to evaluate the correlation

between genera, and a network was constructed using the criteria,

threshold = 0.6 with FDR <0.05. The correlation values and FDR

values were calculated using SAS software. Co-occurrence

networks were visualized using Cytoscape, and genera, with a

relative abundance <10-3 within the network, were excluded as

their amount was considered negligible. Shifts in the potential

pathways and functional genes were predicted using PICRUSt2

(Douglas, G. M., 2019). Pathways that had over 2 log2 fold change

compared to 0 h and P-value <0.05 were selected, and the shift in

predicted pathways was analyzed using heatmaps in R. STAMP

(Parks, D. H., 2014) was used for further statistical tests of

predicted functional profiles and Welch’s t-test with Benjamini-

Hochberg FDR was conducted. The differences among samples

were analyzed using Welch’s t-test in R and GraphPad. The results

with P-values or FDR values less than 0.05 were considered

statistically significant.

12

Table 1. ANI score between E. coli FORC_044 and other EHEC

strains

Strain ANI score (%)

FORC_044 -

EDL933 99.97

Sakai 99.97

Xuzhou21 99.97

TW14359 99.86

EC4115 99.84

MG1655 98.01

K-12_ER3440 98

FORC_082 97.98

FORC_081 97.94

FORC_031 97.88

FORC_042 97.82

VR50 97.71

120009 97.7

FORC_041 97.7

13

Table 2. Primers used in this study

Strain Primer

Name

Primer Sequence Length

(bp)

Tm

(℃)

GC

(%)

Product

size (bp)

Carnobacterium divergens

KCTC 3675

ackA_F TTGTCACCTAGGAAACGGCG 20 60.32 55 116

ackA_R TATCGCCAGAACGAGTTCCC 20 59.54 55

menF_F AAGCCATGCATCCAACTCCA 20 59.96 50 215

menF_R ACCGGCTACTAAGCCACAAC 20 60.04 55

Escherichia coli K12 w3110

ackA_F ATCCGGCGATCATCTTCCAC 20 59.97 55 274

ackA_R GCGGCATTTTCACCGATACC 20 59.97 55

menF_F ACCCGCAATTCTACTGGCAA 20 59.96 50 99

menF_R GAAAACGTTGTGCCTGGTCC 20 59.97 55

14

Escherichia coli FORC_044

stxI_F ACCTCACTGACGCAGTCTGTGG 22 65.9 59 350

stxI_R TCTGCCGGACACATAGAAGGAAA 23 62.9 48

Lactobacillus sakei

KCTC 3603

ackA_F CGCTACTACCAGGTGTGCCC 20 62.57 65 299

ackA_R CCCAGCCAGTGGGGTAAAAC 20 60.9 60

Staphylococcus saprophyticus

KCTC 3345

menF_F TGAATTCGGTACGCGTGGAT 20 59.83 50 139

menF_R CACAATGCCACAACCAGCAA 20 59.9 50

15

Ⅲ. RESULTS AND DISCUSSION

Comparison of bacterial amounts between samples

The amounts of total bacteria and FORC_044 strain were

estimated using qRT-PCR of 16S rRNA and stx1 gene (Fig. 1A, B).

The total amounts of bacteria in beef stored at 25 °C (non-

contaminated: 2.55 x 109 cells/g, contaminated: 3.61 x 109 cells/g)

were higher than those stored at 4 °C (non-contaminated: 1.72 x

107 cells/g, contaminated: 1.61 x 108 cells/g) (P <0.001). The

bacterial amounts in the contaminated samples were higher than

those in non-contaminated samples at both 4 °C (P <0.001) and

25 °C (P <0.01) storage conditions. Furthermore, the amounts of

contaminated FORC_044 were significantly lower in samples stored

at 4 °C (4.94 x 104 cells/g) than those stored at 25 °C (1.39 x 107

cells/g) (P <0.001). These results indicated that contamination and

storage temperature influenced the abundance of bacteria in beef.

16

(A) (B)

0 h 4 h 8 h 1 2 h 2 4 h

5

6

7

8

9

1 0

S to ra g e t im e

To

tal

am

ou

nts

of

ba

cte

ria

(lo

g1

0c

ell

/g)

** *

* * *

*

* * *

*

0 h 8 h 1 2 h 2 4 h

0

1

4

5

6

7

8

S to ra g e t im e

Th

e a

mo

un

ts o

fE

. c

oli

(lo

g1

0c

ell

/g)

** *

Figure 1. Comparison of bacterial cell numbers among samples. (A) Total bacterial amounts in beef plotted against

time under storage. (B) The amount of E. coli FORC_044 in contaminated samples over time. Total bacterial amounts

and E. coli were estimated by quantitative real-time PCR. * P <0.05, ** P <0.01, *** P <0.001.

Non-contaminated_25℃

Non-contaminated_4℃

Contaminated_4℃

Contaminated_25℃

17

Comparison of Shannon diversity index between samples

A total of 3,841,637 sequence reads after trimming were

analyzed (Table S3). The number of reads in each sample was

normalized to 12,200 by random sub-sampling. The diversity of

microbiota was compared among different conditions, and it was

significantly different after 8 h of storage (Fig. 2). The highest

diversity was observed in contaminated samples stored at 4 °C after

12 h (3.29 ± 0.29), and the lowest was detected in contaminated

samples stored at 25 °C after 8 h (1.11 ± 0.10). The diversity of

microbiota in the contaminated samples stored at 4 °C (3.19 ± 0.53

after 8 h and 3.29 ± 0.29 after 12 h) was higher than those stored

at 25 °C (1.11 ± 0.10 after 8 h and 1.24 ± 0.21 after 12 h) (P <0.01

and P <0.001, respectively). The diversity of microbiota in the

non-contaminated samples stored at 4 °C (2.44 ± 0.11) was also

higher than those stored at 25 °C (1.63 ± 0.22) after 12 h storage

(P <0.01). The microbial diversity in the contaminated samples was

higher than that in the non-contaminated samples stored at 4 °C

after 12 h (P <0.01), while the microbial diversity in the non-

contaminated samples was higher than that in the contaminated

samples stored at 25 °C after 8 h (P <0.01).

18

Table 3. Summary of diversity indices obtained from Illumina Miseq

Sampling

temperature

Sampling

time Sampling group

Sample Analyzed

reads

Normalized

reads

Estimated

OTUs (Chao1)

Shannon

diversity index

4℃

0h

Non-contaminated

rc.111 61866 12,200 207.6471 1.467724

rc.112 112572 12,200 313.4286 1.502521

rc.113 62571 12,200 226 1.562666

Contaminated

r.111 60403 12,200 475.5714 2.283696

r.112 56391 12,200 496.6364 2.293656

r.113 52380 12,200 418.2857 2.448299

4h

Non-contaminated

rc.121 39339 12,200 702.0789 2.69688

rc.122 68699 12,200 590.5789 2.389502

19

rc.123 64217 12,200 3 0.012031

Contaminated

r.121 102414 12,200 330.7778 1.517648

r.122 107080 12,200 408.8485 1.569155

r.123 76081 12,200 377.2 1.477902

8h

Non-contaminated

rc.131 23660 12,200 669.6792 2.993406

rc.132 34852 12,200 680.5 2.644146

rc.133 164521 12,200 1083.886 3.405391

Contaminated

r.131 56241 12,200 973.08 3.460266

r.132 16038 12,200 592.4872 2.662675

r.133 52848 12,200 841.88 3.458732

12h Non-contaminated rc.141 83242 12,200 809.0652 2.546459

20

rc.142 41086 12,200 620.4103 2.409403

rc.143 32616 12,200 525.875 2.361775

Contaminated

r.141 26782 12,200 658.25 2.996108

r.142 117643 12,200 1149.821 3.49857

r.143 116216 12,200 1158.857 3.376143

24h

Non-contaminated

rc.151 42902 12,200 716.52 2.944889

rc.152 25194 12,200 727.2273 3.034855

rc.153 56511 12,200 643.0625 2.487126

Contaminated

r.151 97662 12,200 352.7576 2.269347

r.152 56859 12,200 4.5 0.003299

r.153 29923 12,200 4 0.001706

21

25℃

0h

Non-contaminated

rc.211 44889 12,200 562 2.361746

rc.212 36807 12,200 344.6154 2.136071

rc.213 93378 12,200 796.25 2.618868

Contaminated

r.211 91938 12,200 378.2609 1.713709

r.212 51310 12,200 264 1.724152

r.213 92954 12,200 467.129 2.009535

4h

Non-contaminated

rc.221 38082 12,200 425.5833 2.036611

rc.222 135952 12,200 620.2258 2.430011

rc.223 76605 12,200 370.5 2.260002

Contaminated

r.221 34112 12,200 369 1.884932

r.222 94346 12,200 573.0638 2.535436

22

r.223 73598 12,200 591.2273 2.015475

8h

Non-contaminated

rc.231 20437 12,200 603.4 2.752212

rc.232 33458 12,200 592.7885 2.633175

rc.233 54662 12,200 972.2581 3.021465

Contaminated

r.231 25828 12,200 160.3529 1.031955

r.232 51543 12,200 203.5357 1.213556

r.233 34027 12,200 187.05 1.088009

12h

Non-contaminated

rc.241 112554 12,200 331.5357 1.501244

rc.242 109640 12,200 323.5625 1.544121

rc.243 37017 12,200 324.5 1.847059

Contaminated r.241 66237 12,200 264.0909 1.249603

23

r.242 137229 12,200 311.4 1.449259

r.243 50744 12,200 170 1.029922

24h

Non-contaminated

rc.251 39604 12,200 496.7143 2.252539

rc.252 28728 12,200 482.9333 2.673846

rc.253 43855 12,200 209.3529 1.963034

Contaminated

r.251 97990 12,200 310.619 2.140346

r.252 47175 12,200 229 2.213802

r.253 48129 12,200 262.9375 2.069714

24

0 h 4 h 8 h 1 2 h 2 4 h

0

1

2

3

4

S to ra g e t im e

Sh

an

no

n d

ive

rsit

y i

nd

ex

* **** **

**

***

Figure 2. Comparison of Shannon diversity index among samples. * P <0.05, ** P <0.01, *** P <0.001.

Non-contaminated_25℃

Non-contaminated_4℃

Contaminated_4℃

Contaminated_25℃

25

Shift in beef microbiota contaminated with E. coli under different

storage conditions

The shift in beef microbiota composition at different storage

conditions was analyzed at the phylum and genus levels (Fig. 3).

Firmicutes (average 76.32% of all microbiota) and Proteobacteria

(23.47%) were the dominant phyla in all samples (Fig. 3A). The

relative abundances of Firmicutes were lower in samples stored at

4 °C than those stored at 25 °C (P <0.01), whereas Proteobacteria

were higher in samples stored at 4 °C. Carnobacterium,

Lactobacillus, Pseudomonas, and Bacillus were the dominant genera

in all samples (Fig. 3B). Carnobacterium, Lactobacillus,

Staphylococcus, Lactococcus, and Bacillus belong to Firmicutes and

have been reported as spoilage-causing bacteria (Stellato, G.,

2016).

Under 4 °C storage, the proportion of Carnobacterium

increased in the contaminated samples from 4 h to 12 h (P <0.0001,

P <0.05, and P <0.01, respectively), whereas Pseudomonas

increased in the non-contaminated samples over time and was

significantly high at 12 h (P <0.001). The proportion of Escherichia

increased after 8 h and 12 h (P <0.01) in the contaminated samples.

Under 25 °C storage, Carnobacterium was the predominant genus

over time in both non-contaminated and contaminated samples. The

proportions of Bacillus (P <0.05) and Staphylococcus (P <0.05)

26

increased, and that of Pseudomonas decreased in the non-

contaminated samples over time (P <0.05). In contrast, the

proportion of Lactobacillus (P <0.05) and Escherichia increased in

the contaminated samples.

The proportion of Carnobacterium was higher in non-

contaminated samples stored at 25 °C than those in the non-

contaminated samples stored at 4 °C, over time (P <0.01). The

proportions of Lactobacillus and Staphylococcus were higher in

non-contaminated samples stored at 25 °C compared to those in the

non-contaminated samples stored at 4 °C after 8 h. However, the

proportion of Pseudomonas was higher in the non-contaminated

samples stored at 4 °C than that in the non-contaminated samples

stored at 25 °C (P <0.01). In the contaminated samples,

Carnobacterium was the dominant genus over time under both 4 °C

and 25 °C storage. However, the proportion of Carnobacterium

decreased at 4 °C after 24 h (P <0.01), and Pseudomonas and

Bacillus were dominant in these samples. The proportion of

Lactobacillus in samples stored at 25 °C was also higher than those

stored at 4 °C. The proportions of Pseudomonas and Escherichia

were higher in samples stored at 4 °C than those stored at 25 °C (P

<0.05). Although the proportion of Escherichia was higher in the

27

contaminated samples stored at 4 °C than that in samples stored at

25 °C, the cell numbers of Escherichia were higher in samples at

25 °C than those at 4 °C (Fig. 1B). This could be due to the higher

amounts of total bacteria in samples at 25 °C.

Carnobacterium, which is known to be potential spoilage

bacteria in chilled meat products, was the most abundant genus in

all samples. Another dominant genus, Pseudomonas, gradually

increased its proportion when stored at 4 °C (non-contaminated:

47.3%, contaminated: 30.8%), while its proportion decreased when

stored at 25 °C (non-contaminated: 2.2%, contaminated: 0.1%).

Pseudomonas spp. are also known to cause spoilage in beef as they

have proteolytic properties even at low temperatures and cause

undesirable changes (Jay, J. M., 1967). Lactobacillus was also

detected in all samples; Carnobacterium and Lactobacillus are the

frequently found lactic acid bacteria (LAB) in meat products

(Leisner, J. J. et al., 2007; Stiles, M. E., 1996; Zagorec, M. et al.,

2017). Escherichia and Rahnella of the Enterobacteriaceae family

were detected at relatively low proportions in all samples.

Enterobacteriaceae are widespread in the environment, and many

mesophilic species contaminate food in low numbers (Lindberg, A.

M. et al., 1998). Carnobacterium, Pseudomonas spp., Lactobacillus,

and the majority of Enterobacteriaceae are psychrotrophic bacteria

that can grow even at refrigeration temperatures. However, the

28

relative abundances of these genera were significantly higher when

the samples were stored at 25 °C and even higher with E. coli

contamination.

Due to the predominance of Carnobacterium in samples

stored at 25 °C, the microbial diversity was higher in samples

stored at 4 °C than at 25 °C (Fig. 2). Carnobacterium was the

predominant genus in all samples, but the relative abundances were

higher in samples stored at 25 °C than in samples at 4 °C. However,

the relative abundances of other genera, including Pseudomonas,

Rhizobium, Rahnella, and Photobacterium, were higher in samples

stored at 4 °C. These results indicated that the dominant genera

were overgrown in samples stored at 25 °C and that the minor

genera could be influenced by the overgrowth of the dominant

genera under these conditions. Therefore, the diversity decreased

even with an increase in the total bacterial count in samples at

25 °C.

29

(A)

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

4 C

S to ra g e t im e

Re

lati

ve

ab

un

da

nc

e

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

2 5 C

S to ra g e t im e

Re

lati

ve

ab

un

da

nc

e

other

Firmicutes

Proteobacteria

30

(B)

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

4 C

S to ra g e t im e

Re

lati

ve

ab

un

da

nc

e

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

2 5 C

S to ra g e t im e

Re

lati

ve

ab

un

da

nc

e

Figure 3. Shift in beef microbiota composition. Shifts in beef microbiota composition at the (A) phylum and (B) genus

levels following experimental contamination with E. coli and storage under different conditions.

Rhizobium Photobacterium

other

Carnobacterium

Lactobacillus

Rahnella

Escherichia

Bacillus

Staphylococcus Pseudomonas

Lactococcus

Vibrio

31

Comparison of co-occurrence networks

The correlation between microbes over storage time was

analyzed to understand the shift in microbiota under each condition.

The proportion of Escherichia was the highest in contaminated

samples at 4 °C after 8 h. Thus the correlations between microbes

and Escherichia were determined at 8 h in both 4 °C and 25 °C

conditions (Fig. 4). The co-occurrence network showed that the

dominant bacteria in beef microbiota coexist and interact with each

other. The predominant genus, Carnobacterium, was negatively

correlated with genera whose proportions were decreased over

time in non-contaminated samples at both 4 °C and 25 °C and

contaminated samples stored at 4 °C. This suggested that

Carnobacterium could be a critical microbe in the shift in microbiota

over time.

Escherichia was present at 0 h (0.05% of microbiota) in

non-contaminated samples stored at 4 °C and was positively

correlated with Brochothrix, Rhizobium, and Pseudomonas after 4 h

(Fig. 4A, E). However, it was negatively correlated with

Carnobacterium, which was the predominant genus in beef

microbiota; and the proportion of Escherichia decreased after 8 h

(0.5% at 8 h and 0.065% at 24 h). Contaminated Escherichia was

positively correlated with Citrobacter and Pseudocitrobacter and

negatively correlated with Carnobacterium which can be related to

32

the decreased proportion of Escherichia after 8 h at 4 °C (Fig. 4B).

Carnobacterium was positively correlated with Staphylococcus and

negatively correlated with Pseudomonas in non-contaminated

samples stored at 25 °C (Fig. 4C). Thus, the relative abundance of

Staphylococcus increased after 8 h, and that of Pseudomonas

decreased after 8 h (Fig. 3B). Only one negative correlation –

between Staphylococcus and Rahnella – was significant in the

contaminated samples stored at 25 °C (Fig. 4D). Contaminated

Escherichia was not significantly correlated with indigenous

microbes at 25 °C after 8 h, but it was positively correlated with

Pseudomonas after 24 h (Fig. 4F).

These results indicated that the artificially introduced

Escherichia interacted with the dominant Carnobacterium at 4 °C,

and with Pseudomonas at 25 °C with time. These correlations

between Escherichia and other genera are consistent with previous

studies (Koutsoumanis, K., 2009 and Vold, L., 2000). Furthermore,

since Carnobacterium influenced the growth of Escherichia at 4 °C,

it may be assumed that the indigenous microbiota of beef could

influence microbial contamination. Besides, the significant increase

in Escherichia at 25 °C without significant interactions until 24 h

indicates that temperature is also a key factor for its growth in

33

addition to the interactions between microbes. The correlations

between microbes were different at each time point; and, the

composition of the microbiota changed with storage time.

34

(A) 4℃_NC_8h

(C) 25℃_NC_8h (D) 25℃_C_8h

(B) 4℃_C_8h

Proteobacteria

Firmicutes

positive negative

Bacteroidetes

Actinobacteria

35

(E) 4℃_NC_4h (F) 25℃_C_24h

36

Figure 4. Co-occurrence network of microbiota in beef samples following experimental contamination with E. coli and

storage under different conditions. Networks for (A) non-contaminated samples and (B) contaminated samples

storage at 4 °C after 8 h. Networks for (C) non-contaminated samples and (D) contaminated samples storage at

25 °C after 8 h. Spearman coefficient was used to evaluate the correlation between genera (> 0.1% in microbiota),

and the network was constructed using the criteria, threshold = 0.6; Q-value < 0.05. Green line indicates positive

correlation, and red line indicates negative correlation. Circle size represents the proportion of each genus. NC: Non-

contaminated samples, C: Contaminated samples.

37

Shifts in predicted pathways in the microbiota under different storage

conditions

The shifts in microbiota could be related to the different

roles of microbiota under different storage conditions. Thus,

functions of microbiota were predicted using the PICRUSt2 program

and compared among different conditions. A total of 392 pathways

were predicted, and the significantly changed pathways compared to

the 0 h samples were analyzed using heatmaps (Fig. 5A). The

changes in the predicted functions were greater in samples stored

at 25 °C than those at 4 °C, and changes were more significant in

contaminated samples than non-contaminated samples. The

changes in the predicted pathways were more significant in

contaminated samples stored at 4 °C after 4 h, but the changes were

relatively decreased with increasing storage times. However, the

changes in predicted pathways increased over time in the

contaminated samples stored at 25 °C. Moreover, the non-

contaminated samples stored at 4 °C showed lower activation levels

of pathways that were activated under other conditions. This

suggests that temperature and contamination affected the microbial

functions in the beef microbiota.

Twenty pathways were categorized into five major

metabolic pathways (spoilage metabolism, ubiquinone biosynthesis,

nucleotide metabolism, allantoin degradation, and amino acid

38

metabolism), according to the MetaCyc database (Fig. 5A). Six

pathways were grouped under spoilage metabolism, and these were

mainly related to the biosynthesis of acetate and lactate. Four

pathways were involved in ubiquinone biosynthesis; three pathways,

methionine cycle, L-isoleucine biosynthesis, and L-lysine

biosynthesis, were grouped into amino acid metabolism; and five

pathways were grouped under nucleotide metabolism. Two

pathways, which were related to degrading allantoin to CO2 and

glyoxylate, were grouped under allantoin degradation.

Predicted pathways related to spoilage metabolism were

associated with degrading the carbon source to acetic acid and

lactic acid. Acetic acid and lactic acid are common metabolites that

cause off-odor in spoiled beef (Gram, L. et al., 2002; Dainty, R. H.,

1996; Borch, E. et al., 1996). Ubiquinone is an electron transporter

which is essential for the survival of facultative gram-positive

anaerobes and facultative gram-negative anaerobes (Bentley, R., &

Meganathan, R., 1982; Jiang, M. et al., 2007). Allantoin, which is

synthesized by the degradation of nucleic acids, is a marker for

bacterial protein synthesis as it is degraded and recycled as a

nitrogen source (Lamothe, M. et al., 2002; Cusa, E. et al., 1999).

Consequently, nucleotide metabolism and amino acid metabolism

increased along with allantoin degradation. Hence, these shifts in

the predicted pathways and functional genes indicate that spoilage

bacteria grew and survived over time.

39

The changes in the predicted pathways in the samples under

all conditions were significant after 12 h. Therefore, the mean

proportion (%) was determined and compared between samples at 0

h and 12 h under each storage condition (Fig. 5B). The mean

proportions of the twenty pathways increased in all samples after

12 h except in the non-contaminated samples stored at 4 °C. The

differences in the predicted pathways were higher in samples

stored at 25 °C than those stored at 4 °C, and higher in the

contaminated samples than in the non-contaminated samples. The

adenine and adenosine salvage III pathway showed the highest

difference of mean proportions in all conditions (contaminated

samples at 25 °C: 2.37%, contaminated samples at 4 °C: 1.077%,

non-contaminated samples at 25 °C: 0.817%, non-contaminated

samples at 4 °C: 0.391%). This indicates that the microbiota

functions could be altered to a greater extent under 25 °C storage

and that refrigeration could reduce the risks caused by pathway

alteration even with pathogen contamination.

40

(A)

PWY-5837 : 2-carboxy-1,4-naphthoquinol biosynthesis

PWY-922 : mevalonate pathway I PWY-5910 : geranylgeranyldiphosphate biosynthesis I (via mevalonate)

PWY0-41 : allantoin degradation IV PWY-5705 : allantoin degradation to glyoxylate III

PWY-2941 : L-lysine biosynthesis II

PWY-6151 : S-adenosyl-L-methionine cycle I

PWY0-1296 : purine ribonucleosides degradation

PWY-6609 : adenine and adenosine salvage III

PWY-5104 : L-isoleucine biosynthesis IV

ANAGLYCOLYSIS-PWY : glycolysis III (from glucose) ANAEROFRUCAT-PWY : homolactic fermentation

PWY-5100 : pyruvate fermentation to acetate and lactate II PWY-5484 : glycolysis II (from fructose 6-phosphate)

PWY-5863 : superpathway of phylloquinol biosynthesis

PWY-621 : sucrose degradation III (sucrose invertase)

PWY-7199 : pyrimidine deoxyribonucleosides salvage

PWY0-1298 : superpathway of pyrimidine deoxyribonucleosides degradation PWY0-1297 : superpathway of purine deoxyribonucleosides degradation

P161-PWY : acetylene degradation (anaerobic) Spoilage

Metabolism

Nucleotide Metabolism

Amino acid Metabolism

Allantion Degradation

Ubiquinone Biosynthesis

25℃ NC C

4℃ NC C

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

(P-value < 0.05)

log2FoldChange

41

(B)

PWY-5863

PWY-5484 PWY-5705

PWY0-1298 PWY0-41

PWY-621 PWY-5910

PWY-922 PWY0-1297

PWY-6151

PWY-5100 PWY-5104

P161-PWY ANAEROFRUCAT-PWY

PWY-5837

ANAGLYCOLYSIS-PWY

PWY0-1296

PWY-6609 PWY-7199

PWY-2941

Mean proportion (%)

Difference in mean proportions (%)

Mean proportion (%)

Difference in mean proportions (%)

95% confidence intervals 4℃_NC 95% confidence intervals 4℃_C

95% confidence intervals 25℃_C 95% confidence intervals 25℃_NC

Mean proportion (%)

Difference in mean proportions (%)

Mean proportion (%)

Difference in mean proportions (%)

PWY-5863

PWY-5484 PWY-5705

PWY0-1298 PWY0-41

PWY-621 PWY-5910

PWY-922 PWY0-1297

PWY-6151

PWY-5100 PWY-5104

P161-PWY ANAEROFRUCAT-PWY

PWY-5837

ANAGLYCOLYSIS-PWY

PWY0-1296

PWY-6609 PWY-7199

PWY-2941 (P-value < 0.05)

log2FoldChange

12h 0h

42

Figure 5. Shifts in predicted pathways in beef microbiota under different storage conditions. (A) Pathways that had

over 2 log2 fold change compared to 0 h were selected and the shift in the predicted pathways was analyzed using a

heatmap. The pathways showing a significant change (P <0.05) are represented in colors, and the pathways without

any significant changes are shown in gray. (B) Difference in mean proportions (%) of predicted pathways between

samples stored for 0 h and 12 h under different conditions. Twenty pathways that showed a significant increase in

heatmap analysis were further analyzed using an extended error bar plot at 95% confidence intervals. Welch’s t-test

with Benjamini-Hochberg FDR was conducted (Q <0.05). The log2 fold change of samples stored for 12 h compared

to those stored for 0 h (P <0.05) are represented in colors. NC: Non-contaminated samples, C: Contaminated

samples.

43

Shifts in predicted functional genes in the microbiota under different

storage conditions

The metabolic pathways of each category were analyzed at

the functional gene level (Fig. 6). The relative abundance of the

functional genes was higher in the contaminated samples stored at

25 °C than that in other samples. These changes suggest that

pathogen contamination and relatively high temperatures had a

higher impact on microbiota functions. Only five genes, one in

spoilage metabolism and four in amino acid metabolism, decreased

over time at all four storage conditions (Fig. 6A, C).

In the S-adenosyl-L-methionine cycle I pathway (Fig. 6C),

luxS (EC:4.4.1.21), which converts S-ribosyl-L-homocysteine to

autoinducer 2 significantly increased by over 4.5 log2 fold in the

contaminated samples at 25 °C, whereas metE (EC:2.1.1.14)

decreased by over 5 log2 fold. This suggests that this pathway

increased due to the increase in the functional genes involved in the

biosynthesis of autoinducer 2. Autoinducer 2 is a molecule that is

involved in the quorum-sensing system recognized by many

different bacterial species, in particular, E. coli O157:H7. It is also

known to regulate attaching and effacing lesions (Sperandio, V. et

al., 2001; Federle, M. J., 2009). This suggests that the survival and

growth of E. coli could be related to the biosynthesis of autoinducer

2 in the contaminated samples at 25 °C.

44

(A)

(P-value < 0.05)

log2FoldChange

25℃ NC C

4℃ NC C

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

EC Number in Spoilage Metabolism

EC:2.7.2.3

EC:1.1.1.1

EC:5.4.2.11

EC:1.2.1.12

EC:5.3.1.9

EC:4.2.1.11

EC:1.2.1.10

EC:1.1.1.27

EC:4.1.2.13

EC:3.2.1.26 EC:2.7.1.4

EC:2.7.1.11

EC:5.3.1.1

EC:5.4.2.12

EC:2.7.1.40

EC:1.2.7.1

EC:2.7.2.1 EC:2.3.1.8

EC:2.7.1.2

Spoilage Metabolism (biosynthesis of acetate and lactate)

pyruvate

acetyl-CoA

EC:1.2.7.1

acetyl phosphate

acetate

(S)-lactate CO2 EC:1.1.1.27

EC:2.3.1.8

EC:2.7.2.1

acetaldehyde

ethanol EC:1.1.1.1 EC:1.2.1.10

D-glucopyranose 6-phosphate

β-D-fructofuranose 6-phophate

β-D-fructose 1,6-biphophate

EC:5.3.1.9

EC:2.7.1.11

D-glyceraldehyde-3-phosphate

EC:4.1.2.13

3-phospho-D-glyceroyl-phosphate EC:1.2.1.12

3-phospho-D-glycerate EC:2.7.2.3

2-phospho-D-glycerate

phosphoenolpyruvate

D-glucopyranose EC:2.7.1.2

glycerone phosphate

sucrose

EC:2.7.1.4

EC:2.7.1.40

EC:4.2.1.11

EC:5.4.2.12/5.4.2.11

EC:3.2.1.26

β-D-fructofuranose EC:2.7.1.4

EC:5.3.1.1

45

(B)

25℃ NC C

4℃ NC C

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

EC Number in Ubiquinone Biosynthesis

EC:2.7.4.2

EC:5.3.3.2

EC:2.7.1.36

EC:4.2.1.113

EC:2.5.1.1

EC:2.5.1.29

EC:4.1.3.36

EC:2.2.1.9 EC:5.4.4.2

EC:6.2.1.26

EC:2.3.3.10

EC:4.1.1.33

EC:2.5.1.10

EC:4.2.99.20

geranyl diphosphate

chorismate

isochorismate

2-succinyl-5-enolpyruvoyl-6-hydroxyl-3-cyclohexene-1-carboxylate

EC:5.4.4.2

EC:2.2.1.9

(1R,6R)-6-hydroxyl-2-succinylcyclohexa-2,4-diene-1-carboxylate

EC:4.2.1.113

EC:4.2.99.20

4-(2’-carboxyphenyl)-4-oxobutyryl-CoA

EC:6.2.1.26

EC:4.1.3.36

2-succinylbenzoate

1,4-dihydroxy-2-naphthoyl-CoA

isopentenyl diphosphate

acetoacetyl-CoA

(S)-3-hydroxyl-3-methylglutaryl-CoA

EC:2.3.3.10

(R)-mevalonate EC:1.1.1.34

(R)-5-phosphomevalonate

EC:2.7.1.36

(R)-mevalonate diphosphate

EC:2.7.4.2

EC:4.1.1.33

prenyl diphosphate EC:5.3.3.2

(2E,6E)-farnesyl diphosphate

geranylgeranyl diphosphate

EC:2.5.1.1

EC:2.5.1.10

EC:2.5.1.29

Ubiquinone biosynthesis

(P-value < 0.05)

log2FoldChange

46

(C)

(P-value < 0.05)

log2FoldChange

2-oxobutanoate

(S)-2-aceto-2-hydroxybutanoate

(R)-2,3-dihydroxy-3-methylpentanoate

(S)-3-methyl-2-oxopentanoate

L-isoleucine

propanoate

propanoyl-CoA EC:6.2.1.17

EC:1.2.7.7

EC:2.2.1.6

EC:1.1.1.383

EC:4.2.1.9

EC:2.6.1.42

L-asparate

L-aspartyl-4-phosphate

L-aspartate 4-semialdehyde

EC:2.7.2.4

EC:1.2.1.11

(2S,4S)-4-hydroxyl-2,3,4,5-tetrahydrodipicolinate

EC:4.3.3.7

(S)-2,3,4,5-tetrahydrodipicolinate EC:1.17.1.8

L-2-acetamido-6-oxoheptanedioate

EC:2.3.1.89

N-acetyl-L,L-2,6-diaminopimelate

L,L-diaminopimelate EC:3.5.1.47

meso-diaminopimelate EC:5.1.1.7

L-lysine EC:4.1.1.20

S-adenosyl-L-methionine

S-adenosyl-L-homocysteine

S-ribosyl-L-homocysteine

EC:3.2.2.9

L-homocysteine EC:2.1.1.14

L-methionine EC:2.5.1.6

autoinducer 2 EC:4.4.1.21

Amino acid Metabolism

EC:6.2.1.17 EC:2.2.1.6

EC:4.3.3.7 EC:1.2.1.11 EC:2.7.2.4

EC:2.6.1.42 EC:4.2.1.9

EC:4.1.1.20 EC:5.1.1.7

EC:3.5.1.47 EC:2.3.1.89 EC:1.17.1.8

EC:2.1.1.14 EC:4.4.1.21 EC:3.2.2.9

EC:2.5.1.6

EC Number in Amino acid Metabolism

25℃ NC C

4℃ NC C

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

47

(D)

xanthosine

α-D-ribose-1-phosphate EC:2.4.2.1

D-ribose 5-phosphate EC:5.4.2.7

adenosine guanine

inosine hypoxanthine IMP

2’-deoxycytidine

2’-deoxyuridine

dUMP

EC:3.5.4.5

EC:2.7.1.21/2.7.1.145

dTMP EC:2.1.1.45

thymidine EC:2.7.1.21/2.7.1.145

uracil EC:2.4.2.2/2.4.2.3

EC:2.4.2.2/2.4.2.3 2-deoxy-α-D-ribose-1-

phosphate

2-deoxy-D-ribose-5-phosphate

acetaldehyde EC:4.1.2.4

acetyl-CoA EC:1.2.1.10

EC:5.4.2.7

EC:4.1.2.4 D-glyceraldehyde-3-

phosphate

EC:2.4.2.2

2’-deoxyadenosine EC:2.4.2.1

2’deoxyinosine

EC:2.4.2.1 2’-deoxyguanosine

EC:2.4.2.1

Nucleotide Metabolism

EC:2.4.2.1 EC:5.4.2.7

EC:2.7.1.145

EC:2.4.2.3

EC:1.2.1.10 EC:4.1.2.4

EC:2.1.1.45

EC:3.5.4.5 EC:2.7.1.21

EC:2.4.2.2

25℃ NC C

4℃ NC C

4 h

8 h

12 h

24 h

4 h

8h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12h

24 h

EC Number in Nucleotide Metabolism

(P-value < 0.05)

log2FoldChange

48

(E)

(S)-(+)-allantoin

allantoate

(S)-ureidoglycine

EC:3.5.2.5

EC:3.5.3.9

(S)-ureidoglycolate EC:3.5.3.26

N-carbamoyl-2-oxoglycine EC:1.1.1.350

CO2

Allantoin Degradation

EC:3.5.2.5 EC:3.5.3.9

EC:1.1.1.350 EC:3.5.3.26

25℃ NC C

4℃ NC C

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

4 h

8 h

12 h

24 h

EC Number in Allantoin Degradation

(P-value < 0.05)

log2FoldChange

49

Figure 6. Shifts in predicted functional genes of microbiota under different storage conditions. Functional genes that are

involved in the (A) spoilage pathway, (B) ubiquinone biosynthesis, (C) nucleotide metabolism, (D) amino acid

metabolism, and (E) allantoin degradation were further analyzed using a heatmap. A log2 fold change compared to 0 h

(P <0.05) is represented in colors, and those without any significant change are shown in gray. Genes with higher

relative abundances in the contaminated samples are indicated using red arrows, whereas those with higher relative

abundances in the non-contaminated samples are indicated using blue arrows. Genes that decreased over time are

indicated using dotted lines, while those that increased are indicated using solid lines. NC: Non-contaminated samples,

C: Contaminated samples.

50

OTU contribution to the shift in functional genes

In order to determine the genera contributing to the shift in

pathways, the differential abundance of OTUs was identified using

PICRUSt2. One representative functional gene that was significantly

increased in each pathway was analyzed, and the OTU contributing

to its increase was identified. In spoilage metabolism, acetate kinase

(ackA, EC:2.7.2.1), which converts acetyl phosphate to acetate was

selected (Fig. 7A). For ubiquinone biosynthesis, menaquinone-

specific isochorismate synthase (menF, EC:5.4.4.2), which converts

chorismate to isochorismate, the first committed step in the

biosynthesis of menaquinone, was selected (Fig. 7B) (Buss, K. et

al., 2001). Menaquinone is necessary for bacterial vitality and

growth; E. coli, Bacillus subtilis, and Staphylococcus aureus require

menaquinone for their growth (Bentley, R., & Meganathan, R., 1982;

Jiang, M. et al., 2007). Thus, the increase in the biosynthesis of

menaquinone indicates the growth of spoilage-causing bacteria and

foodborne pathogens. Allantoinase (allB, EC:3.5.2.5) was selected

to monitor allantoin degradation, and S-ribosylhomocysteinelyase

(luxS, EC:4.4.1.21) was selected to monitor amino acid metabolism,

and phosphopentomutase (deoB, EC:5.4.2.7) was selected to

monitor nucleotide metabolism (Fig. 7C-E).

The genera contributing to the abundanceof ackA and menF

were compared among samples under different conditions (Fig. 7A,

B). Carnobacterium was the main contributing genus to ackA and

51

menF genes in most samples, except in contaminated samples

stored at 4 °C for 24 h. The most committed genera to the

abundance of ackA were lactic acid bacteria, including

Carnobacterium and Lactobacillus. In contrast, higher normalized

contributions of Escherichia to the abundance of menF compared to

other functional genes were observed. While diverse genera

contributed to the abundance of ackA and menF in the contaminated

samples stored at 4 °C for 24 h, Carnobacterium, Lactobacillus, and

Escherichia were the major contributors to the abundance of ackA

and menF in the contaminated samples stored at 25 °C. The

significant growth of these genera at 25 °C suggests that the

abundances of ackA and menF would be highest in contaminated

samples stored at 25 °C after 24 h.

52

(A) (B)

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

E C :2 .7 .2 .1 ,a c e ta te k in a s e ;a c k A

S to ra g e t im e

Co

ntr

ibu

tio

n c

ou

nt

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

E C :5 .4 .4 .2 ,m e n a q u in o n e -s p e c if ic is o c h o r is m a te s y n th a s e ;m e n F

S to ra g e t im e

Co

ntr

ibu

tio

n c

ou

nt

Brochothrix

Enterobacter

Yersinia

Rouxiella

Serratia

Kosakonia

Rhizobium

other

Carnobacterium

Lactobacillus

Rahnella

Escherichia

Bacillus

Staphylococcus

Vibrio

4℃ 25℃ NC C NC C 4℃ 25℃ NC C NC C

53

(C) (D)

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

E C :4 .4 .1 .2 1 ,S - r ib o s y lh o m o c y s te in e ly a s e ; lu xS

S to ra g e t im e

Co

ntr

ibu

tio

n c

ou

nt

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

E C :5 .4 .2 .7 ,p h o s p h o p e n to m u ta s e ;d e o B

S to ra g e t im e

Co

ntr

ibu

tio

n c

ou

nt

Brochothrix

Enterobacter

Yersinia

Rouxiella

Serratia

Kosakonia

Rhizobium

other

Carnobacterium

Lactobacillus

Rahnella

Escherichia

Bacillus

Staphylococcus

Vibrio

4℃ 25℃ NC C NC C 4℃ 25℃ NC C NC C

54

(E)

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0h

4h

8h

12h

24h

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

E C :3 .5 .2 .5 ,a lla n to in a s e ;a llB

S to ra g e t im e

Co

ntr

ibu

tio

n c

ou

nt

Brochothrix

Enterobacter

Yersinia

Rouxiella

Serratia

Kosakonia

Rhizobium

other

Carnobacterium

Lactobacillus

Rahnella

Escherichia

Bacillus

Staphylococcus

Vibrio

4℃ 25℃ NC C NC C

55

Figure 7. OTUs contributing to the shift in functional genes. OTUs contributing to the shift in(A) acetate kinase (ackA),

menaquinone specific isochorismate (menF), (C) S-ribosylhomocysteine lyase (luxS), (D) phosphopentomutase

(deoB) and (E) allantoinase (allB), were identified using PICRUSt2. NC: Non-contaminated samples, C: Contaminated

samples.

56

Validation of OTU contribution using quantitative real-time PCR

These genera significantly changed their contribution over

time and were selected for validation using qRT-PCR with specific

primers (Table S2) (Fig. 8). The contribution of Staphylococcus to

the abundance of menF was also verified to identify its unique

growth in non-contaminated samples at 25 °C. Change in

ubiquinone biosynthesis was studied as a representative of the

three pathways other than spoilage metabolism, that showed

significant shifts – amino acid metabolism, nucleotide metabolism,

and allantoin degradation as they showed similar shifts in

contribution.

The copy number of ackA and menF in each genus identified

by real-time PCR was used to determine the cell number based on

the genome information in the National Center for Biotechnology

Information (NCBI) database. Consistent with the prediction, both

ackA and menF showed an increase in all samples (Fig. 8). When

beef was stored at 25 °C, ackA was significantly increased in both

non-contaminated samples (average 3.96 x 108 cells/g; P <0.001)

and contaminated samples (2.24 x 108 cells/g; P <0.001). The

abundances of menF were significantly increased at 25 °C in non-

contaminated samples (3.51 x 108 cells/g; P <0.001) and

contaminated samples (1.56 x 108 cells/g; P <0.01). However, the

abundances of ackA and menF genes showed smaller increases in

57

samples stored at 4 °C than samples stored at 25 °C.

Carnobacterium was the predominant genus contributing to

both ackA and menF abundance in the qRT-PCR analysis (Fig. 8).

The proportions of the ackA gene from Lactobacillus increased in

all samples with time (P <0.01) and the Lactobacillus cell number

was exceptionally high in samples at 25 °C (non-contaminated:

1.11 x 108 cells/g, contaminated: 1.20 x 108 cells/g). We also

determined the contribution of Staphylococcus to menF gene

abundance. In the non-contaminated samples stored at 25 °C, the

cell proportion was over 0.05 after 24 h, and the cell number

significantly increased by 1.79 x 107 cells/g from 0 h to 24 h (P

<0.0001) (Fig. 8B). In contrast, Staphylococcus cell number was

significantly low in contaminated samples stored at 25 °C and in

samples at 4 °C, where the cell proportion was less than 0.01 even

after 24 h of storage. This is consistent with the taxonomic

composition results which showed that only non-contaminated

samples stored at 25 °C have a relative abundance of

Staphylococcus over 0.01. This also supports the positive

correlation between Carnobacterium and Staphylococcus in non-

contaminated samples stored at 25 °C. The abundance of

Escherichia increased in contaminated samples stored at 4 °C for 8

h, but decreased after 8 h, whereas the abundance of

58

Carnobacterium gradually increased. This is also consistent with the

shift in taxonomy composition that resulted from the negative

correlation between Carnobacterium and Escherichia at 8 h (Fig.

4B). These qRT-PCR results suggest that the pathway shifts

predicted in this study are reliable.

Together, our results showed that Carnobacterium and

contaminated E. coli interacted with indigenous microbes, and

induced a shift in beef microbiota, which increased the spoilage

metabolism in contaminated samples. The qRT-PCR analysis of

functional genes also showed that the increase in their abundance

was primarily due to the increase in the abundance of

Carnobacterium and Escherichia over time. The relative abundance

of Carnobacterium and Escherichia was especially high in

contaminated samples stored at 25 °C (Carnobacterium: 9.32 x 107

cells/g; ackA, 1.41 × 108 cells/g; menF, Escherichia: 1.07 x 107

cells/g; ackA, 1.40 x 107 cells/g; menF). The alteration of these

microbes in beef with time indicated that the storage temperature

and interactions between microbes are important for maintaining

food quality. Thus, the microbial information of beef distributed

from various regions can be used to predict the spoilage of meat

and provide more detailed guidance to manage those products.

59

(A)

(B)

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

m e n F

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* * *

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

m e n F

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* *

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

m e n F

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* * *

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

m e n F

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* * * *

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

a c k A

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

*

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

a c k A

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* *

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

a c k A

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* * *

0h

8h

12h

24h

0 .0

0 .5

1 .0

4

5

6

7

8

9

a c k A

S to ra g e t im e

Ce

ll p

rop

ort

ion lo

g1

0(c

ell/g

)

* * *

Non-contaminated_25℃

Non-contaminated_4℃

Contaminated_4℃

Contaminated_25℃

Carnobacterium

Lactobacillus

Escherichia

Staphylococcus

60

Figure 8. Validation of OTUs contributing to the shift in predicted functional genes using quantitative real-time PCR.

Bacterial cell number and cell proportion of Carnobacterium, Lactobacillus, and Escherichia in (A) ackA and those of

Carnobacterium, Staphylococcus, and Escherichia in (B) menF were compared between samples under different

conditions. * P <0.05, ** P <0.01, *** P <0.001. NC: Non-contaminated samples, C: Contaminated samples.

61

Ⅳ. CONCLUSIONS

In this study, the influences of storage temperatures and

pathogen contamination on the indigenous microbes in beef were

analyzed. E. coli O157:H7 contamination increased the bacterial cell

number in beef, but refrigeration significantly reduced the growth of

the microbiota. Although the abundances of Escherichia increased in

beef stored at both 4 °C and 25 °C, the cell number of Escherichia

was lower in samples stored at 4 °C. The indigenous beef

microbiota comprised several spoilage-causing microorganisms,

and they had a negative correlation with other genera present in

beef. This resulted in the increased abundance and cell number of

the dominant genera, Carnobacterium, Lactobacillus, and

Escherichia, in the contaminated sample stored at 25 °C. Among

these genera, Carnobacterium was the key microbe that induced the

shift in beef microbiota and influenced the growth of Escherichia at

4 °C. Therefore, spatial and temporal variance among beef

microbiota can indicate different levels of vulnerability of beef

products to the microbial spoilage. Besides, the risk of spoilage,

which is microbe-mediated, was higher when the beef was stored

at 25 °C and pathogen-contaminated conditions. The growth of

spoilage bacteria and foodborne pathogens over time at 25 °C

62

contributed to the increased level of spoilage pathways with a

higher abundance of spoilage associated genes. Thus, storing at

4 °C and avoiding contamination with foodborne pathogens is

essential for the maintenance of beef quality. As E. coli can cause

food poisoning even at very low cell numbers, it was challenging to

determine the effect of E. coli alone on the beef microbiota. This

study provided insights into the effect of E. coli contamination on

beef microbiota and its role in spoilage, under different storage

conditions.

63

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72

Ⅵ. 국문초록

소고기는 세계에서 가장 많이 소비되는 육류 중 하나로 식중독

사고 또한 끊임없이 일어나고 있다. 식중독균은 소고기에 노출이 되었을

때 식중독뿐만 아니라 소고기의 부패를 유발할 수 있다. 따라서,

식품안전을 위해 식중독균이 소고기 마이크로바이옴에 미치는 영향을

연구할 필요가 있다. 본 연구에서는 서로 다른 보관 조건에서

식중독균에의 노출이 소고기 마이크로바이옴과 미생물 종간의

상호작용을 어떻게 변화시키는 지를 살펴보았다. 총 60개의 소고기

샘플을 4℃ 또는 25℃에서 24시간까지 보관하였으며, 마이크로바이옴의

변화는 MiSeq 시스템을 이용해서 분석하였다. 식중독균의 영향은

소고기에 Escherichia coli FORC_044를 인위적으로 노출시킴으로써

확인하였다. FORC_044는 한국에서 식중독환자의 분변에서 분리한

Enterohemorrhagic E. coli (EHEC) 균주이다. 보관 시간에 따른

Escherichia의 균 수와 전체 마이크로바이옴에 대한 비율의 변화를

확인해 보았을 때, 25℃에서 보관하였을 때가 4℃에서 보관하였을

때보다 더욱 많이 증가하는 것을 알 수 있었다. 미생물 종간의 네트워크

분석결과 Escherichia는 Pseudomonas, Brochothrix, Staphylococcus,

Rahnella와 Rhizobium과 같은 소고기 상재균들과 양의 관계를 가지고

있음을 확인하였다. 이와 반대로, 부패 세균 중 하나인

Carnobacterium은 Escherichia를 비롯한 다른 소고기 상재균들과 음의

관계를 가지고 있었다. 보관 시간이 지남에 따라 마이크로바이옴이

나타내는 기능의 변화를 예측해보았을 때, 아세트산과 젖산을 생산하는

반응을 포함하는 부패 과정이 점차 증가하는 것을 확인할 수 있었다.

이러한 변화는 25℃에서 보관한 오염된 소고기에서 가장 크게 나타났다.

73

이와 더불어, Carnobacterium, Lactobacillus와 Escherichia가 이러한

과정에 관여하는 유전자의 변화에 가장 주요한 속임을 확인하였다. 본

연구의 결과는 식중독균이 소고기 마이크로바이옴의 변화와 소고기의

부패에 관여한다는 것을 보여주고 있다. 본 연구는 소고기

마이크로바이옴에 대한 이해를 넓혀줄 것이며, 식중독균과 보관 조건이

소고기의 품질에 어떠한 영향을 미치는 지 알려줄 수 있을 것이다.

주요어: 메타지노믹, 마이크로바이옴, 소고기, 부패 세균, 병원성 대장균,

식중독균 오염, 미생물 상호작용, 식품안전

학번: 2018-22519