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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
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)
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
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
Contents
Abstract∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅰ Contents∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅲ List of Figures∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅴ List of Tables∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Ⅵ Ⅰ. INTRODUCTION∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙1 Ⅱ. MATERIALS AND METHODS∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙4Sample 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
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
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
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
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
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
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.
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)
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
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
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
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
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
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.,
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
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
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
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
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
(A) (B) 0 h 4 h 8 h 1 2 h 2 4 h 5 6 7 8 9 1 0 S t o r a g e t im e T o ta l a m o u n ts o f b a c te r ia ( lo g1 0 c e ll /g ) ** * * * * * * * * * 0 h 8 h 1 2 h 2 4 h 0 1 4 5 6 7 8 S t o r a g e t im e T h e a m o u n ts o f E . c o li ( lo g1 0 c e ll /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℃
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 non-contaminated
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
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
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
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
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
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
0 h 4 h 8 h 1 2 h 2 4 h 0 1 2 3 4 S t o r a g e t im e S h a n n o n d iv e r s it y i n d e x * ** ** * * * * * * *
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℃
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
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
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.
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.
(A) 0h 4h 8h 12h 24h 0h 4h 8h 12h 24h 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 4C S t o r a g e t im e R e la ti v e a b u n d a n c e 0h 4h 8h 12h 24h 0h 4h 8h 12h 24h 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 2 5C S t o r a g e t im e R e la ti v e a b u n d a n c e other Firmicutes Proteobacteria
(B) 0h 4h 8h 12h 24h 0h 4h 8h 12h 24h 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 4C S t o r a g e t im e R e la ti v e a b u n d a n c e 0h 4h 8h 12h 24h 0h 4h 8h 12h 24h 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 2 5C S t o r a g e t im e R e la ti v e a b u n d a n c 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
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
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
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.
(A) 4℃_NC_8h (C) 25℃_NC_8h (D) 25℃_C_8h (B) 4℃_C_8h Proteobacteria Firmicutes positive negative Bacteroidetes Actinobacteria
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.
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,
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.
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.
(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) MetabolismSpoilage
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
(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 95% confidence intervals 4℃_NC 4℃_C 95% confidence intervals 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
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
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
(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
(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
(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
(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 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 Nucleotide Metabolism
(P-value < 0.05) log2FoldChange
(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
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.
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,
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
(A) (B) 0h 4h 8h12h24h 0h 4h 8h12h24h 0h 4h 8h12h24h 0h 4h 8h12h24h 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 E C : 2 . 7 . 2 . 1 , a c e t a t e k in a s e ; a c k A S t o r a g e t im e C o n tr ib u ti o n c o u n t 0h 4h 8h12h24h 0h 4h 8h12h24h 0h 4h 8h12h24h 0h 4h 8h12h24h 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 t e s y n t h a s e ; m e n F S t o r a g e t im e C o n tr ib u ti o n c o u n t Brochothrix Enterobacter Yersinia Rouxiella Serratia Kosakonia Rhizobium other Carnobacterium Lactobacillus Rahnella Escherichia Bacillus Staphylococcus Vibrio 4℃ NC 25℃ C NC C NC 4℃ C NC 25℃ C
(C) (D) 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 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 t e in e ly a s e ; lu x S S t o r a g e t im e C o n tr ib u ti o n c o u n t 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 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 t o m u t a s e ; d e o B S t o r a g e t im e C o n tr ib u ti o n c o u n t Brochothrix Enterobacter Yersinia Rouxiella Serratia Kosakonia Rhizobium other Carnobacterium Lactobacillus Rahnella Escherichia Bacillus Staphylococcus Vibrio 4℃ NC 25℃ C NC C NC 4℃ C NC 25℃ C
(E) 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0h 4h 8h 12 h 24 h 0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 E C : 3 . 5 . 2 . 5 , a lla n t o in a s e ; a llB S t o r a g e t im e C o n tr ib u ti o n c o u n t Brochothrix Enterobacter Yersinia Rouxiella Serratia Kosakonia Rhizobium other Carnobacterium Lactobacillus Rahnella Escherichia Bacillus Staphylococcus Vibrio 4℃ NC 25℃ C NC C
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
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
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
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.