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.
(A) (B) Total amounts of bacteria (log10cell/g)
** * The amounts ofE. coli (log10cell/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℃
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 samples stored at 25 °C after 8 h (P <0.01).
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
Shannon diversity index
* ** ** **
*
*
**
*
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℃
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
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
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,
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)
(B)
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
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
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
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
(E) 4℃_NC_4h (F) 25℃_C_24h
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) Spoilage
Metabolism
(B)
4℃_NC 4℃_C 95% confidence intervals
95% confidence intervals
Difference in mean proportions (%)
Mean proportion (%)
Difference in mean proportions (%)
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.
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
(A) EC Number in Spoilage Metabolism
EC:2.7.2.3 Spoilage Metabolism (biosynthesis of acetate and lactate)
pyruvate
(B) EC Number in Ubiquinone Biosynthesis
EC:2.7.4.2
(C)
EC Number in Amino acid Metabolism
25℃
(D) EC Number in Nucleotide Metabolism
(P-value < 0.05) log2FoldChange
(E) 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, B). Carnobacterium was the main contributing genus to ackA and
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.
(A) (B)
(C) (D)
(E)
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.
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
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