Unpacking Environment Overrides Meat Type in Shaping the Bacterial Community and Potential Spoilage Organisms of Ready-to-Eat Meats During Refrigerated Storage

Sample collection and preparation

In total, 90 samples from 30 prepackaged RTE meat products were collected from Shandong and Henan monitoring stations of China. These samples were produced from 12 regions in China. Table 1 shows the number of samples from different regions. From the perspective of the variety of meat among these 30 samples, the number of beef products, chicken products, duck products, pig products, mutton products, and donkey products were 9, 7, 6, 5, 2, and 1, respectively. Products were stored in the refrigerator. Samples were collected on the 7th, 14th, and 21st day respectively after the package was opened. All samples were uniformly stored in a standard laboratory refrigerator at 4°C after unpackaging to simulate domestic refrigeration conditions and minimize variability introduced by different refrigerator environments.

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Table 1.

The Sample Number from Different Regions^a,b

Region	Number
Shandong	6
Jiangsu	4
Henan	3
Shanxi	3
Anhui	3
Beijing	3
Hunan	2
Zhejiang	2
Shaanxi	1
Inner Mongolia	1
Hebei	1
Hubei	1
In total	30

a

The 30 products comprised the following meat types: beef (9), chicken (7), duck (6), pork (5), mutton (2), and donkey (1).

^bThe larger number of products from Shandong (6/30) is due to station logistics; statistical analyses (PERMANOVA) included production site as a factor to account for the unbalanced design.

DNA extraction and high-throughput sequencing

Two grams of each sample were grinded and suspended in 5 mL phosphate-buffered saline. After shaking at 220 rpm at 4°C for 30 min, the microbial genomic DNA was extracted from each sample. Genomic DNA was extracted from the microbial pellets using the method described by Guo et al (Guo et al., 2014), a widely adopted approach for microbial community analysis of meat products (Zhao et al., 2022). The V3-V4 region of 16S rDNA from each sample was amplified by PCR using the bacterial universal primer pair 341 F (CCTAYGGGRBGCASCAG)/806R (GGACTACNNGGGTATCTAAT). The library was constructed using a TruSeq^® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, USA), and high-throughput sequencing was conducted using a HiSeq2500 platform according to the manufacturer’s instructions.

Bioinformatics analysis

Raw data were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end sequencing reads were merged using FLASH to get the raw tags (Magoč and Salzberg, 2011). Then the raw tags were filtered by QIIME v1.7.0 (Caporaso et al., 2010) to remove low-quality tags. Operational taxonomic unit (OTUs) were picked from the remaining tags using QIIME at a sequence similarity threshold of 97%, and the representative sequence of each OTU was annotated with RDP Classifier v2.2 (Wang et al., 2007) and Green Gene Database (DeSantis et al., 2006). The OTU abundance data was normalized according to the sample with the least sequences. All subsequent analyses were performed based on this normalized data.

For the diversity analysis, Bray-Curtis similarity coefficients were calculated based on OTUs data and plotted on a nonmetric multidimensional scaling (NMDS) graph to show the similarity among samples using the software PAST version 3.11 (Hammer et al., 2001). One-way analysis of similarities (ANOSIM) and one-way PerMANOVA was performed with the distance measure set as Bray-Curtis. The Simpson index was calculated to indicate the α-diversity of each sample. Molecular Ecological Network Analysis (MENA) was conducted on http://ieg4.rccc.ou.edu/mena/main.cgi with the the majority parameter to be “Only keep the genes/OTUs with 30 in total 90 samples” and other parameters to be default. LDA EffectSize (LEfSe) analysis was conducted on http://huttenhower.sph.harvard.edu/galaxy/ with all parameters to be default. Heat maps were generated by Heatmap Illustrator software version 1.0.3.7.

Statistics

The data in this study were analyzed with one-way analysis of variance (ANOVA) using R software. The differences were considered significant at p < 0.05. The resulting data were presented as the means ± SD.

General description of microbial community structure in ready-to-eat meat products

Sampling from these products was conducted on 7 days, 14 days, and 21 days after unpacked. The microbial flora in these 90 samples was determined by high-throughput amplicon sequencing. The results showed that phylum Proteobacteria were the majority in most samples in the first 21 days after package opening (Fig. 1A). The exceptional samples included B04, B14, B27, B30, C14, D10, and D14, in which bacteria of Firmicutes accounted more than half of the total bacteria. In all these samples, Proteobacteria and Firmicutes made up more than 98% of total bacteria, with the remained bacteria belonging to the phyla such as Bacteroidetes, Actinobacteria, and others.

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FIG. 1.

General description of Microbial Community Structure in RTE meat products sampled at 7, 14, and 21 days post-unpacking. (A) The percentages of different phyla in each sample (each bar represents one sample). (B) the Box-plot of high abundance genera. (C) MENA analysis of the major genera showed 6 clusters. The size of the note indicated the node degree (number of lines passing the node).

On the genus level, 124 known genera were identified from the samples. Pseudomonas, Staphylococcus, Psychrobacter, Carnobacterium, and Shewanella were the most abundant genera on average in the 90 samples (Fig. 1B). Fifty-four genera present in more than 30 samples were submitted to MENA Pipeline for analyses of interaction networks in microbial communities. These bacterial genera were grouped into 6 clusters according to their variation in different samples (Fig. 1C), Macrococcus, Serratia, Bacteroides, Yersinia, Desulfovibrio, Lactobacillus, Enhydrobacter, Clostridium, Megamonas, Rhizobium, Streptacidiphilus, and Allobaculum being the central nodes of the interaction networks. Although these genera at the central nodes were not the most abundant ones, their roles in functions might be important due to their abundant interactions with other genera.

The dominance of Pseudomonas and Psychrobacter, which are common psychrotrophic spoilage organisms, along with members of the Firmicutes such as Carnobacterium, is consistent with the refrigerated storage conditions of the products and their recognized role in meat spoilage.

We cannot get species-level names for all bacteria because the 16S rRNA method has limitations. However, for some OTUs, we could name the species. Supplementary Table S2 shows the average read counts of five important species (Enterococcus cecorum, Leuconostoc mesenteroides, Psychrobacter sanguinis, Staphylococcus sciuri, and Pseudomonas fragi) on days 7, 14, and 21.

Evolution of microbial community in ready-to-eat meat products

The microbial community in the RTE meat products changed with time after package opening. The microbial communities in samples from the Shandong and Henan monitoring stations are shown in the NMDS plots (Fig. 2A and B). In both NMDS charts, the microbial communities of samples of day 21 showed separateness from those of samples of day 7 and day 14, while microbial communities of samples of day 7 and day 14 mixed together. Both one-way ANOSIM and one-way PERMANOVA confirmed the above observation by calculation of similarities between samples (Supplementary Table S1). Microbial communities of samples of day 21 showed significant differences (p < 0.05) with microbial communities of samples of day 7 and day 14. According to the MENA result, the main bacteria in RTE meat products were grouped into 6 clusters. The proportion of each cluster in samples of day 7 and day 14 were very similar, while samples of day 21 presented larger ratios in cluster 2 and cluster 4, and less ratios in cluster 1, cluster 3, cluster 5 and cluster 6 (Fig. 2D). The mean value of α diversity (Simpson index) showed a slight increase, but these changes were not significant (p > 0.05) (Fig. 2C). One factor is that the constant refrigerated temperature (4°C) selectively favors psychrotrophic taxa while suppressing mesophilic competitors, which may limit overall richness expansion. (Demaître et al., 2023) Another is that as storage time extends, competitive exclusion among established psychrotrophic species may constrain further increases in diversity. Similar observations have been reported in RTE chicken stored at 4°C. (Qiu et al., 2022) Overall, these observations suggest that refrigerated storage imposes a strong ecological filter that limits α-diversity expansion despite prolonged storage.

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FIG. 2.

Time-dependent microbial community of the RTE meat products after unpacked. NMDS plots of microbial communities in samples from Henan (A) and Shandong (B) monitoring stations; each point represents one sample, colored by storage time (7, 14, or 21 days). (C) α diversity of samples collected on different days. (D) Bubble chart of the abundance of 6 MENA clusters on different days. The size of the bubble indicated the numbers of tags belonging to this cluster.

The NMDS plots clearly illustrated a distinct separation of the microbial communities at day 21 from those at day 7 and day 14 (Fig. 2A,B), indicating a significant temporal shift. This was statistically supported by both ANOSIM and PERMANOVA (p < 0.05). Notably, the communities at day 7 and day 14 largely overlapped, suggesting a relative stability during the first 2 weeks post-unpacking. The PERMANOVA analysis showed that the microbial communities differed significantly between the Shandong and Henan samples on day 14 and day 21 (p < 0.05). However, the overall succession trend was observed in both regions.

The change of specific bacterial genera with the storage time increasing

To find the change of specific bacterial genera with the storage time increasing, the sequencing data were submitted to the online LEfSe tool. As shown in Figure 3A and B, 14 genera decreased and 21 genera increased with the increasing of storage time (samples of day 21 compared with samples of day 7 and day 14) in the samples from Henan monitoring stations. Similarly, 15 genera decreased and 21 genera increased with the increasing of storage time in the samples from Shandong monitoring stations. The consistent changes in the samples from the two monitoring stations included the increase of Lentibacillus, Enterococcus, Leuconostoc, Phascolarctobacterium, Campylobacter, Shewanella, Erwinia, Serratia, Yersinia, and one unknown genera, and the decrease of Brochothrix, Lactococcus, and three unknown genera. Among the increase of the genera, Erwinia, Serratia, Yersinia belonged to Enterobacteriaceae and might be related to the food safety risk of the RTE meat products. The heat map (Fig. 3C) of the above genera with consistent change showed that genera such as Shewanella, Leuconostoc, Enterococcus, Erwinia, Serratia, Yersinia were universally abundant in samples of day 21, but they were present very few in the samples of day 7 and day 14.

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FIG. 3.

Time-dependent specific bacterial genera of the RTE meat products after unpacking. The LEfSe analysis of the significantly changed bacterial genera between samples of day 21 and samples of day 7 and day 14 from Henan (A) and Shandong (B) monitoring stations. The circles from the outside to the inside indicate genus, family, order, class, and phylum. Green nodes indicated this taxon was significantly more in samples of day 21, and red nodes indicated this taxon was significantly less in samples of day 21. (C) Heatmap of significantly changed bacterial genera.

The notable increase in genera belonging to the family Enterobacteriaceae, such as Erwinia, Serratia, and Yersinia (Fig. 3C), is of particular concern. While species-level resolution for Serratia and Yersinia was limited by the 16S V3-V4 region, the enrichment of these Enterobacteriaceae genera after 14 days post-unpackaging suggests an elevated food safety risk when opened RTE meat products are stored more than 2 weeks, consistent in the previous reports in refrigerated RTE meat products. (Qiu et al., 2022) Their proliferation after 14 days underscores a potential elevated food safety risk during extended storage after opening.

Of particular note, the notable increase in genera belonging to the family Enterobacteriaceae, such as Erwinia, Serratia, and Yersinia (Fig. 3C), is consistent with the inference of post-unpackaging environmental contamination. As these genera are commonly found in domestic and retail environments (e.g., on kitchen surfaces or in refrigerators), their proliferation supports the hypothesis that environmental microbiota introduced after unpackaging became the primary source of contamination during extended storage, rather than the initial microbiota derived from the production process. Although previous studies have reported the presence of Listeria monocytogenes in RTE meat products (Yang et al., 2018), no OTUs belonging to the genus Listeria were detected in any of the samples by 16S rRNA amplicon sequencing in this study. One explanation is the low abundance of Listeria in the samples or limited amplification efficiency of the primers, suggesting that Listeria may not be a dominant member of the microbial community in opened RTE meat products under conventional refrigeration conditions.

Unpacking site: a decisive factor for the evolution process of microbial community

Many factors could affect the evolution process of the microbial community of RTE meat products. In this study, the geographical location (unpacking site and production site) and meat species factors were analyzed. Permanova analysis was used to assess the effect of different factors on the microbial community structure. As shown in Figure 4, when samples were grouped by unpacking site, meat species, or production site, the group clusters were not significant on day 7 (Permanova index > 0.05). However, on day 14 and day 21, the microbial communities showed significant separation based on the unpacking site factor (Permanova index < 0.05). Although direct microbial profiling of the unpacking environments was not performed, this result indicates that the microbial composition was subjected to a strong selection pressure associated with the unpacking site during the later storage stages. It is plausible to speculate that bacteria introduced during the unpacking process (e.g., from kitchen surfaces, hands, or the refrigerator) gradually became dominant and ultimately steered the community succession from day 14 onwards. In contrast, the influence of meat species was not a significant factor throughout the entire storage period. This temporal lag likely reflects an initial period during which production-derived microbiota persist, followed by gradual replacement as environmental taxa with superior psychrotrophic fitness and competitive traits proliferate under refrigerated conditions.

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FIG. 4.

PERMANOVA index when the samples were grouped by different factors (unpacking site, meat species, and production site). The NMDS plots of samples were grouped by production sites, unpacking sites and meat species of day 7, 14, and 21.

Influence of meat species on the development of microbial community

The different nutrients in different meat species may have influence on the development of microbial community. The α diversity of the microbial community in chicken products was significantly higher than that in beef products. The α diversity of microbial community in the other meat products showed no significant difference (Fig. 5A). Permanova index showed a decreased trend from day 7 to day 21 in the analysis of meat species in, which means meat species had more influence on the microbial community in the late stage of storage than that in the initial stage. However, the meat species was not the decisive factor throughout whole storage period (Fig. 4). Still, it makes sense to find meat species specific bacteria in the microbial community of RTE meat. The Venn diagrams showed that 295 core OTUs with the mean value of more than 1 tag were present in all kinds of meats (Fig. 5B). There were 97 specific OTUs in beef meat, 121 specific OTUs in chicken meat, 57 specific OTUs in duck meat, 76 specific OTUs in mutton meat, and 75 specific OTUs in pork meat (Fig. 5B). Most of these specific OTUs showed very low abundance in the microbial community. Some of these specific OTUs were present in heat map (Fig. 5C). None of these OTUs showed high abundance in the majority of samples of the same meat species. The consistently low abundance of meat-specific OTUs across samples indicates that, under refrigerated storage, extrinsic factors such as handling and storage environment exert a stronger selective pressure than intrinsic nutrient composition of different meat types, limiting the utility of meat-specific OTUs as biomarkers.

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FIG. 5.

Influence of Meat Species on the Microbial Community. (A) α diversity of samples of different meat species. (B) Venn diagram of samples of different meat species. (C) Heat map of some meat species specific OTUs which generally showed very low abundance.

These findings suggest that opened RTE meats should be consumed within 2 weeks and stored in clean environments.

Future studies should integrate direct environmental sampling with product analysis to explicitly verify the transmission pathways inferred here. Metagenomic shotgun sequencing of both RTE meat samples and corresponding environmental swabs (e.g., refrigerator shelves, kitchen countertops, and packaging materials) would enable source-tracking analyses with tools like FEAST or SourceTracker to quantitatively attribute microbial contamination to specific environmental sources. Additionally, culturomics approaches could help recover viable environmental isolates for comparative genomics, providing mechanistic insights into the adaptive traits that enable certain taxa to dominate during extended refrigerated storage. This would more definitively establish causality between unpacking environment and post-opening microbial succession, guiding evidence-based recommendations for consumer food safety practices.