Overlap of Spoilage-Associated Microbiota between Meat and theMeat Processing Environment in Small-Scale and Large-Scale RetailDistributions

Giuseppina Stellato,a Antonietta La Storia,a Francesca De Filippis,a Giorgia Borriello,b Francesco Villani,a Danilo Ercolinia

Department of Agricultural Sciences, Division of Microbiology, University of Naples Federico II, Portici, Italya; Department of Animal Health, Istituto Zooprofilattico

Sperimentale del Mezzogiorno, Portici, Italyb

ABSTRACT

Microbial contamination in food processing plants can play a fundamental role in food quality and safety. The aims of this studywere to learn more about the possible influence of the meat processing environment on initial fresh meat contamination and toinvestigate the differences between small-scale retail distribution (SD) and large-scale retail distribution (LD) facilities. Sampleswere collected from butcheries (n � 20), including LD (n � 10) and SD (n � 10) facilities, over two sampling campaigns. Sam-ples included fresh beef and pork cuts and swab samples from the knife, the chopping board, and the butcher’s hand. The micro-biota of both meat samples and environmental swabs were very complex, including more than 800 operational taxonomic units(OTUs) collapsed at the species level. The 16S rRNA sequencing analysis showed that core microbiota were shared by 80% of thesamples and included Pseudomonas spp., Streptococcus spp., Brochothrix spp., Psychrobacter spp., and Acinetobacter spp. Hierar-chical clustering of the samples based on the microbiota showed a certain separation between meat and environmental samples,with higher levels of Proteobacteria in meat. In particular, levels of Pseudomonas and several Enterobacteriaceae members weresignificantly higher in meat samples, while Brochothrix, Staphylococcus, lactic acid bacteria, and Psychrobacter prevailed inenvironmental swab samples. Consistent clustering was also observed when metabolic activities were considered by predictivemetagenomic analysis of the samples. An increase in carbohydrate metabolism was predicted for the environmental swabs andwas consistently linked to Firmicutes, while increases in pathways related to amino acid and lipid metabolism were predicted forthe meat samples and were positively correlated with Proteobacteria. Our results highlighted the importance of the processingenvironment in contributing to the initial microbial levels of meat and clearly showed that the type of retail facility (LD or SD)did not apparently affect the contamination.

IMPORTANCE

The study provides an in-depth description of the microbiota of meat and meat processing environments. It highlights the im-portance of the environment as a contamination source of spoilage bacteria, and it shows that the size of the retail facility doesnot affect the level and type of contamination.

Meat is a complex niche with chemical and physical propertiesthat allow the colonization and development of a variety of

microorganisms, especially bacteria (1, 2). Several factors can in-fluence the occurrence of microbes in meat. After slaughtering,meat can be contaminated by microorganisms from water, air,and soil, as well as from the workers and equipment involved inthe processing. In the later processing steps of the fresh meat chain(i.e., handling, cutting, and storage), abiotic factors such as tem-perature, gaseous atmosphere, pH, and NaCl levels select for cer-tain bacteria, allowing colonization of the meat surface by differ-ent spoilage-related species and strains (3, 4).

Microbial growth to large numbers is a prerequisite for meatspoilage that can be considered an ecological phenomenon, en-compassing the changes of the available substrata during the pro-liferation of bacteria (5, 6). Spoilage is the process of food deteri-oration leading to a reduction in its quality, to the point of notbeing edible for humans. Signs of spoilage may include a differentappearance of the food, compared to its fresh form, and altera-tions in the sensorial qualities of the product, particularly the as-pect (including texture and color) and the presence of an off-odor(6–9). The presence of microorganisms on the surface of cut meat

and meat products determines meat spoilage upon their interac-tion and growth under optimal conditions (2, 8).

Although there are many different types of meat, the mainbacterial populations involved in spoilage are common. The mostabundant bacteria causing spoilage of refrigerated beef and porkare Brochothrix thermosphacta, Carnobacterium spp., clostridia,Enterobacteriaceae, Lactobacillus spp., Leuconostoc spp., Pseu-domonas spp., and Weissella spp., and their metabolic activity can

Received 11 March 2016 Accepted 21 April 2016

Accepted manuscript posted online 29 April 2016

Citation Stellato G, La Storia A, De Filippis F, Borriello G, Villani F, Ercolini D. 2016.Overlap of spoilage-associated microbiota between meat and the meatprocessing environment in small-scale and large-scale retail distributions. ApplEnviron Microbiol 82:4045–4054. doi:10.1128/AEM.00793-16.

Editor: C. A. Elkins, FDA Center for Food Safety and Applied Nutrition

Address correspondence to Danilo Ercolini, ercolini@unina.it.

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.00793-16.

Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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lead to defects such as sour flavors, discoloration, gas or slimeproduction, and decreases in pH (2, 6, 10, 11).

The environmental microbiota from processing plants haveoften been addressed as sources of microbes that potentially affectthe quality attributes of meat (1, 12, 13). Indeed, several studiesdemonstrated that the microbiota involved in food-processingsteps are often found on processing surfaces or tools (11, 12, 14–17), underlying the importance of hygienic practices in influenc-ing the food microbiota. However, no studies have investigatedthe differences in the contamination types and levels betweensmall-scale retail distribution (SD) and large-scale retail distribu-tion (LD) facilities. Food handling and cleaning practices can becompletely different according to the size, level of automation,and organization of specific retail facilities.

In meat handling environments, the presence of resident mi-crobiota, possibly contributing to the occurrence of spoilage (3),can lead to economic losses (7, 8) and/or safety issues (12, 18, 19).Various microbial contamination sources can be identified in abutchery, including chopping boards, refrigerators, operators’hands, cloths, and knives and other tools (1, 12). The availabilityof organic residues on surfaces can lead to the growth and aggre-gation of microorganisms and represents a significant source ofcross-contamination (16, 20–22). Good cleaning and sanitizationpractices for surfaces and equipment are thought to solve theproblem of food contamination, since low hygiene standards infood processing plants are the major cause of contamination ofraw meat and meat products (12). The most abundant species

present on processing tools were also found at high levels on meat,suggesting the establishment of an equilibrium between food andthe environment that affects the quality of the final product (1, 12,23). However, the effects of retail size and organization have neverbeen investigated as possible variables affecting the microbiolog-ical quality of meat. In this study, we describe the microbiota inenvironmental swabs and meat samples collected in small-scaleand large-scale retail distribution facilities, in order to explore theinfluence of the microbiota in meat handling environments on theinitial microbiological quality of meat and to assess the effect ofthe type of retail facility on the extent of microbial contamination.

MATERIALS AND METHODSSampling. Samples were collected from 20 butcheries, including 10 small-scale retail distribution (SD) facilities and 10 butcher counters in large-scale retail distribution (LD) facilities, located in the Campania region(southern Italy), all operating under a certified food safety managementsystem (i.e., hazard analysis and critical control points [HACCP]). Samplecollection was replicated twice, with a 3-week interval. The sampling ofthe surfaces took place at least 1 h after routine cleaning and before thestart of sales. Meat samples collected included fresh beef (n � 40) andpork (n � 40) cuts, while surface samples were taken from the knife (n �40), the chopping board (n � 40), and the operator’s hand (n � 40). Adescription of the samples analyzed in this study is presented in Table S1in the supplemental material. The surface sampling was carried out usingsterile sponges (Whirl-Pak Speci-Sponge; Nasco, Fort Ankinson, WI,USA) premoistened with 25 ml sterile peptone buffer solution. Spongeswere rubbed vertically, horizontally, and diagonally across the meat chop-

FIG 1 Box plots showing numbers of observed OTUs (A and B) and Chao1 diversity index values (C and D) for environmental swabs (red) and meat samples(blue) from SD (A and C) and LD (B and D) establishments. Boxes, interquartile ranges (between the first and third quartiles); lines inside boxes, medians(second quartiles); whiskers, lowest and highest values within 1.5 times the interquartile range (from the first and third quartiles, respectively); circles, outliersbeyond the whiskers. *, significant difference obtained with the pairwise Wilcox test (FDR, �0.05).

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ping board surface (100 cm2), both sides of the knife, and the palm of thebutcher’s hand. After collection, samples were cooled at 4°C and analyzedwithin 3 h. All samples were collected with the permission of the butchers.No animals were involved in the present study, only animal products.

Microbiological analysis. Prior to analysis, 25 g of each meat samplewas homogenized in 225 ml sterile quarter-strength Ringer’s solution(Oxoid, Basingstoke, United Kingdom), in a stomacher (Stomacher400circulator; Seward Medical, London, United Kingdom), for 1 min at 230rpm at room temperature. The homogenized meat and surface sampleswere used to perform 10-fold serial dilutions, using sterile Ringer’s solu-tion as the diluent. Pour plating was used to determine total psychro-trophic counts, numbers of lactic acid bacteria, and numbers of Entero-bacteriaceae, by using plate count agar, de Man-Rogosa-Sharpe (MRS)agar, and violet red bile glucose agar (VRBGA), respectively (all fromOxoid). Spread plating was used to determine numbers of Pseudomonasspp. and Brochothrix thermosphacta, by using a Pseudomonas agar basewith a cetrimide-fucidin-cephalosporin (CFC) supplement and strepto-mycin sulfate-thallium acetate-actidione (STAA) agar with STAA selec-tive supplement SR0151E, respectively (all from Oxoid). All the media

were incubated at 20°C for 48 h. Plate counts were determined in tripli-cate. Data were analyzed using analysis of variance (ANOVA) and theTukey post hoc test, using a significance level of 0.05 for sample compari-sons. The statistical analysis was performed using IBM SPSS Statisticssoftware (version 16.0).

DNA extraction. Total DNA extraction from sponges and meat sam-ples was carried out by using a Biostic bacteremia DNA isolation kit (MoBio Laboratories, Inc., Carlsbad, CA). The extraction protocol was ap-plied to the pellet (12,000 � g) obtained from a 10-fold dilution in sterileRinger’s solution for meat samples and from 20 ml of sponge buffer forswabs.

PCR amplifications, 16S gene amplicon library preparation, and se-quencing. The bacterial diversity was studied by pyrosequencing of theamplified V1 to V3 region of the 16S rRNA gene, amplifying a fragment of520 bp (24); 454 adaptors were included in the forward primer, followedby a 10-bp sample-specific multiplex identifier (MID). PCR conditionswere as described previously (1). After agarose gel electrophoresis, PCRproducts were purified twice with an Agencourt AMPure kit (BeckmanCoulter, Milan, Italy) and quantified using a PlateReader AF2200 (Eppen-

FIG 2 Abundance of bacterial species in meat (A and C) and environmental (B and D) samples from SD (A and B) and LD (C and D) facilities. Only OTUsshowing relative abundances of �2% and occurring in �5 samples are reported. Other, all OTUs that failed to reach the cutoff value. Samples are coded asfollows: 1, knife; 2, chopping board; 3, hand; 4, pork; 5, beef. Capital letters, different butcheries, as reported in Table S1 in the supplemental material.

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dorf, Milan, Italy), and equimolar pools were obtained prior to furtherprocessing. The amplicon pools were used for pyrosequencing on a GSJunior platform (454 Life Sciences, Roche), according to the manufactur-er’s instructions, using titanium chemistry. The same DNA templateswere also PCR screened for the presence of Toxoplasma gondii by using the18S rRNA gene as the target (25); the test results were negative for allsamples.

Bioinformatics and data analysis. Raw reads were first filtered ac-cording to the 454 processing pipeline. Sequences were then analyzed andfurther filtered by using QIIME 1.8.0 software (26) and a pipeline de-scribed previously (27). In order to avoid biases due to different sequenc-ing depths, the operational taxonomic unit (OTU) table was rarefied atthe smallest number of reads per sample. Alpha diversity and beta diver-sity were studied by using QIIME, as described previously (27). Coremicrobiota were defined as microbial genera/species present in at least80% of the samples. Statistical analysis and plotting were carried out in theR environment (http://www.r-project.org), by using the packages vegan,stats, psych, corrplot, and made4. Permutational multivariate analysis ofvariance (MANOVA) (nonparametric MANOVA) based on Jaccard andBray-Curtis distance matrices was carried out by using 999 permutationsto detect significant differences in the overall microbial community com-position, as affected by the type of sample or the type of retail facility.Pairwise Wilcox tests were used in order to determine significant differ-ences in alpha diversity parameters, in OTUs, or in predicted pathwayabundances between environmental and meat samples. Correction of Pvalues for multiple testing was performed when necessary (28). Principal-component analysis (PCA) was carried out on logarithmically trans-formed abundance tables by using the dudi.pca function in the veganpackage. Venn diagrams were obtained by using the Bioinformatics andEvolutionary Genomics software (29), in order to describe the microbialcommunity shared by different sets of samples.

PICRUSt (Phylogenetic Investigation of Communities by Reconstruc-tion of Unobserved States) (http://picrust.github.io/picrust) was used topredict the potential functional profiles of the microbial communities inenvironmental swabs and meat samples. For this analysis, OTUs werepicked against the Greengenes database (version 13_5) using QIIME 1.8.The abundances of the predicted metagenomes were normalized withrespect to 16S rRNA gene copy numbers. KEGG orthologs were identifiedfrom the inferred metagenomes and collapsed at hierarchy level 3. Subse-quent analyses were carried out in R as described above.

Nucleotide sequence accession number. The 16S rRNA gene se-quences are available at the Sequence Read Archive (SRA) of the NationalCenter for Biotechnology Information (NCBI), under accession numberSRP072347.

RESULTSEnumeration of bacterial populations. The viable counts on ap-propriate media of the target meat spoilage groups in meat andenvironmental samples are reported in Tables S2 to S6 in the sup-

plemental material. Mean log counts were not significantly differ-ent (P � 0.05) between beef and pork samples (e.g., lactic acidbacteria loads were 3.99 � 0.92 and 3.96 � 0.98 log CFU/g andPseudomonas counts were 4.68 � 1.23 and 4.72 � 1.30 log CFU/gfor beef and pork cuts, respectively). The mean log counts forhand and knife samples were significantly lower than the chop-ping board results (P � 0.05), while knife and hand results did notdiffer significantly (P � 0.05) (e.g., PCA counts were 1.58 � 1.21and 1.40 � 1.03 log CFU/cm2 and Enterobacteriaceae loads were0.24 � 0.86 and 0.41 � 1.07 log CFU/cm2 for hand and knifesamples, respectively). With grouping of the samples into small-scale retail distribution (SD) and large-scale retail distribution(LD) groups, the effect of butchery type was also not significant(see Table S7 in the supplemental material).

Sequencing data analysis and alpha and beta diversity. A totalof 658,572 reads passed the filters applied through the QIIMEsplit_library.py script, with an average length of 454 bp. The di-versity indices varied among the samples, and there was a signifi-cant association between the sample type and the microbial diver-sity (Fig. 1). Interestingly, the chopping board samples showedsignificantly greater diversity, compared to the other surface swabs(false discovery rate [FDR],�0.05), with an average number of 581�303 OTUs and an average Chao1 index of 1,371 � 700. No differ-ence was found between small-scale retail distribution facilitiesand butcher counters in large-scale retail distribution facilities(FDR, �0.05). The principal-coordinate analysis based on aweighted UniFrac distance matrix showed that samples from thetwo samplings did not cluster separately (see Fig. S1 in the supple-mental material) and the microbial composition did not differsignificantly between the two samplings (P � 0.001).

Bacterial diversity in meat and processing environments.The microbial diversity at the species level in small-scale and large-scale retail distributions is shown in Fig. 2, where the averagevalues for the two samplings are reported. Streptococcus spp., Pseu-domonas spp., Brochothrix spp., Psychrobacter spp., and Acineto-bacter were part of the core microbiota, inasmuch as they wereabundant in both types of butcheries and occurred in the 99% ofthe samples, although with different distributions. The highestlevels of Pseudomonas were observed in the SD environment (anaverage of 84% for butcher S) (Fig. 2A) and in meat samples fromLD facilities (an average of 60% for all meat samples) (Fig. 2C).Brochothrix occurred in all of the samples (average of 20%) butshowed a remarkable occurrence in the hand samples. Psychrobac-ter showed a homogeneous distribution among all of the samples,

FIG 3 Venn diagrams showing the numbers of shared genera between groups of samples, as determined by 16S rRNA gene pyrosequencing analysis. Sampleswere grouped as meat versus environmental samples for the LD and SD groups (A) and for the LD and SD groups combined (B).

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FIG 4 Hierarchical average linkage clustering of the samples based on the Pearson’s correlation coefficient for the abundance of genera present in �20% of thesamples. The color scale indicates the scaled abundance of each variable, denoted as the Z-score; red, high abundance; blue, low abundance. Column bars arecolored according to the type of sample (meat or environmental swab) and the type of retail facility (SD or LD), and the row bar is colored according to theclassification at the phylum level. Samples are coded as follows: 1, knife; 2, chopping board; 3, hand; 4, pork; 5, beef. Capital letters, different butcheries, asreported in Table S1 in the supplemental material.

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with remarkable relative abundances in the environmental sam-ples from both SD and LD facilities (averages of 35% and 40%,respectively) and the greatest abundance in pork meat from LDfacilities. Finally, some OTUs were characteristic of specific SDsamples, although with low abundance, such as the case of Acin-etobacter in the beef samples from retail facilities E, F, S, and T(average of 8%) and Leuconostoc in the hand samples from butch-eries O and N (average of 4%).

In Fig. 3, the genera shared among the samples are represented.With meat and environmental samples grouped separately for LDand SD facilities, 31 genera were common to all samples (Fig. 3A);these genera included Streptococcus, Brochothrix, Pseudomonas,Acinetobacter, and Psychrobacter spp., which were also the mostabundant in the core microbiota (average relative abundancesof �10%) (see Table S8 in the supplemental material). Forty-eightgenera were shared by meat and environmental samples for bothtype of retail facilities (Fig. 3B).

Permutational MANOVA based on both Bray-Curtis and Jac-card distance matrices showed a significant difference in the over-all microbiota between swabs and meat samples (P � 0.001). Incontrast, no effect of the type of retail establishment was observed(P � 0.05) (Fig. 1). The hierarchical clustering in Fig. 4 shows acertain degree of separation between meat and environmentalsamples, mostly driven by the abundance of OTUs within theProteobacteria phylum, which was significantly higher in meatsamples than in swab samples (FDR, �0.05). Pseudomonas andseveral Enterobacteriaceae members were significantly more abun-dant in meat samples, while Staphylococcus, Streptococcus, Lacto-coccus lactis, Leuconostoc, Brochothrix, and Psychrobacter showedhigher levels in environmental samples (FDR, �0.05). Accord-ingly, a principal-component analysis based on the compositionof the microbiota showed no clustering of the samples accordingto the retail type (Fig. 1; also see Fig. S2 in the supplemental ma-terial) and, even when SD or LD samples were analyzed separately,the clustering was consistently driven by the sample type (see Fig.S2 in the supplemental material).

The OTU cooccurrence was investigated by considering thegenus-level taxonomic assignment and including OTUs with atleast 0.1% relative abundance in at least 50% of the samples.Significant correlations (FDR, �0.05) are plotted in Fig. S3 inthe supplemental material. Basfia showed strong positive corre-lations with Bordetella and Streptococcus. Gammaproteobacteriacooccurred with OTU core members such as Acinetobacter andMoraxellaceae, while Lactococcus showed weak cooccurrence withLactobacillus.

Predicted metabolic activities. Potential metabolic activitiesof the samples were predicted by using PICRUSt software. A con-sistent grouping of the samples on the basis of the sample type(meat versus environment) was achieved also when the predictedmicrobial pathways were considered (Fig. 5). Pathways related tocarbohydrate metabolism were increased in environmental swabs,while amino acid metabolism and lipid metabolism were moreabundant in meat (FDR, �0.05). In particular, arginine, proline,

and aromatic amino acid metabolism, as well as fatty acid metab-olism, demonstrated higher levels in meat (FDR, �0.05). Spear-man’s correlations between predicted pathways and OTUs arepresented in Fig. 6, in which only Proteobacteria and Firmicutesphyla are shown. Proteobacteria OTUs, particularly for Pseudomo-nas, several Enterobacteriaceae members, and Psychrobacter, werepositively correlated with lipid metabolism and amino acid me-tabolism, while Firmicutes members, such as Brochothrix and lac-tic acid bacteria, cooccurred with carbohydrate-related pathways(FDR, �0.05).

DISCUSSION

In this study, the microbiota in 20 butcheries were studied byrRNA gene-based culture-independent high-throughput se-quencing, in order to identify the relationships between the mi-crobial diversity of processing environments and meat samplesand to compare the microbiota occurring in small-scale retail dis-tribution (SD) facilities and butcher counters in large-scale retaildistribution (LD) facilities. Results showed no significant effect ofthe butchery type on bacterial counts, in agreement with previousreports (30, 31). The microbiota of the environments were com-plex, including more than 500 taxa at the genus/species level, aswere those of fresh meat cuts and environmental samples. Meatmicrobial complexity usually decreases sharply after storage, as aconsequence of the effects of abiotic factors such as storage tem-perature and the type of packaging used, which select a few speciesto become dominant and to spoil the meat (1, 32–34). Signifi-cantly greater microbial diversity and higher viable counts wereobserved for the chopping board samples than for the knife sam-ples. These differences suggest that surface contamination isstrongly affected by the surface material, which represents an im-portant factor to take into account in order to maintain acceptablelevels of hygiene in food processing plants (1, 12, 17, 35). Toolsmade of porous materials, such as wooden chopping boards, areless adequate for thorough cleaning and increase the possibilitiesof adherence of bacteria and establishment of resident microbiota(16, 35, 36). Moreover, ecological factors (pH, water activity, re-dox potential, nutrient availability, and matrix composition), thecapabilities of microbes to develop biofilms and to adhere to sur-faces, cleaning procedures, and staff hygiene training all have im-portant effects on the microbiological quality of fresh meat (2, 37,38). The microbial community composition across surfaces inmeat processing plants is reported to be highly variable, and mostof the OTUs identified in meat samples (raw, spoiled, and pro-cessed) originate from the processing environment (1, 12, 20). Inthe present study, Pseudomonas spp., Brochothrix spp., Psychro-bacter spp., Streptococcus spp., and Acinetobacter spp. were identi-fied as the core microbiota occurring in all of the samples ana-lyzed; they were previously reported as contaminants in foodprocessing environments (39–41). Our results indicate that theyare part of a resident microbiome, but we showed that their prev-alence is not influenced by the type of retail establishment consid-ered. The microorganisms found in the processing facilities sam-

FIG 5 Hierarchical average linkage clustering of the samples based on the Pearson’s correlation coefficient for the abundance of predicted KEGG orthologs collapsed athierarchy level 3, filtered for sample prevalence of �20%. The color scale indicates the scaled abundance of each variable, denoted as the Z-score; red, high abundance;blue, low abundance. Column bars are colored according to the type of sample (meat or environmental swab) and the type of retail facility (SD or LD), and the row baris colored according to the higher hierarchy level in the KEGG classification. Only KEGG orthologs related to carbohydrate, amino acid, or lipid metabolism are reported.Samples are coded as follows: 1, knife; 2, chopping board; 3, hand; 4, pork; 5, beef. Capital letters, different butcheries, as reported in Table S1 in the supplemental material.

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FIG 6 Heatplot showing the correlations between Firmicutes and Proteobacteria members and predicted KEGG orthologs collapsed at hierarchy level 3, bothfiltered for sample prevalence of �20%. Rows and columns are clustered by Euclidean distance and Ward linkage hierarchical clustering. The intensity of thecolors represents the degree of association between the OTUs and the KEGG orthologs, as measured by Spearman’s correlations. The row bar is colored accordingto the OTU classification at the phylum level, and the column bar is colored according to the higher hierarchy level in the KEGG classification. Only KEGGorthologs related to carbohydrate, amino acid, or lipid metabolism are reported.

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pled here occur frequently on freshly cut and aerobically storedmeat (2, 12), and they are recognized as undesirable bacteria infood processing environments (2) and as main contributors tomeat spoilage (5, 7, 9, 12). In particular, Pseudomonas spp. arerecognized as able to form biofilms (36, 42–44), adhering to sur-faces and improving their resistance to sanitation and cleaningprocedures (20, 45, 46).

Predicted metagenomes highlighted the remarkable abun-dance of amino acid metabolism and lipid metabolism in meatsamples and their strong correlation with Proteobacteria, such asPseudomonas and Enterobacteriaceae OTUs. Pseudomonas fragiwas reported previously to be lipolytic and proteolytic, as werespecies belonging to the Enterobacteriaceae family, and they cancontribute to spoilage through the production of volatile organiccompounds and other undesirable metabolites, such as biogenicamines (47–52). In contrast, B. thermosphacta and lactic acid bac-teria were mainly correlated with carbohydrate metabolism. Ac-cordingly, none of the strains of B. thermosphacta or Carnobacte-rium maltaromaticum tested previously was found to beproteolytic and lipolytic, while producing off-flavors arising fromsugar catabolism (53, 54). Lactic acid bacteria are reported to beproducers of polysaccharidic ropy slime (55–57). However, spoil-age-related activities must be considered strain specific and maybe influenced by abiotic factors such as pH, NaCl concentration,or temperature, as well as interactions with other components ofthe microbial community (2, 10, 55).

Our results supported the importance of environmental mi-crobiota in influencing the quality and safety of meat, and theyhighlight the lack of differences in the distributions of microbiotain small-scale versus large-scale meat processing environments.Meat contamination is strongly dependent on the environment inwhich the meat is handled and processed. The initial levels ofmicrobial contamination and the community composition influ-ence the potential shelf-life of meat, depending on storage condi-tions. Therefore, adequate choices of surface materials and ex-tremely accurate cleaning procedures are necessary in order toavoid spreading of bacteria that can contaminate the meat andpotentially cause spoilage.

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