Skip to main content
NCBI home page
Italian Journal of Food Safety logoLink to Italian Journal of Food Safety
. 2024 Oct 10;13(4):12818. doi: 10.4081/ijfs.2024.12818

Comparative analysis of the microbiome composition of artisanal cheeses produced in the Mediterranean area

Contributions: VI, carried out the bioinformatic and biostatistics analysis and writing of the original draft; CO, AL, performed DNA extraction, library preparation and sequencing; FS, contributed to the paper drafting and editing; UGB, AV, FA, collected and shipped the samples from their own country; GM, AS, AD, conceived and planned the study, collected the Italian samples and supported in editing the final draft. All authors have commented and approved the final version as submitted.: Valentina Indio 1,✉, Ursula Gonzales-Barron 2, Chiara Oliveri 1, Alex Lucchi 3, Antonio Valero 4, Fouad Achemchem 5, Gerardo Manfreda 3, Federica Savini 1, Andrea Serraino 1, Alessandra De Cesare 1
PMCID: PMC11694621  PMID: 39749183

Abstract

In the PRIMA project ArtiSaneFood, the microbiological parameters of several artisanal cheeses produced in the Mediterranean area have been quantified. In this pilot study, we selected four of these artisanal cheese products from Italy, Portugal, Spain, and Morocco to investigate and compare their microbiomes in terms of taxonomic composition, presence of reads of foodborne pathogens, as well as virulence and antimicrobial resistance genes. Lactococcus, Streptococcus and Lactobacillus were the most represented genera in the Portuguese and Spanish cheeses, Streptococcus in the Italian cheese, and Enterococcus, Klebsiella, Escherichia, and Citrobacter in the Moroccan products.

The correlation analysis indicated a negative association between the abundance of some lactic acid bacteria (i.e., Lactococcus, Lactobacillus, Streptococcus, and Leuconostoc) and foodborne pathogenic genera, like Escherichia and Salmonella. The analysis of pathogen abundance, virulence factors, and antimicrobial resistance genes showed a strong clusterization based on the cheese type, confirming that the presence of potential human health risk determinants was higher in the artisanal products derived from unpasteurized milk that underwent spontaneous fermentation.

Key words: shotgun metagenomic, artisanal cheese, biological hazards, antimicrobial resistance, Mediterranean products

Introduction

Artisan cheeses are highly appreciated for their genuineness and organoleptic properties. Unlike industrial dairy products, artisanal cheeses are made following traditional practices implemented according to the cheese producer’s experience. As a consequence, during the production process, the time or temperature of single steps might slightly change based on the judgment of the producers assessing the aspect of the products. The use of manual procedures and the lack of strict process parameter control all over the process can make artisanal cheeses more prone to contamination, survival, and growth of foodborne pathogens in comparison to industrial products (Rangel-Ortega et al., 2023). Some artisanal cheese producers in the Mediterranean area prefer to use raw milk instead of pasteurized milk. The pathogens that can be detected in raw milk and which are normally inactivated by pasteurization, have been summarized by EFSA (2015) and are mostly represented by Escherichia coli, Salmonella, Listeria monocytogenes, and Staphylococcus aureus. These pathogens can survive in the final cheese product if not properly handled (Yoon et al., 2016).

In 2022, 30 strong evidence outbreaks, corresponding to 6.2% of the total, were vehiculated by milk and milk products in European countries (EFSA, 2023) but there are no data on artisanal cheeses as possible sources. A less recent US study reported that during 1998-2011, 38 outbreaks were due to artisanal cheeses made with unpasteurized milk (42% of the total), remarking the risk of consuming unpasteurized dairy products, especially for vulnerable consumers, like older adults, pregnant women, and immunocompromised individuals (Gould et al., 2014).

The microbiological safety of artisanal cheeses is certainly affected by the microbiological composition of the raw milk, the level of hygiene in the equipment and environment of the production facility, the type of process, and the staff skills. However, the occurrence, survival, and growth of foodborne pathogens in artisanal cheeses are also affected by the interplay between pathogens and the indigenous microbiota, such as lactic acid bacteria (LAB). LAB usually represent the most abundant bacterial population in artisanal cheeses and are able to produce a variety of antimicrobial compounds against foodborne pathogens (Campagnollo et al., 2022).

The dynamics between LAB, or other beneficial microbial populations, and foodborne pathogens in cheese can be investigated by shotgun metagenomics. Compared to culture-based approaches, which only capture a small portion of the cultivable microbial community, this sequencing technology offers the opportunity to identify all the microbial populations in the cheese, including those represented by non-cultivable or difficult-to-culture microorganisms. Besides the taxonomic profiling, shotgun metagenomic analysis also offers insights into the microbial community’s functional genes, which is essential to understanding functions and interactions between microorganisms within a food ecosystem (Handelsman, 2004). Despite these advantages, metagenomic is also characterized by some weaknesses, limiting its implementation in food and foodborne outbreak investigations, such as a lack of standardized methodologies for the wet lab part and of open access bioinformatic pipelines for the sequence analysis (Koutsoumanis et al., 2019).

In the PRIMA project ArtiSaneFood, the microbiological parameters of a number of artisanal cheeses have been quantified (Faria et al., 2022; Pasquali et al., 2022; Possas et al., 2022). In this pilot study, we selected four of these artisanal cheeses to further investigate and compare their microbiomes in terms of taxonomy composition, presence of reads of foodborne pathogens as well as virulence and antimicrobial resistance (AMR) genes.

Materials and Methods

Investigated samples and shotgun metagenomic sequencing

In this pilot study, 12 cheese samples were analyzed by shotgun metagenomic. The samples were represented by Squacquerone produced in Italy (n=1), Jben produced in Marocco (n=3), Queso Manchego produced in Spain (n=1) and Queijo de Cabra Transmontano produced in Portugal (n=7).

The Squacquerone was the only cheese produced with filtered and pasteurized milk, while raw milk was employed in all the other three artisanal cheese preparations. In Trasmontano, Manchego, and Squacquerone cheeses, the fermentation process and coagulation were supported by the supplementation of starter cultures and rennet, while Jben underwent spontaneous fermentation.

For the metagenomic analysis, 10 g of cheese were diluted in 40 mL of sterile physiological solution (0.90% NaCl) and homogenized using a stomacher (MAYO HG 400V, Stomacher, Baranzate, Italy) at a normal speed for 1 minute. All the solutions of the homogenized samples were centrifuged at 9980× g for 20 minutes at 4°C to collect the cells inside the pellet, which was kept at -80°C until DNA extraction.

To extract DNA from the stored pellets, the PowerFood® Microbial DNA Isolation Kit (MO BIO-Qiagen, Hilden, Germany) was used. The extracted DNAs were quantified with the

BioSpectrometer® (Eppendorf, Milan, Italy) and then fragmentation, tagmentation and indexing were performed using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). Shotgun metagenomic sequencing was carried out using the Illumina sequencing platform NextSeq500 in paired-end mode at 2×150 bp.

Bioinformatic and statistical analysis

Bioinformatic analysis was carried out as previously described (Indio et al., 2024). Briefly, the raw sequences were trimmed with AdapterRemoval v2 to remove sequencing adaptors and low-quality tails. Following cleaning, sequences were aligned using Bowtie2 (Langmead et al., 2019) to the appropriate host genomes, extracted from the UCSC Genome Browser resource (http://hgdownload. soe.ucsc.edu/downloads.html) for cow (bosTau9), sheep (oviAri4), and from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/datasets/ genome/) for goat (ARS1.2).

The web tool “Metagenomics Rapid Annotation using Subsystem Technology” (MG-RAST) was adopted to perform the taxonomic profiling (Keegan et al., 2016), the quantification at the phylum, class, order, family, genus and species levels was retrieved using a suite of scripts included in the MG-RAST-Tools repository.

The R package vegan was used to compute the Inverse Simpson (Fisher) α diversity and Bray-Curtis β diversity indices (respectively with the functions of diversity and vegdist), while corrplot was adopted to evaluate the correlation between taxa relative abundances. Principal coordinate analysis was performed with cmdscale (stats package) and plotted as 3D projections with rgl package (function plot3d). The raw sequences were also de novo assembled with the algorithm SOAPdenovo2 (Luo et al., 2012) using the command SOAPdenovo-63mer and setting the options “K 55 -p 16 -M 3 -F -u”. The obtained scaffolds were analyzed with Resistance Gene Identifier to identify and annotate antibiotic resistance genes (Alcock et al., 2023), and the presence of virulence factors were checked using blastn against the Virulence Factors Database (Liu et al., 2022) and quantified as transcript per million with the program Kallisto (Bray et al., 2016). All the heatmaps and the corresponding hierarchical clustering were obtained using the R package ComplexHeatmap.

The Wilcoxon rank-sum test for comparison of the mean values was applied using the Mann-Whitney U test calculator available at the Statistics Kingdom website (http://www.statskingdom.com/170 median_mann_whitney.html). The statistical analysis was carried out to compare groups containing a number of samples equal to or greater than 3.

Results

The shotgun metagenomic sequencing runs on the tested samples produced 60.5 Gbases, corresponding to an average of 33.6 million reads per sample after trimming and adapters removal. Raw sequences obtained by shotgun metagenomic are publicly available as fastq format at the Sequence Read Archive, available on the NCBI server, under the project named PRJNA1117640. The presence of the host genome (either of goat, sheep, or cow) was very heterogeneous among the cheese samples, ranging between 67% in the Squacquerone to less than 1% in the Jben from Marocco (Supplementary Table 1).

The taxonomic evaluation showed clear differences in the composition of the microbial communities among the cheese types for both phylum and genus level classification. Firmicutes, followed by Proteobacteria and Ascomycota, was the most abundant phylum in all the cheeses, except for Jben in which Proteobacteria was significantly higher (p=0.012) and Ascomycota was <1% (Figure 1A). At the genus level, Lactococcus, Streptococcus and Lactobacillus were the most represented genera in the Portuguese and Spanish cheeses, while the Italian one was characterized by the presence of Streptococcus and the Moroccan showed a high abundance of Enterococcus, Klebsiella, Escherichia and Citrobacter. Average abundances >2% of Enterobacter, Pseudomonas, and Acinetobacter were also highlighted in the dataset, even if not associated with specific cheese types (Figure 1B). Overall, the genera composition was uniform within the same type of cheese, as highlighted by the analysis of β diversity (Figure 1C), with a greater richness in the Moroccan cheese also highlighted by α diversity index calculation (p=0.009) (Figure 1D).

Our data showed the presence of reads of potentially pathogenic species in all the analyzed cheese samples. Interestingly, the unsupervised cluster analysis showed very distinct profiles related to the cheese type (Figure 2A). In particular, the highest relative abundance of pathogenic genera (such as Escherichia, Salmonella, Klebsiella and Shigella) was highlighted in the Jben cheese even if not statistically significant (p=0.064), while Squacquerone almost exclusively hosted Streptococcus genus, mainly represented by the non-pathogenic S. termophilus. Enterococcus was present in both Moroccan and Iberic cheeses, while the relative abundance of Yersinia, Clostridium and Bacillus were <1% in all the tested samples. Among the species belonging to these genera, our data showed several foodborne pathogens with an average abundance >1%, including Salmonella enterica, E. coli, Klebsiella pneumoniae (Supplementary Table 2). The correlation analysis showed a negative association between the abundance of some LAB (i.e., Lactococcus, Lactobacillus, Streptococcus and Leuconostoc) and the reads of foodborne pathogenic genera, like Escherichia, Salmonella, and Klebsiella (Figure 2B). The search for virulence factors highlighted distinct profiles related to cheese origin. In agreement with the presence of reads for zoonotic agents, the Jben showed the highest abundance of virulence factors (p=0.018), while Squacquerone showed the lowest. The most represented virulence factor category, found in all the samples, was “adherence” followed by “immune modulation” and “exoenzyme” which were more abundant in Iberic and Italian cheeses with respect to Moroccan that, conversely, were richer in virulence factors related to motility, biofilm, regulation, antimicrobial activity, nutritional and metabolic processes (Figure 3). Among the most abundant virulence factor genes in Jben, we found fur, rpoS and nmpC related to S. enterica, and the family of fimbrial genes from Citrobacter freundii, wcalJ and ompA belonging to the genome of K. pneumoniae and E. coli, respectively. Many other virulence factors related to the genome of Streptococcus spp. (such as plr/gapA, cshA, rfbB, tig/ropA, cbpD, and eno) were retrieved in Squacquerone, Manchego and Trasmontano; the latter, in addition, showed several virulence genes carried by Enterococcus faecalis, including bee2, str1 and str2 (data not shown).

The analysis of AMR genes suggested a strong cauterization of Jben sample (Figure 4A) where a higher abundance of AMR genes was highlighted (p=0.009). Here we detected AMR genes that are known to confer resistance to several antimicrobial agents, including β-lactam antibiotics (i.e. cephalosporin, cephamycin, penam, penem, monobactam, carbapenem), sulfonamide, rifamicyn, fosfomycin, tetracyclin antibiotics. Conversely, resistance to lincosamide, eflamycin, fusic acid and glycopeptide antibiotics was evidenced in the Portuguese and Spanish cheeses. In addition, genes associated with the resistance to antimicrobial peptides, fluoroquinolone and aminoglycoside antibiotics were detected homogenously through all the samples. Interestingly, we found that the genes coffering AMR in Jben cheese mostly derived from E. coli, K. pneumoniae, S. Enterica and many other less represented bacterial species, while the other chesses showed AMR genes associated to L. monocytogenes, Bacillus cereus, Lactococcus lactis, S. aureus and Enterococcus faecium (Figure 4B).

Figure 1.

Figure 1.

Open in a new tab

A) Relative abundance of the 20 most represented phyla; B) relative abundance of the 20 most represented genera; C) 3D projections of principal coordinate analysis obtained from Bray-Curtis β diversity calculation; D) inverse Simpson α diversity index.

Discussion

In this pilot study performed on a limited number of samples but sequenced at a high coverage (Supplementary Table 1), the shotgun metagenomic approach was adopted to evaluate the microbiome composition of four different artisanal cheeses produced in the Mediterranean area.

Beyond the geographical origin, the cheeses differed also for the manufacturer procedures. Such differences were reflected in the richness and diversity of the microbial populations. As a matter of fact, the Italian cheese obtained from filtrated and pasteurized milk showed lower α diversity with respect to the products made with raw milk. In this regard, Walsh et al. (2020) reported that pasteurization of the milk did not have a significant impact on the α diversity of cheeses. In our dataset, a lack of statistics (due to the presence of a single cheese sample derived from pasteurized milk) did not allow us to determine the significance of the difference, although it is clearly evident (Figure 1D). As far as the raw milk-derived cheeses are concerned, the highest microbial richness (supported by statistics) was found in the Jben which underwent spontaneous fermentation, unlike the Portuguese and Spanish cheeses in which the raw milk was inoculated with started cultures. This evidence was previously described in a metagenomic study in which several fermented foods were analyzed, and the authors reported that the α diversity of spontaneously fermented foods was significantly higher than that of foods produced using starter cultures (Leech et al., 2020).

Our metagenomic dataset showed that the four cheeses analyzed had very distinct microbial profiles based on their geographical origin. Moroccan cheese differed significantly from all the others due to the dominance of coliform bacteria belonging to Klebsiella, Escherichia, and Citrobacter genera.

The presence of these microorganisms had previously been described in Moroccan cheese Jben and associated with inadequate hygienic conditions of preparation of this product or the poor bacteriological quality of raw milk used for this preparation (Hamama and Bayi, 1991; Azzouz et al., 2024). Conversely, the other cheeses showed a microbial composition mostly represented by LAB. In particular, Lactococcus and Lactobacillus were the main LAB populations in the Portuguese and the Spanish cheeses, while a predominance of Streptococcus (mainly S. thermophilus) was found in the Italian product (over 69%).

Figure 2.

Figure 2.

Open in a new tab

A) Heatmap representing the relative abundance of reads of pathogenic genera; rows and columns dendrograms derive from the hierarchical clustering analysis (method: ward.D2, distance: canberra); B) correlation plot representing the association between the relative abundance of the 20 most represented genera: purple and orange dots refer respectively to positive and negative correlation; the larger the dot, the greater the statistical significance of the association.

This result was expected because the guidelines to produce Squacquerone di Romagna allow to use of started culture S. thermophilus only (Aquilanti et al., 2012). The presence of LAB was widely described in artisanal cheeses (Quintana et al., 2020; Walsh et al., 2020), as well as in industrial dairy products where specific strains of LAB were used as starters according to their functional and organoleptic properties, such as the milk acidifying capacity, aromatizing power and the capability to inhibit the undesirable microorganisms (Thierry et al., 2015). The predominance of LAB in our metagenomic analysis is fully in line with what was reported in microbiological studies conducted on Trasmontano (Faria et al., 2022), Manchego (Cabezas et al., 2007) and Squacquerone cheese (Siroli et al., 2020). In this regard, also our correlation analysis indicated an inverse relationship between LAB and pathogens. We found that the relative abundance of pathogenic bacteria genera, like Salmonella and Escherichia, negatively correlated with the LAB component (such as Lactococcus, Lactobacillus, and Streptococcus). This interaction could be due to competitive exclusion (where LAB outcompete pathogens for nutrients and space) and the pathogen growth inhibition mediated by the production of lactic acid and/or antimicrobial agents (bacteriocins, hydrogen peroxide, ethanol) (Adams and Nicolaides, 1997). In our dataset, a higher presence of reads associated with pathogens was found in Jben cheese where, as a matter of fact, the raw milk was subjected to fermentation without the addition of starter culture and the abundance of LAB was the lowest. In this Moroccan product, we found a relevant presence of reads of S. enterica, K. pneumoniae, and E. coli. While S. enterica and Shiga toxin-producing E. coli were widely reported among the main pathogenic bacteria isolated from raw milk and artisanal cheeses that are associated with food-poisoning outbreaks (Baylis, 2009; Rangel-Ortega et al., 2023), K. pneumoniae was lesser known to cause foodborne illness although it is considered a zoonotic pathogen which can originate from a variety of sources in the dairy farm environment including water, plant materials, equipment, dirt, and fecal sources (Martin et al., 2016). However, the presence of multi-drug resistant K. pneumoniae strains in food constitutes a potential risk to public health, not only as a pathogen but also indirectly as a vehicle of AMR genes that could be passed to other pathogens in the microbial community of food and the human gut (Hartantyo et al., 2020). Indeed, our analysis of resistome showed that a significant proportion of AMR determinants in Moroccan cheese were linked to the presence of genes belonging to the genome of K. pneumoniae together with E. coli that, in fact, is also well known to represent one of the most important reservoirs of AMR genes in raw milk cheeses (Ünlü et al., 2011; dos Santos et al., 2022;).

Besides the reads of the pathogens described above, detected in Jben, additional reads associated with the genera Listeria, Yersinia, Enterococcus, Bacillus and Clostridium were found in the other cheese samples, except for Squacquerone cheese which was the only artisanal product obtained from previously filtered and pasteurized milk. However, it is important to highlight that the relative abundance of foodborne pathogens such as L. monocytogenes, Y. enterocolitica, B. cereus and C. botulinum was always lower than <1%. On the contrary, Enterococcus spp. showed an average abundance of 7%, representing the fourth most prevalent microbial group detected in our experiment. Enterococci (i.e., E. faecalis, E. faecium) have recently gained relevance as leading opportunistic pathogens causing various infections, including urinary tract diseases, bacteremia, meningitis, wound infections, and biofilm-associated infections of artificial medical devices that could be also referred to the consumption of contaminated cheese (Anderson et al., 2016). The only zoonotic agent that was present in all the tested cheeses was Streptococcus spp. However, only a very small proportion of the identified Streptococci belonged to foodborne pathogenic species, such as S. agalactiae and S. equi, both quantified in our samples with a relative abundance <1%.

Figure 3.

Figure 3.

Open in a new tab

Heatmap representing the abundance of virulence factors; rows and columns dendrograms derive from the hierarchical clustering analysis (method: ward.D2, distance: canberra).

Figure 4.

Figure 4.

Open in a new tab

A) Heatmap representing the relative abundance of reads associated with genes conferring resistance to antimicrobial agents; rows and columns dendrograms derived from the hierarchical clustering analysis (method: average, distance: euclidean); B) alluvial diagram illustrates the bacteria species associated with the antimicrobial resistance in each sample.

Alongside the study of microbial composition, we investigated the presence in the tested cheeses of genes known to act as virulence factors and AMR genes. Both these two gene categories were higher in the samples where the pathogens were also higher.

This is not surprising since in the host-pathogen interaction the expression of virulence genes allows the bacteria to grow, spread, and cause host damage. In addition, the persistence of such pathogens in the food matrix is strongly related to the ability to escape the action of antimicrobials (Pérez-Rodríguez and Mercanoglu Taban, 2019). In the Moroccan cheese, we observed a wide range of potential virulence factors and AMR genes, mostly in K. pneumoniae, S. enterica and E. coli. These virulence genes were linked to bacterial adherence, biofilm production, motility, and multi-drug resistance mechanism. Conversely, the Iberic cheeses showed a high abundance of virulence determinants from E. faecalis and Streptococcus spp., but most AMR genes were associated with other bacteria genera (such as L. monocytogenes, B. cereus, L. lactis). On the other hand, the Italian product showed very little presence of AMR and virulence determinants, mainly associated with the presence of the starter LAB Streptococcus thermophilus.

Conclusions

This study offers a preliminary overview of the taxonomic composition, presence of virulence factors, and AMR genes in artisanal cheese products collected in different Mediterranean countries. The microbiomes associated with the tested products clustered according to the cheese origin, confirming the impact of the production process, raw materials, and production environment on food microbiomes. Reads of foodborne pathogens were identified in all the productions and were negatively correlated to the relative abundance of LAB. Fighting foodborne pathogen colonization and modulating their interaction with LAB and other beneficial microbes certainly represent one of the most promising strategies to improve the safety of artisanal productions in the future.

Funding Statement

Funding: this study was funded under the PRIMA ARTISANEFOOD project “Innovative Bio-interventions and Risk Modelling Approaches for Ensuring Microbial Safety and Quality of Mediterranean Artisanal Fermented Foods”.

Availability of data and materials

Data and materials are available from the corresponding author upon request.

References

  1. Adams MR, Nicolaides L, 1997. Review of the sensitivity of different foodborne pathogens to fermentation. Food Control 8;227-39. [Google Scholar]
  2. Alcock BP, Huynh W, Chalil R, Smith KW, Raphenya AR, Wlodarski MA, Edalatmand A, Petkau A, Syed SA, Tsang KK, Baker SJC, Dave M, Mccarthy MC, Mukiri KM, Nasir JA, Golbon B, Imtiaz H, Jiang X, Kaur K, Kwong M, Liang ZC, Niu KC, Shan P, Yang JYJ, Gray KL, Hoad GR, Jia B, Bhando T, Carfrae LA, Farha MA, French S, Gordzevich R, Rachwalski K, Tu MM, Bordeleau E, Dooley D, Griffiths E, Zubyk HL, Brown ED, Maguire F, Beiko RG, Hsiao WWL, Brinkman FSL, Van Domselaar G, McArthur AG, 2023. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research 51:D690-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Anderson AC, Jonas D, Huber I, Karygianni L, Wölber J, Hellwig E, Arweiler N, Vach K, Wittmer A, Al-Ahmad A, 2016. Enterococcus faecalis from food, clinical specimens, and oral sites: prevalence of virulence factors in association with biofilm formation. Front Microbiol 6:1534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Aquilanti L, Kahraman O, Zannini E, Osimani A, Silvestri G, Ciarrocchi F, Garofalo C, Tekin E, Clementi F, 2012. Response of lactic acid bacteria to milk fortification with dietary zinc salts. Int Dairy J 25:52-9. [Google Scholar]
  5. Azzouz S, Ahadaf S, Zantar S, El Galiou O, Arakrak A, Bakkali M, Laglaoui A, 2024. Analysis of the bacterial diversity in Moroccan Jben cheese using TTGE, DGGE, and 16S rRNA sequencing. World J Microbiol Biotechnol 40:157. [DOI] [PubMed] [Google Scholar]
  6. Baylis CL, 2009. Raw milk and raw milk cheeses as vehicles for infection by Verocytotoxin producing Escherichia coli. Int J Dairy Technol 62:293-307. [Google Scholar]
  7. Bray NL, Pimentel H, Melsted P, Pachter L, 2016. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525-7. [DOI] [PubMed] [Google Scholar]
  8. Cabezas L, Sánchez I, Poveda JM, Seseña S, Palop MLL, 2007. Comparison of microflora, chemical and sensory characteristics of artisanal Manchego cheeses from two dairies. Food Control 18:11-7. [Google Scholar]
  9. Campagnollo FB, Pedrosa GTS, Kamimura BA, Furtado MM, Baptista RC, Nascimento HM, Alvarenga VO, Magnani M, Sant’Ana AS, 2022. Growth potential of three strains of Listeria monocytogenes and Salmonella enterica in Frescal and semi-hard artisanal Minas microcheeses: Impact of the addition of lactic acid bacteria with antimicrobial activity. LWT 158:113169. [Google Scholar]
  10. dos Santos RA, Araújo GB, Correia EF, de Souza Costa Sobrinho P, 2022. Minas artisanal cheese as potential source of multidrugresistant Escherichia coli. Foodborne Pathog Dis 19:316-23. [DOI] [PubMed] [Google Scholar]
  11. EFSA, 2015. Scientific Opinion on the public health risks related to the consumption of raw drinking milk. EFSA J 13:3940. [Google Scholar]
  12. EFSA, 2023. The European Union one health 2022 zoonoses report. EFSA J 21:e8442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Faria AS, Coelho-Fernande S, Santos-Rodrigue G, Fernandes Â, Barros L, Cadavez V, Gonzales-Barron U, 2022. Microbiological quality profile of goat’s raw milk cheese during ripening as affected by its intrinsic properties. 2th International Conference on Simulation and Modelling in the Food and BioIndustry 2022. Foodsim 46-53. [Google Scholar]
  14. Gould LH, Mungai E, Barton Behravesh C, 2014. Outbreaks attributed to cheese: differences between outbreaks caused by unpasteurized and pasteurized dairy products, United States, 1998–2011. Foodborne Pathog Dis 11:545-51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hamama A, Bayi M, 1991. Composition and microbiological profile of two Moroccan traditional dairy products: raib and jben. Int J Dairy Technol 44:118-20. [Google Scholar]
  16. Handelsman J, 2004. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev 68:669-85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hartantyo SHP, Chau ML, Koh TH, Yap M, Yi T, Cao DYH, Gutiérrez RA, Ng LC, 2020. Foodborne Klebsiella pneumoniae: virulence potential, antibiotic resistance, and risks to food safety. J Food Prot 83:1096-103. [DOI] [PubMed] [Google Scholar]
  18. Indio V, Olivieri C, Lucchi A, Savini F, Gonzales-Barron U, Skandamis P, Achemchem F, Manfreda G, Serraino A, De Cesare A, 2024. Shotgun metagenomic investigation of foodborne pathogens and antimicrobial resistance genes in artisanal fermented meat products from the Mediterranean area. Ital J Food Safety 2024. doi: 10.4081/ijfs.2024.12210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Keegan KP, Glass EM, Meyer F, 2016. MG-RAST, a metagenomics service for analysis of microbial community structure and function. In: Martin F, Uroz S, eds. Microbial environmental genomics (MEG). Methods in molecular biology, vol 1399. Humana Press, New York, NY, USA; pp. 207-33). [DOI] [PubMed] [Google Scholar]
  20. Koutsoumanis K, Allende A, Alvarez Ordóñez A, Bolton D, Bover Cid S, Chemaly M, Davies R, De Cesare A, Hilbert F, Lindqvist R, Nauta M, Peixe L, Ru G, Simmons M, Skandamis P, Suffredini E, Jenkins C, Malorny B, Ribeiro Duarte AS, Torpdahl M, da Silva Felício MT, Guerra B, Rossi M, Herman L, 2019. Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food borne microorganisms. EFSA J 17:e05898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Leech J, Cabrera-Rubio R, Walsh AM, Macori G, Walsh CJ, Barton W, Finnegan L, Crispie F, O’Sullivan O, Claesson MJ, Cotter PD, 2020. Fermented-food metagenomics reveals substrateassociated differences in taxonomy and health-associated and antibiotic resistance determinants. MSystems 5:e00522-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Liu B, Zheng D, Zhou S, Chen L, Yang J, 2022. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res 50:D912-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q, Liu Y, Tang J, Wu G, Zhang H, Shi Y, Liu Y, Yu C, Wang B, Lu Y, Han C, Cheung DW, Yiu SM, Peng S, Xiaoqian Z, Liu G, Liao X, Li Y, Yang H, Wang J, Lam TW, Wang J, 2012. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1:18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Martin NH, Trmčić A, Hsieh TH, Boor KJ, Wiedmann M, 2016. The evolving role of coliforms as indicators of unhygienic processing conditions in dairy foods. Front Microbiol 7:1549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Pasquali F, Valero A, Possas A, Lucchi A, Crippa C, Gambi L, Manfreda G, De Cesare A, 2022. Occurrence of foodborne pathogens in Italian soft artisanal cheeses displaying different intra- and inter-batch variability of physicochemical and microbiological parameters. Front Microbiol 13:959648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Pérez-Rodríguez F, Mercanoglu Taban B, 2019. A state-of-art review on multi-drug resistant pathogens in foods of animal origin: risk factors and mitigation strategies. Front Microbiol 10:2091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Possas A, Hernández M, Esteban-Carbonero Ó, Valero A, Rodríguez-Lázaro D, 2022. Listeria monocytogenes survives better at lower storage temperatures in regular and low-salt soft and cured cheeses. Food Microbiol 104:103979. [DOI] [PubMed] [Google Scholar]
  28. Quintana ÁR, Perea JM, Palop ML, Garzón A, Arias R, 2020. Influence of environmental and productive factors on the biodiversity of lactic acid bacteria population from sheep milk. Animals 10:2180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Rangel-Ortega SdC, Campos-Múzquiz LG, Charles-Rodriguez AV, Chávez-Gonzaléz ML, Palomo-Ligas L, Contreras-Esquivel JC, Solanilla-Duque JF, Flores-Gallegos AC, Rodríguez-Herrera R, 2023. Biological control of pathogens in artisanal cheeses. Int Dairy J 140:105612. [Google Scholar]
  30. Siroli L, Patrignani F, D’Alessandro M, Salvetti E, Torriani S, Lanciotti R, 2020. Suitability of the Nisin Z-producer Lactococcus lactis subsp. lactis CBM 21 to be used as an adjunct culture for Squacquerone cheese production. Animals 10:782. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Thierry A, Valence F, Deutsch SM, Even S, Falentin H, Le Loir Y, Jan G, Gagnaire V, 2015. Strain-to-strain differences within lactic and propionic acid bacteria species strongly impact the properties of cheese - a review. Dairy Sci Technol 95:895-918. [Google Scholar]
  32. Ünlü T, Koluman A, Burkan ZT, Tezel A, Akçelik EN, Çalim HD, Ata Z, 2011. Incidence and antibiotic resistance of Escherichia coli isolated from different kinds of cheese. J Food Safety 31:54-60. [Google Scholar]
  33. Walsh AM, Macori G, Kilcawley KN, Cotter PD, 2020. Meta-analysis of cheese microbiomes highlights contributions to multiple aspects of quality. Nat Food 1:500-10. [DOI] [PubMed] [Google Scholar]
  34. Yoon Y, Lee S, Choi KH, 2016. Microbial benefits and risks of raw milk cheese. Food Control 63:201-15. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data and materials are available from the corresponding author upon request.

Articles from Italian Journal of Food Safety are provided here courtesy of PAGEPress

RESOURCES