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DNA-Based Methods for Cheese Origin Authentication
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This entry is adapted from the peer-reviewed paper 10.3390/foods11213379

Metabolic activities of cheese microbiota play a crucial role in the development of cheese typicity. For geographical origin authentication, microbiota fingerprinting is therefore of high interest as traditional and artisanal cheeses are produced with a more diversified microbiota associated with the cheese-making process (e.g., use of raw milk, starter, brine, equipment and materials, and ripening rooms). While cheese microbial diversity was traditionally investigated using culture-dependent methods, hence overlooking unculturable or subdominant species, nowadays, culture-independent methods (HTS) have unravelled this diversity and provided further means to connect microbiota composition to cheese quality and typicity, but also origin. This success is due to the availability of new sequencing platforms, bioinformatic pipelines, and a continuous decrease in cost. While complete microbial genome sequencing (i.e. metagenomics) can be used, metabarcoding (also known as metagenetics and corresponding to the selective amplification of taxonomically relevant followed by NGS sequencing) is the most widely reported in the scientific literature. Herein researchers report recent scientific literature about metabarcoding analysis of cheese microbiota, highlighting what factors contribute to its formation and how it could be used to authenticate cheese origin.

cheese geographical origin authentication next-generation sequencing

1. Introduction

To perform DNA metabarcoding, cheese samples are first homogenized, then total DNA is most frequently extracted using commercial kits, ad hoc protocols or a combination thereof [1][2]. Hypervariable regions of taxonomically relevant genes (e.g., 16S rDNA for bacteria and archaea, ITS, 18S rDNA, 26S rDNA for fungi) are amplified by PCR reactions, while a second amplification step tags amplicons with specific DNA fragments –barcodes- and dedicated adapters for the final sequencing step using next-generation technologies (e.g., Illumina, Pacbio, Iontorrent, or Nanopore). Typically, 16S rDNA and ITS (internal transcribed spacer) markers, targeting bacteria and fungi, respectively, are employed to generate compositional data describing microbial taxa and their relative abundance in cheese microbial communities. After sequencing, two complementary but different ways can be used for amplicon clustering from quality-checked data, namely, operational taxonomic units (OTUs) and amplicon sequencing variants (ASVs) [3]. Subsequently, taxonomic assignment is performed using a specific classifier tool (BLAST, RDP, UCLUST, SortMeRNA) against various reference databases, such as Greengenes, SILVA, and UNITE [4][5]. Generally, clustered OTUs/ASVs are analyzed from the phylum to the genus level since they can be less precise at the species level [6].

2. Main Factors Affecting Cheese Microbial Diversity

In a recent study by Kamimura et al. [7], the microbiota of 578 traditional Brazilian cheeses were analyzed by metabarcoding. Bacterial communities were distinctly clustered with PCA by cheese type and regional origins while, at the genus level, hierarchical cluster analysis separated production regions. These authors were thus able to identify specific origin-related microbiota. The core microbiota of Brazilian traditional cheeses displayed different relative abundances and oligotypes (i.e. closely related but distinct bacterial taxa) of LAB belonging to the Enterococcus, Lactococcus, Streptococcus, Leuconostoc, and Lactobacillus sensu lato genera, as well as other taxa belonging to the Enterobacteriaceae family and Staphylococcus genus. Within the same regional area, microbiota analysis differentiated the origin of traditional cheeses, namely Cerrado, Araxà, Canastra, Campos des Vertenes and Serro, produced with a similar natural whey starter and ripening period (17 and 22 days). These findings were in agreement with those of another study that analyzed 97 samples of Minas artisanal cheese from 6 different producers located in the same region [8]. Starter cultures, consisting of Streptococcus, Lactococcus and Lactobacillus sensu lato spp., constituted the core microbiota in all farms. However, significant differences in family- and genus-level bacterial community relative abundances were observed between the studied farms due to environmental factors including their geographical location. Even when dominant genera may be inferred to the natural whey cultures used, meta-analysis from amplicon sequencing data of traditional artisanal cheeses from Italy, Belgium, and Kalmykia indicated differences in bacterial structures between cheeses produced in different geographic areas (unweighted PCoA cluster MANOVA, p < 0.001 [9]). Those produced using natural milk cultures showed improved acidification without an effect on the typical cheese microbiota. Indeed, at the end of the ripening period, cheese origins clustered according to producer facilities (PCoA on Bray–Curtis) [10].
Another study was performed by Zago et al. [11], 118 Grana Padano samples were analyzed after 7-8 months of ripening and a common core microbiota composed of Lactobacillus-, Lactococcus-, Lacticaseibacillus-, Limosilactobacillus- and Streptococcus-dominant genera was observed. More precisely, differences in bacterial abundance, richness, and evenness were found for dominant and sub-dominant groups according to production region, a result also confirmed by PERMANOVA b-diversity analysis. The authors also identified specific species that could be linked to several production areas; however, no species biomarkers were identified regardless of production area and non-metric multidimensional scaling did not show any clear clustering profile.
Some cheeses are produced in very small geographical zones by a limited number of producers. This is the case for Plaisentif and Historic Rebel cheeses from the mountainous regions in Italy that are only produced during specific seasons (violet blooming season and grazing season) by 14 and 12 producers, respectively [12][13]. Both are raw milk cheeses produced without starter adjunction. Bacterial amplicon sequencing analysis (16S rDNA V4 region) for Plaisentif cheese identified dominant genera and, more importantly, differences in bacterial community profiles between producers suggesting fraudulent starter addition in some cheeses [13].
Based on a similar analysis for Historical Rebel cheese, the core microbiota was composed of 5 different genera -Streptococcus, Lactobacillus, Lactococcus, Leuconostoc and Pediococcus- with Streptococcus relative abundances ranging from 60% to 85% [12]. Richness and other a-diversity parameters differed among producers as well as in multivariate analysis (PCoA on unweighted Unifrac) and, based on the observed significant differences, pasture area could be linked to the different Historic Rebel cheese producers.

3. Climatic and Environmental Condition

Other factors that can impact microbial communities of traditional cheeses are the climatic and dairy environment conditions that are directly associated with geographical origins. This was observed for traditional Chinese Rushan cheese produced using Chaenomeles sinensis boiled extract as a clotting agent in three different regions. Even if the same UHT milk and production equipment were used, geographical origins significantly impacted the relative abundance of 12 dominant genera, namely Lactobacillus, Acinetobacter, Acetobacter, Lactococcus, Enterobacter, Moraxella, Enterococcus, Streptococcus, Kocuria, Staphylococcus, Chryseobacterium and Exiguobacterium [14], and is likely related to specific house microbiota and open-air drying. This result was also confirmed by PCoA clusters and Anosim analysis.
As typical cheese microbiota can be in part acquired from the specific raw materials used, traditional tools, environmental and production conditions, cheese-making process and geographical area, a comparison between traditional and industrial cheeses may provide additional information to authenticate cheese origin. Noteworthy, some authors have reported that commercial starters, inoculated at ~106 CFU/mL, prevent resident microbiota from developing, especially during ripening [15][16]. Overall, milk pasteurization, use of similar commercial starters, similar industrial equipment, and standardized recipes for cheese production are crucial factors that decrease cheese microbial complexity and biodiversity, and lead to highly standardized productions and thus lower variance is detected (Figure 1). This hypothesis is in accordance with the study by Kamilari et al. [17], in which a significant decrease in bacterial diversity was observed in industrially produced Haloumi cheeses vs. artisanal products. However, the microbial diversity observed for artisanal Haloumi cheeses could not be linked to the producer geographical origins. In another study aiming to authenticate cheese origin at the producer level, no distinction could again be made [18].
Foods 11 03379 g001 550
Figure 1. Technological factors affecting cheese microbiota biodiversity. Green and purple lines show combinations of technological factors during cheese-making that increase or decrease this biodiversity.

4. Cheese Ripening

Cheese ripening is another factor that affects microbial community diversity and cheese typicity. Ripening can be considered as a selection process that leads to cheese microbial composition changes. According to Gobetti et al. [19], intentionally added microorganisms used in cheese-making include primary starters (natural milk culture, natural whey culture, or lyophilized commercial starters), secondary or adjunct LAB starters, and milk autochthonous microbiota (Non-starter lactic acid bacteria -NSLAB- and others). These are the main ripening agents in intermediate to long ripening times, which mainly explain the observed diversity and typicity of the produced cheeses. The relationship between primary starters and NSLAB during maturation is well-known, and involves a progressive reduction of the former in favor of the latter. The role of NSLAB is crucial in maturation and the development of the typical characteristics of traditional cheese. In this case, useful insights can be gained by comparing the genomic features of primary starters for the presence of genes involved in the metabolic pathways important for cheese maturation. While primary starters have important genetic features for the utilization of lactose (mainly connected with their acidification ability) NSLAB possess many more genes coding for peptidases, peptide transporters within cells, and amino acid catabolism that can represent an advantage during cheese maturation [20]. Moreover, compared to primary starters, NSLAB tend to adapt better to the hostile conditions of the cheese ripening environment, such as temperature, salt content, pH, and redox potential. In fact, NSLAB can adopt alternative metabolic pathways to produce energy from unconventional sources while resisting acid conditions. Therefore, NSLAB present in raw milk at a sufficient inoculum to colonize ripened raw milk cheeses, or acquired from the house microbiota, could be an indicator of geographical origin at the producer level. Beyond NSLAB, other microorganisms belonging to various groups can influence cheese ripening. This is the case of fungal communities in many traditional cheeses, such as Queijo de Azeitão in Portugal [21], Tomme d’Orchies in France [22], and Robiola di Roccaverano in Italy [23]. Indeed, fungal communities are well-known for their decisive role in flavor and texture of white and blue-veined mold-ripened cheeses due to lipolytic, proteolytic, and glycolytic activities, leading to high production of aromatic ketones and alcohols [24][25][26]. Generally, fungal species such as Penicillium camemberti, Penicillium roqueforti, Debaryomyces hansenii, Kluyveromyces marxianus, Candida catenulata, Galactomyces geotrichum, and Mucor lanceolatus are either deliberately added as technological adjunct cultures or present in the production environment [26][27][28][29]. Nevertheless, in traditional cheeses, fungal communities were reported to be more diverse than the used starter but, at the same time, not connected with geographical origin [1][21]. Table 1 reports advantages and limitations of amplicon sequencing for cheese origin authentication.
Table
Advantages Reference Limitations Reference
Time- and cost-effective processing of large sample numbers [30] Analyses could be biased by sample processing, DNA extraction methods, and equimolar library preparation [31]
Consolidated pipeline for data analysis [32] PCR amplification steps include errors, e.g., PCR specificity and variation of 16S rRNA copy number per genome [33]
Identification of taxonomic groups associated with typical flavor and cheese-making technology [34] Under- or over-estimation of microbial community diversity [32][35]
Allows improvement of cheese-making to ensure safety while preserving typicity [7] Lack of absolute abundance [36][37]
Evaluation of core microbiome describing facility-associated microbial groups [38][39] Limited and uneven taxonomic resolutions [40][41]
May pinpoint new biotypes [17] DNA amplicon sequencing typically does not discriminate between live and dead microorganisms (except if DNA stains such as propidium monoazide are used) [42]
The mentioned studies showed that in traditional cheeses, the combination of artisanal cheese-making, specific raw materials, and characteristic environmental conditions shape microbial community diversity according to geographical origin. Most analyses conducted using 16S rDNA amplicon sequencing discriminated cheese origin, although taxonomic classification was still limited to genus/family-level descriptions and only a few cheese types per study were considered. To further assess the unicity of typical cheeses against food fraud, more in-depth studies, including meta-analyses on all available cheese data and increased depth of microbial population descriptions (e.g., metagenomics), are of interest.

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Subjects: Food Science & Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Marco Cardin
,
Barbara Cardazzo
,
Jérôme Mounier
,
Enrico Novelli
,
Monika Coton
,
Emmanuel Coton
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Update Date: 11 Nov 2022
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