foods
Review
Omics Approaches to Assess Flavor Development in Cheese
Rania Anastasiou *, Maria Kazou, Marina Georgalaki
and Effie Tsakalidou
, Anastasios Aktypis, Georgia Zoumpopoulou
Laboratory of Dairy Research, Department of Food Science and Human Nutrition, Agricultural University of
Athens, Iera Odos 75, 118 55 Athens, Greece; kmaria@aua.gr (M.K.); mgeor@aua.gr (M.G.); aktyp@aua.gr (A.A.);
gz@aua.gr (G.Z.); et@aua.gr (E.T.)
* Correspondence: ranastasiou@aua.gr
Abstract: Cheese is characterized by a rich and complex microbiota that plays a vital role during
both production and ripening, contributing significantly to the safety, quality, and sensory characteristics of the final product. In this context, it is vital to explore the microbiota composition and
understand its dynamics and evolution during cheese manufacturing and ripening. Application
of high-throughput DNA sequencing technologies have facilitated the more accurate identification
of the cheese microbiome, detailed study of its potential functionality, and its contribution to the
development of specific organoleptic properties. These technologies include amplicon sequencing,
whole-metagenome shotgun sequencing, metatranscriptomics, and, most recently, metabolomics. In
recent years, however, the application of multiple meta-omics approaches along with data integration
analysis, which was enabled by advanced computational and bioinformatics tools, paved the way to
better comprehension of the cheese ripening process, revealing significant associations between the
cheese microbiota and metabolites, as well as their impact on cheese flavor and quality.
Citation: Anastasiou, R.; Kazou, M.;
Keywords: cheese; flavor; omics; lactic acid bacteria; yeasts; cheese microbiome; metagenomics;
metatranscriptomics; metabolomics
Georgalaki, M.; Aktypis, A.;
Zoumpopoulou, G.; Tsakalidou, E.
Omics Approaches to Assess Flavor
Development in Cheese. Foods 2022,
1. Introduction
11, 188. https://doi.org/10.3390/
The history of cheesemaking is lost in the mists of time [1]. Over the years, cheese
making has greatly evolved with a remarkable diversity of cheeses produced worldwide,
with culture and resources shaping the production in each region. Nowadays, the consumer
demand for high-quality products with excellent sensorial characteristics and, at the same
time, at a reasonable cost is growing. According to recent estimations, global cheese
consumption is expected to increase by 13% from 2016 to 2025 [2].
Cheese flavor together with texture and overall appearance are crucial parameters
that determine consumer preference and enjoyment [3,4]. In almost all cheese varieties,
flavor develops mainly during the ripening process, a complex and time-dependent process
that involves microbiological and biochemical changes mediated by the metabolic flux of
starter, adjunct, and non-starter cultures, the action of enzymes from rennet and milk [5],
together with non-enzymatic conversions [6]. Cheese manufacture and ripening are characterized by a succession of microbial communities that contribute via their metabolism to
the production of numerous compounds, which provide aroma (odor) and flavor (taste) to
cheese products [7]. Starter lactic acid bacteria (SLAB), carefully selected and deliberately
added to milk, rapidly metabolize milk lactose, and produce lactic acid, thus improving the
microbial safety and quality of the final product by readily acidifying the curd [8]. However,
throughout ripening, SLAB decline, releasing intracellular enzymes due to autolysis, thus
producing molecules beneficial for the growth of non-starter LAB (NSLAB) that take over,
since they can grow on a food ecosystem that contains lactate, citrate, glycerol, ribose, free
fatty, and amino acids [9]. NSLAB significantly contribute to cheese flavor, texture, nutritional value, and microbial safety in most of the ripened cheese varieties [10,11]; however,
foods11020188
Academic Editors: Javier Carballo
and Juan Antonio Centeno
Received: 15 December 2021
Accepted: 9 January 2022
Published: 11 January 2022
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Foods 2022, 11, 188. https://doi.org/10.3390/foods11020188
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Foods 2022, 11, 188
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some cheese quality defects [9,12] and off-flavors, especially in the later phases of ripening,
have been attributed to NSLAB [11,13,14]. Moreover, the de-acidifying, proteolytic, and/or
lipolytic activities of yeasts and filamentous fungi significantly impact the development
of cheese flavor, texture, and typical appearance, especially of smear- and mold-ripened
cheeses, respectively [15–17].
Study of the complex microbial consortia dynamics and evolution during cheese making and ripening is critical in understanding their contribution to the sensorial characteristics of the final product. Recent advances in high-throughput sequencing (HTS) methodologies (e.g., amplicon metagenomics, shotgun metagenomics, and metatranscriptomics)
and high-resolution metabolomics coupled with the constantly evolving bioinformatics
and statistical tools for data analysis, as well as the improvement of available databases,
have provided deeper insights into cheese microbiota diversity and interactions and thus
revealed the impact of microbial metabolism on flavor compounds formation [18–22]. Furthermore, the integration of multiple meta-omics tools, together with powerful statistical
analysis methods, has allowed detailed functional characterization of the cheese microbiota
and determination of its contribution to the development of characteristic flavor metabolites [23–29]. This in-depth study of cheese microbiota could result in the application of
selected cultures able to enhance or diversify cheese flavor, and thus, leading to cheese
making practices and technologies for accelerated or improved ripening with economic
benefit for both cheese producers and consumers.
In this review article, we first discuss the sources of cheese microbiota and the taxa as
detected by both culture-dependent and -independent approaches, as well as the key microbial metabolic pathways that determine flavor formation. We then focus on studies where
individual -omics technologies have been applied, and finally, on integrated multi-omics
studies that unravel the composition and functionality of the complex cheese microbiota
on flavor formation.
2. The Cheese Microbiome
2.1. Origin of the Cheese Microbiota
Cheese is a high-nutrient fermented dairy food that consists of proteins, sugars, fat,
minerals, and vitamins, while its microbial consortium contributes to an added nutrient
and even probiotic value by its metabolic activity. Knowledge of the cheese microbiome
is important, as the microorganisms and their primarily biochemical processes will drastically affect the final product [30]. Cheese microbiota originates from three sources: the
indigenous milk microbiota, the inoculated starter cultures, and the cheese production
environment [26,31]. The mammalian species seems to influence milk microbiota diversity,
with cow’s milk appearing to be more diverse than that of, e.g., goats and sheep. Moreover,
the fat content also affects the microbial load [32–34]. The milk of a healthy lactating animal
is not, although often considered, sterile, as bacteria isolates present in the mammary
gland have been found to be other than those found on the skin of the same host [35]. In
addition, the ability of some microbes to travel from the intestinal lumen to the mammary
gland through an entero-mammary pathway has also been described [35,36]. More than
100 genera and 400 microbial species have been detected in raw cow, sheep, and goat milk,
and in particular, 90 species of Gram-negative bacteria, 90 species of Gram-positive and
catalase-positive bacteria, 60 species of lactic acid bacteria (LAB), and 70 and 40 species
of yeasts and molds, respectively [31,37]. For all microbial groups, inter-farm variability is wide, while intra-farm variability is generally much lower except from season to
season [38].
2.2. Factors Affecting the Cheese Microbiota
The load and composition of the raw milk microbiota depend directly on the hygiene
conditions of the milking environment and sources that are in contact with the milk, such
as the animal’s teat, milker’s hands, milking machine, milk line, and tank [39,40]. Crucial
factors affecting the microbial dynamics are also the type of forage fed to animals and the
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grazing system employed, the feeding practices, and the overall management system of the
farm including hygiene conditions and drinking and washing water [31,41,42]. Moreover,
udder and teat cleaning as well as disinfection of the milking equipment are of significant
importance as well [43,44].
Nowadays, several approaches are used to decrease the microbial load, thus ensuring
milk safety, as the industrialized production process must meet strict sanitation and quality
standards to ensure cheese safety [45]. Heat treatment and microfiltration are the most
commonly adopted practices in both small- and large-scale dairy units to reduce the
microbial load of milk. Moreover, from the initial step of milking to the shipment of cheeses
to consumers, the product is subjected to rigorous monitoring, quality assurance standards,
and specific microbiological tests. However, milk heat treatment not only reduces the
spoilage and pathogenic population, but also the indigenous pro-technological microbes,
which are key players in shaping the cheese organoleptic characteristics [46]. Therefore,
pasteurization and low pH contribute to the selection of specific bacteria in all types of
cheeses, including some artisanal cheeses made without the use of starter cultures, such
as fresh acid-set cheeses that have been curdled by the addition of acid (e.g., Mascarpone,
Queso blanco, Paneer). On the other hand, the use of raw milk for “traditional cheese” label
production, inoculated or not with starter cultures, results in a richer ripening microbiota
that shapes distinctive cheese sensory properties [47,48].
As mentioned before, the type of milk and its indigenous microorganisms play an
essential role in shaping cheese microbiota and thus, cheese sensory characteristics [49].
Moreover, manufacturing conditions, such as the type of rennet, the type of starters and
adjuncts used, salting, scalding temperature, smoking, as well as ripening conditions, such
as temperature and humidity, are significant factors as well [33,50–54].
2.3. The Cheese Microbial Pool
The cheese microbiome consisting of bacteria and yeasts/fungi is developed during
the two essential manufacturing steps, namely fermentation and ripening. The bacterial
pool includes:
(a)
(b)
The dominant population of the pro-technological SLAB used either individually
or in various combinations depending on the cheese variety, for their metabolic capacity to successfully ferment lactose to lactic acid, as well as their contribution to
the development of cheese sensorial characteristics [12]. The back-slopping technique has been traditionally used for the production of artisanal cheese products,
requiring the inoculation of milk with natural milk or whey cultures consisting of
unknown strains, termed as undefined starters [55]. Nowadays, starters consist
of a specific cocktail of well-defined strains [55], as their survival and spatial distribution within the cheese matrix are strain-dependent properties and can determine the final populations in the curd [56]. Mesophilic cultures, mainly strains
of Lactococcus lactis (both subsp. lactis and cremoris), as well as thermophilic cultures, primarily Streptococcus thermophilus, are the commonly used starters. Additionally, members of the genus Lactobacillus are used as starter cultures, mainly
Lactobacillus delbrueckii subsp. bulgaricus and subsp. lactis and Lactobacillus helveticus,
and rarely Lacticaseibacillus casei, Lacticaseibacillus paracasei, Lactiplantibacillus plantarum/paraplantarum, Levilactobacillus brevis, and Lacticaseibacillus rhamnosus [55,57].
The NSLAB, originating from raw milk as well as the production and ripening environment, may exhibit proteolytic and lipolytic activities and metabolize other carbon
sources than lactose, e.g., citrate, which can be catabolized by Leuconostoc spp., L.
casei, L. plantarum, and Weissella paramesenteroides. NSLAB typically consist of facultative heterofermentative lactobacilli, including L. casei, L. paracasei, L. rhamnosus,
L. plantarum, and Latilactobacillus curvatus, as well as obligate heterofermentative lactobacilli, including L. brevis, Limosilactobacillus fermentum, Lactobacillus wasatchii sp.
nov., and Lentilactobacillus parabuchneri [55]. Other LAB, including species of the
genera Streptococcus, e.g., Streptococcus macedonicus and Streptococcus equi, Pediococcus,
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(c)
Weissella, and Enterococcus, are part of the NSLAB group in many cheeses [58,59].
Enterococci, in some cases, have been used as experimental starter cultures; however, the genus Enterococcus does not possess Qualified Presumption of Safety (QPS)
status, due to its potential to acquire genetic determinants of virulence and antibiotic resistance [60]. Some NSLAB strains can be selected and deliberately used as
adjunct cultures and may dominate the cheese microbial ecosystem, enhance flavor
development, and provide health benefits [11,61,62]. In addition, they help to achieve
a balanced cheese flavor and may exhibit important metabolic activities, such as
proteolytic, lipolytic, and esterolytic activity; amino acid degradation; and citrate
catabolism [63]. Adjunct starters include strains of Lactobacillus spp. as well as the
citrate fermenting Leuconostoc mesenteroides and Leuconostoc pseudomesenteroides [55].
Additionally, genera, such as Corynebacterium, Arthrobacter, and Brevibacterium, which
are very important to produce smear cheeses, as well as Propionibacterium, which is
a significant starter in Swiss-type cheeses, can be also found in cheese even at low
levels [64].
The spoilage and pathogenic bacteria frequently detected in cheese. They derive either
from milk or the processing environment, and when good hygiene practices during
the entire cheese processing and storage are not employed, they can either lead to production losses due to spoilage or undermine the safety of the final product [31]. They
include species of the genera Clostridium, Staphylococcus, Salmonella, Listeria, Brucella,
Shigella, Bacillus, Escherichia, Pseudomonas, Citrobacter, Klebsiella, Enterobacter, Psychrobacter, Proteus, Serratia, and Halomonas, many of which are psychrotolerant [57,65].
As with bacteria, the fungal pool of cheese microbiota includes:
(a)
(b)
The starter cultures that are directly added to the milk or curd. Several cheese varieties are produced with filamentous fungi. They belong to two categories, namely
the internally ripened cheeses, such as blue cheeses and Gamalost produced with
Penicillium roqueforti and Mucor spp., respectively, in which fungal growth is visible
both in the interior part and on the surface of the cheese, and the surface-ripened
cheeses, such as the Camembert-type soft cheeses produced with Penicillium camemberti,
in which fungal growth is visible only on the surface of the cheese [66]. Strains of
Mucor spp., Trichothecium roseum, Fusarium domesticum, Scopulariopsis spp.,
Sporendonema casei, Geotrichum candidum (teleomorph Galactomyces candidus),
Kluyveromyces lactis, and Debaryomyces hansenii are also used for the production of
mold and smear-ripened cheeses [66,67].
The non-starter yeasts and molds (NSYMs), including species of the genera, such as
Issatchenkia, Aspergillus, Cochliobolus, Fusarium, Mucor, Cryptococcus, Pichia, Saccharomyces,
Kluyveromyces, Debaryomyces, Candida, Trichosporon, and Yarrowia, can be transferred
from raw milk to cheese or may be contaminants from the dairy environment and
influence cheese ripening [67]. While some of the NSYMs are known to be spoilers
(e.g., Cladosporium cladosporioides, Mucor racemosus, Penicillium commune), there are
others with an unknown or positive contribution to the cheese sensorial and physicochemical characteristics [67]. Moreover, strains of fungal genera, such as Penicillium,
Aspergillus, and Fusarium spp., are potential producers of mycotoxins, such as aflatoxin M1, ochratoxin A, citrinin, roquefortin C, mycophenolic, and cyclopiazonic acid,
which have been detected in cheeses at various levels, while certain yeast species,
such as Yarrowia lipolytica, are able to produce biogenic amines [68,69].
The use of HTS in profiling cheese microbial diversity revolutionized the way we understand the cheese ecosystem and the dynamic changes in the microbial communities that
take place during cheese ripening [18,57,70]. HTS contributed to unraveling the microbial
diversity of natural whey cultures, which are applied as thermophilic LAB starters in Protected Designation of Origin (PDO) cheeses [57]; detected several novel bacterial genera in
the cheese or cheese rind in various types of cheese varieties, such as Prevotella, Arthrobacter,
Yaniella, Nocardiopsis, Pseudoalteromonas, Facklamia, Vibrio, and the species Geobacillus toebii
and Methylobacterium populi [33,71,72]; and revealed the dominance of bacteria, such as
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Psychrobacter, in some cheese varieties [73]. Furthermore, the potential contribution of
some unusual species, such as Pseudoalteromonas haloplanktis and Psychrobacter immobilis,
has now been revealed by metagenomic profiling of three types of cheese rinds, while four
fungal genera (among them Aspergillus and Sporandonema) have recently been reported as
part of the sub-dominant cheese microbiota, revealing the accessibility of the diversity of
low-abundant taxa by the powerful HTS technologies [72].
Recently, the combination of modern molecular biology tools with conventional
culture-based techniques revealed the presence of 104 bacterial and 39 fungal genera
in the microbial communities of 33 cheese rinds [74], while the characterization of new
metagenomes from 55 kinds of cheese offered 328 metagenome-assembled genomes, including 47 putative new species that could influence taste or color [28]. Such results confirm the
richness of the cheese microbial diversity and inspire the interest for its study ad infinitum.
3. Exploring the Cheese Microbiome
3.1. Culture-Dependent Tools
Exploration of the diverse cheese microbial community, which varies not only among
cheese types but also within the cheese from the core to the surface, commenced using
culture-dependent microbiological methods [22,74,75]. This approach includes bacterial
growth and enumeration on selective growth media, isolation and identification at the
genus and species level, and finally, characterization of biotypes at the intraspecific level.
Many selective media have been developed so far for both bacteria and fungi, regardless
of whether they are pro-technological or spoilage and pathogenic ones. One should
bear in mind, however, that the media selectivity is not necessarily optimal, while at the
same time, they do not always support the growth of viable but not culturable (VNC)
microorganisms [76].
Classification of cheese microorganisms initially relied on the phenotype, e.g., morphological, biochemical, and physiological traits [77]. More advanced phenotypic methods are based on the whole-cell protein profile using either sodium dodecyl sulphatepolyacrylamide gel electrophoresis (SDS-PAGE) or matrix-assisted laser desorption ionizationtime of flight mass spectrometry (MALDI-TOF MS), fatty acid analysis, and immunologybased methods, such as enzyme-linked immunosorbent assay (ELISA) or serological testing [78,79].
Genotypic techniques provide, however, wider and more reliable information concerning the diversity of the cheese microbiota at the genus, species, and even strain level;
they, thus, enhance the potential for exploitation of the cheese microbial community in
this biochemically dynamic product, as well as the development of innovative cheese
products [77]. Outstanding bacteria identification methods include species-specific polymerase chain reaction (PCR), amplified ribosomal DNA restriction analysis (16S-ARDRA),
sequencing of 16S ribosomal RNA gene (16S rRNA PCR), tRNAAla-23S rDNA-restriction
fragment length polymorphism (tRNA Ala–23S rDNA-RFLP), sequencing of the gene encoding for phenylalanyl-tRNA synthase alpha-subunit (pheS), and multiplex real-time PCR
(mRealT-PCR) using the pheS as a molecular target, while DNA fingerprinting techniques
include RFLP of protein-coding genes involved in primary metabolism, restriction enzyme
analysis pulsed-field gel electrophoresis (REA-PFGE), randomly amplified polymorphic
DNA (RAPD), amplified fragment length polymorphism (AFLP), and repetitive extragenic
palindromic-PCR (REP-PCR) [74,76]. Similar methods are used for the identification of
fungi spanning the internal transcribed spacers (ITSs) and targeting the 5.8S, 18S, 26S, or
28S region of the nuclear ribosomal RNA gene [80–85].
3.2. Culture-Independent Tools
3.2.1. Conventional Methods
The limits of the culture-dependent approach due to its inefficiency to detect “difficultto-culture” or sub-dominant microorganisms have moved the trend toward cultureindependent methods that avoid the use of selective cultivation and isolation [22,76]. The
Foods 2022, 11, 188
culture” or sub
dependent approach due to its inefficiency to detect “diffi6 of 32
most common procedure for the culture-independent approach is the analysis of nucleic
acids, either DNA or RNA, isolated from an entire microbial population using PCR amplification of the target sequences. The list of widely applied methods includes polymerase
chain reaction denaturing gradient gel electrophoresis (PCR-DGGE), PCR-temporal temperature gradient gel electrophoresis (PCR-TTGE), single-strand conformation polymorphismPCR (SSCP-PCR), terminal-RFLP (T-RFLP), length heterogeneity-PCR (LH-PCR) analysis,
quantitative real-time PCR (qPCR), and reverse transcription-qPCR (RT-qPCR), as well as
denaturing high-performance liquid chromatography (DHPLC) and fluorescence in situ
hybridization (FISH), which do not rely on PCR amplification [76,86,87]. The 16S and 26S
rRNA-encoding genes are the most frequently employed targets for identifying bacterial
and eukaryote species, respectively [88,89], although other genes, such as pheS and RNA
polymerase B subunit (rpoB), have also been used as targets for bacterial identification in
cheese [90,91].
3.2.2. Novel Omics Approaches
Given the complexity of the cheese microbiota as well as its plasticity, i.e., the dynamic
succession of microbial groups during cheese making and ripening, the major challenge is
to reliably assess the route and fate of each microbial group or even certain microbial strains
during cheese making, and to understand their role and contribution in the quality and
safety of the final product. The above challenges surpass the limits of the established classical and advanced microbiological and chemical analyses and can nowadays be overcome
by the application of the so-called -omics approaches.
The term-omics refers to their ability to characterize in a single analysis all or most
members of a family of molecules involved in the development and maintenance of life
in a non-targeted and non-biased manner [92] and they include high-throughput nextgeneration sequencing (NGS) -based methods [93], such as genomics/metagenomics and
metatranscriptomics targeting DNA and RNA, respectively. Additionally, metaproteomics
and metabolomics, targeting proteins and metabolites, respectively, are of high significance as well (Figure 1). Their application to cheese microbiota will be discussed in the
following sections.
Figure 1. Integrating multi-omics approaches for the study of cheese flavor development.
4. Cheese Microbiota Metabolic Pathways for Flavor Development
Cheese flavor development is a dynamic process that is influenced by the type and
composition of milk, processing conditions, and biochemical reactions of microorganisms
present in the cheese matrix [94]. Besides rennet and milk indigenous enzymes, cheese
microbiota is a primary source of enzymes participating in biochemical pathways involved
in cheese manufacture and ripening; hence, it plays a key role in shaping the sensorial
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properties of the final product. In fact, population interactions among both bacteria and
fungi (yeasts and molds) lead to a variety of flavor compounds present in the different
types of cheese contributing to differentiate them [95]. Substrates for biochemical processes
are carbohydrates, lipids, and proteins, with the latter being considered as the principal
ones for cheese flavor formation [55].
4.1. Carbohydrate Catabolism
The catabolism of the principal milk carbohydrate lactose is the first fermentation
step, upon which all milk-based fermentation processes are based [20]. The pathways
through which lactose is catabolized depend considerably on the starter cultures, as well
as the type and abundance of NSLAB for each cheese variety [96]. Starter activity, curd
washing, and cheese salting impact the amount of residual lactose available to bacteria
after cheese manufacture as most of the lactose is lost in the whey. Initial catabolism of
–
–
lactose, via the Embden–Meyerhof–Parnas (EMP) pathway and/or the Leloir pathway
after uptake and cleavage to glucose and galactose moieties, gives a mixture of L- and
D-lactate depending on the starter cultures [96,97]. Moreover, the tagatose-6P pathway is
also involved in the catabolism of galactose-6P, with end products also entering the EMP
pathway (Figure 2) [98]. Residual lactose in the curd is rapidly depleted, mainly by NSLAB,
during the early stages of ripening [99]. Heterofermentative NSLAB bacteria, such as
Leuconostoc and certain Lactobacillus species, produce, besides lactate, ethanol, acetate, and
CO2 via the phosphoketolase (PK) pathway [100]. Furthermore, Leuconostoc spp. along with
Lactococcus lactis subsp. lactis biovar. diacetylactis, can convert pyruvate, an intermediate
molecule in several metabolic pathways, to diacetyl and acetaldehyde, products commonly
found in Gouda and fresh milk cheeses. Finally, lactate, derived from the action of both
starters and NSLAB, becomes the substrate for a range of biochemical reactions that lead to
the production of flavor compounds. For instance, Propionibacterium spp. can convert lactate
to flavor-forming compounds, such as propionate and acetate that contribute to the Swisstype cheese flavor. Regarding yeasts, lactate catabolism leads to the production of CO2 and
ethanol, which have been correlated to non-desirable off-flavors of cheeses [101]. Moreover, lactose-fermenting yeasts, namely K. lactis, Kluyveromyces marxianus, and D. hansenii,
that are present in certain cheese varieties are able to assimilate lactose, leading to the
accumulation of ethanol and acetic acid [102].
Figure 2. Schematic overview of carbohydrate catabolism pathways by LAB leading to the generation
of flavor compounds in cheese. Asterisks denote flavor compounds [6,103,104].
4.2. Citrate Catabolism
Interestingly, some LAB also utilize citrate in the absence of carbohydrates. Citrate,
although present at relatively low concentrations in milk (approximately 10 mM), can
have a profound impact on cheese aroma. Only few LAB species, such as L. lactis and
Leuc. mesenteroides, are able to utilize citrate and this property is linked to plasmid-encoded
citrate permease genes (citP) regulated under low pH conditions in the absence of sugars [97,105]. Once inside the cell, citrate is cleaved into acetate and oxaloacetate by the
Foods 2022, 11, 188
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enzyme citrate lyase, and then, oxaloacetate is converted into pyruvate and CO2 by oxaloacetate decarboxylase [103] (Figure 3). Although carbon dioxide is responsible for cavity
formation in certain cheese types, regarding flavor development, the co-metabolism of citrate and lactose leads to characteristic C4 aroma compounds, such as diacetyl, acetoin, and
butanediol.
In detail,
bacterial
α α-acetolactate synthase catalyzes the condensation of two
2,3-butanediol.
In detail,
bacterial
pyruvate
pyruvatemolecules
moleculesto
togive
giveαα-acetolactate, especially under conditions of pyruvate excess
ce
synthesized,
α
and acidic pH. Once synthesized, α-acetolactate
is unstable and is either decarboxylated to
to
acetoin
by
α
acetoin by α-acetolactate decarboxylases or to diacetyl by non-enzymatic decarboxylation
(in the presence of oxygen, though). Acetoin can also be synthesized from diacetyl by
diacetyl-acetoin reductase, an enzyme that converts acetoin to 2,3-butanediol [106].
Figure 3. Schematic overview of citrate catabolism by LAB leading to the formation of flavor
compounds in cheese. Asterisks denote flavor compounds [6,103,104].
4.3. Lipid Catabolism
Cheese, in general, has a high fat content (even reaching 35% w/w in certain varieties) [107] and lipids may undergo oxidative or hydrolytic degradation. As lipid oxidation
does not occur at a significant extent in cheese due to its low redox potential and the
presence of natural and synthetic antioxidants, its contribution to flavor formation is limited [108]. However, enzymatic hydrolysis, especially during cheese ripening, has a major
contribution to flavor development in many cheese varieties [109].
Foods 2022, 11, 188
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Raw milk contains a potent indigenous lipoprotein lipase (LPL), unstable to heat,
which generally causes low levels of lipolysis in cheeses made from pasteurized milk, but
its action is significant in raw milk cheeses [110]. Consequently, it is the cheese microbiota
that contributes more than endogenous enzymes to flavor formation in cheeses produced
from pasteurized milk. In general, most lipolytic enzymes are specific for fatty acids (FAs)
esterified at
positions
of milk of
triglyc
esterified
at the
thesn−1
sn −or1sn−3
or sn
− 3 positions
milk triglycerides, such as butyric acid
and other short- and medium-chain FAs [111]. There are two types of lipolytic enzymes,
namely lipases that are active on lipids, and esterases that are active on water-soluble ester
substrates [111].
LAB are generally considered to be weakly lipolytic; however, their activity is substantially higher on diglycerides and monoglycerides, especially when the esterified fatty acid
is a short-chain fatty acid (SCFA) [97]. Esterase activities have been detected in streptococci,
lactococci, and mesophilic and thermophilic lactobacilli [111]. SCFAs can directly contribute to flavor, but the actual flavor formation follows, as free fatty acids (FFAs) can act as
precursors for the production of a wide range of flavor compounds [6]. Specifically, esters
can be formed in cheese via esterification (reaction of FFAs and alcohols) and alcoholysis
(transfer of an acyl group from glycerides to an alcohol), with the involved pathways being
species and strain dependent [112]. The presence of ethanol as the most abundant alcohol
in cheese explains the prevalence of ethyl esters, compounds associated with fruity notes
fruity
notes
in cheese
[111]. Other
enzymatic
reactions
FFAs are
as substrates
areand
β
in
cheese
[111].
Other enzymatic
reactions
using
FFAs asusing
substrates
β-oxidation
decarboxylation and the produced methyl ketones and secondary alcohols play a major
role in cheese flavor. Additionally, esterification of hydroxy-fatty acids produces lactones
that also contribute to cheese flavor [113] (Figure 4).
Figure 4. Schematic representation of fatty acids catabolism biochemical pathways leading to flavor
compound formation in cheese. Asterisks denote potent flavor compounds [7,95,108,111].
Microorganisms other than LAB significantly contribute to flavor development through
lipolysis, for many cheese varieties. Propionic acid bacteria (PAB) are well known for their
lipolytic activity, which was found to be 10–100 times higher than the one attributed to
LAB. For instance, Propionibacterium freudenreichii releases FFAs during the ripening of
Swiss-type cheeses and lipolysis occurs with an initial preferential release of butyric acid
followed by palmitic acid [114]. Regarding mold-ripened cheeses, fungi possess significant
lipid degradation ability contributing to flavor formation [20]. For instance, P. roqueforti
produces extracellular lipases, which are responsible for the extensive lipolysis of Roquefort
–
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cheese, while P. camemberti, along with the complex bacterial surface microbiota, produce
lipases that contribute to the characteristic flavor development in white mold-ripened
cheese varieties [96].
4.4. Protein and Amino Acid Catabolism
Proteolysis can be considered the key biochemical event in cheese flavor formation,
leading to the release of peptides and amino acids that can be further catabolized to various
flavor compounds. Proteolysis in cheese starts in the coagulant with enzymes (e.g., rennin),
resulting in the formation of large- or intermediate-sized peptides. With the major part
of rennin being lost in the whey during curd drainage, LAB play a significant role in
milk proteins breakdown, in particular during cheese ripening, despite their weak proteolytic system. Generally, SLAB have a superior role in protein/peptide breakdown than
NSLAB [11]. The LAB proteolytic system comprises cell envelope-associated extracellular
proteinases; cell membrane transport systems for oligopeptides, di-/tripeptides, and free
amino acids (FAAs); and intracellular peptidases [115]. FAAs are key substrates for a
variety of biochemical reactions, leading to the production of major flavor compounds
identified in many cheese varieties [116] (Figure 5).
Figure 5. Schematic representation of amino acid catabolism pathways involved in the development
of cheese flavor. Asterisks denotes potent flavor compounds [6,103].
Conversion of branched-chain amino acids (leucine, isoleucine, and valine) and aromatic amino acids (tryptophan, phenylalanine, and tyrosine) to the respective α-keto acids
begins with a transamination step, and the aforementioned α-keto acids can then be converted into their corresponding aldehydes, carboxylic acids, alcohols, and their related
(thio)esters [7]. Sulfur-containing amino acid catabolism (methionine and cysteine) leads to
the formation of methanethiol (MTL) and other sulfur derivatives. Specifically, methionine
catabolic pathways leading to MTL vary among LAB, with two enzymatic pathways being
suggested: the most direct pathway involves C–S lyases (cystathionine β- or γ-lyases),
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while the other one involves the formation of α-keto-γ-methylthiobutyric acid (KMBA) that
is further converted to MTL [117]. MTL, as well as dimethyl sulfide, dimethyl disulfide,
and dimethyl trisulfide, all three obtained after MTL auto-oxidization, are regarded as
essential cheese flavor components [103].
Regarding yeasts, it is generally recognized that the species G. candidum, Yersinia lipolytica,
and K. marxianus are more proteolytic than D. hansenii. In fact, yeasts are able to produce
volatile compounds that contribute to cheese flavor, such as branched-chain aldehydes
and alcohols [15]. Moreover, fungi belonging to the genus Penicillium are responsible for
intense proteolytic activity during cheese maturation in mold-ripened varieties, such as
Camembert and Brie, with the released ammonium compounds playing a vital role in
cheese flavor development [118].
5. Omics Insights into Flavor Formation in Cheese
5.1. Genomics
The first omics technique to appear, genomics, focused on sequencing and annotation
of the complete genome of an organism in order to reveal the genetic structure and to
predict gene functions that are encoded within the genome. Since the publication of the
long-awaited whole genome sequence of the first LAB strain in 2001, namely L. lactis subsp.
lactis IL1403 [119], the number of sequenced LAB genomes has grown exponentially, due to
the advent of HTS, with the genome sequences of thousands of LAB species and strains
currently being deposited in public databases [120].
The study of Bolotin et al. [119] was the first genomic milestone in LAB research and
laid the groundwork for a better understanding of the many aspects of bacterial physiology,
metabolic pathways, and regulatory mechanisms, as well as how phenotypes are affected
by genetic variations. In the following years, numerous genomics studies were conducted
regarding strains of the “laboratory workhorse” species L. lactis [121–125]. As mentioned
before, S. thermophilus, L. bulgaricus, and L. helveticus are also the main SLAB used in
cheese production along with L. lactis. Therefore, due to their industrial and economic
importance, efforts are constantly being made to fully sequence and annotate genomes of
these taxa [126–129]. In recent years though, genomics studies included NSLAB as well,
due to their considerable impact on flavor development in cheese [98,130–140].
Proteins Associated with Flavor Formation in Cheese
Proteolysis, lipolysis, and AAs/FAs catabolism have all been studied in detail before
whole genome sequencing (WGS) appeared. However, the available genome sequences
allowed an in-depth analysis of the respective genes, so as to understand the genetic
instability of several traits and unravel strain-specific differences. From this perspective,
a comparative genomics analysis of all the proteins involved in the proteolytic system
of 22 completely sequenced LAB strains was performed, including the cell-wall-bound
proteinase, peptide transporters, and peptidases [115]. Based on the results, the genomes
of the Lactobacillus acidophilus, Lactobacillus johnsonii, Lactobacillus gasseri, L. bulgaricus, and
L. helveticus strains studied encoded a relatively higher number of proteolysis-related genes.
Furthermore, the cell-wall-bound proteinase PrtP was solely identified in the chromosomes
of L. acidophilus, L. johnsonii, L. bulgaricus, L. casei, L. rhamnosus, and S. thermophilus strains, as
well as in the plasmid of L. lactis subsp. cremoris SK11. On the other hand, endopeptidases
PepE/PepG and proline peptidases PepI, PepR, and PepL were absent in Lactococcus and
Streptococcus strains, while aminopeptidases PepC, PepN, and PepM and proline peptidases
PepX and PepQ were present in all genomes analyzed [115]. More recently, comparative
genomics analysis among 213 assemblies, of which 175 belonged to Lactobacillus species
and 38 to associated genera, was performed [141]. Regarding the metabolic potential of
the 213 strains analyzed, the authors found genes for 60 cell envelope proteinases, which
are important for cleaving casein during growth in milk and thus, contribute to cheese
flavor, ranging in length from 1097 to 2270 amino acids. In addition, a broad repertoire of
glycoside hydrolases and glycosyltransferases was identified, which are both important
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in carbohydrate metabolism [141]. Therefore, genome-scale metabolic models have been
constructed and applied for the in silico prediction of the metabolic patterns of LAB strains
under various conditions [142–147]. It should be noted, however, that the accuracy of these
models depends on the quality of the genome sequencing and the correct annotation.
Furthermore, genomes of the non-LAB genera Propionibacterium and Brevibacterium
have also been sequenced and annotated, as they also contribute to the flavor of certain
types of cheeses. Genomics analysis of Propionibacterium assemblies mainly focuses on
the identification of genes that are involved in the two key metabolic pathways for the
propionate production, i.e., the Wood–Werkman and the tricarboxylic acid cycles, the amino
acid catabolic pathways, which result in the formation of volatile compounds, and the
detection of esterases involved in the formation of FFAs and esters [148–150]. Similarly,
WGS studies of Brevibacterium assemblies reported, among others, the gene repertoire
responsible for the catabolism of lactose, galactose, citrate, lipids, proteins, and amino acids,
which contribute to the flavor, texture, and appearance of cheeses [151–154].
Apart from bacteria, several fungal species are also used as starters in internally and
surface-ripened cheeses, such as Roquefort and Camembert, respectively, with a vital
role in the flavor formation of the final product. Albeit their importance, there is still a
limited number of sequenced genomes in the NCBI database, e.g., only five and six partially
sequenced assemblies for P. roqueforti and P. camemberti, respectively, which are two of
the main fungal species used in cheese production. However, most of the WGS studies
performed focused on the phylogeny and not the metabolic pathways regarding flavor
formation [155–159].
Linking genotype to phenotype is of paramount importance to an in-depth understanding of the technological potential of a strain and therefore, to a better selection of
candidates with flavor-forming metabolic potential to be used as starter or adjunct cultures in cheese production. However, as cheese ripening depends on a complex microbial
community, the need for metagenomics analysis quickly arose.
5.2. Metagenomics
Metagenomics encompasses two different HTS approaches, namely amplicon sequencing and shotgun metagenome sequencing. In amplicon sequencing, a highly conserved
marker gene or genome’s region of DNA directly extracted from a food microbial community, e.g., that of cheese, is amplified and sequenced. These markers are of taxonomic
relevance, with the 16S rRNA gene being used for the identification of bacteria taxa and
the 18S rRNA gene together with the ITS DNA region for the yeasts/fungal taxa. On
the other hand, in shotgun metagenomics, the extracted DNA is fully sequenced in a
non-targeted manner, thus providing not only taxonomical identification results, but also
information on the metabolic potential of the microbial community by reconstructing
metabolic pathways [19].
From this perspective, a shotgun metagenomics study was recently performed in
25 Cotija cheese samples [160]. This artisanal Mexican cheese is produced by raw milk
without the addition of starter cultures and is ripened in an open environment. Therefore,
the organoleptic characteristics of Cotija cheese depend on a range of biotic and abiotic
factors. Taxonomic identification revealed that the bacterial microbiota is mainly composed
of Firmicutes, followed by Actinobacteria and Proteobacteria. The authors were able to
reconstruct the genome assemblies of the three dominant species detected, i.e., L. plantarum,
Leuc. mesenteroides, and W. paramesenteroides, which accounted for more than 80% of the
total bacterial sequences. Gene functional annotation related to Cotija cheese flavor resulted
in the identification of genes involved in the catabolism of phenylalanine, branched-chain
amino acids, and fatty acids [160].
5.3. Transcriptomics
Genomics is widely applied to study the technological potential of a strain; however,
it can only in silico predict its functional potential. Therefore, transcriptomics (RNA-seq),
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although a relatively new field of research, has gained much attention as it provides an
accurate profile of a strain’s functional activity at a given time point.
Regarding cheese microbiota, the majority of the transcriptomic studies have been
performed on L. lactis, due to its importance in cheese production [120,161–169]. In particular, the transcriptional responses of L. lactis have been investigated during different
stresses, such as cold, heat, acid, osmotic, oxidative, and starvation [170–176], or during
growth in media with different carbon sources [177–179]. Apart from L. lactis, transcriptomics studies have also been performed in other important cheese bacterial [163,180–186],
yeasts [187–189], and fungal species [190–196]. Most interestingly, Dalmasso et al. [197]
reported that P. freudenreichii CIRM-BIA1T adapted from warm (28 ◦ C) to cold storage (4 ◦ C)
was able to express genes involved in the formation of important cheese flavor compounds
through the catabolism of branched-chain amino acids. Similarly, the transcriptome profile of P. freudenreichii CIRM-BIA138 was analyzed during the adaptation of the strain to
starvation [198], while that of P. freudenreichii ITG P14 during different stresses, i.e., cold,
heat, and starvation [199]. Moreover, the transcriptome profile of L. rhamnosus PR1019 was
recently evaluated in a cheese-like medium during carbon source starvation [200]. The
analysis revealed that the strain was able to adapt under these conditions using alternative
metabolic pathways, such as pyruvate degradation and ribose catabolism.
5.4. Metatranscriptomics
In contrast to the plethora of transcriptomics studies in single microbes, a limited
number of metatranscriptomics projects have been performed to assess the entire gene
expression of a food microbial community, such as that of cheese. Among the key problems
are the short half-life of mRNA, the large amount of mRNA needed for analysis, the experimental design, and, most importantly, the selection of the proper sampling points and some
technology-specific limitations, including the available bioinformatics tools and workflows
for data processing and analysis [201]. The first comprehensive metatranscriptomics study
was performed by Lessard et al. [202]. The authors monitored the metatranscriptome
profiles of G. candidum and P. camemberti in the rind of an industrial Canadian Camemberttype cheese. Based on the functional annotation performed, transcripts related to energy
metabolism, such as glycolysis/gluconeogenesis, the pentose phosphate pathway, the tricarboxylic acid cycle, and oxidative phosphorylation, were identified. Furthermore, lyases
involved in the production of volatile sulfur compounds during methionine catabolism as
well as transcripts related to cabbage sulfur aroma development and ammonia production
were also detected [202].
More recently, Monnet et al. [203] evaluated the functional activity of the microbiome
in the rind of a French Reblochon-type cheese, which was produced by S. thermophilus,
L. bulgaricus, G. candidum, and D. hansenii. Metatranscriptomics analysis revealed that
during ripening, only minor changes occurred in the LAB, such as an upregulation of genes
involved in lipid and carbohydrate metabolism, while in yeasts, a significant upregulation
of genes related to amino acid catabolism occurred from day 14 to day 35 (end of ripening),
suggesting their contribution to flavor formation [203].
However, discrepancies observed between mRNA levels and protein abundance,
which have been attributed to the variable half-lives of mRNA, variable rates of protein
synthesis, and possible post-translational modifications of proteins [18], make the analysis
of expressed proteins a valuable tool for investigating the functional activity of microbiota.
5.5. Metaproteomics
Metaproteomics is the large-scale characterization of the entire protein complement
of microbial communities in a biological system of increased complexity at a given time
point [204]. The progress of proteomics has been driven by the development of new
technologies for peptide/protein separation, isotope labeling for quantification, and bioinformatics data analysis, while the analysis and quantification of proteins has been revolutionized by MS-based methods [205]. There are several challenges that must be overcome to
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address a metaproteomic study in animal food products. Among them are the presence of
raw material proteins that interfere with the detection of microbial proteins, the complexity
of the microbiota-expressed proteins in a fermented food present at concentration ranges
that may vary dramatically, and finally, the presence of certain highly abundant proteins
that are often not interesting for metaproteomic analysis, while the microbiota proteins may
be several orders of magnitude less abundant. For these reasons, large-scale metaproteomic
studies are limited. However, they are gradually gaining attention in the field of fermented
food research, including cheese [206,207], to assess the functional diversity of microbial
communities. To our knowledge, there are no metaproteomics studies on cheese flavor
formation and development. However, the proteomic approach applied in a survey of bacterial proteins released in Emmental cheese revealed functional groups of proteins involved
in proteolysis and glycolysis, pathways that influence the organoleptic characteristics of
cheese [208].
5.6. Metabolomics
5.6.1. Analytical Techniques
Metabolomics is a relatively new field of omics research dealing with the simultaneous and high-throughput identification and quantification of low molecular mass
(<1500 Da) metabolites that are not genetically encoded and are produced and modified
by the metabolism of living organisms, such as organic acids, carbohydrates, amino acids,
peptides, nucleic acids, vitamins, polyphenols, alkaloids, and minerals [209]. Metabolomic
analyses have been classified as targeted (specific) or untargeted (non-selective) analyses.
The vast chemical diversity of the compounds in the metabolome requires efficient
metabolite extraction, chromatographic separation, mass spectral detection, identification,
quantification, and multivariate data analysis. Various analytical techniques are used for
separation, such as liquid chromatography (LC), including high-performance LC (HPLC)
or ultra-performance LC (UPLC), gas chromatography (GC), and capillary electrophoresis
(CE), coupled to mass spectrometry (MS). MS-based platforms predominate because of
both their ability to identify a wide range of compounds and their high throughput capacity.
Apart from MS, other commonly used detection techniques are nuclear magnetic resonance
(NMR) and near infrared spectrometry (NIR). However, none of the individual analytical
methods are capable of effectively analyzing all the metabolites of a sample.
5.6.2. Metabolomics to Assess Flavor Formation in Cheese
Metabolomic studies in cheese provide insights on specific metabolites produced
by cheese microbiota, with special interest in flavor compounds, and have significantly
enabled characterization of the metabolite diversity in cheese, as well as the factors affecting
it. According to Ochi et al. [210], GC/TOF-MS fingerprinting of hydrophilic low-molecularmass compounds can be the basis of prediction models for sensory attributes, such as “rich
flavor” and “sour flavor” in ripened Cheddar and Gouda, while specific amino acids [211]
or volatile compounds, such as hexanoic acid, heptanoic acid, octanoic acid, 2-decenal, and
acetoin [212], may be used as ripening markers in Cheddar cheese.
The cheese metabolome is affected by the degree of LAB autolysis. Grana-Padano
cheeses produced in two different dairies and with different volatile profiles, as shown
by solid phase micro-extraction (SPME) GC-MS, were analyzed with respect to their total
culturable lactic microbiota and starter lysis. Remarkably, it was shown that increased
complexity of microbial origin volatiles, such as ketones, alcohols, hydrocarbons, acetic acid,
and propionic acid, was associated with the complex microbiota composition, with NSLAB
(mainly L. rhamnosus/L. casei) being dominant. On the other hand, intracellular enzymes
released due to SLAB cell lysis were involved in a higher content of FFAs, benzaldehyde,
and organic acids, such as pyroglutamic and citric acid [213].
LC high-resolution MS (LC–HRMS) metabolic fingerprinting in model cheese revealed that the cheese metabolome is influenced by the spatial distribution of the starter
L. lactis colonies throughout ripening [214]. By varying the time of renneting (at 0 h,
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e.g., simultaneously, or after 8 h from starter inoculation), they generated two different
spatial distributions of immobilized bacterial colonies (few big colonies spread away from
each other, or numerous small colonies close to each other, respectively), and identified
26 metabolites, including amino acids, organic acids, vitamins, nucleotides, and proteolysis
products, being more abundant in small-colony cheeses. Moreover, according to Le Boucher
et al., bacterial cells forming small colonies use the same metabolic pathways but display
higher metabolic activity than big colonies, resulting in higher concentrations of metabolites
being accumulated due to proteolysis or carbohydrate catabolism [215].
Investigation of the flavor compounds produced by the whole cheese microbiota
or the individual contribution of SLAB or NSLAB strains is of great interest for cheese
producers. Studies of this type are often performed in a cheese-based medium, mimicking
the cheese environment, thus providing the nutrients and precursors present in cheese
during ripening.
Sgarbi et al. explored the volatile compounds produced by different L. casei and
L. rhamnosus strains grown on either a cheese-based medium (CBM) or on a LAB cell lysatebased medium (LCM). Volatile analysis by SPME GC-MS showed differences between
NSLAB strains grown in CBM (where pyruvate could be a common precursor for all
compounds produced) and LCM (where in the absence of lactate and citrate, NSLAB
strains used mainly FFAs and FAAs), thus providing a better understanding of how NSLABproduced volatile flavor compounds contribute to the development of cheese flavor during
ripening, a first step toward the selection of wild NSLAB possessing a specific aromatic
profile, for use as adjunct culture [216].
Similarly, Pogačić et al. evaluated the potential of Lactobacillus spp. and Leuconostoc spp.
in the production of aroma compounds by incubating single strains in a curd-based slurry
medium [217]. Analysis of volatiles by GC–MS revealed strain to strain variation. Acetoin,
diacetyl, acids, and esters were mainly produced by L. rhamnosus and L. paracasei, while
Leuconostoc spp. were major producers of alcohols and esters. The same curd-based slurry
medium has been used by Pogačić et al. to screen LAB, Actinobacteria, P. freudenreichii,
and Hafnia alvei strains for their ability to produce aroma compounds. Forty-nine out of
52 aroma compounds identified differed in their abundance among the bacteria [218].
A cheese-based medium was also used by Guarrasi et al. to grow SLAB and NSLAB
strains isolated from Caciocavallo Palermitano cheese to assess their contribution to the
volatile organic compounds (VOCs) production in ripened cheese [219]. By applying
head space (HS)-SPME GC-MS to the fermented substrates, they found that strains of
L. delbrueckii, L. casei, L. paracasei, L. rhamnosus, and Enterococcus gallinarum mainly influenced the development of the characteristic ripened-cheese aromatic compounds.
Furthermore, according to Yee et al., strains of dairy propionibacteria exhibited large
inter-species and intra-species diversity in their ability to produce different aroma compounds when grown in a cheese curd-based medium [220]. GC-MS data differentiated
P. freudenreichii strains from each other, as well as from Propionibacterium acidipropionici
strains. For the same compound, differences between strains of the same species were as
high as ~500-fold, with P. freudenreichii strains harboring the widest potential to produce
cheese aroma compounds, making it a potential candidate to modulate cheese flavor.
Except cheese-based medium, cheese model systems are important tools that facilitate
the study of the impact of microbial metabolism on cheese sensorial characteristics formation since they can be prepared under controlled microbiological conditions, are more
economical, reproducible, and easier to obtain.
Miniature cheeses were prepared by Ruggirello et al. [221] to evaluate L. lactis commercial starter cultures’ viability and ability to produce aroma compounds during ripening.
Culture-independent and -dependent approaches, as well as HS-SPME GC-MS, revealed
the persistence of L. lactis in cheese throughout cheese making and ripening, although
in a metabolically active hypothetical viable but not culturable (VBNC) state, since the
expression of L. lactis cystathionine β-lyase (metC) and α-acetolactate synthase (als) genes,
involved in the biosynthesis of sulfur aroma compounds and diacetyl/acetoin, respectively,
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was partially associated with acetoin, diacetyl, 2,3-butanediol, and dimethyl disulfide
production in ripened cheeses.
Irlinger et al. [222] applied GC-MS for metabolic profiling of the Gram-negative bacteria Psychrobacter celer and H. alvei when grown on a mini smear cheese. Both bacteria were
able to colonize the cheese surface and compete in the microbial community, modifying
at the same time the aromatic content of cheeses, highlighting the fact that less abundant
microorganisms can have a significant impact on cheese flavor.
In a recent study, Suzuki-Iwashima et al. [223] investigated the combined effects of
LAB starters and the white fungus P. camemberti on the production of volatile compounds
during the ripening of a model white mold surface-ripened cheese. Metabolomics analysis
by GC-MS showed that the early ripening period was characterized by metabolites, such as
lactose, galactose, lactic acid, ethanol, diacetyl, acetoin, ethyl acetate, and sulfur compounds
(dimethyl sulfide and dimethyl disulfide), that derived from carbohydrate metabolism of
LAB, while fungal metabolism of proteins (with amino acid-derived compounds including
branched aldehydes, such as 3-methyl butanal), and of fatty acids and proteins (with methyl
ketones, fatty acids and amino acids) characterized the intermediate- and the late-ripening
stages, respectively.
5.6.3. Metabolomics Shed Light on the Ability of Adjunct Cultures to Produce Flavor
Compounds
Starter bacteria combined with adjunct LAB strains selected for desirable metabolic
potential is a common tool used to control and accelerate cheese ripening [224], enhance and
improve flavor intensity [62], especially in cheeses made with pasteurized milk [11], and
accelerate flavor development [103]. Metabolomics approaches have been a valuable tool to
assess the contribution of the potential adjuncts on cheese flavor development [18,55,225].
So far, several studies have been performed evaluating not only the contribution of
potential adjunct cultures to cheese flavor development, but also their persistence, technological performance, together with interactions with the starter cultures in cheese making
experiments. In a recent study, Stefanovic et al. [138], selected three L. paracasei strains
based on their proteolytic enzyme activities and ability to produce flavor compounds in
cheese model systems [226] to be used as adjunct cultures in Cheddar cheese manufacture.
The authors found that the L. paracasei strains contributed to the development and diversification of flavor-related compounds in short-aged cheeses. Different adjunct cultures
did not influence the gross cheese composition, nor primary or secondary proteolysis or
lipolysis. However, cheese volatile analysis by GC-MS showed variation in long-chain
aldehydes, acids, and esters that originated from the metabolism of FFAs, suggesting that
starter lipolytic activity produced the primary metabolites, which were further metabolized
by the adjuncts into flavor-contributing compounds.
Bancalari et al. [227] evaluated the use of a wild L. paracasei strain, selected for its
ability to produce in vitro acetoin and diacetyl, as adjunct culture to enhance the flavor
formation in Caciotta-type cheese. Indeed, the adjunct strain was able to develop in
curd and cheese, producing higher amounts of volatile compounds and organic acids
as monitored through SPME GC-MS, thus differentiating the experimental Caciotta with
respect to the control cheese. Moreover, Belkheir et al. [228] prepared a Tetilla-type cheese
using as adjunct cultures a high-diacetyl producer L. plantarum strain together with a
peptidolytic L. brevis strain producing volatile sulfur compounds, to study the volatile
profile and sensory characteristics of the model cheese and to compare it with the PDO
Tetilla cheese. Volatile analysis with SPME GC-MS showed an increased abundance of
acetic acid, hexanoic acid, ethyl butanoate, and ethyl hexanoate, as well as higher scores
for flavor preference in the cheese made with the two adjuncts than in the control cheese,
highlighting the fact that the use of selected adjunct strains would differentiate the cheese
sensory properties. Interestingly, when Kocuria varians (Micrococcaceae) and Y. lipolytica,
selected for their proteolytic and lipolytic activities, were used as adjunct cultures, for the
manufacture of experimental Tetilla cheese from pasteurized cow’s milk, Centeno et al. [229]
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were able to recover the traditional flavor and sensory characteristics of raw-milk PDO
Tetilla cheese. The volatile profiles of cheese manufactured with both adjuncts, detected by
GC-MS, showed enhanced formation of fatty acids, esters, and sulfur compounds, thus the
modifying the flavor profile of the experimental Tetilla cheese, which was considered very
similar to good-quality artisanal raw-milk cheese.
In a potentially probiotic Caciotta cheese, industrially produced with autochthonous
putative probiotic Lactobacillus and Kluyveromyces strains as adjunct cultures, Pisano et al. [230]
investigated the adjunct’s influence on the cheese chemical and microbiological composition and sensory properties. Cheese metabolome characterization by means of 1 H NMR
spectroscopy (for amino acids, organic acids, and carbohydrates) together with HPLCdiode array detector/evaporative light scattering detector (DAD/ELSD) for the cholesterol,
α-tocopherol, and fatty acid composition, highlighted significant variations in the cheese
metabolome both in terms of the ripening time and strain combination, with Kluyveromyces
and Lactobacillus strains surviving the manufacturing process and retaining their viability
till the end of ripening, suggesting that Caciotta cheese can be used as a carrier for probiotic
bacteria delivery.
5.6.4. Metabolomics as a Means of Cheese Authentication
Effective and reliable analytical methods are of paramount importance to securing the
authenticity of PDO cheeses, aiming to protect both the product value and consumers. To
this end, metabolomics-based approaches represent a powerful method to discriminate
fraudulent varieties of a given food product [57,231,232].
Pisano et al. [233] analyzed the polar metabolite profiles by GC-MS, together with
the predominant cultivable microbiota from buffalo and cow Mozzarella, in order to discriminate them. PDO buffalo Mozzarella exhibited a higher microbial diversity together
with less psychrotrophic bacteria, while cow Mozzarella showed the highest counts of
S. thermophilus, originating from the commercial starter culture. Furthermore, the polar
metabolites reflected differences in the production protocols and microbiota complexity
of theses cheeses, suggesting that the polar metabolite profile can be a promising tool to
characterize and verify the authenticity of Italian buffalo Mozzarella. Moreover, Rocchetti
et al. [234] characterized low-molecular-mass metabolites based on ultra-high-pressure
liquid chromatography coupled with quadrupole time-of-flight MS (UHPLC/QTOF-MS),
aiming to reveal differences between genuine PDO and non-PDO Grana Padano cheeses.
Amino acids, oligopeptides, and fatty acids were the biomarkers with the highest discriminatory power.
Another authenticity problem involves adulteration related to non-declared processing
methods, such as in the case of Fiore Sardo (FS) cheese, where the use of raw ovine milk
is mandatory. Caboni et al. studied the polar low-molecular-mass metabolites, by GCMS, aiming to discriminate FS cheese produced from raw or thermized milk. FAAs and
saccharides were the metabolites that mostly changed, suggesting the polar low-molecularmass metabolites as a potential biomarker for detecting milk thermization in ovine PDO
cheeses [235].
5.7. Integration
Studies combining multiple-omics approaches, integrating genomics, transcriptomics,
together with metabolomics, that is, a systems biology approach, provide a detailed picture
of the cheese microbiota dynamics, as well as information on potential microbial interactions
and their contribution with respect to cheese sensorial characteristics development. An
overview of the most comprehensive studies combining multiple omics approaches to
study flavor development in cheese can be found in Supplementary Table S1. In this section,
integrating omics studies, which have been performed not only in mature cheese, but also
during cheese ripening, are discussed, to gain deeper insights on microbial succession and
metabolite production.
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5.7.1. Amplicon Metagenomics-Shotgun Metagenomics
The first pioneering study incorporating different -omics techniques was performed
by Wolfe et al. [72]. The authors analyzed the rind microbial communities of 137 cheese
samples, including 61 natural cheeses that were left undisturbed during the aging process,
52 washed with 20% w/w NaCl, and 24 bloomy cheese rinds, from different geographic
regions, milk types, and milk treatments. Using amplicon-based metagenomics analysis, only 14 bacterial and 10 fungal genera were identified at higher than 1% average
abundance. Interestingly, the microbiota was found to be associated with the rind type
(natural, washed, and bloomy) and moisture instead of the geographic origin. Additionally,
shotgun metagenomics was performed to assess the functional potential of the microbiota
based on the different rind types. In particular, shotgun data identified genes involved in
several metabolic pathways associated with flavor formation, such as the catabolism of
cysteine and methionine, which are known to contribute to the production of volatile sulfur
compounds, and that of valine, leucine, and isoleucine, which provide sweaty and putrid
aromas. It should be noted that the majority of these genes were detected in the washed rind
cheeses. Furthermore, the halotolerant γ-proteobacteria genus Pseudoalteromonas, originally
associated with marine environments, was found for the first time in cheese microbiota
and, in particular, in the natural and bloomy cheese rinds. The shotgun metagenomics
results identified a few cold-adapted enzymes produced by Pseudoalteromonas spp. that
participate in lipolysis and proteolysis. Therefore, the presence of Pseudoalteromonas, as
part of the cheese microbial community, could be considered beneficial, as it can contribute
to the development of flavor compounds in cheeses during ripening and storage at low
temperatures [72].
5.7.2. Amplicon Metagenomics-Metabolomics-Metatranscriptomics
De Pasquale et al. [236] used Fiore Sardo, Pecorino Siciliano, and Pecorino Toscano
cheeses as hard cheese model systems to study the spatial distribution of metabolically
active microbiota and its effect on secondary proteolysis and VOC production, since these
cheeses present a decreasing NaCl gradient from the surface to the center and an opposite
moisture trend, properties that affect the microbiota distribution. By combining 16S rRNA
gene pyrosequencing (targeting RNA) and Purge and Trap coupled with GC-MS (PT GCMS), they found that in all cheese varieties, the poorest VOC profile was detected in the
core region, due to the low oxygen availability, with high levels of alcohols originating
from aldehydes and methyl-ketones reduction. Mesophilic lactobacilli (predominantly
L. plantarum) positively correlated to alcohols, aldehydes, methyl and branched esters,
and sulfur compounds. Thermophilic LAB in Pecorino Siciliano (including L. delbrueckii
and S. thermophilus) positively correlated with the total concentration of FAAs, in different
cheese regions, as well as with alcohols and related esters, while Brevibacterium sp. present
on the surface of Pecorino Toscano correlated with alcohols, aldehydes, ketones, esters, and
sulfur compounds.
Recently, Turri et al. [237] characterized the mature Historic Rebel (HR) cheese, an Italian heritage cheese produced from raw cow milk in the Alps. Microbiota diversity, assessed
by 16S rRNA gene amplicon sequencing, revealed that the core microbiota comprising
Streptococcus, Lactobacillus, Lactococcus, Leuconostoc, and Pediococcus genera correlated positively with the VOCs hexanal, 2-heptanal, 3-hydroxybutan-2-one (or acetoin), and ethanol
respectively, as determined by SPME GC-MS. Moreover, a lipidomics approach was included by applying Dynamic Headspace (DHS) GC-MS to analyze the terpene fraction and
the polyunsaturated fatty acids composition of HR cheese, parameters that were closely
related with pasture vegetation and feeding, respectively, and contribute to the richness of
cheese flavor.
In a sophisticated study [188], metabolite analysis by Ultra HPLC-MS (UHPLC-MS)
and HPLC-UV in combination with dual RNA-seq analysis were applied in ripened labscale cheeses to provide insight into the metabolic interactions in a simple synthetic community composed of three species commonly used for the production of smear-ripened
Foods 2022, 11, 188
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cheese. A strong mutualistic interaction between Brevibacterium aurantiacum and H. alvei
was proposed, according to which, proteases and lipases secreted by B. aurantiacum liberate
energy compounds from caseins and triglycerides that stimulate H. alvei, which in turn
produces siderophore that increases iron availability for B. aurantiacum. Furthermore, the
proteolytic activity of B. aurantiacum led to increased methionine catabolism in H. alvei
producing methanethiol, a precursor for a wide variety of volatile sulfur compounds that
contribute to cheese flavor.
5.7.3. Shotgun Metagenomics-Metatranscriptomics
Recently, Duru et al. [49] and DeFilippis et al. [26] studied the effect of modifying
the cheese ripening temperature on microbial community structure and function. More
specifically, in a thorough study, the metagenome and metatranscriptome profiles of the
semi-hard Swiss-type Maasdam cheese were studied during warm (20 ◦ C) and cold (5 ◦ C)
room ripening, using L. lactis subsp. lactis and L. lactis subsp. cremoris as starter strains
and as adjunct cultures strains of P. freudenreichii subsp. shermanii, L. rhamnosus, and
L. helveticus [49]. The authors constructed four genomes (one genome per species) from
the shotgun data to near completeness (higher than 97%), and based on the mean DNA
read coverage, L. lactis was found to be the dominant species. Annotation of genome
assemblies and pathways reconstruction identified genes required for FFAs biosynthesis in
all genomes, proteolytic enzymes only in the LAB, and lipolytic enzymes in the genomes of
L. lactis, L. rhamnosus, and P. freudenreichii. Furthermore, genes for valine catabolism were
found in L. lactis and P. freudenreichii, while those for methionine and cysteine catabolism in
L. lactis, L. rhamnosus, and L. helveticus genomes. Therefore, all species are important for
the flavor formation of Maasdam cheese to a different extent. RNA-seq analysis confirmed
the dominance of L. lactis, as more than 85% of the transcript reads mapped uniquely to
this species. Moreover, metatranscriptomic data showed that L. lactis was metabolically
active despite the ripening temperature. However, this was not the case for the other
species, as genes related to the central metabolism were downregulated during cold room
ripening, suggesting that fewer flavor compounds were produced. On the other hand,
according to De Filippis et al. [26], elevation of the ripening temperature of Caciocavallo
Silano cheese from standard (16 ◦ C) to experimental (20 ◦ C) temperatures directly affected
microbiota diversity and metabolism. More specifically, 16S rRNA amplicon and shotgun
metatranscriptome sequencing revealed an increased relative abundance of NSLAB in
cheese ripened at higher temperatures, together with differential expression of 651 genes.
Furthermore, overexpression of proteolysis, lipolysis, amino, and fatty acid catabolismrelated genes at 20 ◦ C correlated with increased production of cheese VOCs, as found by
SPME GC-MS, and significantly increased the cheese maturation rate.
The functional potential of the microbiome in an experimental surface-ripened
cheese was recently assessed by Dugat-Bony et al. [238]. Using reference genomes,
the assembly of the shotgun metagenomics data resulted in the genome construction
of the nine microbes used for the production of the cheese, i.e., six bacteria species
(L. lactis, Staphylococcus equorum, Corynebacterium casei, H. alvei, B. aurantiacum, and
Arthrobacter arilaitensis), and three yeast species (D. hansenii, G. candidum, and K. lactis).
In addition to the shotgun metagenomics, the authors also employed metatranscriptomics
to link metabolically related transcript reads to the microbiome at specific time points
during cheese ripening. RNA-seq analysis revealed that enzymes involved in lactose
fermentation were found to be expressed by L. lactis and K. lactis. Moreover, lactate degradation was attributed to D. hansenii and G. candidum, due to the high levels of lactate
dehydrogenase transcripts detected in these species. Furthermore, although several transcript reads associated with protein and lipid metabolism were mapped to L. lactis, it was
found that G. candidum was the main contributor to proteolysis and lipolysis. In addition,
G. candidum was also found to be a key microbe regarding the catabolism of amino acids
and thus, the production of flavor compounds throughout ripening. However, it should be
noted that transcripts related to amino acid catabolism also mapped to the L. lactis genome,
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mainly at the early stage of ripening, as well as to the C. casei and H. alvei genomes at the
end of ripening [238].
5.7.4. Genomics-Amplicon Metagenomics-Shotgun Metagenomics-Metabolomics
Furthermore, Zheng et al. [29] studied the correlations between microbial dynamics
and evolution and flavor production during ripening of Kazak artisanal cheese by amplicon
sequencing (16S rRNA and ITS loci) for microbiota diversity together with SPME GCMS for the analysis of neutral VOCs and volatile FFAs. A total of eight bacterial and
seven fungal genera were identified across all time points of the Kazak cheese ripening
process, the most abundant being Lactobacillus and Streptococcus together with Kluyveromyces
and Torulaspora, respectively. Different bacteria and yeast genera were considered as
functional core microbiota for producing amino acids, fatty acids, and volatiles. Acetobacter,
Lactococcus, Staphylococcus, and Bacillus positively correlated with 2-nonanone, acetoin,
and benzaldehyde; Kluyveromyces with butanoic acid; and ethyl ester, Issatchenkia and
Candida with n-decanoic acid and hexanoic acid, respectively, while Aspergillus had a
positive correlation with heptanal and n-decanoic acid. Various correlations with fatty
acids were also assigned. Recently, in a similar study, Penland et al. [239] characterized
the Pélardon cheese microbiota and VOCs throughout cheese making and ripening by
16S rRNA and ITS gene sequencing, and HS GC-MS analysis, respectively. L. lactis, the
main acidifying bacterium, decreased during ripening. Leuc. mesenteroides and G. candidum
correlated with amino acid catabolism at the early ripening stages, while L. paracasei
and Enterococcus faecalis together with the fungi P. commune and Scopulariopsis brevicaulis
dominated during prolonged ripening and were positively correlated to major volatile
compounds responsible for the goaty and earthy Pélardon cheese aroma.
Moreover, Bertuzzi et al. [25] applied whole-metagenome shotgun sequencing to
study rind microbiota succession and metabolic potential in surface-ripened cheeses and
to associate it with volatile compounds detected with HS-SPME GC-MS. Over the course
of ripening, correlation analysis between microbiome and volatile data revealed strong
relationships between individual microorganisms and volatiles. D. hansenii correlated with
the production of alcohols and carboxylic acids originating from FAA and FFA metabolism;
Brevibacterium linens, G. candidum, and Staphylococcus xylosus with sulfur compounds and
2-methyl-1-butanol; Corynebacterium variablile with ketones; and Glutamicibacter arilaitensis
with ketones, alcohols, and acids.
Macrococcus caseolyticus subsp. caseolyticus strains have been associated with the
secondary microflora of Ragusano and Fontina cheeses and it has been suggested that
they may have a positive impact on the cheese flavor profile [240]. According to Mazhar
et al. [241], who studied Macrococcus strains of dairy and non-dairy origin, whole-genome
sequencing and comparative genome analysis, further supported with enzymatic assays,
revealed the strains’ limited ability to catabolize amino acids and consequently to produce
amino acid-derived flavor compounds. Interestingly, lipase and high esterolytic activities
were detected and correlated with diverse volatiles detected by GC-MS, since most of them
were mainly associated with FFA metabolism. This type of study would help to identify
strains potentially useful for further investigation, as adjuncts producing novel and distinct
flavor profiles.
Moreover, multi-omics analyses have been applied to group artisanal and industrial
Cheddar cheeses based on type and brand [242], differentiate Cheddar cheese based on age
and brand [23], and compare cheese of varying quality [24]. More specifically, three omics
datasets (16S rRNA amplicon sequencing, untargeted GC-MS, and LC-MS metabolomics)
were analyzed to identify relationships between the cheese microbiota and metabolites
of artisanal and industrial Cheddar cheeses. Metabolites with extensive diversity were
detected in both artisanal and industrial cheeses, with many of them significantly associated with each cheese type and specific LAB genera, thus making the discrimination
between industrial and artisanal Cheddar cheeses possible. Among them, the metabolites
O-methoxycatechol O-sulphate and 3-hydroxy propanoic acid in artisanal cheese were
Foods 2022, 11, 188
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reported for the first time in foods. The former originating from the metabolism of dietary
phenolics [243] correlated positively with Streptococcus, while the latter compound with several industrial applications [244], positively correlated with Lactobacillus and Streptococcus.
Furthermore, Pediococcus, present only in artisanal cheese, was correlated to 21 metabolites
that may influence cheese flavor [242]. In a subsequent study, microbiota and metabolite
profiles of industrial Cheddar cheeses of different ripening ages made by different manufacturers were analyzed [23]. Age-specific markers including numerous amino acids and
carboxylic acids, such as malic acid, hydroxy-glutaric acid, citric acid, lauric acid, myristic
acid, pentadecanoic acid, and hexadecenoic acid, were found to be positively associated
with the ripening age. New significant associations existing between cheese microbiota
and metabolites were described, such as the levels of phenylalanine correlating positively
with the presence of Thermus sp., which may have originated from hot water sources in
the factory and has been implicated with the pink discoloration in cheese [245], as well
as the negative association between cheese cholesterol and S. thermophilus abundance,
which confirms previous reports on the cholesterol-lowering activity of S. thermophilus
strains in vitro [246]. Finally, Cheddar cheeses of different qualities made by the same
manufacturer were investigated in an attempt to identify biomarkers (microbiota taxa and
metabolites) that could discriminate, at the molecular level, Cheddar cheeses of different
sensory qualities [24]. The metabolites with greatest discriminatory power included proline, histidine, isoleucine, and aspartic acid, present in greater amounts in the high-quality
cheese samples, together with stearic acid and octadecanol, which were more abundant
in the low-quality cheese samples. The key discriminatory taxa were Streptococcus (presumably S. thermophilus) and Lactococcus (L. lactis), which were found in a higher relative
abundance in the high-quality and the low-quality cheese samples, respectively.
New insights into cheese microbiome were obtained from an extensive meta-analysis
of cheese microbiomes and corresponding volatilomes, where 328 metagenome-assembled
genomes from 184 cheese metagenomes were recovered including 47 putative novel species,
the majority of which belonged to halophilic genera (such as Psychrobacter and Halomonas)
or to genera associated with the rind (for example, Brevibacterium, Corynebacterium, and
Arthrobacter) [28]. Metabolic modeling of their genomes predicted that they could influence
cheese taste or color through the secretion of volatiles or pigment biosynthesis. Moreover,
the integration of strain-level metagenomics with metabolomics indicated that variations
in the abundancies of strains corresponded to differences in the volatilome. Except the
detailed characterization of cheese microbiota, this study highlights the combination of
strain-level metagenomics with metabolomics to correlate strain abundance with volatile
levels, therefore evaluating the effect of specific strains on flavor, since different strains of
the same species may produce different metabolites [247].
Indeed, the impact of strain diversity on cheese rind microbial dynamics and functional outputs has recently been documented by Niccum et al. [248]. The authors constructed several synthetic cheese rind communities by inoculating onto cheese curd agar
distinct combinations of strains from the species Staphylococcus equorum, B. auranticum, and
Brachybacterium alimentarium, with comparative genomics demonstrating the communities’
phylogenomic diversity and variable genome content. The initial identical community composition diverged over time and resulted in substantial differences in dominant community
taxa, possibly due to strain-level variations, resulting in different interactions with other
community members. Differing responses were observed upon communities’ exposure to
abiotic (6% w/w salt) or biotic (addition of a Penicillium strain) perturbations. Furthermore,
divergence in community composition also drives community functional diversity, since
variations were observed in pigment production, as well as in the composition of volatile
organic compounds detected by HSSE GC-MS across the communities.
6. Conclusions
The cheese microbiome is a dynamic ecosystem that develops and evolves during
cheese manufacture and ripening and shapes the quality, organoleptic properties, and safety
Foods 2022, 11, 188
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of the final product, factors that determine consumers’ preferences. Application of -omics
techniques has greatly facilitated the study of microbiota dynamics and evolution during
cheese ripening, together with its contribution to the organoleptic properties’ formation.
Furthermore, integrating individual meta-omics approaches in combination with data
integration analysis provided deeper insights into microbiota–metabolite interactions that
influence cheese flavor and quality.
With genomics, the genetic structure of cheese microbiota is revealed, gene functions
that are encoded within the genome are predicted, while WGS allows an in-depth analysis,
among others, of the genes involved in glycolysis, proteolysis, lipolysis, and AAs/FAs
catabolism, leading to exploitation of the metabolic diversity of cheese microbiota. Metagenomics analysis, i.e., amplicon and shotgun sequencing, provides not only taxonomical
identification results, but also information on the metabolic potential of the microbial
community. Furthermore, metatranscriptomics studies assess the entire gene expression
of cheese microbiota, and the regulation of genes involved in lipid, carbohydrate, and
AA catabolism, revealing their contribution to flavor formation. However, it is clear that
genomic or metagenomic studies cannot fully account for the strains’ flavor potential, and
integration with metabolomic-based approaches is essential. The information provided
by this approach can be used not only to explore the flavor metabolites produced, but
also to elucidate the role of LAB strains used for cheese production and evaluate NSLAB’s
contribution to cheese flavor development.
Despite the many advantages associated with the use of -omics, there are still several
limitations. However, considering the economic impact associated with the optimization
of cheese production, it is certain that -omics studies will greatly contribute to cheese
production standardization, diversification, and optimization.
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/foods11020188/s1, Table S1: Integrative studies combining different
omics approaches with respect to cheese flavor formation.
Author Contributions: Conceptualization, R.A. and E.T.; writing and original draft preparation, R.A.,
M.K., M.G., A.A. and G.Z.; revision, E.T. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is contained within the article or supplementary material.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
2.
3.
4.
5.
6.
7.
Litopoulou-Tzanetaki, E.; Tzanetakis, N. The microfloras of traditional Greek cheeses. Microbiol. Spectr. 2014, 2, Cm-0009-2012.
[CrossRef] [PubMed]
OECD/Food and Agriculture Organization of the United Nations. Dairy and Dairy Products. In OECD-FAO Agricultural Outlook
2016–2025; OECD Publishing: Paris, France, 2016. [CrossRef]
Drake, M.A.; Delahunty, C.M. Sensory Character of Cheese and Its Evaluation. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F.,
Cotter, P.D., Everett, D.W., Eds.; Academic Press: San Diego, CA, USA, 2017; Volume 1, pp. 517–545. [CrossRef]
Kilcawley, K. Cheese Flavour. In Fundamentals of Cheese Science, 2nd ed.; Fox, P.F., Guinee, T.P., Cogan, T.M., McSweeney, P.L.H.,
Eds.; Springer: Boston, MA, USA, 2017; pp. 443–474. [CrossRef]
Fox, P.F.; Guinee, T.P.; Cogan, T.M.; McSweeney, P.L.H. Biochemistry of Cheese Ripening. In Fundamentals of Cheese Science, 2nd
ed.; Fox, P.F., Guinee, T.P., Cogan, T.M., McSweeney, P.L.H., Eds.; Springer: Boston, MA, USA, 2017; pp. 391–442. [CrossRef]
Smit, G.; Smit, B.A.; Engels, W.J.M. Flavour formation by lactic acid bacteria and biochemical flavour profiling of cheese products.
FEMS Microbiol. Rev. 2005, 29, 591–610. [CrossRef]
McSweeney, P.L.H.; Sousa, M.J. Biochemical pathways for the production of flavour compounds in cheeses during ripening: A
review. Lait 2000, 80, 293–324. [CrossRef]
Foods 2022, 11, 188
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
23 of 32
Fox, P.F.; McSweeney, P.L.H.; Cogan, T.M.; Guinee, T.P. Cheese: Chemistry, Physics and Microbiology, 3rd ed.; Elsevier Academic
Press: Cambridge, MA, USA, 2004; pp. 1–617. [CrossRef]
Broadbent, J.R.; Houck, K.; Johnson, M.E.; Oberg, C.J. Influence of adjunct use and cheese microenvironment on nonstarter
bacteria in reduced-fat Cheddar-type cheese. J. Dairy Sci. 2003, 86, 2773–2782. [CrossRef]
De Pasquale, I.; Di Cagno, R.; Buchin, S.; De Angelis, M.; Gobbetti, M. Microbial ecology dynamics reveal a succession in the core
microbiota involved in the ripening of pasta filata Caciocavallo Pugliese cheese. Appl. Environ. Microbiol. 2014, 80, 6243–6255.
[CrossRef]
Gobbetti, M.; De Angelis, M.; Di Cagno, R.; Mancini, L.; Fox, P.F. Pros and cons for using non-starter lactic acid bacteria (NSLAB)
as secondary/adjunct starters for cheese ripening. Trends Food Sci. Technol. 2015, 45, 167–178. [CrossRef]
Beresford, T.P.; Fitzsimons, N.A.; Brennan, N.L.; Cogan, T.M. Recent advances in cheese microbiology. Int. Dairy J. 2001, 11,
259–274. [CrossRef]
Antonsson, M.; Molin, G.; Ardö, Y. Lactobacillus strains isolated from Danbo cheese as adjunct cultures in a cheese model system.
Int. J. Food Microbiol. 2003, 85, 159–169. [CrossRef]
Crow, V.; Curry, B.; Hayes, M. The ecology of non-starter lactic acid bacteria (NSLAB) and their use as adjuncts in New Zealand
Cheddar. Int. Dairy J. 2001, 11, 275–283. [CrossRef]
Fröhlich-Wyder, M.-T.; Arias-Roth, E.; Jakob, E. Cheese yeasts. Yeast 2019, 36, 129–141. [CrossRef] [PubMed]
Quigley, L.; O’Sullivan, O.; Stanton, C.; Beresford, T.P.; Ross, R.P.; Fitzgerald, G.F.; Cotter, P.D. The complex microbiota of raw
milk. FEMS Microbiol. Rev. 2013, 37, 664–698. [CrossRef]
Spinnler, H.-E. Surface Mold–Ripened Cheeses. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F., Cotter, P.D., Everett, D.W., Eds.;
Academic Press: San Diego, CA, USA, 2017; Volume 1, pp. 911–928. [CrossRef]
Afshari, R.; Pillidge, C.; Dias, D.; Osborn, A.; Gill, H. Cheesomics: The future pathway to understanding cheese flavour and
quality. Crit. Rev. Food Sci. Nutr. 2018, 60, 33–47. [CrossRef]
De Filippis, F.; Parente, E.; Ercolini, D. Recent past, present, and future of the food microbiome. Annu. Rev. Food Sci. Technol. 2018,
9, 589–608. [CrossRef]
McAuliffe, O.; Kilcawley, K.; Stefanovic, E. Symposium review: Genomic investigations of flavor formation by dairy microbiota. J.
Dairy Sci. 2019, 102, 909–922. [CrossRef] [PubMed]
Wolfe, B.E.; Dutton, R.J. Fermented foods as experimentally tractable microbial ecosystems. Cell 2015, 161, 49–55. [CrossRef]
Yeluri Jonnala, B.R.; McSweeney, P.L.H.; Sheehan, J.J.; Cotter, P.D. Sequencing of the cheese microbiome and its relevance to
industry. Front. Microbiol. 2018, 9, 1020. [CrossRef] [PubMed]
Afshari, R.; Pillidge, C.J.; Dias, D.A.; Osborn, A.M.; Gill, H. Microbiota and metabolite profiling combined with integrative
analysis for differentiating cheeses of varying ripening ages. Front. Microbiol. 2020, 11, 2991. [CrossRef] [PubMed]
Afshari, R.; Pillidge, C.J.; Dias, D.A.; Osborn, A.M.; Gill, H. Biomarkers associated with cheese quality uncovered by integrative
multi-omic analysis. Food Control 2021, 123, 107752. [CrossRef]
Bertuzzi, A.S.; Walsh, A.M.; Sheehan, J.J.; Cotter, P.D.; Crispie, F.; McSweeney, P.L.H.; Kilcawley, K.N.; Rea, M.C. Omics-based
insights into flavor development and microbial succession within surface-ripened cheese. mSystems 2018, 3, e00211–e00217.
[CrossRef]
De Filippis, F.; Genovese, A.; Ferranti, P.; Gilbert, J.A.; Ercolini, D. Metatranscriptomics reveals temperature-driven functional
changes in microbiome impacting cheese maturation rate. Sci. Rep. 2016, 6, 21871. [CrossRef]
Walsh, A.M.; Crispie, F.; Claesson, M.J.; Cotter, P.D. Translating Omics to Food Microbiology. Annu. Rev. Food Sci. Technol. 2017, 8,
113–134. [CrossRef] [PubMed]
Walsh, A.M.; Macori, G.; Kilcawley, K.N.; Cotter, P.D. Meta-analysis of cheese microbiomes highlights contributions to multiple
aspects of quality. Nat. Food 2020, 1, 500–510. [CrossRef]
Zheng, X.; Liu, F.; Shi, X.; Wang, B.; Li, K.; Li, B.; Zhuge, B. Dynamic correlations between microbiota succession and flavor
development involved in the ripening of Kazak artisanal cheese. Food Res. Int. 2018, 105, 733–742. [CrossRef] [PubMed]
Mayo, B.; Rodríguez, J.; Vázquez, L.; Flórez, A.B. Microbial interactions within the cheese ecosystem and their application to
improve quality and safety. Foods 2021, 10, 602. [CrossRef] [PubMed]
Montel, M.C.; Buchin, S.; Mallet, A.; Delbes-Paus, C.; Vuitton, D.A.; Desmasures, N.; Berthier, F. Traditional cheeses: Rich and
diverse microbiota with associated benefits. Int. J. Food Microbiol. 2014, 177, 136–154. [CrossRef]
Fotou, K.; Tzora, A.; Voidarou, C.; Alexopoulos, A.; Plessas, S.; Avgeris, I.; Bezirtzoglou, E.; Akrida-Demertzi, K.; Demertzis, P.G.
Isolation of microbial pathogens of subclinical mastitis from raw sheep’s milk of Epirus (Greece) and their role in its hygiene.
Anaerobe 2011, 17, 315–319. [CrossRef]
Quigley, L.; O’Sullivan, O.; Beresford, T.P.; Ross, R.P.; Fitzgerald, G.F.; Cotter, P.D. High-throughput sequencing for detection
of subpopulations of bacteria not previously associated with artisanal cheeses. Appl. Environ. Microbiol. 2012, 78, 5717–5723.
[CrossRef] [PubMed]
Yabrir, B.; Hakem, A.; Laoun, A.; Labiad, M.; Attia, H.; Mati, A. Composition and nitrogen distribution of Ouled-Djellal and
Rumbi Algerian ewe’s milk. Adv. J. Food Sci. Technol. 2013, 5, 1220–1226. [CrossRef]
Addis, M.F.; Tanca, A.; Uzzau, S.; Oikonomou, G.; Bicalho, R.C.; Moroni, P. The bovine milk microbiota: Insights and perspectives
from -omics studies. Mol. Biosyst. 2016, 12, 2359–2372. [CrossRef]
Foods 2022, 11, 188
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
24 of 32
Young, W.; Hine, B.C.; Wallace, O.A.; Callaghan, M.; Bibiloni, R. Transfer of intestinal bacterial components to mammary
secretions in the cow. PeerJ 2015, 3, e888. [CrossRef] [PubMed]
Parente, E.; Ricciardi, A.; Zotta, T. The microbiota of dairy milk: A review. Int. Dairy J. 2020, 107, 104714. [CrossRef]
Desmasures, N.; Bazin, F.; Guéguen, M. Microbiological composition of raw milk from selected farms in the Camembert region of
Normandy. J. Appl. Microbiol. 1997, 83, 53–58. [CrossRef] [PubMed]
Ceniti, C.; Britti, D.; Santoro, A.M.L.; Musarella, R.; Ciambrone, L.; Casalinuovo, F.; Costanzo, N. Phenotypic antimicrobial
resistance profile of isolates causing clinical mastitis in dairy animals. Ital. J. Food Saf. 2017, 6, 6612. [CrossRef]
Stellato, G.; De Filippis, F.; La Storia, A.; Ercolini, D. Coexistence of lactic acid bacteria and potential spoilage microbiota in a
dairy processing environment. Appl. Environ. Microbiol. 2015, 81, 7893–7904. [CrossRef] [PubMed]
Buffa, M.n.; Guamis, B.; Saldo, J.; Trujillo, A.J. Changes in organic acids during ripening of cheeses made from raw, pasteurized or
high-pressure-treated goats’ milk. LWT Food Sci. Technol. 2004, 37, 247–253. [CrossRef]
Frétin, M.; Martin, B.; Rifa, E.; Isabelle, V.-M.; Pomiès, D.; Ferlay, A.; Montel, M.-C.; Delbès, C. Bacterial community assembly
from cow teat skin to ripened cheeses is influenced by grazing systems. Sci. Rep. 2018, 8, 200. [CrossRef] [PubMed]
Mallet, A.; Guéguen, M.; Kauffmann, F.; Chesneau, C.; Sesboué, A.; Desmasures, N. Quantitative and qualitative microbial
analysis of raw milk reveals substantial diversity influenced by herd management practices. Int. Dairy J. 2012, 27, 13–21.
[CrossRef]
Marchand, S.; De Block, J.; De Jonghe, V.; Coorevits, A.; Heyndrickx, M.; Herman, L. Biofilm formation in milk production and
processing environments; influence on milk quality and safety. Compr. Rev. Food Sci. Food Saf. 2012, 11, 133–147. [CrossRef]
EFSA Guidance for those carrying out systematic reviews European Food Safety Authority. Application of systematic review
methodology to food and feed safety assessments to support decision making. EFSA J. 2010, 8, 1637. [CrossRef]
Kheadr, E.E.; Vuillemard, J.C.; El-Deeb, S.A. Acceleration of Cheddar cheese lipolysis by using liposome-entrapped lipases. J.
Food Sci. 2002, 67, 485–492. [CrossRef]
Bachmann, H.P.; Fröhlich-Wyder, M.T.; Jakob, E.; Roth, E.; Wechsler, D.; Beuvier, E.; Buchin, S. Cheese|Raw Milk Cheeses. In
Encyclopedia of Dairy Sciences, 2nd ed.; Fuquay, J.W., Ed.; Academic Press: Cambridge, MA, USA, 2011; pp. 652–660. [CrossRef]
De Filippis, F.; La Storia, A.; Stellato, G.; Gatti, M.; Ercolini, D. A selected core microbiome drives the early stages of three popular
italian cheese manufactures. PLoS ONE 2014, 9, e89680. [CrossRef]
Duru, I.C.; Laine, P.; Andreevskaya, M.; Paulin, L.; Kananen, S.; Tynkkynen, S.; Auvinen, P.; Smolander, O.P. Metagenomic and
metatranscriptomic analysis of the microbial community in Swiss-type Maasdam cheese during ripening. Int. J. Food Microbiol.
2018, 281, 10–22. [CrossRef] [PubMed]
Alegría, Á.; Szczesny, P.; Mayo, B.; Bardowski, J.; Kowalczyk, M. Biodiversity in Oscypek, a traditional Polish cheese, determined
by culture-dependent and -independent approaches. Appl. Environ. Microbiol. 2012, 78, 1890–1898. [CrossRef] [PubMed]
Donnelly, C.W. Cheese and Microbes; ASM Press: Washington, DC, USA, 2014; pp. 1–300. [CrossRef]
Hickey, C.D.; Fallico, V.; Wilkinson, M.G.; Sheehan, J.J. Redefining the effect of salt on thermophilic starter cell viability,
culturability and metabolic activity in cheese. Food Microbiol. 2018, 69, 219–231. [CrossRef] [PubMed]
Pintado, A.; Pinho, O.; Ferreira, I.; Pintado, M.; Gomes, A.; Malcata, F. Microbiological, biochemical and biogenic amine profiles
of Terrincho cheese manufactured in several dairy farms. Int. Dairy J. 2008, 18, 631–640. [CrossRef]
Porcellato, D.; Skeie, S.B. Bacterial dynamics and functional analysis of microbial metagenomes during ripening of Dutch-type
cheese. Int. Dairy J. 2016, 61, 182–188. [CrossRef]
Blaya, J.; Barzideh, Z.; LaPointe, G. Symposium review: Interaction of starter cultures and nonstarter lactic acid bacteria in the
cheese environment1. J. Dairy Sci. 2018, 101, 3611–3629. [CrossRef]
Wilkinson, M.G.; LaPointe, G. Starter lactic acid bacteria survival in cheese: New perspectives on cheese microbiology. J. Dairy
Sci. 2020, 103, 10963–10985. [CrossRef]
Kamilari, E.; Tomazou, M.; Antoniades, A.; Tsaltas, D. High throughput sequencing technologies as a new toolbox for deep
analysis, characterization and potentially authentication of protection designation of origin cheeses? Int. J. Food Sci. 2019,
2019, 5837301. [CrossRef]
Kuusi, M.; Lahti, E.; Virolainen, A.; Hatakka, M.; Vuento, R.; Rantala, L.; Vuopio-Varkila, J.; Seuna, E.; Karppelin, M.; Hakkinen,
M.; et al. An outbreak of Streptococcus equi subspecies zooepidemicus associated with consumption of fresh goat cheese. BMC Infect.
Dis. 2006, 6, 36. [CrossRef]
Georgalaki, M.; Manolopoulou, E.; Anastasiou, R.; Papadelli, M.; Tsakalidou, E. Detection of Streptococcus macedonicus in Greek
cheeses. Int. Dairy J. 2009, 19, 96–99. [CrossRef]
Dapkevicius, M.D.; Sgardioli, B.; Câmara, S.P.A.; Poeta, P.; Malcata, F.X. Current trends of enterococci in dairy products: A
comprehensive review of their multiple roles. Foods 2021, 10, 821. [CrossRef]
Drici, H.; Gilbert, C.; Kihal, M.; Atlan, D. Atypical citrate-fermenting Lactococcus lactis strains isolated from dromedary’s milk. J.
Appl. Microbiol. 2010, 108, 647–657. [CrossRef]
Settanni, L.; Moschetti, G. Non-starter lactic acid bacteria used to improve cheese quality and provide health benefits. Food
Microbiol. 2010, 27, 691–697. [CrossRef] [PubMed]
Irlinger, F.; Helinck, S.; Jany, J.L. Secondary and Adjunct Cultures. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F., Cotter, P.D.,
Everett, D.W., Eds.; Academic Press: San Diego, CA, USA, 2017; Volume 1, pp. 273–300. [CrossRef]
Foods 2022, 11, 188
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
25 of 32
Fernández, M.; Hudson, J.A.; Korpela, R.; de los Reyes-Gavilán, C.G. Impact on human health of microorganisms present in
fermented dairy products: An overview. BioMed Res. Int. 2015, 2015, 412714. [CrossRef]
Choi, K.-H.; Lee, H.; Lee, S.; Kim, S.; Yoon, Y. Cheese microbial risk assessments—A review. Asian-Australas J. Anim. Sci. 2016, 29,
307–314. [CrossRef]
Metin, B. Filamentous Fungi in Cheese Production. In Microbial Cultures and Enzymes in Dairy Technology, 1st ed.; Budak, S.O.,
Akal, H.C., Eds.; IGI Global: Hershey, PA, USA, 2018; pp. 257–275. [CrossRef]
Lavoie, K.; Touchette, M.; St-Gelais, D.; Labrie, S. Characterization of the fungal microflora in raw milk and specialty cheeses of
the province of Quebec. Dairy Sci. Technol. 2012, 92, 455–468. [CrossRef]
Garnier, L.; Valence, F.; Mounier, J. Diversity and control of spoilage fungi in dairy products: An update. Microorganisms 2017,
5, 42. [CrossRef] [PubMed]
Hymery, N.; Vasseur, V.; Coton, M.; Mounier, J.; Jany, J.-L.; Barbier, G.; Coton, E. Filamentous fungi and mycotoxins in cheese: A
review. Compr. Rev. Food Sci. Food Saf. 2014, 13, 437–456. [CrossRef] [PubMed]
Ceugniez, A.; Taminiau, B.; Coucheney, F.; Jacques, P.; Delcenserie, V.; Daube, G.; Drider, D. Use of a metagenetic approach to
monitor the bacterial microbiota of “Tomme d’Orchies” cheese during the ripening process. Int. J. Food Microbiol. 2017, 247, 65–69.
[CrossRef] [PubMed]
Delgado, S.; Rachid, C.T.C.C.; Fernández, E.; Rychlik, T.; Alegría, Á.; Peixoto, R.S.; Mayo, B. Diversity of thermophilic bacteria in
raw, pasteurized and selectively-cultured milk, as assessed by culturing, PCR-DGGE and pyrosequencing. Food Microbiol. 2013,
36, 103–111. [CrossRef]
Wolfe, B.E.; Button, J.E.; Santarelli, M.; Dutton, R.J. Cheese rind communities provide tractable systems for in situ and in vitro
studies of microbial diversity. Cell 2014, 158, 422–433. [CrossRef]
Dugat-Bony, E.; Garnier, L.; Denonfoux, J.; Ferreira, S.; Sarthou, A.-S.; Bonnarme, P.; Irlinger, F. Highlighting the microbial
diversity of 12 French cheese varieties. Int. J. Food Microbiol. 2016, 238, 265–273. [CrossRef]
Irlinger, F.; Layec, S.; Hélinck, S.; Dugat-Bony, E. Cheese rind microbial communities: Diversity, composition and origin. FEMS
Microbiol. Lett. 2015, 362, 1–11. [CrossRef] [PubMed]
Cogan, T.M.; Beresford, T.P. Microbiology of Hard Cheese. In Dairy Microbiology Handbook; Robinson, R.K., Ed.; John Wiley &
Sons: Hoboken, NJ, USA, 2002; pp. 515–560. [CrossRef]
Neviani, E.; Bottari, B.; Lazzi, C.; Gatti, M. New developments in the study of the microbiota of raw-milk, long-ripened cheeses
by molecular methods: The case of Grana Padano and Parmigiano Reggiano. Front. Microbiol. 2013, 4, 36. [CrossRef]
Beresford, T.; Williams, A. The Microbiology of Cheese Ripening. In Cheese: Chemistry, Physics and Microbiology, 3rd ed.; Fox,
P.F., McSweeney, P.L.H., Cogan, T.M., Guinee, T.P., Eds.; Elsevier Academic Press: Cambridge, MA, USA, 2004; pp. 287–317.
[CrossRef]
El Soda, M.; Ahmed, N.; Omran, N.; Osman, G.; Morsi, A. Isolation identification and selection of lactic acid bacteria cultures for
cheese making. Emir. J. Agric. Sci. 2003, 15, 51–71. [CrossRef]
Ferone, M.; Gowen, A.; Fanning, S.; Scannell, A.G.M. Microbial detection and identification methods: Bench top assays to omics
approaches. Compr. Rev. Food Sci. Food Saf. 2020, 19, 3106–3129. [CrossRef] [PubMed]
Banjara, N.; Suhr, M.J.; Hallen-Adams, H.E. Diversity of yeast and mold species from a variety of cheese types. Curr. Microbiol.
2015, 70, 792–800. [CrossRef]
Lopandic, K.; Zelger, S.; Bánszky, L.K.; Eliskases-Lechner, F.; Prillinger, H. Identification of yeasts associated with milk products
using traditional and molecular techniques. Food Microbiol. 2006, 23, 341–350. [CrossRef]
Petersen, K.M.; Møller, P.L.; Jespersen, L. DNA typing methods for differentiation of Debaryomyces hansenii strains and other
yeasts related to surface ripened cheeses. Int. J. Food Microbiol. 2001, 69, 11–24. [CrossRef]
Settanni, L.; Sannino, C.; Francesca, N.; Guarcello, R.; Moschetti, G. Yeast ecology of vineyards within Marsala wine area (western
Sicily) in two consecutive vintages and selection of autochthonous Saccharomyces cerevisiae strains. J. Biosci. Bioeng. 2012, 114,
606–614. [CrossRef]
Vasdinyei, R.; Deák, T. Characterization of yeast isolates originating from Hungarian dairy products using traditional and
molecular identification techniques. Int. J. Food Microbiol. 2003, 86, 123–130. [CrossRef]
Wrent, P.; Rivas, E.M.; Gil de Prado, E.; Peinado, J.M.; de Silóniz, M.I. Development of species-specific primers for rapid
identification of Debaryomyces hansenii. Int. J. Food Microbiol. 2015, 193, 109–113. [CrossRef]
Babot, J.D.; Hidalgo, M.; Argañaraz-Martínez, E.; Apella, M.C.; Perez Chaia, A. Fluorescence in situ hybridization for detection of
classical propionibacteria with specific 16S rRNA-targeted probes and its application to enumeration in Gruyère cheese. Int. J.
Food Microbiol. 2011, 145, 221–228. [CrossRef] [PubMed]
Cocolin, L.; Alessandria, V.; Dolci, P.; Gorra, R.; Rantsiou, K. Culture independent methods to assess the diversity and dynamics
of microbiota during food fermentation. Int. J. Food Microbiol. 2013, 167, 29–43. [CrossRef] [PubMed]
Cocolin, L.; Manzano, M.; Rebecca, S.; Comi, G. Monitoring of yeast population changes during a continuous wine fermentation
by molecular methods. Am. J. Enol. Vitic. 2002, 53, 24–27.
Flórez, A.; Mayo, B. PCR–DGGE as a tool for characterizing dominant microbial populations in the Spanish blue-veined Cabrales
cheese. Int. Dairy J. 2006, 16, 1205–1210. [CrossRef]
Deperrois-Lafarge, V.; Meheut, T. Use of the rpoB gene as an alternative to the V3 gene for the identification of spoilage and
pathogenic bacteria species in milk and milk products. Lett. Appl. Microbiol. 2012, 55, 99–108. [CrossRef]
Foods 2022, 11, 188
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
26 of 32
Moser, A.; Berthoud, H.; Eugster, E.; Meile, L.; Irmler, S. Detection and enumeration of Lactobacillus helveticus in dairy products.
Int. Dairy J. 2017, 68, 52–59. [CrossRef]
Horgan, R.P.; Kenny, L.C. ‘Omic’ technologies: Genomics, transcriptomics, proteomics and metabolomics. Obstet. Gynecol. 2011,
13, 189–195. [CrossRef]
Metzker, M.L. Sequencing technologies—The next generation. Nat. Rev. Genet. 2010, 11, 31–46. [CrossRef] [PubMed]
Steele, J.; Broadbent, J.; Kok, J. Perspectives on the contribution of lactic acid bacteria to cheese flavor development. Curr. Opin.
Biotechnol. 2013, 24, 135–141. [CrossRef] [PubMed]
Zheng, X.; Shi, X.; Wang, B. A Review on the general cheese processing technology, flavor biochemical pathways and the influence
of yeasts in cheese. Front. Microbiol. 2021, 12, 703284. [CrossRef] [PubMed]
McSweeney, P.L.H. Biochemistry of Cheese Ripening: Introduction and Overview. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F.,
Cotter, P.D., Everett, D.W., Eds.; Academic Press: San Diego, CA, USA, 2017; Volume 1, pp. 379–387. [CrossRef]
Parente, E.; Cogan, T.M.; Powell, I.B. Starter Cultures: General Aspects. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F., Cotter,
P.D., Everett, D.W., Eds.; Academic Press: San Diego, CA, USA, 2017; Volume 1, pp. 201–226. [CrossRef]
Iskandar, C.F.; Cailliez-Grimal, C.; Borges, F.; Revol-Junelles, A.-M. Review of lactose and galactose metabolism in lactic acid
bacteria dedicated to expert genomic annotation. Trends Food Sci. Technol. 2019, 88, 121–132. [CrossRef]
Guinee, T.P.; Fox, P.F. Salt in Cheese: Physical, Chemical and Biological Aspects. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F.,
Cotter, P.D., Everett, D.W., Eds.; Academic Press: San Diego, CA, USA, 2017; Volume 1, pp. 317–375. [CrossRef]
Valk, L.C.; Luttik, M.A.H.; de Ram, C.; Pabst, M.; van den Broek, M.; van Loosdrecht, M.C.M.; Pronk, J.T. A novel d-galacturonate
fermentation pathway in Lactobacillus suebicus links initial reactions of the galacturonate-isomerase route with the phosphoketolase
pathway. Front. Microbiol. 2020, 10, 3027. [CrossRef] [PubMed]
Geronikou, A.; Srimahaeak, T.; Rantsiou, K.; Triantafillidis, G.; Larsen, N.; Jespersen, L. Occurrence of yeasts in white-brined
cheeses: Methodologies for identification, spoilage potential and good manufacturing practices. Front. Microbiol. 2020, 11, 582778.
[CrossRef]
Beniwal, A.; Saini, P.; Kokkiligadda, A.; Vij, S. Physiological growth and galactose utilization by dairy yeast Kluyveromyces marxianus
in mixed sugars and whey during fermentation. 3 Biotech 2017, 7, 349. [CrossRef]
Smid, E.J.; Kleerebezem, M. Production of aroma compounds in lactic fermentations. Annu. Rev. Food Sci. Technol. 2014, 5,
313–326. [CrossRef]
Thierry, A.; Pogačić, T.; Weber, M.; Lortal, S. Production of Flavor Compounds by Lactic Acid Bacteria in Fermented Foods. In
Biotechnology of Lactic Acid Bacteria; Mozzi, F., Raya, R.R., Vignolo, G.M., Eds.; Elsevier: Amsterdam, The Netherlands, 2015;
pp. 314–340. [CrossRef]
Bintsis, T. Lactic acid bacteria as starter cultures: An update in their metabolism and genetics. AIMS Microbiol. 2018, 4, 665–684.
[CrossRef]
Zuljan, F.A.; Repizo, G.D.; Alarcon, S.H.; Magni, C. α-Acetolactate synthase of Lactococcus lactis contributes to pH homeostasis in
acid stress conditions. Int. J. Food Microbiol. 2014, 188, 99–107. [CrossRef] [PubMed]
Feeney, E.L.; Lamichhane, P.; Sheehan, J.J. The cheese matrix: Understanding the impact of cheese structure on aspects of
cardiovascular health—A food science and a human nutrition perspective. Int. J. Dairy Technol. 2021, 74, 656–670. [CrossRef]
Collins, Y.F.; McSweeney, P.L.H.; Wilkinson, M.G. Lipolysis and free fatty acid catabolism in cheese: A review of current
knowledge. Int. Dairy J. 2003, 13, 841–866. [CrossRef]
Wolf, I.V.; Meinardi, C.A.; Zalazar, C.A. Production of flavour compounds from fat during cheese ripening by action of lipases
and esterases. Protein Pept. Lett. 2009, 16, 1235–1243. [CrossRef]
Deeth, H.C. Lipoprotein lipase and lipolysis in milk. Int. Dairy J. 2006, 16, 555–562. [CrossRef]
Thierry, A.; Collins, Y.F.; Abeijón Mukdsi, M.C.; McSweeney, P.L.H.; Wilkinson, M.G.; Spinnler, H.E. Lipolysis and Metabolism
of Fatty Acids in Cheese. In Cheese, 4th ed.; McSweeney, P.L.H., Fox, P.F., Cotter, P.D., Everett, D.W., Eds.; Academic Press: San
Diego, CA, USA, 2017; Volume 1, pp. 423–444. [CrossRef]
Abeijón Mukdsi María, C.; Falentin, H.; Maillard, M.-B.; Chuat, V.; Medina Roxana, B.; Parayre, S.; Thierry, A. The secreted
esterase of Propionibacterium freudenreichii has a major role in cheese lipolysis. Appl. Environ. Microbiol. 2014, 80, 751–756.
[CrossRef]
Marilley, L.; Casey, M.G. Flavours of cheese products: Metabolic pathways, analytical tools and identification of producing strains.
Int. J. Food Microbiol. 2004, 90, 139–159. [CrossRef]
Dherbécourt, J.; Bourlieu, C.; Maillard, M.-B.; Aubert-Frogerais, L.; Richoux, R.; Thierry, A. Time course and specificity of lipolysis
in Swiss cheese. J. Agric. Food Chem. 2010, 58, 11732–11739. [CrossRef]
Liu, M.; Bayjanov, J.R.; Renckens, B.; Nauta, A.; Siezen, R.J. The proteolytic system of lactic acid bacteria revisited: A genomic
comparison. BMC Genom. 2010, 11, 36. [CrossRef] [PubMed]
van Kranenburg, R.; Kleerebezem, M.; van Hylckama Vlieg, J.; Ursing, B.M.; Boekhorst, J.; Smit, B.A.; Ayad, E.H.E.; Smit, G.;
Siezen, R.J. Flavour formation from amino acids by lactic acid bacteria: Predictions from genome sequence analysis. Int. Dairy J.
2002, 12, 111–121. [CrossRef]
Hanniffy, S.B.; Peláez, C.; Martínez-Bartolomé, M.A.; Requena, T.; Martínez-Cuesta, M.C. Key enzymes involved in methionine
catabolism by cheese lactic acid bacteria. Int. J. Food Microbiol. 2009, 135, 223–230. [CrossRef]
Foods 2022, 11, 188
27 of 32
118. Galli, B.D.; Martin, J.G.P.; da Silva, P.P.M.; Porto, E.; Spoto, M.H.F. Sensory quality of Camembert-type cheese: Relationship
between starter cultures and ripening molds. Int. J. Food Microbiol. 2016, 234, 71–75. [CrossRef] [PubMed]
119. Bolotin, A.; Wincker, P.; Mauger, S.; Jaillon, O.; Malarme, K.; Weissenbach, J.; Ehrlich, S.D.; Sorokin, A. The complete genome
sequence of the lactic acid bacterium Lactococcus lactis ssp. lactis IL1403. Genome Res. 2001, 11, 731–753. [CrossRef]
120. Kok, J.; van Gijtenbeek, L.A.; de Jong, A.; van der Meulen, S.B.; Solopova, A.; Kuipers, O.P. The evolution of gene regulation
research in Lactococcus lactis. FEMS Microbiol. Rev. 2017, 41, S220–S243. [CrossRef] [PubMed]
121. Kelleher, P.; Bottacini, F.; Mahony, J.; Kilcawley, K.N.; van Sinderen, D. Comparative and functional genomics of the
Lactococcus lactis taxon; insights into evolution and niche adaptation. BMC Genom. 2017, 18, 267. [CrossRef]
122. Kok, J.; Buist, G.; Zomer, A.L.; van Hijum, S.A.F.T.; Kuipers, O.P. Comparative and functional genomics of lactococci. FEMS
Microbiol. Rev. 2005, 29, 411–433. [CrossRef] [PubMed]
123. Linares, D.M.; Kok, J.; Poolman, B. Genome sequences of Lactococcus lactis MG1363 (revised) and NZ9000 and comparative
physiological studies. J. Bacteriol. 2010, 192, 5806–5812. [CrossRef] [PubMed]
124. Oliveira, A.P.; Nielsen, J.; Förster, J. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol. 2005, 5, 39.
[CrossRef] [PubMed]
125. Wels, M.; Siezen, R.; van Hijum, S.; Kelly, W.J.; Bachmann, H. Comparative genome analysis of Lactococcus lactis indicates niche
adaptation and resolves genotype/phenotype disparity. Front. Microbiol. 2019, 10, 4. [CrossRef] [PubMed]
126. Alexandraki, V.; Kazou, M.; Blom, J.; Pot, B.; Papadimitriou, K.; Tsakalidou, E. Comparative genomics of Streptococcus thermophilus
support important traits concerning the evolution, biology and technological properties of the species. Front. Microbiol. 2019,
10, 2916. [CrossRef]
127. Broadbent, J.R.; Cai, H.; Larsen, R.L.; Hughes, J.E.; Welker, D.L.; De Carvalho, V.G.; Tompkins, T.A.; Ardo, Y.; Vogensen, F.; De
Lorentiis, A.; et al. Genetic diversity in proteolytic enzymes and amino acid metabolism among Lactobacillus helveticus strains. J.
Dairy Sci. 2011, 94, 4313–4328. [CrossRef]
128. Broadbent, J.R.; Hughes, J.E.; Welker, D.L.; Tompkins, T.A.; Steele, J.L. Complete genome sequence for Lactobacillus helveticus
CNRZ 32, an industrial cheese starter and cheese flavor adjunct. Genome Announc. 2013, 1, e00590-13. [CrossRef]
129. Makarova, K.; Slesarev, A.; Wolf, Y.; Sorokin, A.; Mirkin, B.; Koonin, E.; Pavlov, A.; Pavlova, N.; Karamychev, V.; Polouchine, N.;
et al. Comparative genomics of the lactic acid bacteria. Proc. Natl. Acad. Sci. USA 2006, 103, 15611–15616. [CrossRef] [PubMed]
130. Buron-Moles, G.; Chailyan, A.; Dolejs, I.; Forster, J.; Mikš, M.H. Uncovering carbohydrate metabolism through a genotypephenotype association study of 56 lactic acid bacteria genomes. Appl. Microbiol. Biotechnol. 2019, 103, 3135–3152. [CrossRef]
131. Cai, H.; Thompson, R.; Budinich, M.F.; Broadbent, J.R.; Steele, J.L. Genome sequence and comparative genome analysis of
Lactobacillus casei: Insights into their niche-associated evolution. Genome Biol. Evol. 2009, 1, 239–257. [CrossRef] [PubMed]
132. Claesson, M.J.; van Sinderen, D.; O’Toole, P.W. The genus Lactobacillus—A genomic basis for understanding its diversity. FEMS
Microbiol. Lett. 2007, 269, 22–28. [CrossRef]
133. Jiang, Y.; Zhang, J.; Zhao, X.; Zhao, W.; Yu, Z.; Chen, C.; Yang, Z. Complete genome sequencing of exopolysaccharide-producing
Lactobacillus plantarum K25 provides genetic evidence for the probiotic functionality and cold endurance capacity of the strain.
Biosci. Biotechnol. Biochem. 2018, 82, 1225–1233. [CrossRef]
134. Kazou, M.; Alexandraki, V.; Blom, J.; Pot, B.; Tsakalidou, E.; Papadimitriou, K. Comparative genomics of Lactobacillus acidipiscis
ACA-DC 1533 isolated from traditional Greek Kopanisti cheese against species within the Lactobacillus salivarius clade. Front.
Microbiol. 2018, 9, 1244. [CrossRef]
135. Liu, J.; Chan, S.H.J.; Chen, J.; Solem, C.; Jensen, P.R. Systems biology—A guide for understanding and developing improved
strains of lactic acid bacteria. Front. Microbiol. 2019, 10, 876. [CrossRef] [PubMed]
136. Liu, M.; Nauta, A.; Francke, C.; Siezen Roland, J. Comparative genomics of enzymes in flavor-forming pathways from amino
acids in lactic acid bacteria. Appl. Environ. Microbiol. 2008, 74, 4590–4600. [CrossRef]
137. Stefanovic, E.; Casey, A.; Cotter, P.; Cavanagh, D.; Fitzgerald, G.; McAuliffe, O. Draft genome sequence of Lactobacillus casei
DPC6800, an isolate with the potential to diversify flavor in cheese. Genome Announc. 2016, 4, e00063-16. [CrossRef]
138. Stefanovic, E.; Kilcawley, K.N.; Roces, C.; Rea, M.C.; O’Sullivan, M.; Sheehan, J.J.; McAuliffe, O. Evaluation of the potential
of Lactobacillus paracasei adjuncts for flavor compounds development and diversification in short-aged Cheddar cheese. Front.
Microbiol. 2018, 9, 1506. [CrossRef]
139. Wu, C.; Huang, J.; Zhou, R. Genomics of lactic acid bacteria: Current status and potential applications. Crit. Rev. Microbiol. 2017,
43, 393–404. [CrossRef] [PubMed]
140. Zheng, J.; Ruan, L.; Sun, M.; Gänzle, M. A genomic view of lactobacilli and pediococci demonstrates that phylogeny matches
ecology and physiology. Appl. Environ. Microbiol. 2015, 81, 7233–7243. [CrossRef]
141. Sun, Z.; Harris, H.M.B.; McCann, A.; Guo, C.; Argimón, S.; Zhang, W.; Yang, X.; Jeffery, I.B.; Cooney, J.C.; Kagawa, T.F.; et al.
Expanding the biotechnology potential of lactobacilli through comparative genomics of 213 strains and associated genera. Nat.
Commun. 2015, 6, 8322. [CrossRef]
142. Flahaut, N.A.; Wiersma, A.; van de Bunt, B.; Martens, D.E.; Schaap, P.J.; Sijtsma, L.; Dos Santos, V.A.; de Vos, W.M. Genome-scale
metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation. Appl. Microbiol. Biotechnol.
2013, 97, 8729–8739. [CrossRef]
Foods 2022, 11, 188
28 of 32
143. Koduru, L.; Kim, Y.; Bang, J.; Lakshmanan, M.; Han, N.S.; Lee, D.-Y. Genome-scale modeling and transcriptome analysis of
Leuconostoc mesenteroides unravel the redox governed metabolic states in obligate heterofermentative lactic acid bacteria. Sci. Rep.
2017, 7, 15721. [CrossRef] [PubMed]
144. Kristjansdottir, T.; Bosma, E.F.; Branco dos Santos, F.; Özdemir, E.; Herrgård, M.J.; França, L.; Ferreira, B.; Nielsen, A.T.;
Gudmundsson, S. A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory. Microb.
Cell Fact. 2019, 18, 186. [CrossRef]
145. Pastink, M.I.; Teusink, B.; Hols, P.; Visser, S.; de Vos, W.M.; Hugenholtz, J. Genome-scale model of Streptococcus thermophilus
LMG18311 for metabolic comparison of lactic acid bacteria. Appl. Environ. Microbiol. 2009, 75, 3627–3633. [CrossRef] [PubMed]
146. Vinay-Lara, E.; Hamilton, J.J.; Stahl, B.; Broadbent, J.R.; Reed, J.L.; Steele, J.L. Genome-scale reconstruction of metabolic networks
of Lactobacillus casei ATCC 334 and 12A. PLoS ONE 2014, 9, e110785. [CrossRef]
147. Xu, N.; Liu, J.; Ai, L.; Liu, L. Reconstruction and analysis of the genome-scale metabolic model of Lactobacillus casei LC2W. Gene
2015, 554, 140–147. [CrossRef] [PubMed]
148. Falentin, H.; Deutsch, S.-M.; Jan, G.; Loux, V.; Thierry, A.; Parayre, S.; Maillard, M.-B.; Dherbécourt, J.; Cousin, F.J.; Jardin, J.;
et al. The complete genome of Propionibacterium freudenreichii CIRM-BIA1T, a hardy actinobacterium with food and probiotic
applications. PLoS ONE 2010, 5, e11748. [CrossRef]
149. Koskinen, P.; Deptula, P.; Smolander, O.-P.; Tamene, F.; Kammonen, J.; Savijoki, K.; Paulin, L.; Piironen, V.; Auvinen, P.; Varmanen,
P. Complete genome sequence of Propionibacterium freudenreichii DSM 20271(T). Stand. Genom. Sci. 2015, 10, 83. [CrossRef]
[PubMed]
150. Piwowarek, K.; Lipińska, E.; Hać-Szymańczuk, E.; Kieliszek, M.; Kot, A.M. Sequencing and analysis of the genome of Propionibacterium Freudenreichii T82 strain: Importance for industry. Biomolecules 2020, 10, 348. [CrossRef]
151. Anast, J.M.; Dzieciol, M.; Schultz, D.L.; Wagner, M.; Mann, E.; Schmitz-Esser, S. Brevibacterium from Austrian hard cheese harbor
a putative histamine catabolism pathway and a plasmid for adaptation to the cheese environment. Sci. Rep. 2019, 9, 6164.
[CrossRef] [PubMed]
152. Levesque, S.; de Melo, A.G.; Labrie, S.J.; Moineau, S. Mobilome of Brevibacterium aurantiacum sheds light on its genetic diversity
and its adaptation to smear-ripened cheeses. Front. Microbiol. 2019, 10, 1270. [CrossRef] [PubMed]
153. Olender, A.; Rutyna, P.; Niemcewicz, M.; Bogut, A.; Ciesielka, M.; Teresiński, G. Draft whole-genome sequence of Brevibacterium
casei strain isolated from a bloodstream infection. Braz. J. Microbiol. 2020, 51, 685–689. [CrossRef] [PubMed]
154. Pham, N.-P.; Layec, S.; Dugat-Bony, E.; Vidal, M.; Irlinger, F.; Monnet, C. Comparative genomic analysis of Brevibacterium strains:
Insights into key genetic determinants involved in adaptation to the cheese habitat. BMC Genom. 2017, 18, 955. [CrossRef]
155. Cheeseman, K.; Ropars, J.; Renault, P.; Dupont, J.; Gouzy, J.; Branca, A.; Abraham, A.-L.; Ceppi, M.; Conseiller, E.; Debuchy,
R.; et al. Multiple recent horizontal transfers of a large genomic region in cheese making fungi. Nat. Commun. 2014, 5, 2876.
[CrossRef] [PubMed]
156. Dumas, E.; Feurtey, A.; Rodríguez de la Vega, R.C.; Le Prieur, S.; Snirc, A.; Coton, M.; Thierry, A.; Coton, E.; Le Piver, M.; Roueyre,
D.; et al. Independent domestication events in the blue-cheese fungus Penicillium roqueforti. Mol. Ecol. 2020, 29, 2639–2660.
[CrossRef]
157. Gillot, G.; Jany, J.-L.; Coton, M.; Le Floch, G.; Debaets, S.; Ropars, J.; López-Villavicencio, M.; Dupont, J.; Branca, A.; Giraud, T.;
et al. Insights into Penicillium roqueforti morphological and genetic diversity. PLoS ONE 2015, 10, e0129849. [CrossRef]
158. Perkins, V.; Vignola, S.; Lessard, M.-H.; Plante, P.-L.; Corbeil, J.; Dugat-Bony, E.; Frenette, M.; Labrie, S. Phenotypic and genetic
characterization of the cheese ripening yeast Geotrichum candidum. Front. Microbiol. 2020, 11, 737. [CrossRef]
159. Ropars, J.; Didiot, E.; Rodríguez de la Vega, R.C.; Bennetot, B.; Coton, M.; Poirier, E.; Coton, E.; Snirc, A.; Le Prieur, S.; Giraud, T.
Domestication of the emblematic white cheese-making fungus Penicillium camemberti and its diversification into two varieties.
Curr. Biol. 2020, 30, 4441–4453.e4444. [CrossRef]
160. Escobar-Zepeda, A.; Sanchez-Flores, A.; Quirasco Baruch, M. Metagenomic analysis of a Mexican ripened cheese reveals a unique
complex microbiota. Food Microbiol. 2016, 57, 116–127. [CrossRef] [PubMed]
161. de Jong, A.; Hansen, M.E.; Kuipers, O.P.; Kilstrup, M.; Kok, J. The transcriptional and gene regulatory network of Lactococcus lactis
MG1363 during growth in milk. PLoS ONE 2013, 8, e53085. [CrossRef] [PubMed]
162. Del Rio, B.; Redruello, B.; Martin, M.C.; Fernandez, M.; de Jong, A.; Kuipers, O.P.; Ladero, V.; Alvarez, M.A. Transcriptome
profiling of Lactococcus lactis subsp. cremoris CECT 8666 in response to agmatine. Genom. Data 2016, 7, 112–114. [CrossRef]
163. Desfossés-Foucault, É.; LaPointe, G.; Roy, D. Transcription profiling of interactions between Lactococcus lactis subsp. cremoris
SK11 and Lactobacillus paracasei ATCC 334 during Cheddar cheese simulation. Int. J. Food Microbiol. 2014, 178, 76–86. [CrossRef]
[PubMed]
164. Dressaire, C.; Redon, E.; Gitton, C.; Loubiere, P.; Monnet, V.; Cocaign-Bousquet, M. Investigation of the adaptation of
Lactococcus lactis to isoleucine starvation integrating dynamic transcriptome and proteome information. Microb. Cell Fact.
2011, 10 (Suppl. S1), S18. [CrossRef]
165. Gupta, J.A.; Thapa, S.; Verma, M.; Som, R.; Mukherjee, K.J. Genomics and transcriptomics analysis reveals the mechanism of
isobutanol tolerance of a laboratory evolved Lactococcus lactis strain. Sci. Rep. 2020, 10, 10850. [CrossRef]
Foods 2022, 11, 188
29 of 32
166. Kuipers, O.P.; de Jong, A.; Baerends, R.J.S.; van Hijum, S.A.F.T.; Zomer, A.L.; Karsens, H.A.; den Hengst, C.D.; Kramer, N.E.;
Buist, G.; Kok, J. Transcriptome analysis and related databases of Lactococcus lactis. In Proceedings of the 7th Symposium on
Lactic Acid Bacteria, Egmond aan Zee, The Netherlands, 1–5 September 2002; Siezen, R.J., Kok, J., Abee, T., Schasfsma, G., Eds.;
Springer: Dordrecht, The Netherlands, 2002; pp. 113–122. [CrossRef]
167. Nouaille, S.; Even, S.; Charlier, C.; Le Loir, Y.; Cocaign-Bousquet, M.; Loubière, P. Transcriptomic response of Lactococcus lactis in
mixed culture with Staphylococcus aureus. Appl. Environ. Microbiol. 2009, 75, 4473–4482. [CrossRef]
168. Puri, P.; Wetzel, C.; Saffert, P.; Gaston, K.W.; Russell, S.P.; Cordero Varela, J.A.; van der Vlies, P.; Zhang, G.; Limbach, P.A.; Ignatova,
Z.; et al. Systematic identification of tRNAome and its dynamics in Lactococcus lactis. Mol. Microbiol. 2014, 93, 944–956. [CrossRef]
169. Wu, H.; Liu, J.; Miao, S.; Zhao, Y.; Zhu, H.; Qiao, M.; Saris, P.E.J.; Qiao, J. Contribution of YthA, a PspC family transcriptional
regulator of Lactococcus lactis F44 acid tolerance and nisin yield: A transcriptomic approach. Appl. Environ. Microbiol. 2018,
84, e02483-17. [CrossRef] [PubMed]
170. Cretenet, M.; Laroute, V.; Ulvé, V.; Jeanson, S.; Nouaille, S.; Even, S.; Piot, M.; Girbal, L.; Le Loir, Y.; Loubière, P.; et al. Dynamic
analysis of the Lactococcus lactis transcriptome in cheeses made from milk concentrated by ultrafiltration reveals multiple strategies
of adaptation to stresses. Appl. Environ. Microbiol. 2011, 77, 247–257. [CrossRef] [PubMed]
171. Duwat, P.; Sourice, S.; Cesselin, B.; Lamberet, G.; Vido, K.; Gaudu, P.; Le Loir, Y.; Violet, F.; Loubière, P.; Gruss, A. Respiration
capacity of the fermenting bacterium Lactococcus lactis and its positive effects on growth and survival. J. Bacteriol. 2001, 183,
4509–4516. [CrossRef] [PubMed]
172. Larsen, N.; Moslehi-Jenabian, S.; Werner, B.B.; Jensen, M.L.; Garrigues, C.; Vogensen, F.K.; Jespersen, L. Transcriptome analysis
of Lactococcus lactis subsp. lactis during milk acidification as affected by dissolved oxygen and the redox potential. Int. J. Food
Microbiol. 2016, 226, 5–12. [CrossRef] [PubMed]
173. Lu, J.; Cui, L.; Lin, S.; Hao, L.; Cao, N.; Yi, J.; Liu, X.; Lu, L.; Kang, Q. Short communication: Global transcriptome analysis of
Lactococcus lactis ssp. lactis in response to gradient freezing. J. Dairy Sci. 2019, 102, 3933–3938. [CrossRef] [PubMed]
174. Taïbi, A.; Dabour, N.; Lamoureux, M.; Roy, D.; LaPointe, G. Comparative transcriptome analysis of Lactococcus lactis subsp.
cremoris strains under conditions simulating Cheddar cheese manufacture. Int. J. Food Microbiol. 2011, 146, 263–275. [CrossRef]
[PubMed]
175. van der Meulen, S.B.; de Jong, A.; Kok, J. Early transcriptome response of Lactococcus lactis to environmental stresses reveals
differentially expressed small regulatory RNAs and tRNAs. Front. Microbiol. 2017, 8, 1704. [CrossRef]
176. Zhu, Z.; Yang, P.; Wu, Z.; Zhang, J.; Du, G. Systemic understanding of Lactococcus lactis response to acid stress using transcriptomics
approaches. J. Ind. Microbiol. Biotechnol. 2019, 46, 1621–1629. [CrossRef]
177. Dressaire, C.; Redon, E.; Milhem, H.; Besse, P.; Loubière, P.; Cocaign-Bousquet, M. Growth rate regulated genes and their wide
involvement in the Lactococcus lactis stress responses. BMC Genom. 2008, 9, 343. [CrossRef]
178. van der Meulen, S.B.; de Jong, A.; Kok, J. Transcriptome landscape of Lactococcus lactis reveals many novel RNAs including a
small regulatory RNA involved in carbon uptake and metabolism. RNA Biol. 2016, 13, 353–366. [CrossRef]
179. Zomer, A.L.; Buist, G.; Larsen, R.; Kok, J.; Kuipers, O.P. Time-resolved determination of the CcpA regulon of Lactococcus lactis
subsp. cremoris MG1363. J. Bacteriol. 2007, 189, 1366–1381. [CrossRef] [PubMed]
180. Chen, C.; Wang, L.; Lu, Y.; Yu, H.; Tian, H. Comparative transcriptional analysis of Lactobacillus plantarum and its ccpA-knockout
mutant under galactooligosaccharides and glucose conditions. Front. Microbiol. 2019, 10, 1584. [CrossRef] [PubMed]
181. Chun, B.H.; Kim, K.H.; Jeon, H.H.; Lee, S.H.; Jeon, C.O. Pan-genomic and transcriptomic analyses of Leuconostoc mesenteroides
provide insights into its genomic and metabolic features and roles in kimchi fermentation. Sci. Rep. 2017, 7, 11504. [CrossRef]
[PubMed]
182. Giaretta, S.; Treu, L.; Vendramin, V.; da Silva Duarte, V.; Tarrah, A.; Campanaro, S.; Corich, V.; Giacomini, A. Comparative
Transcriptomic analysis of Streptococcus thermophilus TH1436 and TH1477 showing different capability in the use of galactose.
Front. Microbiol. 2018, 9, 1765. [CrossRef]
183. Jääskeläinen, E.; Vesterinen, S.; Parshintsev, J.; Johansson, P.; Riekkola, M.-L.; Björkroth, J. Production of buttery-odor compounds
and transcriptome response in Leuconostoc gelidum subsp. gasicomitatum LMG18811T during growth on various carbon sources.
Appl. Environ. Microbiol. 2015, 81, 1902–1908. [CrossRef]
184. Kim, S.Y.; Kim, S.A.; Jang, Y.J.; Seo, S.O.; Han, N.S. Screening of endogenous strong promoters of Leuconostoc citreum EFEL2700
based on transcriptome analysis and its application for food-grade production of β-galactosidase. J. Biotechnol. 2021, 325, 1–6.
[CrossRef]
185. Li, J.-s.; Bi, Y.-t.; Dong, C.; Yang, J.-f.; Liang, W.-d. Transcriptome analysis of adaptive heat shock response of Streptococcus
thermophilus. PLoS ONE 2011, 6, e25777. [CrossRef]
186. Padmanabhan, A.; Tong, Y.; Wu, Q.; Zhang, J.; Shah, N.P. Transcriptomic insights into the growth phase- and sugar-associated
changes in the exopolysaccharide production of a high eps-producing Streptococcus thermophilus ASCC 1275. Front. Microbiol.
2018, 9, 1919. [CrossRef]
187. Malek, R.; Bonnarme, P.; Irlinger, F.; Frey-Klett, P.; Onésime, D.; Aubert, J.; Loux, V.; Beckerich, J.-M. Transcriptomic response of
Debaryomyces hansenii during mixed culture in a liquid model cheese medium with Yarrowia lipolytica. Int. J. Food Microbiol. 2018,
264, 53–62. [CrossRef]
Foods 2022, 11, 188
30 of 32
188. Pham, N.-P.; Landaud, S.; Lieben, P.; Bonnarme, P.; Monnet, C. Transcription profiling reveals cooperative metabolic interactions
in a microbial cheese-ripening community composed of Debaryomyces hansenii, Brevibacterium aurantiacum, and Hafnia alvei. Front.
Microbiol. 2019, 10, 1901. [CrossRef]
189. Prista, C.; Michán, C.; Miranda, I.M.; Ramos, J. The halotolerant Debaryomyces hansenii, the Cinderella of non-conventional yeasts.
Yeast 2016, 33, 523–533. [CrossRef]
190. Becerra, M.; González-Siso, M.I.; Cerdán, M.E. A transcriptome analyis of Kluyveromyces lactis growing in cheese whey. Int. Dairy
J. 2006, 16, 207–214. [CrossRef]
191. Castellote, J.; Fraud, S.; Irlinger, F.; Swennen, D.; Fer, F.; Bonnarme, P.; Monnet, C. Investigation of Geotrichum candidum gene
expression during the ripening of Reblochon-type cheese by reverse transcription-quantitative PCR. Int. J. Food Microbiol. 2015,
194, 54–61. [CrossRef] [PubMed]
192. Pracharova, P.; Lieben, P.; Pollet, B.; Beckerich, J.M.; Bonnarme, P.; Landaud, S.; Swennen, D. Geotrichum candidum gene expression
and metabolite accumulation inside the cells reflect the strain oxidative stress sensitivity and ability to produce flavour compounds.
FEMS Yeast Res. 2019, 19, foy111. [CrossRef]
193. Punt, M.; van den Brule, T.; Teertstra, W.R.; Dijksterhuis, J.; den Besten, H.M.W.; Ohm, R.A.; Wösten, H.A.B. Impact of maturation
and growth temperature on cell-size distribution, heat-resistance, compatible solute composition and transcription profiles of
Penicillium roqueforti conidia. Food Res. Int. 2020, 136, 109287. [CrossRef]
194. Rojas-Aedo, J.F.; Gil-Durán, C.; Del-Cid, A.; Valdés, N.; Álamos, P.; Vaca, I.; García-Rico, R.O.; Levicán, G.; Tello, M.; Chávez, R.
The biosynthetic gene cluster for andrastin A in Penicillium roqueforti. Front. Microbiol. 2017, 8, 813. [CrossRef] [PubMed]
195. Suleau, A.; Gourdon, P.; Reitz-Ausseur, J.; Casaregola, S. Transcriptomic analysis of extensive changes in metabolic regulation in
Kluyveromyces lactis strains. Eukaryot. Cell 2006, 5, 1360–1370. [CrossRef]
196. Suleau, A.; Jacques, N.; Reitz-Ausseur, J.; Casaregola, S. Intraspecific gene expression variability in the yeast Kluyveromyces lactis
revealed by micro-array analysis. FEMS Yeast Res. 2005, 5, 595–604. [CrossRef] [PubMed]
197. Dalmasso, M.; Aubert, J.; Briard-Bion, V.; Chuat, V.; Deutsch, S.-M.; Even, S.; Falentin, H.; Jan, G.; Jardin, J.; Maillard, M.-B.; et al.
A temporal -omic study of Propionibacterium freudenreichii CIRM-BIA1T adaptation strategies in conditions mimicking cheese
ripening in the cold. PLoS ONE 2012, 7, e29083. [CrossRef]
198. Aburjaile, F.F.; Rohmer, M.; Parrinello, H.; Maillard, M.B.; Beaucher, E.; Henry, G.; Nicolas, A.; Madec, M.N.; Thierry, A.; Parayre,
S.; et al. Adaptation of Propionibacterium freudenreichii to long-term survival under gradual nutritional shortage. BMC Genom.
2016, 17, 1007. [CrossRef]
199. Falentin, H.; Postollec, F.; Parayre, S.; Henaff, N.; Le Bivic, P.; Richoux, R.; Thierry, A.; Sohier, D. Specific metabolic activity
of ripening bacteria quantified by real-time reverse transcription PCR throughout Emmental cheese manufacture. Int. J. Food
Microbiol. 2010, 144, 10–19. [CrossRef]
200. Lazzi, C.; Turroni, S.; Mancini, A.; Sgarbi, E.; Neviani, E.; Brigidi, P.; Gatti, M. Transcriptomic clues to understand the growth of
Lactobacillus rhamnosus in cheese. BMC Microbiol. 2014, 14, 28. [CrossRef]
201. Shakya, M.; Lo, C.-C.; Chain, P.S.G. Advances and challenges in metatranscriptomic analysis. Front. Genet. 2019, 10, 904.
[CrossRef]
202. Lessard, M.H.; Viel, C.; Boyle, B.; St-Gelais, D.; Labrie, S. Metatranscriptome analysis of fungal strains Penicillium camemberti and
Geotrichum candidum reveal cheese matrix breakdown and potential development of sensory properties of ripened Camemberttype cheese. BMC Genom. 2014, 15, 235. [CrossRef]
203. Monnet, C.; Dugat-Bony, E.; Swennen, D.; Beckerich, J.-M.; Irlinger, F.; Fraud, S.; Bonnarme, P. Investigation of the activity of the
microorganisms in a Reblochon-style cheese by metatranscriptomic analysis. Front. Microbiol. 2016, 7, 536. [CrossRef]
204. Wilmes, P.; Bond, P.L. The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a
mixed community of prokaryotic microorganisms. Environ. Microbiol. 2004, 6, 911–920. [CrossRef]
205. Zhang, Y.; Fonslow, B.R.; Shan, B.; Baek, M.-C.; Yates, J.R. Protein analysis by shotgun/bottom-up proteomics. Chem. Rev. 2013,
113, 2343–2394. [CrossRef]
206. Soggiu, A.; Piras, C.; Mortera, S.L.; Alloggio, I.; Urbani, A.; Bonizzi, L.; Roncada, P. Unravelling the effect of clostridia spores and
lysozyme on microbiota dynamics in Grana Padano cheese: A metaproteomics approach. J. Proteom. 2016, 147, 21–27. [CrossRef]
207. Yang, L.; Fan, W.; Xu, Y. Metaproteomics insights into traditional fermented foods and beverages. Compr. Rev. Food Sci. Food Saf.
2020, 19, 2506–2529. [CrossRef]
208. Gagnaire, V.; Piot, M.; Camier, B.; Vissers, J.P.; Jan, G.; Léonil, J. Survey of bacterial proteins released in cheese: A proteomic
approach. Int. J. Food Microbiol. 2004, 94, 185–201. [CrossRef]
209. Mozzi, F.; Ortiz, M.E.; Bleckwedel, J.; De Vuyst, L.; Pescuma, M. Metabolomics as a tool for the comprehensive understanding of
fermented and functional foods with lactic acid bacteria. Food Res. Int. 2013, 54, 1152–1161. [CrossRef]
210. Ochi, H.; Bamba, T.; Naito, H.; Iwatsuki, K.; Fukusaki, E. Metabolic fingerprinting of hard and semi-hard natural cheeses using
gas chromatography with flame ionization detector for practical sensory prediction modeling. J. Biosci. Bioeng. 2012, 114, 506–511.
[CrossRef]
211. Ochi, H.; Sakai, Y.; Koishihara, H.; Abe, F.; Bamba, T.; Fukusaki, E. Monitoring the ripening process of Cheddar cheese based on
hydrophilic component profiling using gas chromatography-mass spectrometry. J. Dairy Sci. 2013, 96, 7427–7441. [CrossRef]
212. Gan, H.H.; Yan, B.; Linforth, R.S.T.; Fisk, I.D. Development and validation of an APCI-MS/GC-MS approach for the classification
and prediction of Cheddar cheese maturity. Food Chem. 2016, 190, 442–447. [CrossRef]
Foods 2022, 11, 188
31 of 32
213. Lazzi, C.; Povolo, M.; Locci, F.; Bernini, V.; Neviani, E.; Gatti, M. Can the development and autolysis of lactic acid bacteria
influence the cheese volatile fraction? The case of Grana Padano. Int. J. Food Microbiol. 2016, 233, 20–28. [CrossRef]
214. Le Boucher, C.; Courant, F.; Royer, A.L.; Jeanson, S.; Lortal, S.; Dervilly-Pinel, G.; Thierry, A.; Le Bizec, B. LC–HRMS fingerprinting
as an efficient approach to highlight fine differences in cheese metabolome during ripening. Metabolomics 2015, 11, 1117–1130.
[CrossRef]
215. Le Boucher, C.; Gagnaire, V.; Briard-Bion, V.; Jardin, J.; Maillard, M.B.; Dervilly-Pinel, G.; Le Bizec, B.; Lortal, S.; Jeanson, S.;
Thierry, A. Spatial distribution of Lactococcus lactis colonies modulates the production of major metabolites during the ripening of
a model cheese. Appl. Environ. Microbiol. 2016, 82, 202–210. [CrossRef]
216. Sgarbi, E.; Lazzi, C.; Tabanelli, G.; Gatti, M.; Neviani, E.; Gardini, F. Nonstarter lactic acid bacteria volatilomes produced using
cheese components. J. Dairy Sci. 2013, 96, 4223–4234. [CrossRef]
217. Pogačić, T.; Maillard, M.B.; Leclerc, A.; Hervé, C.; Chuat, V.; Valence, F.; Thierry, A. Lactobacillus and Leuconostoc volatilomes in
cheese conditions. Appl. Microbiol. Biotechnol. 2016, 100, 2335–2346. [CrossRef] [PubMed]
218. Pogačić, T.; Maillard, M.-B.; Leclerc, A.; Hervé, C.; Chuat, V.; Yee, A.L.; Valence, F.; Thierry, A. A methodological approach to
screen diverse cheese-related bacteria for their ability to produce aroma compounds. Food Microbiol. 2015, 46, 145–153. [CrossRef]
[PubMed]
219. Guarrasi, V.; Sannino, C.; Moschetti, M.; Bonanno, A.; Di Grigoli, A.; Settanni, L. The individual contribution of starter and
non-starter lactic acid bacteria to the volatile organic compound composition of Caciocavallo Palermitano cheese. Int. J. Food
Microbiol. 2017, 259, 35–42. [CrossRef]
220. Yee, A.L.; Maillard, M.B.; Roland, N.; Chuat, V.; Leclerc, A.; Pogačić, T.; Valence, F.; Thierry, A. Great interspecies and intraspecies
diversity of dairy propionibacteria in the production of cheese aroma compounds. Int. J. Food Microbiol. 2014, 191, 60–68.
[CrossRef] [PubMed]
221. Ruggirello, M.; Giordano, M.; Bertolino, M.; Ferrocino, I.; Cocolin, L.; Dolci, P. Study of Lactococcus lactis during advanced ripening
stages of model cheeses characterized by GC-MS. Food Microbiol. 2018, 74, 132–142. [CrossRef]
222. Irlinger, F.; Yung, S.A.; Sarthou, A.S.; Delbès-Paus, C.; Montel, M.C.; Coton, E.; Coton, M.; Helinck, S. Ecological and aromatic
impact of two Gram-negative bacteria (Psychrobacter celer and Hafnia alvei) inoculated as part of the whole microbial community
of an experimental smear soft cheese. Int. J. Food Microbiol. 2012, 153, 332–338. [CrossRef] [PubMed]
223. Suzuki-Iwashima, A.; Matsuura, H.; Iwasawa, A.; Shiota, M. Metabolomics analyses of the combined effects of lactic acid bacteria
and Penicillium camemberti on the generation of volatile compounds in model mold-surface-ripened cheeses. J. Biosci. Bioeng. 2020,
129, 333–347. [CrossRef] [PubMed]
224. Gobbetti, M.; Di Cagno, R.; Calasso, M.; Neviani, E.; Fox, P.F.; De Angelis, M. Drivers that establish and assembly the lactic acid
bacteria biota in cheeses. Trends Food Sci. Technol. 2018, 78, 244–254. [CrossRef]
225. Khattab, A.R.; Guirguis, H.A.; Tawfik, S.M.; Farag, M.A. Cheese ripening: A review on modern technologies towards flavor
enhancement, process acceleration and improved quality assessment. Trends Food Sci. Technol. 2019, 88, 343–360. [CrossRef]
226. Stefanovic, E.; Thierry, A.; Maillard, M.B.; Bertuzzi, A.; Rea, M.C.; Fitzgerald, G.; McAuliffe, O.; Kilcawley, K.N. Strains of the
Lactobacillus casei group show diverse abilities for the production of flavor compounds in 2 model systems. J. Dairy Sci. 2017, 100,
6918–6929. [CrossRef]
227. Bancalari, E.; Montanari, C.; Levante, A.; Alinovi, M.; Neviani, E.; Gardini, F.; Gatti, M. Lactobacillus paracasei 4341 as adjunct
culture to enhance flavor in short ripened Caciotta-type cheese. Food Res. Int. 2020, 135, 109284. [CrossRef]
228. Belkheir, K.; Zadi-Karam, H.; Karam, N.-E.; Carballo, J.; Centeno, J.A. Effects of selected mesophilic Lactobacillus strains obtained
from camel milk on the volatile and sensory profiles of a model short-ripened pressed cows’ milk cheese. Int. Dairy J. 2020,
109, 104738. [CrossRef]
229. Centeno, J.; Garabal, J.I.; Docampo, F.; Lorenzo, J.M.; Carballo, J. Recovering traditional raw-milk Tetilla cheese flavour and
sensory attributes by using Kocuria varians and Yarrowia lipolytica adjunct cultures. Int. J. Food Microbiol. 2017, 251, 33–40.
[CrossRef]
230. Pisano, M.B.; Rosa, A.; Putzu, D.; Cesare Marincola, F.; Mossa, V.; Viale, S.; Fadda, M.E.; Cosentino, S. Influence of autochthonous
putative probiotic cultures on microbiota, lipid components and metabolome of Caciotta cheese. Front. Microbiol. 2020, 11, 583745.
[CrossRef]
231. Checa, A.; Saurina, J. Metabolomics and PDO. In Comprehensive Analytical Chemistry; de la Guardia, M., Gonzálvez, A., Eds.;
Elsevier: Amsterdam, The Netherlands, 2013; Volume 60, pp. 123–143.
232. Danezis, G.P.; Pappas, A.C.; Tsiplakou, E.; Pappa, E.C.; Zacharioudaki, M.; Tsagkaris, A.S.; Papachristidis, C.A.; Sotirakoglou, K.;
Zervas, G.; Georgiou, C.A. Authentication of Greek Protected Designation of Origin cheeses through elemental metabolomics.
Int. Dairy J. 2020, 104, 104599. [CrossRef]
233. Pisano, M.B.; Scano, P.; Murgia, A.; Cosentino, S.; Caboni, P. Metabolomics and microbiological profile of Italian mozzarella
cheese produced with buffalo and cow milk. Food Chem. 2016, 192, 618–624. [CrossRef]
234. Rocchetti, G.; Lucini, L.; Gallo, A.; Masoero, F.; Trevisan, M.; Giuberti, G. Untargeted metabolomics reveals differences in chemical
fingerprints between PDO and non-PDO Grana Padano cheeses. Food Res. Int. 2018, 113, 407–413. [CrossRef]
235. Caboni, P.; Maxia, D.; Scano, P.; Addis, M.; Dedola, A.; Pes, M.; Murgia, A.; Casula, M.; Profumo, A.; Pirisi, A. A gas
chromatography-mass spectrometry untargeted metabolomics approach to discriminate Fiore Sardo cheese produced from raw
or thermized ovine milk. J. Dairy Sci. 2019, 102, 5005–5018. [CrossRef] [PubMed]
Foods 2022, 11, 188
32 of 32
236. De Pasquale, I.; Di Cagno, R.; Buchin, S.; De Angelis, M.; Gobbetti, M. Spatial distribution of the metabolically active microbiota
within Italian PDO ewes’ milk cheeses. PLoS ONE 2016, 11, e0153213. [CrossRef] [PubMed]
237. Turri, F.; Cremonesi, P.; Battelli, G.; Severgnini, M.; Brasca, M.; Gandini, G.; Pizzi, F. High biodiversity in a limited mountain area
revealed in the traditional production of Historic Rebel cheese by an integrated microbiota–lipidomic approach. Sci. Rep. 2021,
11, 10374. [CrossRef]
238. Dugat-Bony, E.; Straub, C.; Teissandier, A.; Onésime, D.; Loux, V.; Monnet, C.; Irlinger, F.; Landaud, S.; Leclercq-Perlat, M.N.; Bento, P.; et al. Overview of a surface-ripened cheese community functioning by meta-omics analyses. PLoS ONE 2015,
10, e0124360. [CrossRef] [PubMed]
239. Penland, M.; Falentin, H.; Parayre, S.; Pawtowski, A.; Maillard, M.-B.; Thierry, A.; Mounier, J.; Coton, M.; Deutsch, S.-M. Linking
Pélardon artisanal goat cheese microbial communities to aroma compounds during cheese-making and ripening. Int. J. Food
Microbiol. 2021, 345, 109130. [CrossRef]
240. Giannino, M.L.; Marzotto, M.; Dellaglio, F.; Feligini, M. Study of microbial diversity in raw milk and fresh curd used for Fontina
cheese production by culture-independent methods. Int. J. Food Microbiol. 2009, 130, 188–195. [CrossRef]
241. Mazhar, S.; Kilcawley, K.N.; Hill, C.; McAuliffe, O. A systems-wide analysis of proteolytic and lipolytic pathways uncovers
the flavor-forming potential of the gram-positive bacterium Macrococcus caseolyticus subsp. caseolyticus. Front. Microbiol. 2020,
11, 1533. [CrossRef]
242. Afshari, R.; Pillidge, C.J.; Read, E.; Rochfort, S.; Dias, D.A.; Osborn, A.M.; Gill, H. New insights into cheddar cheese microbiotametabolome relationships revealed by integrative analysis of multi-omics data. Sci. Rep. 2020, 10, 3164. [CrossRef]
243. Pimpão, R.C.; Ventura, M.R.; Ferreira, R.B.; Williamson, G.; Santos, C.N.D. Phenolic sulfates as new and highly abundant
metabolites in human plasma after ingestion of a mixed berry fruit purée. Br. J. Nutr. 2015, 113, 454–463. [CrossRef] [PubMed]
244. Matsakas, L.; Hrůzová, K.; Rova, U.; Christakopoulos, P. Biological production of 3-hydroxypropionic acid: An update on the
current status. Fermentation 2018, 4, 13. [CrossRef]
245. Quigley, L.; O’Sullivan Daniel, J.; Daly, D.; O’Sullivan, O.; Burdikova, Z.; Vana, R.; Beresford Tom, P.; Ross, R.P.; Fitzgerald Gerald,
F.; McSweeney Paul, L.H.; et al. Thermus and the pink discoloration defect in cheese. mSystems 2016, 1, e00023-16. [CrossRef]
246. Belviso, S.; Giordano, M.; Dolci, P.; Zeppa, G. In vitro cholesterol-lowering activity of Lactobacillus plantarum and Lactobacillus
paracasei strains isolated from the Italian Castelmagno PDO cheese. Dairy Sci. Technol. 2009, 89, 169–176. [CrossRef]
247. Thierry, A.; Valence, F.; Deutsch, S.-M.; Even, S.; Falentin, H.; Le Loir, Y.; Jan, G.; Gagnaire, V. Strain-to-strain differences within
lactic and propionic acid bacteria species strongly impact the properties of cheese—A review. Dairy Sci. Technol. 2015, 95, 895–918.
[CrossRef]
248. Niccum Brittany, A.; Kastman Erik, K.; Kfoury, N.; Robbat, A.; Wolfe Benjamin, E.; Cotter Paul, D. Strain-level diversity impacts
cheese rind microbiome assembly and function. mSystems 2020, 5, e00149-20. [CrossRef]