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Article

Grape Pomace in Ewes Diet Affects Metagenomic Profile, Volatile Compounds and Biogenic Amines Contents of Ripened Cheese

by
Francesca Bennato
1,
Marco Di Domenico
2,
Andrea Ianni
1,
Luigina Di Gialleonardo
2,
Cesare Cammà
2 and
Giuseppe Martino
2,*
1
Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, 64100 Teramo, Italy
2
Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise “G. Caporale”, Via Campo Boario, 64100 Teramo, Italy
*
Author to whom correspondence should be addressed.
Fermentation 2022, 8(11), 598; https://doi.org/10.3390/fermentation8110598
Submission received: 15 October 2022 / Revised: 28 October 2022 / Accepted: 30 October 2022 / Published: 2 November 2022
(This article belongs to the Special Issue Dairy Fermentation)
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Abstract

:
The main objective of this research was to evaluate the development of volatile organic compounds (VOCs) and the accumulation of biogenic amines (BAs) in relation to the dynamic of microbial population composition in fresh and ripened cheese produced from raw milk of ewes fed a diet containing grape pomace (GP+) and fed a standard diet (Ctrl). Genomic DNA was extracted from the cheeses at 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening and prepared for 16S rRNA-gene sequencing to characterize the cheese microbiota; furthermore, VOCs were determined via solid-phase microextraction combined with gas chromatography-mass spectrometry and biogenic amines by HPLC analyses. Diet did not affect the relative abundance of the main phyla identified, Proteobacteria characterized T2 samples, but the scenario changed during the ripening. At genus level, Pseudomonas, Chryseobacterium and Acinetobacter were the dominant taxa, however, a lower percentage of Pseudomonas was detected in GP+ cheeses. Enterococcus became dominant in ripened cheeses followed in Ctrl cheeses by Lactobacillus and in GP+ cheeses by Lactococcus. The diet affected the development of carboxylic acids and ketones but not of aldehydes. Low levels of esters were identified in all the samples. In total, four biogenic amines were determined in cheeses samples and their levels differed between the two groups and during ripening time. In 60, T90 and T120 GP+ cheeses, a lower amount of 2-phenylethylamine was found compared to Ctrl. Putrescine was detected only in GP+ samples and reached the highest level at 120 days. Conversely, the amount of cadaverine in GP+ samples was invariable during the ripening. The concentration of tyramine in GP+ samples was compared to Ctrl during the ripening. Overall, significant positive correlations between some families of bacteria and the formation of VOCs and BAs were found.

1. Introduction

The name Pecorino is commonly given to Italian cheeses made exclusively from raw or pasteurized ewes’ milk with or without the use of natural or commercial starter cultures. Raw milk, due to its high nutritional content, is a complex environment characterized by a rich microbiota in comparison with pasteurized milk [1]. Milk microbiota accounts for a high diversity of microorganisms, with bacteria being the most represented, followed by molds and yeasts [2]. These microorganisms enter milk from a variety of sources and some species present in raw milk can grow, survive and even become dominant during the cheese process attending in many processes, such as dairy fermentations (e.g., Lactococcus, Lactobacillus, Streptococcus), spoilage (e.g., Pseudomonas, Clostridium, Bacillus), human health (e.g., Lactobacilli and Bifidobacteria) and safety (e.g., Listeria, Salmonella, Escherichia coli, Campylobacter and mycotoxin-producing fungi).
The specific composition of the milk microbiota directly impacts on the development of the organoleptic properties of derived cheeses characterized by strong and unique features, highly appreciated by the consumers. Understanding the dairy microbiota dynamics is an important aspect for controlling the qualitative, sensorial and biosafety features of the dairy products. Although many studies investigated the contribution of single or few microorganisms, there is still some information lacking about microbial communities. In the last decade, the classical cultivation-based approach has been substituted by the development of next-generation sequencing techniques [3]. The use of 16S rRNA gene sequencing allowed for the identification and quantification of the taxonomic composition of dairy microbial population and to obtain information about the functional and taxonomic features of microbial communities in fermented food [4,5].
The characteristics and the microbiota composition of the milk can be influenced by several factors, such as animal health with special attention to mammary gland infections, stage and the number of lactations, but also by ruminant feeding strategies. Specific nutrients are taken by animals within their diet which can directly affect animal health and performance; therefore, they have a strong impact on milk composition and its derived dairy products. These changes can affect the growth of specific bacterial taxa [6,7]. Diet supplementation of dairy cows with a marine alga, Ascophyllum nodus, reduces the quantity of free fatty acids (FFAs) and causes a significant increase of Firmicutes and a decrease of Proteobacteria [6]. An increase of milk lactose in the milk of ewes fed a dietary hemp seed supplementation did not affect milk microbiological composition. However, significant differences in the growth rate of starter cultures, Streptococcus thermophilus and Lactobacillus lactis, appear in ripened cheeses. The cheeses produced with milk of ewes fed a standard diet were dominated by S. thermophilus. Conversely, cheese produced with milk of ewes fed with hemp seed showed a greater homogeneity between S. thermophilus and L. lactis compared to control group [7]. Milk samples of Holstein dairy cows fed a diet supplemented with bamboo leaf extract, with high content of flavonoids, showed an increase of protein content and a decrease of somatic cell count. In addition, the relative abundance of Firmicutes was significantly decreased while a higher relative abundance of Proteobacteria was seen in the group receiving bamboo leaf extract compared to the control group [8].
In this context, grape pomace (GP) represents a rich source of bioactive compounds, especially polyphenols and fiber, able to improve immune status of animal, rumen metabolism and milk characteristics [9,10,11]. Therefore, it has been hypothesized that it could affect the milk and cheese microbiological composition.
The use of raw milk is considered essential for a better and stronger flavor compared to pasteurized milk, primarily due to greater proteolysis and lipolysis by the raw milk microbiota in the cheese [12]. Milk type, milk constituents and the combined activity of native microflora in raw milk and enzymes affect the volatile organic compounds (VOCs) generated during cheese production and ripening.
During the cheesemaking process, lactic acid bacteria (LAB) converts lactose to lactate that can be further processed in formate, acetaldehyde, ethanol, and acetate [13,14]. In addition, the microorganisms release enzyme involved in lipolysis associated to the release of FFAs and proteolysis that is responsible for casein degradation to peptides with different molecular weights and free amino acids (FAAs) [15]. These biochemical processes that occur during the ripening produce hundreds of VOCs such as carboxylic acids, lactones, ketones, alcohols, and aldehydes that have a direct impact on cheese flavor, a key parameter in consumer choice, cheese quality, and variety [16,17].
Milk characteristics and natural microbiota play a key role during ripening, not only for the development of the flavor and typical organoleptic properties of Pecorino cheeses, but also for the accumulation of undesirable substances, such as biogenic amines (BAs) [18]. The presence of BAs (tryptamine, 2-phenylethylamine, putrescine, cadaverine, histamine, serotonin, tyramine, spermidine, spermine) in fermented cheese is influenced by a lot of factors such as production technology, ripening period, storage and primarily by the presence of microorganism possessing amino acid decarboxylase activity [19]. The indigenous microorganisms present in milk and coming from the environment during milking and cheesemaking have been reported to be capable of BAs production, above all some Gram-negative (e.g., Pseudomonaceae and Enterobacteriaceae) and non-starter lactic acid bacteria (e.g., Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, and Streptococcus).
The aim of the present study was to evaluate the development of VOCs and the accumulation of BAs in relation to the dynamic of microbial population composition in fresh and ripened cheese obtained from lactating ewes fed a diet containing 10% of GP.

2. Methods and Materials

2.1. Cheese Manufacturing

This study is part of a project studying the effects of a diet containing 10% of GP on the quality of milk and derived dairy products. Details on the experimental design have been reported in a previous study [20]. On day 58, 59 and 60, bulk milk was collected separately from each group and used partly for chemical analysis and partly for Pecorino cheesemaking. The cheeses were produced in a local cheese factory. Raw milk was heated at 60 °C for 15, cooled quickly at 40 °C and transferred to a container in which rennet (15 g/100 kg) was added (75% of chymosin and 25% of pepsin; 1:18,000 strength; Naturen Premium 225, Chr. Hansen, Parma, Italy), no starter cultures were added during the cheesemaking. Milk coagulated within 40 min at 38–40 °C. The curd was cut into small cheese grains pieces, drained for whey and portioned in aliquots of ca. 500 g, inserted and pressed into round plastic molds and kept at 37 °C for 5 h. After this, the cheeses were taken out of the molds and a 20% NaCl water solution was used to salt the cheese in brine. Thereafter, the salted, fresh cheese was stored in the ripening room at a controlled temperature (10 ± 0.5 °C) and a relative humidity of 85%. From each cheese-making process, 12 forms of cheese for each group were produced, 3 were analyzed after 2 (T2) day of ripening, 3 subjected to a maturation process of 60 (T60), 3 of 90 (T90) and 3 of 120 (T120) days.

2.2. DNA Extraction, 16S rRNA Gene Amplicon Library Preparation and Sequencing

Total DNA was extracted from each sample of cheese as reported by Ianni et al. [7]. Briefly, 2 g of cheese, taken to different parts of the cheese shape to obtain a representative sample, were homogenized with 20 mL of PBS for 5 min in a stomacher. The samples were centrifugated for 5 min a 1400 rpm. The fatty layer was removed, the supernatant was collected and centrifuged for 5 min at 14,000 rpm. Then, the supernatant was discarded, and the pellet resuspended in 400 μL of PBS. The DNA was extracted and purified by the Maxwell 16 Tissue DNA Purification Kit (Promega, Madison, WI, USA) following the manufacturer’s instructions. The DNA was quantified by Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, MA, USA).
DNA was then diluted with 10 mM Tris pH 8.5 to a final concentration of 5 ng/μL according to the Illumina standard protocol [21]. PCR amplification was performed with V3–V4 sequencing primers as reported in the same protocol. Master mixtures were made up to final volumes of 25 µL by mixing 12.5 µL 2× KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Potters Bar, UK), 2.5 µL of template DNA and 5 μL of each forward and reverse primer (1 μM). Amplification reactions were then performed in a ProFlex PCR System (Thermo Fisher Scientific, Waltham, MA, USA) setting 25 cycles (denaturation 95 °C for 30 s, annealing 55 °C for 30 s, extension 72 °C for 30 s). The PCR products were checked by TapeStation 4200 (Agilent Technologies, Santa Clara, CA, USA). The 16S rRNA PCR amplicon of each sample was barcoded via a secondary PCR according to the Illumina standard protocol [21]. The samples were then sequenced up to about 360 K reads on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) at Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise “G. Caporale”, using Illumina MiSeq Reagent v2 kits (500 cycles, Part # MS-102-2003).

2.3. Determination of Volatile Compound in Cheese

The extraction and the analysis of VOCs from cheese samples were as reported by Ianni et al. [7] with slight modification. Briefly, 3 g of cheese samples were grated into small granules and placed in a glass bottle with 13 mL of saturated NaCl solution (360 g/L) and 10 μL of internal standard solution (4-methyl-2-heptanone; 10 mg/kg in ethanol) and allowed to equilibrate at 60 °C for 60 min.
Extraction of the volatiles was carried out using headspace solid phase microextraction (SPME) technique, using a divinylbenzene-Carboxen-polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Bellefonte, PA, USA). VOCs were separated on a gas chromatograph (Clarus 580; Perkin Elmer, Waltham, MA, USA) coupled with a mass spectrometer (SQ8S; Perkin Elmer, Waltham, MA, USA). Chromatographic conditions were the same as described in Bennato et al. [22]. The data were expressed as standardized relative abundance, as a percentage of each compound on the sum of the total VOCs.

2.4. Determination of Biogenic Amines in Cheese

For the determination of Bas (Histamine, Tyramine, 2-Phenylethylamine, Putrescine, Cadaverine, Spermine, Spermidina, Tryptamine), 5 g of cheese were homogenized with 20 mL of perchloric acid and then centrifuged at 4000 rpm. The supernatant was recovered, filtered and 900 μL of internal standard, 1–7 diamino heptane (100 μg/mL) were added. Then, 200 μL of 2 N sodium hydroxide, 300 μL of saturated bicarbonate and 1 mL of Dansyl Chloride (10 mg/mL) were added.
The samples were placed in the bath for 45 min at 40 °C, 150 μL of 30% (v/v) ammonia were added and left in the dark for 45 min. After this time, 2 mL of acetonitrile were added and the samples were centrifuged again for 15 min at 4000 rpm. The supernatant was recovered and injected into HPLC. Separation of the analytes was performed using a Supelcosil LC-18 column (25 cm × 4.6 mm, 5 μm). The biogenic amines were separated through a linear gradient, 0.1 M ammonium acetate (A) and acetonitrile (B) were used for the separation of Bas. At the beginning of the analysis, A represented 50% of the mobile phase, and this percentage underwent a linear reduction of up to 10% in 20 min; then, the A content increased up to 50% in 5 min and this condition was held for 10 min until the end. A flow rate of 1 mL/min was applied and the column temperature was set at 38 °C ± 0.1 °C.
The identification of the BA was carried out by comparing the chromatographic traces obtained in the different samples with those of mixtures of standard amines at a known concentration.
In addition, to the identification of BA on the basis of retention times, the standard solutions were used, after appropriate dilution, also for the preparation of calibration lines (0.5–50 µg/mL) for quantitative calculations, performed by comparing the areas of the sample peaks with those of the standard BA mixture. More precisely, the concentrations were obtained by comparing the area of each amine detected in the sample to the area of the internal standard added to the same sample and thus reporting the value obtained on the calibration line relative to the same standard amine.

2.5. Bioinformatic and Statistical Analysis

Bcl2fastq (Illumina, San Diego, CA, USA) was used to combine per-cycle BCL files from the sequencing run, to translate them into FASTQ files (raw reads) and to separate multiplexed samples (demultiplexing). The raw reads were then merged using FLASH Version 1.2.7 (Magoc et al., 2011) and filtered to obtain high-quality clean reads under specified filtering conditions using QIIME (Version 1.7.0) [23]. Clean tags were aligned to Gold database (http://drive5.com/uchime/uchime_download.html; accessed on 15 April 2022) for detection and removal of chimera sequences using UCHIME algorithm (http://www.drive5.com/usearch/manual/uchime_algo.html, accessed on 15 April 2022) [24]. The effective tags were clustered into OTUs with ≥ 97% similarity by Uparse (Version 7.0.1001; http://drive5.com/uparse/, accessed on 15 April 2022) [25]. The representative sequence for each OTU was selected and the taxonomic information was annotated using Mothur software, version 1.43.0, [26] and SILVA Database (http://www.arb-silva.de/, accessed on 15 April 2022) [27].
The taxonomic classification generated by centrifuge was used to measure α-diversity in R package (software for statistical computing and graphics) by betapart. Alpha diversity was used to define the richness (total operational taxonomic units, OTUs and Chao1) and evenness (Shannon and Simpson indices).
The statistical analysis was performed using the statistical software JMP 14 program (SAS Institute, Cary, NC, USA). All data were processed with ANOVA (Analysis of Variance) to analyze the impact of the diet (D), the ripening (R) and diet × ripening interaction (D × R). The means were assessed by HSD Tukey’s test and differences were considered significant for p < 0.05. All the analyses were carried out on all samples of cheese (three samples for each group and for each ripening time). Data were reported as least square means ± pooled standard error of the mean (SEM).
Principal component analysis (PCA) was performed on bacteria families, volatile compounds and biogenic amines as the variables using XLSTAT statistical software for Windows (Addinsoft XLSTAT 2016.02.28451). Pearson’s correlation was applied to evaluate the correlation among bacteria families, biogenic amines and volatile compounds.

3. Results

3.1. Microbiota Cheese Characterization

The identified microorganisms in the cheese samples belong to Firmicutes, Proteobacteria and Bacteroidetes phyla (Figure 1A). Proteobacteria was the dominant phyla identified in T2 samples, independently of diet (66.27% and 61.43% relative abundance in Ctrl and GP+, respectively). During the ripening, the scenario changed and Firmicutes become the dominant phyla followed by Bacteroidetes. No significant differences in the relative abundance of the main phyla were detected in relation to the diet and to the combinate effect of diet and ripening. The ripening represents the main source of variation in the relative abundance of Firmicutes, Proteobacteria and Bacteroidetes.
Fourteen families were present in all the samples with Pseudomonaceae (42.11% and 25.70% relative abundance in Ctrl and GP+, respectively), Weeksellaceae (22.41% and 26.03% relative abundance in Ctrl and GP+, respectively) and Moraxellaceae (23.27% and 30.69% relative abundance in Ctrl and GP+, respectively) prevalent in T2 samples. During the ripening, the relative abundance of Pseudomonaceae, Weeksellaceae and Moraxellaceae decrease in both groups and the dominant families in Ctrl cheeses become Enterococcaceae and Lactobacillaceae, while in GP+ samples, Enterococcaceae and Streptococcacea were the familes most represented. Significant differences in relation to the diet were observed in the relative abundance of Streptococcaceae (p < 0.0001), Sphingobacteriaceae (p = 0.0003), Enterococcaceae (p = 0.0039), Lactobacillaceae (p < 0.0001) and Staphylococcaceae (p = 0.0019). The diet x ripening interaction exerted a significant effect on the relative abundances of Pseudomonaceae (p < 0.0001), Streptococcaceae (p = 0.0008), Carnobacteriaceae (p = 0.0011), Lactobacillaceae (p = 0.0155), Staphylococcaceae (p = 0.0129) (Figure 1B).
At genus level, T2 samples were characterized by Pseudomonas (42.11% and 25.70% relative abundance in Ctrl and GP+, respectively), Chryseobacterium (22.41% and 26.03% relative abundance in Ctrl and GP+, respectively) and Acinetobacter (23.10% and 21.06% relative abundance in Ctrl and GP+, respectively). In the ripened cheeses of both groups, Enterococcus was the most abundant genus with a relative abundance between 30 and 57% followed in Ctrl samples by Lactobacillus with a relative abundance of 17.72%, 14.01% and 13.74% in T60, T90 and T120, respectively and in GP+ cheeses by Lactococcus with a relative abundance of 23.60%, 19.43% and 13.84% in T60, T90 and T120, respectively. Diet affects the growth of many genera such as Lactococcus (p < 0.0001), Streptococcus (p = 0.0162), Sphingobacterium (p = 0.0003), Enterococcus (p = 0.0242), Lactobacillus (p < 0.0001), Leuconostoc (p = 0.0039) and Staphylococcus (p = 0.0019). Pseudomonas, Lactococcus, Enterobacter, Carnobacterium, Lactobacillus, Staphylococcus were influenced by diet x ripening interaction (Figure 1C).
The α-diversity was measured as the richness (OTUs and Chao1) and diversity, accounting both species richness and evenness (Shannon and Simpson indices) of and within samples. As showed in Figure 2, OTUs and Chao1 indices were the highest in cheeses samples at T2 and lowest in cheese samples at T60, T90 and T120. Significant differences related to the diet were observed in T2 samples between Ctrl and GP+ groups both in OTUs (p = 0.019) and Chao1 (p = 0.007). On the contrary, the Shannon and Simpson indices did not differ between the two groups across the whole ripening time.

3.2. Volatile Compounds in Cheese

Thirty-four volatile compounds were detected in the volatile fraction of the cheeses, including three alcohols, three aldehydes, eight carboxylic acids, seven ketones, six esters, four lactones and three miscellaneous compounds (Table 1). In total, three alcohols were identified: 1-pentanol, 3-methyl-, 2-nonanol, 1-decanol; however, these compounds were found only in T120 cheeses and significant differences related to the diet were found only for 1-pentanol, 3-methyl. The effect of ripening time was important for the formation of aldehydes (hexanal, p < 0.0001; octanal, p = 0.0005; nonanal, p < 0.0002), on the contrary, no significant differences correlated to the treatment were observed. In details, the principal aldehydes identified in T2 samples of both groups were octanal and nonanal. The levels of these compounds were significantly affected by ripening time, octanal was not identified in T60, T90, and T120 samples and nonanal decreased between T2 and T60 both in Ctrl and GP+ groups until to disappear at 120 days of ripening. Among the identified carboxylic acids, octanoic acid and n-decanoic acid were principal in T2 samples. The levels of octanoic acid were affected by the diet (p < 0.0001), the ripening (p = 0.0022) and diet and ripening interaction (p = 0.0009); higher levels in GP+ samples compared to Ctrl were observed at 60 and 120 days of ripening. The different strategy feeding did not change the levels of n-decanoic acid, on the contrary, the diet affected hexanoic (p < 0.0001), nonanoic (p < 0.0001) and dodecanoic acids (p < 0.0003) levels. In particular, lower levels of hexanoic acid were detected in GP+ samples both at 60 and 120 days. On the contrary, the levels of nonanoic acid were higher in GP+ samples both after 60 and after 90 and 120 days of ripening. Regarding ketones, the concentrations of 2-heptanon (p = 0.0001) and 2-nonanone (p = 0.001) were influenced by the diet; while the levels of 2-pentanone, 4,6 octadiyn-3-one, 2-methyl, and 8-nonen-2-one changed both by diet and ripening. Low levels of esters were identified in all the samples. Decanoic acid-, ethyl ester was the only one detected in T2 samples, while at 60 days hexanoic acid, ethyl ester, octanoic acid, and ethyl ester were found both in Ctrl and GP+, however, no significant differences of these compounds between the two groups were highlighted neither in T60, not in T90 and T120. Lactones were not identified in T2 samples. The ripening affected pantolactone (p = 0.0028), δ-decalactone (p < 0.0001), γ-dodecalactone (p = 0.0005) and δ-dodecalactone (p < 0.0001) levels while only δ-decalactone levels were influenced by the diet (p < 0.0001).

3.3. Biogenic Amines in Cheese

In total, four biogenic amines were determined in cheeses samples and their levels differed between the two groups and during ripening time (Table 2). In T2 samples of both groups, none of the listed compounds were found. In GP+ samples ripened for 60 days, 2-phenylethylamine, putrescine, cadaverine and tyramine were identified. The amount of 2-phenylethylamine was affected both by the diet (p < 0.001) and the ripening (p < 0.001). In GP+ cheeses, 2-phenylethylamine levels were lower compared to Ctrl both after 60 days and after 90 and 120 days of ripening. Putrescine, detected only in GP+ samples, was affected by the ripening (p = 0.0144) and increased at 120 days compared to 60 and 90 days. Conversely, the amount of cadaverine in GP+ samples was invariable during the ripening. Tyramine concentration increased during the ripening, however, its quantity in GP+ samples was compared to Ctrl in any time of ripening.

3.4. Microbioma, Volatile Profile and Biogenic Amines Correlation

Principal component analysis was performed to evaluate the quality of cheese samples by including bacteria families, volatile compounds and biogenic amines as the variables (Figure 3A). The PCA results can explain 59.90% of the total variance and the first and second components represented 39.66% and 20.24% of the total variance, respectively. Biogenic amines appeared to correlate with the positive F1 and F2, while no specific distribution was found in bacteria families and volatile compounds.
In the score plot (Figure 3B), it is possible to distinguish four different cheese groups. The samples ripened at 2 days are well differentiated from the other ripening time and characterized by the higher presence of Moraxellaceae, Weeksellaceae, Flavobacteriaceae and Pseudomonaceae and aldehydes compared to the other ripening times. Cheeses ripened at 60, 90 and 120 days were well separated according to the treatment; Ctrl cheeses appeared to correlate with the positive F1 and negative F2 coordinates characterized by Lactobacillaceae, Lactobacillales and Leuconostocacaceae, esteres, lactones and ketones. On the contrary, GP+ samples were less clustered and GP+-T120 well separated from T60 and T90. Cadaverine, putrescine and Streptococcaceae were major descriptors of GP+-T120 cheeses.
In order to evaluate the possible relationship between bacteria families and the variable BAs and VOCs, Pearson’s test was used (Table 3). Significant positive correlations between some families of bacteria and the formation of VOCs and BAs were found. The presence of Moraxellaceae, Weeksellaceae, Pseudomonadaceae, Carnobacteriaceae was positively correlated with the production of aldehydes and negatively correlated with the presence of 2-phenylethylamine, tyramine and lactones. Conversely, Streptococcaceae were negatively correlated to aldehydes and ketones and positively to carboxylic acid, putrescine and cadaverine. Enterococcaceae positively correlated to ketones, esters, lactones, 2-phenylethylamine and tyramine and negatively with aldehydes, Enterobacteriaceae affected the development of alcohol, esters and lactones, Lactobacillaceae of ketones and lactones and negatively correlated to aldehydes and carboxylic acids. Leuconostocaceae and Lactobacillales correlated positively to ketones and esters and negatively to acids. Sphingobacteriaceae correlated positively to acids and negatively to ketones, esters and lactones.

4. Discussion

This study aimed at giving new insights on how the diet can exert effects on the microbiological composition of cheese made from raw ewes’ milk, without the addition of a starter, and on how the microbial dynamics affects the development of VOCs and BAs. To the best of our knowledge, no information could be found in the literature concerning the effect of a diet containing GP on the microbiological characteristics of raw ewes’ cheese.
The analyses of α-diversity, used to examine the evolution of bacterial richness, evenness and biodiversity showed a negative trend during the ripening suggesting that a non-negligible number of bacteria disappeared during the ripening and few bacterial genera predominated. These results agree with what reported for other raw ewes’ cheese [28,29,30]. Chao1 and OTUs indices showed a lower bacterial richness in T2 cheeses made with raw milk of ewes fed GP even if the differences with the Ctrl reduced during the ripening. This finding could be explained, at last in part, with the antibacterial properties of GP bioactive substances able to inhibit the growth of some bacterial species [31]. Shannon and Simpson indices confirmed a downward trend from fresh cheese to 60-, 90- and 120-day-old ripened cheeses, however no significant differences were found in relation to the diet.
According to previous studies, Proteobacteria and Firmicutes were the predominant phyla detected in T2 cheeses made with raw ewes’ milk, independent of diet [32,33,34]. On the contrary, the cheese ripening was characterized by an increase in Firmicutes abundance and a decrease in Proteobacteria. These results are consistent with what has been previously observed for raw ewes’ milk derived cheese as Canestrato Pugliese [32] and Idiazabal, a traditional cheese produced from raw ewes’ milk in the Basque Country [30].
Microorganisms belonging to the phylum of Proteobacteria such as Acinetobacter (family Moraxellaceae) and Pseudomonas (family Pseudomonaceae) characterize Ctrl and GP+ cheeses after 2 days of ripening. Acinetobacter is a Gram-negative, psychrotrophic bacteria, that colonize teat canal and has been found in feces samples and as contaminant of raw milk [35]. Pseudomonas is the most important psychrotrophic bacteria in raw milk, coming from natural environment [36] and has been related to hygiene conditions [37,38]. These microorganisms, likely found in the milking environment, can come into contact with the teat and enter into the udder through the milking process [39]. The presence, in fresh cheeses (T2), of microorganism of possible environmental contamination of milk suggests that the thermization treatment of the milk used for cheese manufacturing was not able to neutralize these bacteria so they remain in less ripened cheese. However, the lower levels of Pseudomonas in T2 GP+ cheeses could be attributed to a better sanity state of the udder since Pseudomonas spp. have been associated with clinical mastitis [40]. In addition, Pseudomonas spp. play a key role in milk spoilage after a long period of cold incubation [41]. Therefore, its reduction is considered positive also for cheese microbiological safety.
As the ripening progressed, LAB (Enterococcus, Lactococcus, Lactobacillus, Streptococcus) proliferated and Enterococcus became the dominant genus independently of the diet and the ripening. However, during the ripening, the diet with GP influenced the growth of other LAB, such as Lactococcus, Lactobacillus, Streptococcus and Leuconostoc. After 60 days of ripening, cheeses produced with raw milk of ewes fed GP were characterized by a higher relative abundance of Lactococcus and Streptococcus (family Streptococcaceae) and a lower relative abundance of Lactobacillus (family Lactobacillaceae) compared to Ctrl cheeses. Conversely, Lactobacillus was the most dominant genera after Enterococcus in T60 Ctrl cheeses. The presence of LAB, Gram-positive bacteria, has been well documented in dairy products which play an essential role in traditional cheese making, contributing to a rapid acidification of milk [15,42]. The different abundance of LAB in the two groups and the changes in their growth during the ripening could be due to the ability of microbial populations to adapt to the specific environmental conditions that prevail in cheese such as heat, acidity, energy sources but also to the biochemical composition of raw milk that has a great impact on microbial dynamics as well as the presence of compounds taken with the diet that could influence the growth of selected bacterial genera in cheeses.
Microorganisms contribute to cheese flavor formation through the assimilation of substrates that are present and/or produced during cheese making. In our study, VOCs analysis was performed in order to evaluate the development of aroma and taste in relation to microbial changes. VOCs cheese composition was characterized by a high amount of carboxylic acid (butanoic, hexanoic, octanoic, nonanoic, n-decanoic, undecanoic, dodecanoic and tetradecanoic) whose content changes in relation to the diet and the ripening. Diet induced a decrease of hexanoic acid and an increase of octanoic and nonanoic acids suggesting a different activity of lipase involved in the lipolytic process. Pearson’s correlation demonstrated a positive correlation between carboxylic acid and Streptococcaceae, Sphingobacteriaceae and Carnobacteriaceae suggesting that these families play a key role in their production. According to other studies, bacteria belonging to Streptococcaceae family induce the production of carboxylic acids, specifically, Lactoccocus spp. play a greater role in the production of hexanoic and octanoic acid [5,43]. The higher presence of Lactococcus in GP+ samples could explain the higher percentage of octanoic acid that is the most representative carboxylic acid in GP+ samples at any time of ripening. Among the family of VOCs identified in cheese samples, esters are common volatile constituents of some dairy products such as cheese and their contribution to the flavors of dairy products is concentration-dependent. Ethyl esters of fatty acids of C2–C10 are the predominant volatile compounds that are responsible for the fruity character of Italian-type cheeses made from cow milk [44] but also from goats’ and ewes’ milk [22,45]. Two different mechanisms, esterification and alcoholysis, are involved in the biosynthesis of esters and enzyme as lipase and esterase and LAB play a key role. However, the role of microorganisms involved in ester biosynthesis in fermented dairy products has not been well defined. Starter Lactococci, psychrotrophic bacteria, heterofermentative NSLAB are known as microorganisms responsible for ester production in cheese [46]. In our study, positive correlations were observed between esters and bacteria belonging to Enterobacteriaceae, Enterococcaceae, Leuconostocaceae and Lactobacillales. Higher concentrations of ester were found in cheeses ripened at 120 days, however differences in relation to the diet were found only in octanoic acid, ethyl ester. Cheeses made from the milk of GP+ group were characterized by lower ketones concentrations. Ketones are intermediate compounds, produced by lipid degradation by β-oxidation and decarboxylation of fatty acids and can be reduced to secondary alcohols [47]. Ketones and methyl ketones also impart fruity notes to cheeses. However, the concentrations of these compounds in most cheese varieties are generally low except in mould-ripened and surface mould-ripened cheeses, which contain relatively high levels of ketones and methyl ketones [48,49]. Their concentration increased in Ctrl, on the contrary in GP+ a decrease during the ripening was found. Enterococcaceae, Lactobacillaceae, Leuconostocaceae and Lactobacillales were positively correlated to ketones production. As also highlighted by PCA, aldehydes characterize fresh cheeses (T2) and disappear during the ripening, on the contrary lactone increase during the ripening reaching the higher concentration at 120 days of ripening. Alcohols were found at low concentration only in T120 cheeses.
As reported in other studies, both the type of the milk used for the production and the ripening are important factors affecting the formation of BAs [50]. Amines can be produced by the metabolism of FAAs by decarboxylation, and they are not associated with good quality cheese, due to their potentially adverse health effects and often poor flavor. Among the BAs identified in cheese samples, tyramine resulted to be the BA present in the highest concentration in all the cheeses examined, starting from 60 days of ripening and its concentration increased during the ripening. This finding agrees with a lot of studies that reported a high incidence of tyramine in cheese manufactured from raw ewes’ milk [18,51,52,53]. Enterobacteriaceae and certain LAB, especially enterococci and lactobacilli, are particularly active in the production of BA. In particular, it has been demonstrated that Enterococcus spp. possess tyrosine decarboxylase activity [54,55].
The high concentration of the precursor amino acid tyrosine and the activity of decarboxylating enzymes are limiting factors on the formation of tyrosine. In the present study, a positive correlation was found between tyramine and Enterococcaceae. These data are in agreement with those of Suzzi et al. [56] who found that great part of enterococci isolated from Semicotto caprine cheese produce tyramine. So, the increased amount of Enterococcaceae during the ripening was associated with the increased levels of tyramine independently by the diet. A positive correlation was also found between Enterococcaceae and 2-phenilethylamine. It has been demonstrated that stains of Enterococcaceae (Enterococcus faecium and Enterococcus faecalis) produce proteins having phenylalanine and tyrosine decarboxylase activity [57,58]. The higher levels of bacteria of Streptococcaceae (Lactococcus and Streptococcus) justify the higher number of diamines, cadaverine and putrescine, found in GP+ cheeses. Putrescine is synthesized via ornithine decarboxylation or agmatine deamination, and cadaverine has originated by lysine decarboxylation [59]. These BAs have been found in high concentrations in hard ripened cheese made from raw milk [60]. Cadaverine and putrescine impart unpleasant flavors to cheese and other foodstuffs, which are related to putrefaction and rotten meat [61]. Putrescine and cadaverine production have mainly been related to Gram-negative bacteria, especially Enterobacteriaceae [62,63] however, some Lactococcus lactic strains have been recently identified as putrescine-producing from agmatine and can be found in large numbers in cheeses with high putrescine concentrations [64,65].

5. Conclusions

The results of the present research indicate that animal feeding can affect the complex microbiota of cheese made with raw ewes’ milk. Both the diet and ripening time had a remarkable impact on bacterial communities and considerable differences were observed between the two groups. The lower relative abundance of Pseudomonas in fresh cheese made with milk of ewes fed grape pomace may suggest a better sanity state of the udder as well as being considered positive for cheese microbiological safety. Grape pomace affects the growth of specific LAB, promoting the growth of Lactobacillus through the ripening. The changes in cheese microbiota influence the development of different volatile organic compounds with a possible impact on cheese aroma and taste, a key parameter in consumer choice. However, it would be necessary to verify whether these changes may have any effect on consumer acceptability. The presence of amine biogenes, such as cadaverine and putrescine, may prejudice the cheese safety. This data suggests the importance to control every step of the entire process, from the quality of the raw milk used, the cheese-making process and aging, in order to produce a product of sanitary quality.

Author Contributions

Conceptualization, G.M. and C.C.; methodology, F.B. and M.D.D.; software, L.D.G.; validation, L.D.G.; formal analysis, F.B. and M.D.D.; investigation, A.I.; resources, G.M.; data curation, F.B. and A.I.; writing—original draft preparation, F.B.; writing—review and editing, A.I.; visualization, F.B.; supervision, G.M.; project administration, G.M.; funding acquisition, G.M. 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

All data are available following reasonable request to the corresponding author.

Acknowledgments

The authors are grateful to Lisa Di Marcantonio for technical support and to “Caseificio Sapori del Gran Sasso” for the kind cooperation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Quigley, L.; O’Sullivan, O.; Stanton, C.; Beresford, T.P.; Ross, R.; Fitzgerald, G.F.; Cotter, P.D. The complex microbiota of raw milk. FEMS Microbiol. Rev. 2013, 37, 664–698. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. 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. [Google Scholar] [CrossRef]
  3. Afshari, R.; Pillidge, C.J.; Dias, D.A.; Osborn, A.M.; Gill, H. Cheesomics: The future pathway to understanding cheese flavour and quality. Crit. Rev. Food Sci. Nutr. 2018, 60, 33–47. [Google Scholar] [CrossRef] [PubMed]
  4. 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. [Google Scholar] [CrossRef]
  5. Gezginc, Y.; Karabekmez-Erdem, T.; Tatar, H.D.; Dağgeçen, E.C.; Ayman, S.; Akyol, I. Metagenomics and volatile profile of Turkish artisanal Tulum cheese microbiota. Food Biosci. 2021, 45, 101497. [Google Scholar] [CrossRef]
  6. Lopez, C.C.; Serio, A.; Rossi, C.; Mazzarrino, G.; Marchetti, S.; Castellani, F.; Grotta, L.; Fiorentino, F.P.; Paparella, A.; Martino, G. Effect of diet supplementation with Ascophyllum nodosum on cow milk composition and microbiota. J. Dairy Sci. 2016, 99, 6285–6297. [Google Scholar] [CrossRef]
  7. Ianni, A.; Di Domenico, M.; Bennato, F.; Peserico, A.; Martino, C.; Rinaldi, A.; Candeloro, L.; Grotta, L.; Cammà, C.; Pomilio, F. Metagenomic and volatile profiles of ripened cheese obtained from dairy ewes fed a dietary hemp seed supplementation. J. Dairy Sci. 2020, 103, 5882–5892. [Google Scholar] [CrossRef]
  8. Jing-Wei, Z.; Yi-Yuan, S.; Xin, L.; Hua, Z.; Hui, N.; Luo-Yun, F.; Ben-Hai, X.; Jin-Jin, T.; Lin-Shu, J. Microbiome and Metabolic Changes of Milk in Response to Dietary Supplementation With Bamboo Leaf Extract in Dairy Cows. Front. Nutr. 2021, 8, 723446. [Google Scholar] [CrossRef]
  9. Correddu, F.; Nudda, A.; Battacone, G.; Boe, R.; Francesconi, A.H.D.; Pulina, G. Effects of grape seed supplementation, alone or associated with linseed, on ruminal metabolism in Sarda dairy sheep. Anim. Feed Sci. Technol. 2014, 199, 61–72. [Google Scholar] [CrossRef]
  10. Moate, P.J.; Williams, S.R.O.; Torok, V.A.; Hannah, M.C.; Ribaux, B.E.; Tavendale, M.H.; Eckard, R.J.; Jacobs, J.L.; Auldist, M.J.; Wales, W.J. Grape marc reduces methane emissions when fed to dairy cows. J. Dairy Sci. 2014, 97, 5073–5087. [Google Scholar] [CrossRef]
  11. Iannaccone, M.; Elgendy, R.; Giantin, M.; Martino, C.; Giansante, D.; Ianni, A.; Dacasto, M.; Martino, G. RNA Sequencing-Based Whole-Transcriptome Analysis of Friesian Cattle Fed with Grape Pomace-Supplemented Diet. Animals 2018, 8, 188. [Google Scholar] [CrossRef] [Green Version]
  12. Buffa, M.; Guamis, B.; Pavia, M.; Trujillo, A.J. Lipolysis in cheese made from raw, pasteurized or high-pressure-treated goats’ milk. Int. Dairy J. 2001, 11, 175–179. [Google Scholar] [CrossRef]
  13. 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. [Google Scholar] [CrossRef]
  14. McSweeney, P.L.H. Biochemistry of cheese ripening. Int. J. Dairy Technol. 2004, 57, 127–144. [Google Scholar] [CrossRef]
  15. Fox, P.F.; Guinee, T.P.; Cogan, T.M.; McSweeney, P.L.H. Microbiology of cheese ripening. In Fundamentals of Cheese Science. In Fundamentals of Cheese Science, 2nd ed.; Fox, P.F., Guinee, T.P., Cogan, T.M., McSweeney, P.L.H., Eds.; Springer: New York, NY, USA, 2017; pp. 333–390. [Google Scholar] [CrossRef]
  16. Bertuzzi, A.S.; McSweeney, P.L.H.; Rea, M.C.; Kilcawley, K.N. Detection of Volatile Compounds of Cheese and Their Contribution to the Flavor Profile of Surface-Ripened Cheese. Compr. Rev. Food Sci. Food Saf. 2018, 17, 371–390. [Google Scholar] [CrossRef] [Green Version]
  17. Drake, M.A.; Delahunty, C.M. Sensory character of cheese and its evaluation. In Cheese: Chemistry, Physics, and Microbiology, 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. 455–487. [Google Scholar]
  18. Schirone, M.; Tofalo, R.; Mazzone, G.; Corsetti, A.; Suzzi, G. Biogenic amine content and microbiological profile of Pecorino di Farindola cheese. Food Microbiol. 2011, 28, 128–136. [Google Scholar] [CrossRef]
  19. Galgano, F.; Suzzi, G.; Favati, F.; Caruso, M.C.; Martuscelli, M.; Gardini, F.; Salzano, G. Biogenic amines during ripening in ‘Semicotto Caprino’ cheese: Role of enterococci. Int. J. Food Sci. Technol. 2001, 36, 153–160. [Google Scholar] [CrossRef]
  20. Bennato, F.; Ianni, A.; Florio, M.; Grotta, L.; Pomilio, F.; Saletti, M.A.; Martino, G. Nutritional Properties of Milk from Dairy Ewes Fed with a Diet Containing Grape Pomace. Foods 2022, 11, 1878. [Google Scholar] [CrossRef]
  21. Illumina. 16S Metagenomic Sequencing Library Preparation Protocol: Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System; Part No. 15044223 Rev. B.; Illumina: San Diego, CA, USA, 2013; Available online: https://www.illumina.com/content/dam/illuminasupport/documents/documentation/chemistrydocumentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf (accessed on 12 April 2022).
  22. Bennato, F.; Ianni, A.; Innosa, D.; Grotta, L.; D’Onofrio, A.; Martino, G. Chemical-nutritional characteristics and aromatic profile of milk and related dairy products obtained from goats fed with extruded linseed. Asian-Australas. J. Anim. Sci. 2019, 33, 148–156. [Google Scholar] [CrossRef]
  23. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Gonzalez Peña, A.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  24. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996–998. [Google Scholar] [CrossRef] [PubMed]
  26. Schloss, P.D.; Westcott, S.L.; Ryabin, T.; Hall, J.R.; Hartmann, M.; Hollister, E.B.; Lesniewski, R.A.; Oakley, B.B.; Parks, D.H.; Robinson, C.J.; et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol. 2009, 75, 7537–7541. [Google Scholar] [CrossRef] [Green Version]
  27. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef]
  28. Cardinali, F.; Osimani, A.; Taccari, M.; Milanović, V.; Garofalo, C.; Clementi, F.; Polverigiani, S.; Zitti, S.; Raffaelli, N.; Mozzon, M.; et al. Impact of thistle rennet from Carlina acanthifolia All. subsp. acanthifolia on bacterial diversity and dynamics of a specialty Italian raw ewes’ milk cheese. Int. J. Food Microbiol. 2017, 255, 7–16. [Google Scholar] [CrossRef] [PubMed]
  29. Ramezani, M.; Hosseini, S.M.; Ferrocino, I.; Amoozegar, M.A.; Cocolin, L. Molecular investigation of bacterial communities during the manufacturing and ripening of semi-hard Iranian Liqvan cheese. Food Microbiol. 2017, 66, 64–71. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Santamarina-García, G.; Hernández, I.; Amores, G.; Virto, M. Characterization of Microbial Shifts during the Production and Ripening of Raw Ewe Milk-Derived Idiazabal Cheese by High-Throughput Sequencing. Biology 2022, 11, 769. [Google Scholar] [CrossRef]
  31. Hassan, Y.I.; Kosir, V.; Yin, X.; Ross, K.; Diarra, M.S. Grape Pomace as a Promising Antimicrobial Alternative in Feed: A Critical Review. J. Agric. Food Chem. 2019, 67, 9705–9718. [Google Scholar] [CrossRef]
  32. De Pasquale, I.; Calasso, M.; Mancini, L.; Ercolini, D.; La Storia, A.; De Angelis, M.; Di Cagno, R.; Gobbetti, M. Causal Relationship between Microbial Ecology Dynamics and Proteolysis during Manufacture and Ripening of Protected Designation of Origin (PDO) Cheese Canestrato Pugliese. Appl. Environ. Microbiol. 2014, 80, 4085–4094. [Google Scholar] [CrossRef]
  33. Endres, C.M.; Castro, M.S.; Trevisol, L.D.; Severo, J.M.; Mann, M.B.; Varela, A.P.M.; Frazzon, A.P.G.; Mayer, F.Q.; Frazzon, J. Molecular characterization of the bacterial communities present in sheep’s milk and cheese produced in South Brazilian Region via 16S rRNA gene metabarcoding sequencing. LWT 2021, 147, 111579. [Google Scholar] [CrossRef]
  34. Esteban-Blanco, C.; Gutiérrez-Gil, B.; Puente-Sánchez, F.; Marina, H.; Tamames, J.; Acedo, A.; Arranz, J.J. Microbiota characterization of sheep milk and its association with somatic cell count using 16s rRNA gene sequencing. J. Anim. Breed. Genet. 2020, 137, 73–83. [Google Scholar] [CrossRef] [PubMed]
  35. Yuan, H.; Han, S.; Zhang, S.; Xue, Y.; Zhang, Y.; Lu, H.; Wang, S. Microbial Properties of Raw Milk throughout the Year and Their Relationships to Quality Parameters. Foods 2022, 11, 3077. [Google Scholar] [CrossRef] [PubMed]
  36. Falardeau, J.; Keeney, K.; Trmčić, A.; Kitts, D.; Wang, S. Farm-to-fork profiling of bacterial communities associated with an artisan cheese production facility. Food Microbiol. 2019, 83, 48–58. [Google Scholar] [CrossRef]
  37. Bellassi, P.; Rocchetti, G.; Nocetti, M.; Lucini, L.; Masoero, F.; Morelli, L. A Combined Metabolomic and Metagenomic Approach to Discriminate Raw Milk for the Production of Hard Cheese. Foods 2021, 10, 109. [Google Scholar] [CrossRef]
  38. Jonnala, B.R.Y.; 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. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Delbès, C.; Ali-Mandjee, L.; Montel, M.-C. Monitoring Bacterial Communities in Raw Milk and Cheese by Culture-Dependent and -Independent 16S rRNA Gene-Based Analyses. Appl. Environ. Microbiol. 2007, 73, 1882–1891. [Google Scholar] [CrossRef] [Green Version]
  40. Ruegg, P.L. Investigation of mastitis problems on farms. Vet. Clin. North Am. Food Anim. Pract. 2003, 19, 47–73. [Google Scholar] [CrossRef]
  41. Raats, D.; Offek, M.; Minz, D.; Halpern, M. Molecular analysis of bacterial communities in raw cow milk and the impact of refrigeration on its structure and dynamics. Food Microbiol. 2011, 28, 465–471. [Google Scholar] [CrossRef]
  42. García-Cano, I.; Rocha-Mendoza, D.; Ortega-Anaya, J.; Wang, K.; Kosmerl, E.; Jiménez-Flores, R. Lactic acid bacteria isolated from dairy products as potential producers of lipolytic, proteolytic and antibacterial proteins. Appl. Microbiol. Biotechnol. 2019, 103, 5243–5257. [Google Scholar] [CrossRef]
  43. Liang, L.; Liu, G.; Zhang, F.; Li, Q.; Linhardt, R.J. Digestibility of squash polysaccharide under simulated salivary, gastric and intestinal conditions and its impact on short-chain fatty acid production in type-2 diabetic rats. Carbohydr. Polym. 2020, 235, 115904. [Google Scholar] [CrossRef]
  44. Ianni, A.; Innosa, D.; Martino, C.; Bennato, F.; Martino, G. Short communication: Compositional characteristics and aromatic profile of caciotta cheese obtained from Friesian cows fed with a dietary supplementation of dried grape pomace. J. Dairy Sci. 2019, 102, 1025–1032. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Padilla, B.; Belloch, C.; López-Díez, J.J.; Flores, M.; Manzanares, P. Potential impact of dairy yeasts on the typical flavour of traditional ewes’ and goats’ cheeses. Int. Dairy J. 2014, 35, 122–129. [Google Scholar] [CrossRef] [Green Version]
  46. Dopieralska, P.; Barłowska, J.; Teter, A.; Król, J.; Brodziak, A.; Domaradzki, P. Changes in Fatty Acid and Volatile Compound Profiles During Storage of Smoked Cheese Made from the Milk of Native Polish Cow Breeds Raised in the Low Beskids. Animals 2020, 10, 2103. [Google Scholar] [CrossRef] [PubMed]
  47. Bontinis, T.G.; Mallatou, H.; Pappa, E.C.; Massouras, T.; Alichanidis, E. Study of proteolysis, lipolysis and volatile profile of a traditional Greek goat cheese (Xinotyri) during ripening. Small Rumin. Res. 2012, 105, 193–201. [Google Scholar] [CrossRef]
  48. Sablé, S.; Cottenceau, G. Current Knowledge of Soft Cheeses Flavor and Related Compounds. J. Agric. Food Chem. 1999, 47, 4825–4836. [Google Scholar] [CrossRef]
  49. Moio, L.; Piombino, P.; Addeo, F. Odour-impact compounds of Gorgonzola cheese. J. Dairy Res. 2000, 67, 273–285. [Google Scholar] [CrossRef]
  50. Novella-Rodríguez, S.; Veciana-Nogués, M.; Izquierdo-Pulido, M.; Vidal-Carou, M. Distribution of Biogenic Amines and Polyamines in Cheese. J. Food Sci. 2003, 68, 750–756. [Google Scholar] [CrossRef]
  51. Roig-Sagués, A.X.; Molina, A.P.; Hernández-Herrero, M.M. Histamine and tyramine-forming microorganisms in Spanish traditional cheeses. Eur. Food Res. Technol. 2002, 215, 96–100. [Google Scholar] [CrossRef]
  52. Martuscelli, M.; Gardini, F.; Torriani, S.; Mastrocola, D.; Serio, A.; Chaves-López, C.; Schirone, M.; Suzzi, G. Production of biogenic amines during the ripening of Pecorino Abruzzese cheese. Int. Dairy J. 2005, 15, 571–578. [Google Scholar] [CrossRef]
  53. Pintado, A.I.E.; Pinho, O.; Ferreira, I.M.P.M.V.O.; Pintado, M.M.E.; Gomes, A.M.P.; Malcata, F.X. Microbiological, biochemical and biogenic amine profiles of Terrincho cheese manufactured in several dairy farms. Int. Dairy J. 2008, 18, 631–640. [Google Scholar] [CrossRef]
  54. Ladero, V.; Fernández, M.; Calles-Enríquez, M.; Sánchez-Llana, E.; Cañedo, E.; Martín, M.C.; Alvarez, M.A. Is the production of the biogenic amines tyramine and putrescine a species-level trait in enterococci? Food Microbiol. 2012, 30, 132–138. [Google Scholar] [CrossRef] [PubMed]
  55. Kučerová, K.; Svobodova, H.; Tůma, S.; Ondráčková, I.; Plocková, M. Production of biogenic amines by Enterococci. Czech J. Food Sci. 2010, 27, 50–55. [Google Scholar] [CrossRef] [Green Version]
  56. Suzzi, G.; Caruso, M.; Gardini, F.; Lombardi, A.; Vannini, L.; Guerzoni, M.E.; Andrighetto, C.; Lanorte, M.T. A survey of the enterococci isolated from an artisanal Italian goat’s cheese (semicotto caprino). J. Appl. Microbiol. 2000, 89, 267–274. [Google Scholar] [CrossRef] [PubMed]
  57. Marcobal, A.; Rivas, B.D.L.; Muã±Oz, R. First genetic characterization of a bacterial β-phenylethylamine biosynthetic enzyme in Enterococcus faecium RM58. FEMS Microbiol. Lett. 2006, 258, 144–149. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Liu, F.; Xu, W.; Du, L.; Wang, D.; Zhu, Y.; Geng, Z.; Zhang, M.; Xu, W. Heterologous Expression and Characterization of Tyrosine Decarboxylase from Enterococcus faecalis R612Z1 and Enterococcus faecium R615Z1. J. Food Prot. 2014, 77, 592–598. [Google Scholar] [CrossRef]
  59. Linares, D.M.; Martín, M.; Ladero, V.; Alvarez, M.A.; Fernández, M. Biogenic Amines in Dairy Products. Crit. Rev. Food Sci. Nutr. 2011, 51, 691–703. [Google Scholar] [CrossRef] [PubMed]
  60. Poveda, J.M.; Chicón, R.; Cabezas, L. Biogenic amine content and proteolysis in Manchego cheese manufactured with Lactobacillus paracasei subsp. paracasei as adjunct and other autochthonous strains as starters. Int. Dairy J. 2015, 47, 94–101. [Google Scholar] [CrossRef]
  61. Alvarez, M.A.; Moreno-Arribas, M.V. The problem of biogenic amines in fermented foods and the use of potential biogenic amine-degrading microorganisms as a solution. Trends Food Sci. Technol. 2014, 39, 146–155. [Google Scholar] [CrossRef] [Green Version]
  62. Brink, B.T.; Damink, C.; Joosten, H.M.; Veld, J.H.I. Occurrence and formation of biologically active amines in foods. Int. J. Food Microbiol. 1990, 11, 73–84. [Google Scholar] [CrossRef]
  63. Pircher, A.; Bauer, F.; Paulsen, P. Formation of cadaverine, histamine, putrescine and tyramine by bacteria isolated from meat, fermented sausages and cheeses. Eur. Food Res. Technol. 2007, 226, 225–231. [Google Scholar] [CrossRef]
  64. Linares, D.M.; Del Río, B.; Ladero, V.; Martínez, N.; Fernández, M.; Martín, M.C.; Álvarez, M.A. Factors Influencing Biogenic Amines Accumulation in Dairy Products. Front. Microbiol. 2012, 3, 180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Ladero, V.; Rattray, F.P.; Mayo, B.; Martin, M.C.; Fernandez, M.; Alvarez, M.A. Sequencing and Transcriptional Analysis of the Biosynthesis Gene Cluster of Putrescine-Producing Lactococcus lactis. Appl. Environ. Microbiol. 2011, 77, 6409–6418. [Google Scholar] [CrossRef] [PubMed]
Fermentation 08 00598 g001 550
Figure 1. Bar plots of the relative abundance (%) bacterial community composition at phylum (A), family (B) and genus (C) level identified in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed a standard diet (Ctrl) and grape pomace diet (GP+).
Figure 1. Bar plots of the relative abundance (%) bacterial community composition at phylum (A), family (B) and genus (C) level identified in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed a standard diet (Ctrl) and grape pomace diet (GP+).
Fermentation 08 00598 g001
Fermentation 08 00598 g002 550
Figure 2. Alpha diversity measured as observed OTUs (A), Chao1 (B), Simpson (C) and Shannon (D) indices in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+). Box-plots labeled with different letters are significantly different from each other.
Figure 2. Alpha diversity measured as observed OTUs (A), Chao1 (B), Simpson (C) and Shannon (D) indices in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+). Box-plots labeled with different letters are significantly different from each other.
Fermentation 08 00598 g002
Fermentation 08 00598 g003 550
Figure 3. Score plot (A) and loading plot (B) of the principal component analysis among bacteria families, volatile compounds and biogenic amines in cheese samples after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+).
Figure 3. Score plot (A) and loading plot (B) of the principal component analysis among bacteria families, volatile compounds and biogenic amines in cheese samples after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+).
Fermentation 08 00598 g003
Table
Table 1. Volatile organic compounds detected in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+).
Table 1. Volatile organic compounds detected in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+).
T2T60T90T120SEMP
CtrlGP+CtrlGP+CtrlGP+CtrlGP+ DRD × R
Alcohols
1-Pentanol, 3-methyl-ndndndndndndnd b0.13 a<0.010.02190.00330.0033
2-Nonanolndndndndndnd0.150.130.020.89280.04320.9953
1-Decanolndndndndndnd0.100.08<0.010.0977<0.00010.041
Aldeydes
Hexanalndndndndndnd1.851.140.310.2482<0.00010.2161
Octanal0.200.27ndndndndndnd0.020.63460.00050.8407
Nonanal3.461.960.150.06ndndndnd1.340.30900.00020.3727
Carboxylic Acids
Butanoic acidndnd1.92 a,b1.21 a,b0.95 a,b0.48 b3.26 a1.92 a,b0.610.05180.00010.3756
Hexanoic acid5.89 d5.68 d20.23 a14.88 b,c12.22 c10.62 c18.56 a,b10.71 c1.88<0.0001<0.00010.0021
Octanoic Acid24.50 c,d27.97 b,c28.87 b,c44.47 a32.59 a,b,c39.11 a,b16.49 d42.95 a13.63<0.00010.00220.0009
Nonanoic acidndnd0.16 b0.37 cnd c0.31 a,bnd c0.30 a0.00<0.0001<0.0001< 0.0001
n-Decanoic acid53.31 a46.10 a,b21.13 b32.78 a,b27.47 a,b41.52 a,b26.93 b26.94 b61.520.22110.00110.1698
Undecanoic acid3.6 a2.48 a,bnd b,c0.05 b,cnd b,c0.09 b,cnd c0.04 c0.610.5170<0.00010.515
Dodecanoic acidndnd0.71 b1.08 a,b1.03 a,b1.36 a0.77 b1.13 a,b0.020.0003<0.00010.0574
Tetradecanoic acidndndndndndnd0.08 a0.06 a<0.010.6132<0.00010.8149
Ketones
2-Pentanonendnd2.84 a1.08 b,c0.65 c0.99 b,c2.28 a,b1.04 b,c0.180.0012<0.00010.0019
2-Heptanon1.81 b5.07 a,b9.79 a1.47 b7.76 a1.49 b5.68 a,b2.00 b2.130.00010.16110.0002
4,6 Octadiyn-3-one, 2-methyl-ndndndndndnd0.43 a0.08 b0.010.0083<0.00010.0007
2-Nonanone2.39 c8.44 a,b,c11.51 a,b1.74 b,c12.89 a2.74 b,c13.39 a5.9 a,b,c7.420.00100.10360.001
8-Nonen-2-onendnd0.64 a0.05 b0.35 a,b0.09 b0.62 a0.3 b0.01<0.0001<0.00010.0002
2-Decanonendnd0.17 a0.06 andndndnd<0.010.05530.00010.0345
2-Undecanonendndndnd0.44 a0.17 a0.64 a0.56 a0.050.27120.00010.6036
Esters
Butanoic acid, ethyl esterndndndndndnd0.5 a0.39 a0.010.3833<0.00010.4451
Hexanoic acid, ethyl esterndnd0.26 b0.11 b1.36 a,b0.07 b2.81 a1.02 a,b0.520.01130.00060.0758
Octanoic acid, ethyl esterndnd0.12 b0.24 b0.89 b0.23 b2.80 a1.19 b0.120.0012<0.00010.0009
Decanoic acid, ethyl ester1.05 b,c0.26 cndnd1.01 b,c0.52 b,c2.75 a2.02 a,b0.270.0363<0.00010.5851
Dodecanoic acid-, ethyl esterndndndndndnd0.07 a0.10 a<0.010.5967<0.00010.7936
Valeric acid, 3-tridecyl esterndndndndndndnd b0.01 a<0.010.00800.00070.0007
Lactones
Pantolactonendndndnd0.09 and a1.05 a0.48 a0.190.26390.00280.3892
δ-Decalactonendnd0.37 a0.13 c0.22 b,c0.14 c0.35 a,b0.20 c<0.01<0.0001<0.00010.0004
γ-Dodecalactonendndndndndnd0.08 a0.06 a<0.010.56460.00050.7491
δ-Dodecalactonendndndndndnd0.09 a0.07 a<0.010.5288<0.00010.6952
Others
Ethylbenzene0.8 a0.24 b0.52 a,b0.1 b0.02 b0.01 b0.12 b0.05 b0.030.00310.00080.0471
P-xylene1.13 a0.4 b0.61 a,b0.13 b0.01 b0.01 bndnd0.040.0018<0.00010.0082
1,1-Dodecanediol, diacetatendndndnd0.06 a0.06 a0.04 a0.11 a0.010.61480.32130.8223
Data are reported as relative percentage. a–d Means within a row with different superscripts are significantly different. nd: not detected; SEM: pooled standard error of the mean. D: diet; R: ripening.
Table
Table 2. Concentrations of biogenic amines in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+).
Table 2. Concentrations of biogenic amines in cheese after 2 (T2), 60 (T60), 90 (T90) and 120 (T120) days of ripening, obtained from raw milk of ewes fed standard diet (Ctrl) and grape pomace diet (GP+).
T2T60T90T120SEMP
CtrlGP+CtrlGP+CtrlGP+CtrlGP+ DRD × R
Triptaminendndndndndndndnd
2-phenylethylaminendnd38.11 c13.35 d46.28 c15.04 d85.37 a63.51 b12.12<0.001<0.0010.1523
Putresceinndndnd7.63 bnd5.34 bnd14.11 a3.22 0.0144
Cadaverinendndnd18.33nd10.6715.7420.008.62
Hystaminendndndndndndndnd
Serotoninendndndndndndndnd
Tyraminendnd32.53 b42.72 b40.18 b34.55 b101.23 a103.03 a86.310.6861<0.00010.4532
Spermidinendndndndndndndnd
Sperminendndndndndndndnd
Data are reported as μg/g cheese on dry matter basis. a–d Means within a row with different superscripts are significantly different. SEM: pooled standard error of the mean. D: diet; R: ripening.
Table
Table 3. Pearson’s correlations values between bacteria families and volatile organic compounds and biogenic amines.
Table 3. Pearson’s correlations values between bacteria families and volatile organic compounds and biogenic amines.
AlcoholsAldeydesCarboxylic AcidsKetonesEstersLactones2-phenylethylaminePutresceinCadaverineTyramine
Moraxellaceae−0.2540.8110.139−0.272−0.400−0.417−0.568−0.371−0.464−0.615
Weeksellaceae−0.4520.7060.338−0.393−0.647−0.616−0.604−0.280−0.353−0.613
Pseudomonadaceae−0.3210.9180.259−0.430−0.463−0.478−0.629−0.340−0.443−0.655
Streptococcaceae0.030−0.5480.563−0.419−0.169−0.0970.1760.4590.6470.401
Enterobacteriaceae0.487−0.236−0.2550.1860.6060.4980.1880.2820.4020.319
Sphingobacteriaceae−0.2260.1430.589−0.537−0.555−0.500−0.3780.0910.104−0.236
Carnobacteriaceae−0.2400.5490.464−0.562−0.508−0.418−0.476−0.172−0.166−0.436
Enterococcaceae0.364−0.819−0.2990.4230.5290.5180.7030.3090.3630.685
Lactobacillaceae0.036−0.436−0.7100.7990.4010.4420.320−0.165−0.2480.127
Leuconostocaceae−0.005−0.390−0.5500.6010.4150.3580.325−0.171−0.1400.122
Staphylococcaceae−0.240−0.1180.345−0.265−0.250−0.265−0.2390.2580.361−0.037
Lactobacillales0.173−0.197−0.5220.5290.4110.3200.076−0.071−0.1930.005
Flavobacteriaceae−0.1580.294−0.003−0.053−0.149−0.160−0.143−0.209−0.270−0.233
Values in bold are different from 0 with a significance level alpha = 0.05.
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Bennato, F.; Di Domenico, M.; Ianni, A.; Di Gialleonardo, L.; Cammà, C.; Martino, G. Grape Pomace in Ewes Diet Affects Metagenomic Profile, Volatile Compounds and Biogenic Amines Contents of Ripened Cheese. Fermentation 2022, 8, 598. https://doi.org/10.3390/fermentation8110598

AMA Style

Bennato F, Di Domenico M, Ianni A, Di Gialleonardo L, Cammà C, Martino G. Grape Pomace in Ewes Diet Affects Metagenomic Profile, Volatile Compounds and Biogenic Amines Contents of Ripened Cheese. Fermentation. 2022; 8(11):598. https://doi.org/10.3390/fermentation8110598

Chicago/Turabian Style

Bennato, Francesca, Marco Di Domenico, Andrea Ianni, Luigina Di Gialleonardo, Cesare Cammà, and Giuseppe Martino. 2022. "Grape Pomace in Ewes Diet Affects Metagenomic Profile, Volatile Compounds and Biogenic Amines Contents of Ripened Cheese" Fermentation 8, no. 11: 598. https://doi.org/10.3390/fermentation8110598

APA Style

Bennato, F., Di Domenico, M., Ianni, A., Di Gialleonardo, L., Cammà, C., & Martino, G. (2022). Grape Pomace in Ewes Diet Affects Metagenomic Profile, Volatile Compounds and Biogenic Amines Contents of Ripened Cheese. Fermentation, 8(11), 598. https://doi.org/10.3390/fermentation8110598

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