). For example, aflatoxin B1, when ingested by a lactating cow through contaminated feed, is bio-transformed into the toxic and carcinogenic metabolite aflatoxin M1 (AFM1), which excreted into milk. The amount of AFM1 present in milk is generally only about 1 to 2% of the amount of ingested aflatoxin B1, although in high-yielding dairy cows, this percentage can be higher, reaching values >6% (
). Given that AFM1 represents a potential risk for public health, several countries have regulated its maximum residue level in milk: the United States, China, and Brazil have set a maximum residue level for AFM1 of 0.5 µg/kg (
), and in Europe, milk concentrations of AFM1 must not exceed 0.05 µg/kg (
). In addition, regulatory limits for cheese have been introduced by some countries, including the Netherlands (200 ng/kg), Austria, Italy, Switzerland (250 ng/kg), China, and the United States (500 ng/kg) (
). Different authors have shown that the AFM1 concentration in cheese is related to different factors, including cheese type and technology factors, such as the amount of water removed during cheese-making and ripening (
). Cheese production is usually based on fermentation processes carried out by starter cultures used to generate lactic acid–enabling gel syneresis, whey expulsion, and curd formation. Natural whey cultures (NWC) are undefined cheese starters obtained by the traditional back-slopping procedure (
) and that play a key role in cheese specificity, uniqueness, and development of sensory characteristics. Specifically, NWC ensure rapid and complete lactose fermentation and contribute to casein proteolysis and to aroma and flavor development through AA catabolism (
). Currently, NWC are used in the manufacture of different European long-ripened cheeses (Grana Padano, Parmigiano Reggiano, Comté, and Gruyère), and their core microbiome mainly comprises Lactobacillus helveticus, Lactobacillus delbrueckii, Limosilactobacillus fermentum (formerly known as Lb. fermentum), and Streptococcus thermophilus (
). The relationships among these lactic acid bacteria (LAB) species and their diversity can be affected by many biotic and abiotic drivers, such as bacteriophages, environmental conditions, and changes in whey and cheese-making parameters (
). A recent study demonstrated that milking equipment cleaning procedures can modify the microbial composition of NWC and, consequently, the core microbiome of the cheese (
). In recent years, it has become common practice to add inert sorbents to dairy cattle diets to reduce bioavailability of mycotoxins (
), no information is available about their effects on the microbiota of dairy products.
). Microorganisms present in the environment are transferred to raw milk during and after milking through feces that adheres to the udders (
). Detoxifying agents that may exhibit antimicrobial action (
) can be blended into the feed, pass through the digestive tract of animals, and then be discharged with feces (
). Our hypothesis is that these agents could alter the fecal microbiota and, consequently, the biodiversity of raw milk and whey. Therefore, in this study, we investigated the effect of 2 mycotoxin-detoxifying agents on the microbiota of NWC used in production of a Grana-like cheese.
MATERIALS AND METHODS
Experimental Design and NWC Collection
). Thirty clinically healthy dairy cows in mid lactation were used in experiments conducted in the Eco-Farm Carpaneta experimental center (San Giorgio Bigarello, Mantova, Italy). The animals were divided into 3 groups, each consisting of 10 animals. The first (control group, CTRL) was fed a TMR; the second group (B1) received the CTRL diet (TMR) with the addition of B1 (115 ± 1 g/cow per day corresponding to 5 g/kg of DM in the TMR), and the third group (B2) was fed the TMR with the addition of B2 (115 ± 1 g/cow per day corresponding to 5 g/kg of DM in the TMR). The trial consisted of 2 periods, an initial period for adaptation (7 d) followed by the experimental period (7 d), and it aimed to evaluate the potential effect of the B1 and B2 additives on NWC microbiota. Two separate tests were conducted sequentially: the adaptation period of CTRL and B1 was conducted from d 0 to 7 and the experimental period from d 8 to 14. In the second trial, the adaptation period of the CTRL and B2 groups was conducted from d 15 to 21, and the experimental period from d 22 to 28 (Figure 1).
Bacterial Enumeration and LAB Identification and Typing
). The resulting fingerprints were compared using the BioNumeric 5.0 software package (Applied Maths), using the unweighted pair group method with arithmetic averages (UPGMA). The biotype was defined at a minimum similarity of 90%. The diversity within the LAB isolates was measured by the Simpson (1 − D) and Shannon-Wiener (H′) indices. These indices were calculated based on RAPD-PCR patterns using scripts available in the BioNumerics 5.0 software package (Applied Maths;
). The Simpson index value of diversity ranges between 0 and 1, where a higher value indicates greater biodiversity; the Shannon-Wiener index value is generally between 1.5 and 3.5, and again, a higher value indicates greater biodiversity richness of the community (
Evaluation of Viability of Whey Samples
). Briefly, whey samples were diluted in PBS (NaCl 9 g/L, Na2HPO4 0.421 g/L, KH2PO4 0.144 g/L; pH 7.4), stained with 10 µL of propidium iodide (PI; 0.2 mmol/L in water) and 10 µL of SYTO 24 (0.1 mmol/L in water), and incubated in the dark for 15 min at 37°C. The staining method is based on dual nucleic acid staining with the cell-permeant dye SYTO 24 (fluorescent emission maximum at 515 nm), and cell-impermeant dye PI (fluorescent emission maximum at 620 nm). SYTO 24 permeates the membrane of total cells and stains the nucleic acids with green fluorescence; PI penetrates only bacteria with damaged membranes, reducing the SYTO 24 fluorescence when both dyes are present. Thus, live bacteria with intact cell membranes fluoresce bright green (measured in active fluorescence units, AFU), bacteria with slightly damaged membranes exhibit both green and red fluorescence (damaged cells), whereas bacteria with broken membranes fluoresce red (non-AFU). The samples were analyzed using an Accuri C6 Plus flow cytometer (BD Biosciences), equipped with a 488-nm laser beam excitation.
Acidification Activity of NWC Samples
Detection of LAB Species in NWC Samples by Pentaplex PCR Analysis
). The DNA was directly extracted from 1 mL of NWC according to
. All determinations were performed in duplicate.
RAPD-PCR Fingerprinting Analysis of Whey Samples
. The DNA was directly extracted from 1 mL of NWC according to
, and RAPD-PCR was applied to the total DNA of each whey sample using 4 primers: M13 (
) and OPA-4, OPA-13, and OPA-18 (
). The RAPD-PCR fingerprints were imported into BioNumeric 5.0 (Applied Maths) and analyzed using composite data set and UPGMA cluster analysis.
Genomic DNA Extraction, Library Preparation, and Sequencing
). Bacterial DNA was then amplified using primers (
) that target the V3-V4 hypervariable regions of the 16S rRNA gene. All PCR amplifications were performed as previously described by
) and then joined into single reads using the C++ program SeqPrep (
). After joining, reads were filtered for quality based on (1) maximum of 3 consecutive low-quality base calls (Phred <19) allowed; (2) fraction of consecutive high-quality base calls (Phred >19) in a read over total read length ≥0.75; and (3) no “N”-labeled (missing/uncalled) bases. Reads that did not match all criteria were filtered out. The remaining reads were combined in a single FASTA file for the identification and quantification of operational taxonomic units (OTU). Reads were aligned against the SILVA closed reference sequence collection release 132, with 97% cluster identity (
), applying the Cd-hit clustering algorithm (
). A predefined taxonomy map of reference sequences to taxonomies was then used for taxonomic identification from the main taxa ranks to the genus level (domain, phylum, class, order, family, genus). By counting the abundance of each OTU, an OTU table was created and then grouped at each phylogenetic level; OTU with total counts <10 in <2 samples were filtered out. The whey core microbiota was identified by selecting OTU that were shared by at least 95% of the samples. All the above steps, except the FastQC quality check, were performed with the QIIME 1.9 open-source bioinformatics pipeline for microbiome analysis (
). The command lines and parameters that were used to process 16S rRNA gene sequence were adapted from
Alpha and Beta Diversity Indices and OTU
) OTU table. In addition to the number of observed OTU directly counted from the OTU table, within-sample microbial richness, diversity, and evenness were estimated using the following indices: Chao1 and abundance-based coverage estimator (ACE) for richness; Shannon, Simpson, and Fisher’s α for diversity (
); and Simpson E and Pielou’s J (Shannon’s evenness) for evenness (
). The across-sample microbiota diversity was quantified by calculating Bray-Curtis dissimilarities (
). Details on the calculation of the mentioned α and β diversity indices can be found in
. Analysis of variance was used to test differences between CTRL and B1 and B2 samples in terms of α diversity and OTU abundances at various taxonomic levels. P-values < 0.05 were considered significant. For Bray-Curtis dissimilarities (β diversity), differences were tested nonparametrically using permutational ANOVA (PERMANOVA), with 999 permutations (
), which was also used to estimate most diversity indices. The ACE index and sample-base rarefaction were estimated using custom Python scripts (https://github.com/filippob/Rare-OTUsACE.git) and R (https://github.com/filippob/sampleBasedRarefaction). The plots were generated using the ggplot2 R package (
). Additional data handling and statistical analysis were performed in R (
Acidifying Activity of LAB Isolates and Growth Kinetics
). Streptococcus thermophilus isolates were enumerated on M17 agar 0.5% (wt/vol) lactose (Biolife Italiana), whereas Lb. helveticus were counted on MRS agar (pH 5.4; Biolife Italiana) under anaerobic conditions (Anaerocult A, Merck). Both media were incubated at 42°C for 48 and 72 h.
RESULTS AND DISCUSSION
(n = 4 each)
|LAB in MRS agar||7.7 ± 0.1||7.8 ± 0.1||7.5 ± 0.5||7.5 ± 0.7|
|LAB in M17 agar||7.3 ± 0.2||7.4 ± 0.2||7.8 ± 0.1||7.6 ± 0.3|
|Live cells||8.4 ± 0.9||8.8 ± 0.1||9.1 ± 0.2||9.1 ± 0.2|
|Damaged cells||8.0 ± 0.3||8.2 ± 0.2||8.3 ± 0.2||8.4 ± 0.2|
|Dead cells||8.8 ± 0.2||8.8 ± 0.0||8.6 ± 0.1||8.6 ± 0.2|
|Total count||9.1 ± 0.1||9.2 ± 0.1||9.3 ± 0.1||9.3 ± 0.1|
|Kinetic parameters of acidification|
|Vm (pH/h)||0.69 ± 0.01||0.66 ± 0.02||0.58 ± 0.13||0.60 ± 0.13|
|Tm (h)||3.5 ± 0.2||3.5 ± 0.0||3.7 ± 0.2||3.9 ± 0.3|
|TΔpH (h)||4.6 ± 0.1||4.6 ± 0.1||4.7 ± 0.2||4.8 ± 0.2|
|Lactobacillus delbrueckii ssp. lactis||–||–||–||+|
|Lb. delbrueckii ssp. bulgaricus||–||–||–||–|
). As shown in Table 1, no significant differences were detected among the 3 subpopulations identified (live, damaged, and dead cells), and neither binder affected the total counts of the samples examined (∼9.0 log10 cfu/mL). This result could indicate a nontoxic activity of these compounds on the cheese whey microbiota overall. Values obtained from flow cytometry were ∼1.0 to 1.5 orders of magnitude higher than those obtained by the plate counting technique. Therefore, the differences between flow cytometry data and plate counting are likely due to the presence in the whey samples of viable but nonculturable cells that are not enumerated by the culture method (
) because lactobacilli and streptococci culturable in MRS and M17 are the main components of the whey microbiota (
). Lactobacillus delbrueckii ssp. lactis, one of the most abundant LAB species in NWC, was recovered only in B2 whey samples (Table 1). This result confirms the findings of recent studies (
), which indicate a drastic reduction or absence of Lb. delbrueckii ssp. lactis in Grana Padano whey cultures.
); similar community structures were observed in β diversity for B1 and B2 treatments.
). Bacteroidetes represented the remaining dominant microbiota, increasing from 5% (CTRL) to 13.3% for B1 and from 8.5% (CTRL) to 15.6% for B2 (Figure 3). An increase in Bacteroidetes in ruminal microbiota was also shown by
when dairy cows were fed a TMR containing bentonite clay. Bacteroidetes are present in raw milk and usually come from the bovine environment (feces and teat skin;
). These findings support our hypothesis that addition of detoxifying agents to feed can affect the ruminal and fecal microbiota and, consequently, the microbial composition of raw milk and whey starter. Several other genera of gram-negative microorganisms were detected (Figure 3).
|Species||Biotype||CTRL samples||B1 samples|
|No. of strains||Day||No. of strains||Day|
|Total number of strains||23||24|
|CTRL samples||B2 samples|
|Lb. delbrueckii ssp. lactis||LDL1||—||1||X|
|Total number of strains||22||23|
). Despite this, our results indicated that the mycotoxin-detoxifying agents can affect the LAB biotypes present in NWC, especially with regard to Lb. helveticus. Our hypothesis is that these agents can shift the fecal microbiota and consequently the biodiversity of the raw milk and whey. Thus, in addition to investigating the microbial biodiversity of the different NWC, we evaluated their growth rate and metabolism through their acidifying activity to further explore whether the presence of the 2 additives could differentially inhibit or stimulate individual bacterial strains. The acidification capability of the different biotypes was evaluated in pH-controlled fermentation either in the presence (0.25% wt/vol) or absence of B1 and B2 compounds in reconstituted milk, taking in account the difficulty of growing strains isolated from NWC in synthetic medium. The percentage of detoxifying agent was chosen to be consistent with the concentration of B1 and B2 present in dairy cow feces.
|Source of LAB biotypes||Biotype||TΔpH (h)||GT (h)||MIC (%)|
|CTRL||ST2||7.2 ± 0.0||6.7 ± 0.3||2.1 ± 0.0||2.2 ± 0.1||>0.25|
|ST4||7.0 ± 0.0||6.8 ± 0.1||2.1 ± 0.0||2.1 ± 0.0||>0.25|
|LH2||>20.0||>20.0||3.1 ± 1.0||4.6 ± 0.3||0.25|
|CTRL and B1||ST1||6.7 ± 0.0||5.5 ± 0.0||2.1 ± 0.0||2.0 ± 0.0||>0.25|
|ST3||8.8 ± 0.2||7.0 ± 0.0||2.1 ± 0.0||2.0 ± 0.0||>0.25|
|LH3||17.2 ± 0.2||15.0 ± 0.1||2.9 ± 0.1||2.9 ± 0.2||>0.25|
|B1||LH1||17.6 ± 0.0||15.2 ± 0.0||3.6 ± 0.5||2.5 ± 0.2||>0.25|
|LH4||18.7 ± 0.1||16.8 ± 0.1||2.3 ± 0.1||1.8 ± 0.0||>0.25|
|CTRL||ST4||6.3 ± 0.1||10.7 ± 0.1||2.9 ± 0.1||4.9 ± 0.1||0.13|
|LH5||17.2 ± 0.2||18.3 ± 0.2||3.1 ± 0.1||4.3 ± 0.0||0.25|
|LH6||>20.0||>20.0||2.4 ± 0.0||3.4 ± 0.2||0.25|
|LH7||16.2 ± 0.1||>20.0||3.1 ± 0.3||4.6 ± 0.5||0.13|
|LH8||>20.0||>20.0||3.2 ± 0.0||5.0 ± 0.2||0.25|
|CTRL and B2||ST5||10.2 ± 0.0||>20.0||3.0 ± 0.0||3.1 ± 0.0||>0.25|
|ST6||10.2 ± 0.0||18.5 ± 0.0||3.3 ± 0.1||3.2 ± 0.0||>0.25|
|B2||LH1||14.8 ± 0.1||17.7 ± 0.1||3.8 ± 0.5||2.6 ± 0.1||>0.25|
|LH9||>20.0||>20.0||2.8 ± 0.1||2.1 ± 0.0||>0.25|
|LH10||18.7 ± 0.1||>20.0||3.2 ± 0.2||2.4 ± 0.1||>0.25|
). In this study, GT was calculated to assess the effect of B1 and B2 on LAB growth kinetics. As reported in Table 3, the addition of B1 to the culture medium did not significantly affect the growth of St. thermophilus biotypes. However, B1 slowed the growth of the only Lb. helveticus biotype isolated from the CTRL samples (LH2), whereas it did not affect the LH3 strain recovered from both CTRL and B1 whey cultures and significantly stimulated the growth of biotypes LH1 and LH4 isolated only from the B1 NWC (P < 0.05; Table 3). A similar scenario was observed using B2 (Table 3). The addition of B2 to the culture medium significantly (P < 0.05) increased the GT values of all strains recovered from CTRL samples and promoted the growth of the Lb. helveticus strains found in B2 whey cultures. In addition, B2 did not affect the growth of strains isolated from CTRL and B2 NWC. As reported in Table 3, we observed a poor correlation between the variation in acidifying activity and growth rate. These discrepancies were probably due to the composition of B1 and B2 and their buffering power.
reported that a variety of physical and chemical processes can confer an antibacterial effect on clays. It is known that clay minerals can adhere by physical attraction to cell walls, hampering the passive and active uptake of essential nutrients and thus, changing the bacterial metabolism. Alternatively, clays may deprive bacteria of essential nutrients by adsorption. Moreover, environmental conditions can affect the potential bacteriostatic activity of these; specifically, at pH <5.0 or >9.0, many metals present in hydrated clay minerals become soluble, increasing the oxidation state (>400 mV), which affects bacterial defenses (
). Polyphenols from lignin and their metabolites can act as activators or inhibitors of bacterial growth. Studies conducted in humans have shown that these compounds can stimulate the development of Lactobacillus in the gut, thus changing the gut microbiota composition (
). Nevertheless, how these compounds affect bacterial development remains partly unknown but seems related to their chemical structure and concentration. Previous studies also reported that the effect of polyphenols on LAB growth was strain dependent (
), and the addition of these compounds to skim milk can improve the acidification activity of LAB strains (
). Humic acids present in B2 could also promote or hinder bacterial growth. At the present time, no literature is currently available on the effect of humic acids on LAB strains, because these compounds have been tested only against soil bacteria and pathogenic species. In this case, the chemical composition of the humic acids was considered a key factor determining the positive or negative effect on bacterial growth (
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