1. Introduction
Goat cheese is a key element in the Mediterranean diet and is among the most frequently consumed dairy products globally. The texture, flavor, and organoleptic properties of the cheese depend on several factors, including (but not limited to) the cheesemaking process, the animal breed, and the breeding management. Emerging evidence underlines the pivotal role of the microbiota, and its continuous evolution, in the conditioning of a cheese’s characteristics. The complex microbial diversity harbored in the milk converts its components, mainly carbohydrates and proteins, into secondary products and/or substrates that further trigger the growth and metabolism of microorganisms. This results in the continuous restructuring of the microbiota and the accumulation of myriads of molecules of microbial origin constituting the cheese mass, such as fatty acids, volatile organic compounds (VOCs), amines, ketones, free amino acids, phenols, alcohols, aldehydes, lactones, and sulfur compounds [
1,
2,
3,
4]. On the other hand, the presence of harmful elements, either of animal or environmental origin, poses health and hygiene problems. As of today, most commercial and large-scale cheese factories carry out milk treatment procedures (e.g., thermization and pressurization) to standardize the milk quality and drastically reduce the milk’s microbial load and diversity [
5]. In contrast, the great majority of traditional dairy products are still produced using raw milk [
6], thus benefitting from a high level of microbial biodiversity operating across the stages of the cheesemaking process. Nevertheless, higher hygienic standards and precautions are required throughout the whole production process [
6,
7].
Milk and cheese act as the “point of contact” between animal, human, and environmental health; therefore, the accurate assessment of the microbiota contained in these products is an important tool of One-Health relevance, besides being of great importance in other respects, such as biosafety, technological processing, and nutritional and nutraceutical value. The rapid advance of the metaomics discipline has enabled the detailed characterization of the microbial communities harbored by virtually all ecological niches. Metagenomic and metabolomic investigations are the most commonly employed approaches in cheese microbiota research, providing information on the composition and genetic potential of the sampled microbial community along with the overall array of metabolites produced by the consortia of microorganisms associated with cheese. The supplementation of this information with metaproteomics is desirable, as it would provide valuable information on the protein repertoire and the biochemical pathways being effectively implemented by the microbiota components under the sampling conditions. Nevertheless, metaproteomic investigations of milk by-products are rare owing to the technical difficulties that prevent the common adoption of the metaproteomics approach in the characterization of cheese microbiota. To the best of our knowledge, only a few studies available in the literature have employed metaproteomics for the investigation of cheese microbiota [
8,
9,
10], and no metaproteomics studies are available on raw goat milk cheese.
Considering the potential of studying cheese microbiota and the paucity of cheese metaproteomics studies, we conducted 16S rRNA gene sequencing and a metaproteomics study to obtain a comprehensive picture of a typical raw goat cheese microbiota in terms of both composition and activity. This is the first metaomics-based study of a typical raw goat milk cheese investigating the microbial community dynamics on the rind and in the core of the cheese wheel across different ripening periods. Moreover, we provide insights into the microbial interactions occurring among the naïve and environmental bacteria and biochemical strategies for guaranteeing the biosafety of typical raw milk by-products.
3. Discussion
The microbial consortia of milk and its by-products are important bioindicators of animal health and the microbial exchange occurring through the human–animal–environment network. The fine orchestration of microbial metabolic functions is the foundation of the cheesemaking technological process, including the development of the gustatory and/or olfactive nuances peculiar to each cheese and the maintenance of the biosafety of dairy products. Most typical cheeses are made with raw, unprocessed milk carrying a high level of microbial diversity, whose importance is still largely debated. On the one hand, employing milk rich in microbial biodiversity enables the control of food biosafety along with the development of unique characteristics through the exploitation of the variable and versatile arrays of metabolic routes. Contrary to this, a higher level of microbial diversity might involve pathobionts and/or spoilage organisms; thus, a reduction in the naïve milk-associated microbial flora is thought to be key for guaranteeing the quality and biosafety of milk and its by-products [
6,
11]. Although both arguments are scientifically sound, the myriad of variables influencing both the structural and functional networks of the microbiota make the effect of the microbial consortia on various aspects of the cheesemaking process unpredictable. This prompted us to conduct a thorough investigation into each microbiota to elucidate how the microbial interconnections were shaped throughout the experimental duration.
In this study, we conducted the first metaomics survey of the microbiota associated with a traditional cheese made with raw goat milk. The cheese selected as the study model was Caprino Nicastrese, an artisanal goat cheese produced in Calabria, in the south of Italy. To the best of our knowledge, two studies have so far been performed implementing “pre-omics” approaches to evaluate the presence of selected bacterial specimens [
12,
13], but a comprehensive analysis of the typical goat cheese microbiota is lacking. The sampling strategy we adopted relied on our initial hypothesis that cheese regions with diverse physicochemical features (e.g., oxygen availability) would host different microbial communities, whose overall metabolism was likely pivotal to specific aspects of the cheese. The ripening timepoints selected in our study mirrored the variants of the cheese that are currently sold (i.e., Caprino Nicastrese ripened at 30, 60, and 90 days).
Both DNA and protein datasets depicted distinct microbiotas in the rind and core of the cheese wheel. The core-associated microbiota was characterized by the emergence of new bacterial families (e.g.,
Brevibacteriaceae and
Micrococcaceae) along with the increased abundance of other bacterial families such as
Lactobacillaceae and
Paenibacillaceae. Altogether, this elucidates how the ecological niche (i.e., cheese rind or core) shapes the microbiota architecture and microbial metabolism to transform the dairy product and preserve it from spoilage. Our observations diverged from other descriptions of the microbiota of this cheese, which identify
Lactobacillus spp. and
Enterococcus spp. as the most abundant organisms [
12,
13]. These variations in microbiota composition were primarily attributed to the different investigation methods. Pino and colleagues [
12] employed culture-dependent methods, which intrinsically overestimate the most common bacterial organisms at the expense of others. Additionally, farm-to-farm variability is to be expected due to the lack of strict production specifications and the changing environmental variables that influence the microbiota composition [
12]. Moreover, this previous investigation did not distinguish between core and surface microbiota while assessing the microbial consortia composition. As the current study shows, the different cheese-wheel depths are associated with specific microbiota compositions and functions. Altogether, these factors mean that the studies are not easily comparable; rather, an integration and complementation of the outcomes should be considered.
Different pictures of the general microbiota composition were drawn by the 16S rRNA gene-sequencing and metaproteomics approaches. For instance, the family
Streptococcaceae was identified as the most abundant in the core and rind microbiota by the DNA-based approach, whereas the most abundant protein profile belonged to the family
Bacillaceae, regardless of the cheese-wheel depth. Additionally, the taxonomic assessment by metaproteomics identified a higher bacterial heterogeneity at the family level than 16S rRNA gene sequencing. The variations in the observations within the present study stemmed from the different principles these methods are based on. Each method targets different biological macromolecules and thus presents diverse technical drawbacks [
14]. In addition, metaproteomics enables the identification of a higher level of bacterial complexity, since the changes in the abundance of expressed proteins are detected earlier than the changes in the number of DNA copies targeted by 16S rRNA gene sequencing [
14,
15,
16,
17].
Surprisingly, a stable microbiota composition was described by both 16S rRNA gene sequencing and metaproteomics in the samples stratified according to the ripening timepoint. This observation was unexpected. In light of this, we believe that the major structural rearrangements occurred in the early stages of the cheesemaking process (i.e., before 30 days, which was our earliest sampling timepoint), and that only minor reshaping took place after the “microbiologically driven” ripening stage, resulting in no statistically significant differences in the microbial consortia composition across the sampling timepoints. An alternative/complementary interpretation of this outcome would support the slow and continuous shaping of the microbiota, so that only a longer ripening window could highlight any statistically significant structural changes. This view is also supported by a recent study performed on Cheddar cheese made from raw milk. Here, the major shaping of the microbial consortia occurred in the very early stages of the cheese production, and a relatively stable microbiota composition was reported over the next 26 weeks of ageing [
18]. Another recent study on raw goat cheese ripened for 5–15 days reported a stable microbiota composition, again supporting the notion of a slow-but-continuous shaping of the microbial consortia [
19].
The functional characterization of the microbial communities was in agreement with the previous observations made by grouping the samples according to the cheese-wheel depth only. This clustering was entirely supported by the functional categorization of the protein repertoire, underlining the different functional concerns of the microbiota harbored in the rind and core of the cheese wheel.
The ontology of the rind protein repertoire identified a microbial community mostly involved in the maintenance of food biosafety by preventing the cheese surface from being colonized by foreign microbial organisms such as pathobionts or spoilage bacteria. This microbial consortium was, indeed, involved in “cellulose biosynthesis”, a biological process carried out by aerobic acetic acid bacteria that perform the oxidative fermentation of a variety of sugar substrates and, once they have exhausted the lactose as the main carbon source, produce cellulose as a by-product [
20]. The bacterial cellulose produced on the rind surface “wraps” the dairy product, providing physical support and facilitating symbiotic interconnections among the microorganisms that preserve the food from colonization by external microorganisms [
21]. In line with the above, the overexpression of the “antibiotic catabolic process” supported the occurrence of competition mechanisms between the naïve and environmental flora, suggesting that the bacterial families encoding for this biological process had experienced a chemical attack. Additionally, the increased abundance profile of proteins related to the “glutamine metabolic process” might have indicated the involvement of the rind microbiota in the biosynthesis of nitrogen-containing compounds, to which are attributed several physiological and technological functions, such as antimicrobial properties and the development of typical organoleptic features [
22].
In comparison to the rind microbiota, the microbial community in the core of the cheese wheel was more heterogeneous, in both structural and functional terms. The bacterial involvement in the maintenance of product biosafety remained, although other biochemical routes were employed. In addition, the core microbiota seemed focused on more complex and diverse biological functions, ranging from the conservation of bacterial metabolism to the array of processes involved in the development of the so-called “added values” of typical cheeses in both nutraceutical and sensorial terms. The metabolic processes performed by the core microbiota indicated the greater participation of this microbial community in biological processes such as DNA replication, protein biosynthesis, and cellular respiration. The latter is interestingly represented by the case of H
2O
2 catabolic processes. Besides the well-known role of hydrogen peroxide in microbial interactions [
23,
24], it is also one of the major metabolic by-products of many lactic acid bacteria [
25], as these often lack respiratory chains and opt to reduce molecular oxygen to recycle NAD+ from NADH, with increased energetic yield as compared to the classical fermentation process. Analogous cases of hybrid metabolism have been recently reported by Marco et al. [
26] in
Lactobacillus plantarum, a microorganism with a pivotal role in fermented food production technology. Here, the authors described how combining features of respiration and fermentation would improve lactic acid bacteria function, thus enhancing product biosafety and quality [
26]. The resistance of lactic bacteria to hydrogen peroxide is granted by the absence of oxidant-sensitive dehydratases and mononuclear Fe(II) enzymes [
25,
27]. Instead, the extensive involvement of the core microbiota in the biosynthesis of Fe(III)-chelating substances produced by aerobic or facultatively anaerobic bacteria (i.e., siderophores) suggests the activation of hybrid metabolism in the core microbial consortium, although tailored investigations would be necessary to confirm this unconventional metabolic route.
The core microbiota was also focused on biological processes linked to the development of the typical nutraceutical and gustatory characteristics of the dairy product, as was supported by the overall involvement of the microbiota in the biosynthesis and/or transformation of a variety of proteins, lipids, and amino acids. Specifically, the way in which bacterial activities related to lipid metabolism and fatty acid biosynthesis are linked to improved organoleptic properties in cheese and dairy products has already been described [
28,
29]. In addition, the microbiota involvement in the “arginine biosynthetic process via ornithine” indicated the continuous control exerted by the whole microbiota composition on the development of nutraceutical features, considering the role of ornithine in the production of bacteriocins and natural antibiotics. Additionally, arginine affects a variety of human physiological processes, such as growth/tissue repair, immune support, and cellular communications [
30]. Moreover, the microbiota engagement in the 7,8-dihydroneopterin 3’-triphosphate biosynthetic process was suggestive of the production of B-group vitamins and folate, whose health-promoting effects range from anticarcinogenic activity to a reduced risk of cardiovascular diseases [
31,
32]. In line with biopterin production, the thiamine production further supported the beneficial effects exerted by the core-associated microbial community on the cheese organoleptic and health-promoting characteristics [
33,
34].
4. Materials and Methods
4.1. Cheese Samples and Experimental Design
The present work explored the microbial community associated with a typical raw goat milk cheese. Caprino Nicastrese cheese was employed as the study object, as an example of a traditional raw goat milk cheese. Following collection, the raw goat milk was coagulated for 60 min at 36 °C using 0.4 g/L goat rennet and without the addition of any starter culture. The resulting curd was manually cut into rice-sized pieces, shaped, and stored at room temperature for 48 h to drain out the residual whey. Cheese wheels were then salted for 24 h in brine with 30% (w/v) NaCl. Finally, the cheese was ripened in wooden axis in the storage basement of the cheese farms at 10–15 °C and 70–85% humidity.
Samples from the surface and inner mass (i.e., rind and core, respectively) of the cheese wheels were aseptically collected with a sterile knife from 30-, 60-, and 90-day-ripened cheese wheels. Biological replicates were sampled as defined in
Figure 7 and transported on ice to the laboratory for the subsequent isolation and analysis of the harbored microbiota.
Bacterial fractions were isolated from the rind and core of the cheese wheel at 30, 60, and 90 days of ripening. Pooling was performed, with each pool including bacterial extracts from three samples. Eighteen pools were subjected to 16S rRNA gene sequencing, 9 from the rind and 9 from the core. Each depth was composed of 3 sample pools taken from cheese wheels ripened for 30, 60, and 90 days. The metaproteomics survey relied on a total of 10 sample pools, 4 for the rind and 6 for the core depth. Two pools were considered from each ripening timepoint of the core depth, whereas two pools were excluded in the rind groups due to technical issues encountered during the analytical workflow. Specifically, one pool was omitted from the pool at 60 days and one from the group at 90 days of ripening.
4.2. Bacterial Fraction Enrichment
To avoid alterations to the microbiota composition and/or activity, all the steps of the bacterial fraction enrichment were performed at 4 °C, with the temperature kept under strict control. Briefly, independent aliquots of 0.5 g of each biological replicate per sample type were finely grated and homogenized with 15 mL buffer containing 50 mM Na
2HPO
4 and 0.1% Tween 80 at pH 8.0. Samples were then shaken on an orbital shaker at 1600 rpm for 10 min. Following this, the samples were centrifuged for 20 min at 2500×
g. The supernatant was collected in a new tube and subjected to four more rounds of shaking/centrifuge/resuspension, whereas the pellet from each step was gently resuspended and collected in a single clean “pool vial”. The “pool vial” was finally centrifuged at 12,000×
g for 20 min, resulting in the collection of a bacterial pellet from an original amount of 0.5g cheese aliquots [
1,
14,
35].
The enriched bacterial fractions represented the common starting point for the two analytical approaches employed in the present study: 16S rRNA gene sequencing and metaproteomics (
Figure 7).
4.3. 16S rRNA Gene Sequencing and Metataxonomic Analysis
DNA Extraction and Library Preparation
Cheese DNA was extracted from 9 rind and 9 core samples, respectively, 3 for each ripening time point (Figure. 7) according to the EZ1 DNA Tissue protocol (Qiagen, Hilden, Germany). Starting from 40 mg, 190 µL of buffer G2 and 10 µL of proteinase K solution were added to each sample aliquot, before incubation at 56 °C in an Eppendorf® Thermomixer until complete sample lysis, with vortexing 2–3 times per hour to disperse the sample. Two hundred microliters of supernatant were transferred to a new 2 mL sample tube, and the automated EZ1 extraction was finalized. The amplification of the V3–V4 variable region from the bacterial 16S rRNA gene (∼460 bp) was carried out using the primers 16S_F 5′-(TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG)-3′ and 16S_R 5′-(GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C)-3′, according to the MiSeq rRNA Amplicon Sequencing protocol (Illumina, San Diego, CA, USA). The PCR reactions were set up using 2 × KAPA Hifi HotStart ready Mix kits (KAPA Biosystems Inc., Wilmington, MA, USA). DNA amplicons were cleaned up using CleanNGS kit beads (CleanNA, Waddinxveen, The Netherlands). A second amplification step was performed to obtain a unique combination of Illumina Nextera XT dual indices for each sample. The final libraries were cleaned up using CleanNGS kit beads; quantified by a Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA); and normalized to 4 nM. To generate 250 × paired-end 2 bp length reads, normalized libraries were pooled together and run on the Illumina MiSeq platform, according to manufacturer’s specifications.
4.4. Biocomputational and Statistical Analysis for Cheese Microbiota Profile Analysis
QIIME2 was used to analyze the paired-end sequencing reads [
36]. Quality control, denoising, chimera removal, trimming, and the construction of the amplicon sequence variant (ASV) table were performed by the means of DADA2, implemented as a plugin in QIIME2 [
37]. The taxonomy was assigned using a Naive Bayes model pre-trained on SILVA through the QIIME2 plugin q2-feature classifiers [
38]. Alpha and beta diversity were computed by skbio.diversity using analysis of variance (ANOVA) and permutational analysis of variance (PERMANOVA), respectively; the latter was applied on phylogenetically informed weighted and unweighted UniFrac and Bray–Curtis distance matrices [
39] with 9999 permutations to perform a paired comparison of the rind and core samples at different timepoints. Principal coordinate analysis (PCoA) plots were used to illustrate the beta diversity of samples. The ASV table was normalized using the cumulative sum scaling (CSS) method [
40]; hence, the Kruskal–Wallis test was applied to compare taxonomic differences at the phylum (L2), family (L5), and genus (L6) levels. Python 3.7 was used to perform ecological statistical analyses. Three different levels of statistical significance were identified based on different
p values (
p ≤ 0.001) and false-discovery rate (FDR) thresholds (
p ≤ 0.05,
p ≤ 0.001) [
41]. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) [
42], employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO) database, was used to determine ASVs and their microbiome functions. In addition, LEfSe (linear discriminant analysis effect size) was independently used to determine the features most likely to explain the differences between the rind and core of the cheese wheel at 30, 60, and 90 days of ripening.
4.5. Metaproteome Extraction and Quantification
Bacterial pellets obtained via the above-described bacterial fraction enrichment protocol were resuspended in protein extraction buffer (7M UREA, 2M Thiourea, 4% CHAPS) and subjected to 6 cycles of 1 min bead beating (Minilys, Bertin Tecnologies, Montigny-le-Bretonneux, France), interspersed with 1 min resting on ice. Bead-beating steps were performed by shaking each sample at 4000 rpm with an equal amount (1:1 v/w) of 0.1 mm zirconium silica beads. Following bead beating, the samples were heated up to 60 °C for 10 min and centrifuged for 20 min at 12,000× g and 4 °C. The supernatant containing the extracted metaproteome was collected in a clean tube and further processed for the metaproteomic analytical workflow.
Extracted proteins were quantified using Bio-Rad Protein Assay Dye Reagent Concentrate (Bio-Rad, Hercules, CA, USA) following the manufacturer’s instructions. Approximately 50 μg of the extracted proteins was precipitated by incubation (30 min at 4 °C) with precooled 20% trichloroacetic acid (TCA) and kept for further processing.
4.6. Trypsin Digestion and Mass Spectrometry Analysis
Precipitated proteins were digested in solution. Briefly, 50 μg of total proteins for each sample was treated for disulfide bond reduction with 10 mM DTT for 1 h at +37 °C and alkylated with 20 mM IAA at +37 °C for 1 h in the dark. Iodoacetamide excess was removed by the incubation of the sample with 1.61 mM DTT at +37 °C for 20 min. Sample digestion was carried out overnight at +37° C using trypsin in a 1: 50 (
w/
w) ratio with respect to the protein content. Enzymatic digestion was stopped by the addition of 0.1% FA (
v/
v). Tryptic peptides were purified and desalted using self-assembled C18 Stage Tips [
43]. Tips containing the C18 membranes with the bounded peptide mixture were eluted with 5% acetonitrile (5% ACN/0.1% TFA), dried in the vacuum centrifuge, and stored at −20 °C until mass spectrometry measurements.
Prior to MS/MS measurement, the dried peptide mixture was suspended in 0.1% FA and loaded onto a precolumn Acclaim PepMap100 C18 (5 μm, 100 Å, 300 μm i.d. × 5 mm) (Thermo Scientific, San Jose, CA, USA). Following 5 min of trapping, operating at 10 μL/min in eluent A, peptides were separated by an Easy-Spray PepMap C18 column (2 µm 100 Å 15 cm × 50 µm ID) with a Thermo Scientific Dionex UltiMate 3000 RSLC nano system (Sunnyvale, CA, USA).
Analyses were performed using an aqueous solution of FA (0.1%, v/v) as eluent A and ACN/FA (99.9:0.1, v/v) as eluent B in the following gradient elution: (i) 5% of eluent B (7 min); (ii) from 5 to 35% of eluent B (113 min); (iii) from 35 to 99% of B (15 min); (iv) 99% of B (10 min); (v) from 99 to 5% of B (2 min); (vi) 5% of B for column conditioning (13 min). The column was kept at 35 °C and operated at a flow rate of 300 nL/min; the injection volume was set at 5.0 μL.
Peptides were directly eluted into Orbitrap Elite nanoESI-MS/MS (Thermo Fisher Scientific, Waltham, MA, USA). Tandem mass spectrometry measurements were performed in positive full-scan acquisition mode in the 350–2000 m/z range and with a resolution power of 60,000. The nanoESI tuning parameters were as follows: capillary temperature 250 °C, source voltage 1.5 kV, sheath gas 0, auxiliary gas 0, and S-lens RR level 50%. MS/MS analyses were performed in data-dependent scan (DDS) mode by selecting and fragmenting the twenty most intense multiple-charged ions of the collected full-scan spectra using collision-induced dissociation (CID, 35% normalized collision energy) with a resolution power of 60,000. Only precursors with a charge state of 2–7 and an intensity above the threshold of 5 × 103 were collected for MS/MS. The DDS parameters were set as follows: exclusion mass width relative reference mass in the low and high range 10 ppm, minimum signal threshold (counts) 500, default charge state 2, activation time 10 ms [
44].
4.7. Bioinformatics Data Analysis and Data Integration
4.7.1. Protein Identification and Quantification
MS raw spectra were processed through Proteome Discoverer and MaxQuant software following a two-step database-dependent search (DDS) approach, as reported previously [
15]. Briefly, raw files were first processed by Thermo Proteome Discoverer software (v.2.2) and searched against the UniProt KB bacteria database. Methionine oxidation was set as the variable modification and the carbamidomethylation of cysteine as the fixed modification. The SequestHT node thresholds were set to “automatic”, and a filter considering only entries with at least one peptide per protein was chosen. All other filters and settings of the software were kept as default, including protein grouping with peptide confidence set as “high” and a delta Cn of 0.1. The percolator node supporting a strict maximum parsimony principle was activated with a false discovery rate of 1%.
The first DDS enabled the assessment of the microbial community composition at the family level, leading to the construction of a smaller in-house database accounting for the bacterial families identified in both the metaproteomics and 16S rRNA gene-sequencing investigations. The customized database was employed in the second DDS of the MS raw data performed on MaxQuant (v 1.6.17.0) set to LFQ modality for peptide identification and protein inference and quantification. Cysteine carbamidomethylation was set as the fixed modification and methionine oxidation as the variable modification. Two missed cleavage sites were allowed for in-silico protease digestion, and peptides had to be fully tryptic. All other parameters of the software were set as default, including peptide and protein FDR < 1%, at least 1 peptide per protein, precursor mass tolerance of 4.5 ppm after mass recalibration, and a fragment ion mass tolerance of 20 ppm. The mass spectrometry proteomics data were deposited into the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD032280.
4.7.2. Ecological and Functional Characterization of Microbiota by Metaproteomics
Information on the taxonomic composition of the microbiota, as assessed by the identified protein repertoire, was gathered from the protein annotation of the UniProt KB database, whereas the quantitative microbiota composition was determined based on the LFQ intensities relative to each bacterial member on a family basis. The logarithmic transformation of the cumulative intensities on a family basis was accomplished while comparing the microbiota composition in the diverse sample groups.
Identified protein repertoires were functionally categorized into biological processes and molecular functions according to the Gene Ontology (GO) data repository. Abundance profiles of the identified proteins (LFQ values) were subjected to statistical investigation using Primer7 v.7 statistical software (PRIMER-E, Plymouth, UK). Principal component analysis (PCO) was conducted based on the square root transformation of the protein LFQs. Statistical differences across the samples were calculated by performing ANOVA and PERMANOVA. Parametric T-tests assessing the discriminating role of the bacterial families in the microbiota composition were conducted, and the results were visualized in iMetalab using shiny apps (
https://shiny.imetalab.ca/). Linear discriminant analysis effect size (LEfSe) was calculated using the galaxy platform (
https://usegalaxy.org). Heat maps visualizing microbial community composition across the samples and the functional classification of the identified proteins were drawn using heatmap.2, provided by the gplots package implemented in R v.4.2.0 software (
http://www.R-project.org). Correlation analysis was performed through the corrplot package implemented in R v.4.2.0 software (
http://www.R-project.org).