Ten samples of inoculated PDHM (hereafter named as IM1 to IM10) were obtained by adding ten different fresh PM samples (hereafter named as PM1 to PM10) at 10% (v/v). Then, the IM samples were incubated at 37 °C for 4 h. The PDHM and IM samples were collected and analyzed at different time points: at the baseline (T0), and 2h (T1) and 4h (T2) after inoculation. The adopted experimental design is summarized in Fig. 1. Microbiological and metataxonomic analysis were performed on PDHM and PM samples at baseline and on PDHM and IM samples at T1 and T2. Peptidomic analysis was conducted on the same samples except for T1.
Mothers who delivered prematurely between November 2018 and January 2019 at the Neonatal Intensive Care Unit (NICU) of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy), were included in the study. Eligible criteria were: delivery before the 37th week of gestational age and absence of maternal antibiotic therapy at the time of the milk collection. Basic maternal and infants’ clinical characteristics were collected using the computed medical records.
Term-delivering mothers, included in this study, donated their milk to the HMB of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, according to HMB Italian guidelines (13). Written informed consent was obtained for each participating mother. The study was approved by Ethical Committee of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Approval nr. 289_2017).
Preterm mother’s own milk collection
Preterm-delivering mothers, prior to milk collection, had to perform an accurate hand washing using a hand sanitizer and a breast washing, using exclusively running water, in accordance with the NICU internal procedure. Each sample was collected, using an electric breast pump of breastfeeding room and a personal breast pump kit, into a sterile bottle. Breast pump kit had to be cleaned and sterilized before every collection.
Enrolled preterm-delivering mothers collected a sample of 50 mL of their own fresh milk between the 30th and the 60th day post-delivery. All PM samples were collected during the first hours of the morning at the breastfeeding room of the NICU, immediately refrigerated and delivered to the laboratories for analyses. PM3 and PM5 samples were obtained from the same mother in different days.
Donor HM collection and pasteurization
Term delivering mothers, belonging to the HMB, collected their milk at home following the
the same instructions of mothers who delivered preterm, in terms of personal hygiene and material disinfection. Each sample was collected using an electric breast pump and a personal breast pump kit, and placed into sterile bottle. Breast pump kit had to be cleaned and sterilized before every collection.
Donor HM was stored at donors’ home in a refrigerator for maximum 24 h and thereafter frozen at -20° C. During the transport, donor HM was preserved by maintaining the cold chain until the arriving to the HMB.
Donor HM samples, used to perform inoculation, were collected by donors in different periods of lactation. These samples were thawed, pooled (total volume of 3 L) and then Holder pasteurized at 62.5 °C for 30 min. PDHM was stored at -80° C until inoculation with fresh PM samples.
Inoculation of PDHM with PM
To perform the inoculation, frozen PDHM was gently thawed and 100 mL and inoculated with 10 mL of each fresh PM sample. The low milk volume expressed by preterm-delivering mothers, which necessarily must be used to breastfeed their babies, did not allow to consider higher inoculation level.
After inoculation, PDHM and IM samples were kept at 37 °C for 4 h (incubation time).
PDHM and PM were analyzed immediately after their arrival in the laboratory. PDHM and IM samples were analyzed after incubation at different time points too, as described in the experimental design (Fig. 1).
Enumeration of microorganisms
Serial decimal dilutions of the inoculated aliquots in sterile quarter-strength Ringer’s solution (Scharlab, Barcelona, Spain) were prepared, and the following microbiological determinations were carried out. Mesophilic aerobic bacteria were counted on Petrifilm Aerobic Count Plate (3M, Minneapolis, MN, USA) after incubation at 30 °C for 72 h (ISO 4833-1:2013). Enterobacteriaceae were enumerated on Petrifilm Enterobacteriaceae Count Plate (3M) at 37 °C for 24 h (ISO 21528-1:2017). Coliforms and Escherichia coli were determined on Petrifilm E. coli/Coliform Count Plate (3M) at 37 °C for 24-48 h. De Man–Rogosa–Sharpe (MRS) agar (Biolife Italiana, Milan, Italy), M17 agar (Biolife Italiana) and Kanamycin Aesculin Azide (KAA) agar (Scharlab) were used for the enumeration of rod-shaped lactic acid bacteria (LAB), cocci LAB (lactococci and streptococci) and enterococci, respectively. MRS agar was incubated at 37 °C for 72 h under anaerobic conditions (AnaerocultA, Merck, Darmstad, Germany), while M17 and KAA agar were kept at 37 °C for 48 h. TOS-propionate agar (Sigma-Aldrich, St. Louis, MO, USA) with MUP selective supplement (Sigma-Aldrich) incubated at 37 °C for 72 h under anaerobic conditions (AnaerocultA, Merck) was used to count Bifidobacterium spp. (14), whereas P2 agar (peptone, 5 g; beef extract, 3 g; yeast extract, 5 g; sodium lactate, 1 g; agar, 15 g/L) was used for anaerobic enumeration of Propionibacterium spp. Cultivating at 30 °C for 7 days (15). Chloramphenicol Glucose Yeast Extract agar (Sacco Srl, Cadorago, Italy) after incubation at 25 °C for 5 days was used to culture yeasts (16). Pseudomonas agar (Biolife Italiana) with PP Pseudomonas supplement (Biolife Italiana) kept at 30 °C for 48 h (17) and Bacillus cereus agar base (PEMBA) agar (Biolife Italiana) with Bacillus cereus Antimicrobic Supplement (Biolife Italiana) incubated at 30 °C for 24 h (18) were used for the detection of Pseudomonas spp. and Bacillus cereus, respectively. Baird Parker (BP) agar (Biolife Italiana) with RPF Supplement (Biolife Italiana) was used for coagulase-positive and negative staphylococci counting after incubation at 37 °C for 48 h (19). At any sampling time, PDHM sample was also analysed for detection of mesophilic aerobic bacteria, Enterobacteriaceae, coliforms, E. coli, yeasts, Pseudomonas spp., coagulase-positive and negative staphylococci and B. cereus.
Search for Listeria monocytogenes and Pseudomonas aeruginosa
Listeria monocytogenes was searched in all IM and PDHM samples by SureFast® Listeria monocytogenes PLUS real-time PCR (RT-PCR) assay (R-Biopharm, Darmstadt, Germany) according to the manufacturer’s instructions. The RT-PCR amplification reactions were performed on an Eco Real-Time PCR System (Illumina, San Diego, CA, USA). Detection of Pseudomonas aeruginosa was performed in samples where the presence of Pseudomonas spp. had been detected by microbiological cultivation method. The adopted protocol was as follows: ten colonies from PP agar plates were randomly picked and sub-cultured overnight in Brain Heart Infusion (BHI) broth (Scharlab) at 30 °C. After growth, the DNA was extracted by the MicroLYSIS kit (Clent Life Science, Stourbridge, UK). The identification of isolates was carried out using P. aeruginosa specific primer as previously reported in Cremonesi et al. (20).
Search for Staphylococcus aureus virulence and enterotoxin genes
Staphylococcus aureus virulence and enterotoxin genes were explored in samples found to be contaminated with coagulase-positive staphylococci. The DNA was extracted from 1 mL of breast milk as previously described by Cremonesi et al. (21). The extracted DNA was amplified by a multiplex PCR for the detection of genes encoding for the coagulase (coa) and thermonuclease (nuc) regions and for the main staphylococcal enterotoxins (sea, sec, sed, seg, seh, sei, sej and sel) according to Cremonesi et al. (22).
Five mL of milk sample were centrifuged at 500 g for 10 min at 4 °C; the supernatant was discarded, and the pellet was washed with one mL of saline solution (0.9% NaCl) and centrifuged at 500 g for 5 min at 4 °C. The supernatant was discarded, and the bacterial DNA was extracted from the samples as described previously (23), by using a method based on the combination of a chaotropic agent, guanidium thiocyanate, with silica particles, to obtain bacterial cell lysis and nuclease inactivation. DNA quality and quantity were assessed using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The isolated DNA was then stored at -20 ˚C until use.
Metagenomic library preparation and sequencing
Bacterial DNA was amplified using the primers described by Caporaso et al. (24), which targeted the V3-V4 hypervariable regions of the 16S rRNA gene. All PCR amplifications were performed in 25 μL volumes per sample. A total of 12.5 μL of Phusion High-Fidelity Master Mix 2× (Thermo Fisher Scientific, Waltham, MA, USA) and 0.2 μL of each primer (100 μM) were added to 2 μL of genomic DNA (5 ng/μL). Amplification was performed in an Applied Biosystem 2700 thermal cycler (Thermo Fisher Scientific) using the amplification cycle as follows: samples were denatured at 98 ˚C for 30 s, followed by 25 cycles with a denaturing step at 98 ˚C for 30 s, annealing at 56 ˚C for 1 min and extension at 72˚C for 1 min, and a final extension at 72˚C for 7 min. Amplicons were cleaned with Agencourt AMPure XP kit (Beckman Coulter, Brea, CA, USA) and libraries were prepared following the 16S Metagenomic Sequencing Library Preparation Protocol (Illumina, San Diego, CA, USA). The libraries obtained were quantified by RT-PCR with KAPA Library Quantification Kit (KapaBiosystems, Cape Town, South Africa), pooled in equimolar proportion and sequenced during a single MiSeq (Illumina) run with 2×250-base paired-end reads.
Bioinformatics and Statistical analysis
Demultiplexed paired-end reads from 16S rRNA-gene sequencing were first checked for quality using FastQC (25) for an initial assessment. Forward and reverse paired-end reads were joined into single reads using the C++ program SeqPrep (26). After joining, reads were filtered for quality based on: i) maximum three consecutive low-quality base calls (Phred < 19) allowed; ii) fraction of consecutive high-quality base calls (Phred > 19) in a read over total read length ≥ 0.75; iii) no ”N”-labeled bases (missing/uncalled) allowed. Reads that did not match all the above criteria were filtered out. All remaining reads were combined in a single FASTA file for the identification and quantification of OTUs (operational taxonomic units). Reads were aligned against the SILVA closed reference sequence collection release 123, with 97% cluster identity (27; 28) applying the CD-HIT clustering algorithm (29). A pre-defined taxonomy map of reference sequences to taxonomies was then used for taxonomic identification along the main taxa ranks down to the genus level (domain, phylum, class, order, family, genus). By counting the abundance of each OTU, the OTU table was created and then grouped at each phylogenetic level. OTUs with total counts lower than 10 in fewer than 2 samples were filtered out. All of the above steps, except the FastQC reads quality check, were performed with the QIIME open-source bioinformatics pipeline for microbiome analysis (30).
The milk microbial diversity was assessed within- (alpha diversity) and across- (beta diversity) samples. All indices (alpha and beta diversity) were estimated from the complete OTU table (at the OTU level), filtered for OTUs with more than 10 total counts distributed in at least two samples. Besides the number of observed OTUs directly counted from the OTU table, within-sample microbial richness and diversity were estimated using the following indices: Chao1 and ACE (Abundance-based coverage Estimator) for richness, Shannon, Simpson and Fisher’s alpha for diversity (31; 32; 33; 34; 35; 36), Simpson E and Pielou’s J (Shannon’s evenness) for evenness (37). The across- sample milk microbiota diversity was quantified by calculating Bray-Curtis dissimilarities (38). Prior to the calculation of the Bray-Curtis dissimilarities, OTU counts were normalized for uneven sequencing depth by cumulative sum scaling CSS, (39). Among groups (PDHM, PM, IM) and pairwise Bray-Curtis dissimilarities at different timepoints were evaluated non-parametrically using the permutational analysis of variance approach (999 permutations; (40). Details on the calculation of the mentioned alpha- and beta-diversity indices can be found in Biscarini et al. (41).
Descriptive data related to clinical demographic data of preterm/term - delivering mothers and respective newborns, involved in this study, were reported as mean and standard deviation. Bacterial counts were expressed as mean and standard deviation and the significance level was set at 0.01. Metataxonomic data were reported as mean and relative abundance (%) and the significance level was set at 0.05.
Peptidomic proﬁling by UPLC/HR-MS/MS
Peptidomic analyses were performed using an Acquity UPLC module (Waters, Milford, MA, USA) coupled to a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientiﬁc, San Jose, CA, USA) and the peptides were identiﬁed using the Proteome Discoverer v1.4 software (Thermo Fisher Scientiﬁc). The MS data were processed, and the peptides were identiﬁed using the Proteome Discoverer v1.4 software (Thermo Fisher Scientiﬁc). Automatic peak detection was performed setting signal-to-noise ratio to 4 as suggested by Mangé et al. (42). The sequences of peptides were identiﬁed from MS/MS spectra using SequestHT algorithm (43) against a HM protein library constructed based on results from previous studies (42; 44; 45; 46; 47). A non-speciﬁc enzyme cleavage pattern was deﬁned, and 12 missed cleavage sites (maximum allowed for the algorithm) were allowed. No static modiﬁcations were set. Phosphorylation of serine and threonine, deamidation of asparagine, glutamine and arginine, oxidation of methionine and cyclisation of an N-terminal glutamine to pyro-glutamic acid were selected as dynamic modiﬁcations. Mass error tolerance for precursor ions was 5 ppm and for fragment ions was 0.02 Da. A strict false discovery rate of peptide identiﬁcation was set (FDR = 0.01).