Metagenomic and bacterial culture studies of human breast milk microbiota: factors influencing its bacterial composition and diversity

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In hospitals, it is critical for premature infants, protecting them from illnesses such as necrotizing enterocolitis. It has its own companion microbiota, whose bacterial composition and diversity are affected by several factors linked to the mother and her baby. However, little is known about the breast milk microbiota of mothers who delivered premature infants. The goal of the present study was to examine the breast milk microbiota and determine the factors influencing its composition and diversity over the time. Results We analysed 137 breast milk samples collected from 40 women who delivered premature and extremely premature infants. Each sample was both analysed by bacterial culture and the V3-V4 region of the bacterial 16S rRNA gene was sequenced using an Illumina platform to characterize microbial communities. For the samples analysed, a diverse community of breast milk bacteria was found with the most dominant phyla being Firmicutes , Proteobacteria , Actinobacteria and Bacteroidetes . At the genus level, Staphylococcus , Acinetobacter and Corynebacterium were the most prevalent. A core milk microbiota has been defined, composed of seven genera. The pregnancy number, lactation stage and mother’s body mass index were shown to influence bacterial diversity. In contrast, no statistically significant differences were observed for maternal age, gestational age, parity, population, mother’s blood group, mode of delivery, infant gender, infant weight, and use of antibiotic prophylaxis during delivery. Most of the bacterial species detected by bacterial culture were also identified to genus taxonomic level by 16S rRNA gene sequencing. Longitudinal analyses showed that bacterial diversity tended to increase over time, while the aerobic bacterial flora was rather stable over time. Moreover, LEfSe analysis allowed us to make associations between bacteria in breast milk with certain maternal conditions or factors. Conclusions Our study showed that the bacterial composition and diversity of breast milk are influenced by several maternal factors. We have observed a variability in bacterial diversity and composition during the 3 stages of lactation, which differs between mothers. We have identified bacterial signatures associated with different clinical conditions in the mother and child. human breast milk bacteria microbiota metagenomic 16S rRNA gene bacterial culture bacterial diversity bacterial composition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Human breast milk is considered the optimum feeding regime for newborn infants. It is a dynamic biological fluid that provides critical nutrients (human milk oligosaccharides, lipids, proteins, minerals...) and bioactive compounds such as antibodies, cytokines, defensins and certain bacteria that promote the establishment of the gut microbiome in newborns and support growth and immune development during early childhood. Breast milk feeding confers protection to the infant against several diseases such as gastrointestinal tract infections, necrotizing enterocolitis, respiratory tract infections and reduces the incidence of sudden infant death syndrome [1- Eidelman AI]. Infants fed with breast milk have reduced risk of chronic diseases such as allergies, asthma, diabetes, obesity, irritable bowel syndrome and Crohn's disease in both childhood and adult life [ 1 , 2 , 3 ]. Breastfeeding has also been associated with higher cognitive development in children, with a dose-dependent effect (exclusive breastfeeding is more beneficial than partial breastfeeding) and a time-dependent effect (breastfeeding > 3 months is more beneficial than ≤ 3 months) [ 4 , 5 ]. Breast milk promotes intestinal health in the preterm infant but also a dynamic interplay of host and dietary factors that stimulate the colonization of and enrichment for specific bacteria during establishment of the preterm infant microbiota [ 6 ] Initially, bacteria isolated from breast milk were considered as contaminants from the mother's skin and infant oral cavity or from incorrect handling methods [ 7 , 8 ]. Indeed, breast milk has long been considered a sterile biological fluid, but several studies have identified it as an integral source of microorganisms for the developing infant [ 9 ]. Breast milk has its own microbiota, including beneficial, commensal and potentially probiotic bacteria ( Bifidobacterium spp. , Lactobacillus spp .). Breastfed infants consume on average up to 8 x 10 5 bacteria every day, with a breast milk considered the second most important integral source of microorganisms for the infant after the birth canal in vaginally born infants [ 10 ]. Evidence of breast milk containing its own microbiota has been demonstrated by studies based on bacterial culture and metagenomics analysis. Some studies based only on aerobic and anaerobic culture have identified and isolated bacterial genera from breast milk, such as Lactobacillus and Bifidobacterium [ 11 ]. However, this approach has many limitations as some bacterial are strict anaerobes (e.g. Bifidobacterium ) and therefore more difficult to isolate. One of the most comprehensive methods for studying microbiota is metagenomics which enabled a more exhaustive identification of non-cultivable bacteria in breast milk. Unlike genomics, which usually consists in sequencing a single genome of a single bacterial strain, metagenomics simultaneously sequences genomes of several different strains from the same ecosystem. A metagenomic analysis provides the overall composition of microbiota: all the bacteria present with their respective abundances and diversities. A metagenomic approach by sequencing makes it possible to study the microbiota according to two complementary methodologies. Either by sequencing the whole genomes of the organisms present in the environment (shotgun metagenomics), or by sequencing one or more genes specific to each bacterial species (targeted 16S rRNA gene) [ 12 ]. Using this culture-independent method, Hunt et al. identified eight bacterial genera ( Staphylococcus , Streptococcus , Serratia , Pseudomonas , Corynebacterium , Ralstonia , Propionibacterium and Sphingomonas ) in breast milk samples from 16 women [ 13 ]. In the different published studies analysing milk microbiota, there are variations that can be attributed to many factors such as mode of delivery and health status of the mother [ 14 , 15 , 16 ]. In addition, the bacterial composition and diversity of breast milk changes throughout the lactation period, from colostrum to transitional milk, and then to mature milk. Colostrum, the first liquid produced by mothers when their babies are born, is present from the first to the fourth day. Transitional milk is produced between five days and two weeks after birth and from the 15th day after delivery, milk is considered mature [ 10 ]. The conventional methods of bacterial culture (streaking by using calibrated sterile loops and plating by using calibrated micropipettes following serial dilutions) for quantifying and identifying the bacteria composing the microbiota of breast milk are not standardized, which makes it difficult to carry out an accurate assessment of its composition. Strictly anaerobic bacteria can only be cultured in the absence of O2, requiring more complex bacteriological methods for sampling, transport and subsequent culture and/or isolation, especially in hospital environments, where time is limited and samples are abundant. These technical constraints may explain why only a few laboratories master these analyses. It is therefore important to be able to use an alternative method. A study of the composition of the bacterial microbiota of breast milk using standard methods of bacterial culture on agar and metagenomics analysis methods would allow for more precise and comprehensive knowledge. It is in this scientific and medical context that we decided to carry out a collaborative study between two expert teams, the Department of Hygiene, Prevention and Infection Control (HPIC) and Neonatal Intensive Care Unit (NICU) of the Centre Hospitalier Sud Francilien (CHSF), which has milk samples from the hospital's lactarium and the Centre National de Recherche en Génomique Humaine (CNRGH), which enables a response to scientific questions necessitating high throughput sequencing and bioinformatics analysis thanks to the development and deployment of innovative integrated technologies. The study of breast milk by bacterial culture on agar and metagenomics sequencing (targeted 16S rRNA gene) on the same samples constitutes one of the originalities of our research, with the aim of determining the factors influencing its bacterial composition and diversity. The analysis of numerous samples per mother enabled us to carry out a longitudinal study to investigate intra-individual variations in bacterial composition and diversity during the three lactation stages. Moreover, linear discriminant analysis effect size (LEfSe) allowed us to explore many clinical parameters (e.g. mode of delivery, lactation stage...) to identify bacterial markers related to clinical conditions. We report the outcomes of a cohort of 40 women who delivered premature and extremely premature infants, therefore with a breast milk microbiota highly specific. The challenge of breast milk nutrition for premature infants is increasingly significant. Indeed, preterm birth rates are rising in most countries. A study examining the trends of birth rates found that the global preterm birth rate rose from 9.8% in 2000 to 10.6% in 2014 [ 17 ]. Birth prematurity condition of the 40 newborns have prevented them from breastfeeding directly - a hospital grade electric breast pump was an effective option to express milk and establish and maintain an adequate nutrition supply. Thus, to determine the factors influencing bacterial composition and diversity we studied breast milk from 40 mothers along the three lactation phases. We explored a wide range of clinical parameters to highlight the association of bacterial markers with clinical conditions. Methods Setting The CHSF has approximately 1,200 beds across medicine, surgery and obstetrics, including three intensive care units, with over 80,000 admissions per year. The NICU has 58 beds, including 16 in the resuscitation department, 12 in the intensive care unit, 18 in medicine and 12 in the kangaroo unit, with 1,500 admissions per year. Participants and inclusion criteria A prospective study was carried out on a cohort of 40 mothers who had given birth in our hospital or another in the surrounding area, and whose newborns were cared for in the NICU of our establishment. All mothers who delivered premature and extremely premature infants, with available milk samples who had decided to pump their milk without breastfeeding, were eligible for this study. Mothers who had received an antibiotic in the three months prior to delivery were excluded from the study, except for those who received antibiotic prophylaxis during delivery by injection. Informed consent was obtained from all mothers enrolled in the study. Patient data Maternal and gestational age (weeks of amenorrhea), geographical origin, blood group, mode of delivery, parity, pregnancy number, postpartum body mass index (BMI), lactation stage (colostrum, transitional and mature milk), infant gender and birth weight, and use of antibiotic prophylaxis during delivery were obtained from hospital records. Sample collection Each mother provided one or more milk samples collected a few days to a few weeks postpartum in a sterile milk container. All collections were performed using a breast pump either at NICU or at home. Before sample collection, the breast pump was decontaminated with a detergent and disinfectant solution. The mother cleaned her hands and breasts with a mild liquid soap. The breast area was wiped while leaving the areolas and nipples wet without putting any clothes on them. If the collection was done in the NICU, a nurse collected the samples directly and immediately stored them at + 4°C. If collection was done at home, the samples were immediately stored at + 4°C for a maximum of 48 h before being transported in an isotherm container with a cold system and then stored in the NICU at + 4°C. Samples were partitioned into five mL tubes prior to transfer directly by the nurses with a cold system, from the NICU to the HPIC, and stored at + 4°C. Culture dependent bacterial analysis Breast milk was screened by culture on Columbia + 5% sheep blood agar (COS, bioMérieux SA, Marcy-l’Etoile, France) for the Total Aerobic Flora (TAF) counts, and Chapman agar (MSA2, bioMérieux SA) for the Staphylococcus aureus counts. Four plates of blood agar were inoculated by depositing with a calibrated pipette 100 µL of pure samples or diluted at 1/21th, 7/441th and 1/9261th with NaCl 0.85% Medium and then spread out with a rake. Two plates of Chapman agar were inoculated by depositing 100 µL of pure samples or diluted at 1/21th with NaCl 0.85% Medium. After 48 h incubation at 37 ± 2°C, TAF and S. aureus were enumerated. All bacterial species were identified using standard bacteriological methods based on colony morphology, hemolytic activity, catalase production, coagulase test reactions and Gram staining. All enumerations were performed with an automatic colonies counter Scan 1200® (Interscience, St Nom, France). Finally, these same milk samples were stored at -80°C until transported to the CNRGH, for metagenomics analysis with a cold system, and stored at -20°C. 16S rRNA gene sequencing of the milk microbiota DNA extraction was performed from whole breast milk (including lipids) using the ZymoBIOMICS™ DNA Miniprep Kit according to the manufacturer's instructions (Zymo Research®, Irvine, California, USA). Briefly, 950 µL aliquots of each milk sample were submitted to a chemical lysis. Mechanical lysis was then performed by bead-beating with 0.1- and 0.5-mm diameter ceramic beads (Zymo Research®, Irvine, California, USA) using the FastPrep-24™ machine (MP Biomedicals®, Illkirch-Graffenstaden, France). The samples were centrifuged (11,000 g at room temperature for 1 min), and the supernatant, containing the metagenomic DNA, was recovered in a volume of 500 µL. After several filtration/purification steps, the bacterial DNA was ready for quantitative and qualitative analyses. PCR reactions were performed using primers to amplify the V3-V4 variable segment of the 16S rRNA gene, which yielded approximately 550 bp. The primer sequences were as follows: 16S Amplicon PCR Forward Primer: 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3' 16S Amplicon PCR Reverse Primer: 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3'. Negative control was carried out alongside for each PCR performed. A mock community consisting of DNA extracted from 10 species with known relative abundances (Zymo Research® D6300, Irvine, California, USA) was also run as a positive control (more details in Additional file 1: Table S1 ). Each 25 µL PCR reaction included 10.5 µL DNA template, 1 µL of both forward and reverse primers (5 µM) and 12.5 µL of dNTP mix (KAPA HiFi HotStart ReadyMix PCR Kit, Roche). Thermal cycler settings included a 3 min denaturation step at 95°C followed by 28 cycles of 95°C, 55°C, and 72°C at each temperature for 30 seconds. A final elongation step at 72°C for 5 min was then performed to complete each reaction before storing PCR products at -20°C until further use. A second series of PCR reactions was performed to integrate the Illumina® indexes and adapters, resulting in fragments of about 630 bp. Reactions were initially carried out adding 5 µL of DNA template. Thermal cycler settings included a 3 min denaturation step at 95°C followed by 8 cycles of 95°C, 55°C, and 72°C at each temperature for 30 seconds. A final elongation step at 72°C for 5 min was then performed to complete each reaction before storing PCR products at -20°C until further use. Purification steps were performed after each PCR using AMPure XP® paramagnetic beads. Finally, the 16S libraries were then normalized to 4 nM to form an equimolar pool. The DNA libraries were sequenced on an Illumina® MiSeq sequencer using the kit (MiSeq® Reagent Kit v3 600 Cycles PE, Illumina®) which sequenced 2x300 bp at the ends of the 16S amplicons. A sample of sterile water (5 Prime®, Germany) was used as a negative control. Demultiplexed sequencing data were deposited into the European Nucleotide Archive under accession number PRJEB73593. Bioinformatics processing Raw sequences were trimmed to remove poor-quality tails (potentially clustering nucleotides associated to a Phred quality score < 30), PCR primers, indexes and adapters by using cutadapt v3.5 software [ 18 ]. Trimmed reads shorter than 100 bp were discarded. Then, the sequences were assembled into a single fragment per 16S amplicon (referred to hereafter as merged sequences) with the usearch v11.0.667 fastq_mergepairs command [ 19 ]. The size of the conserved fragments was between 350 and 500 bp. Samples with less than 50,000 assembled sequences or less than 40% assembled sequences were excluded from the study. A downsampling to 150,000 sequences per sample with seqtk v1.4.130 software was then performed to reduce the memory usage and the computation time of mothur software [ 20 ] used afterwards. Further sequence processing was performed with mothur v1.48.0 following the MiSeq standard operating procedures [ 21 ]. Specifically, sequences were filtered to remove those with a homopolymer longer than 7 bp, those with more than 4 ambiguous bases and those shorter than 400 bp or longer than 500 bp. Sequences were de-replicated. Unique sequences were aligned to the SILVA v138.1 [ 22 ], and unaligned sequences were removed. To account for PCR and sequencing errors, sequences were binned together at 98% identity and chimeras were removed. Quality filtered reads were taxonomically classified by using the k-Nearest Neighbor algorithm and the SILVA v138.1 reference database. Sequences belonging to the taxa Eukaryota , Chloroplast , Mitochondria and Archaea were discarded from the dataset. Sequences were subsequently binned into operational taxonomic units (OTU) by using a 97% similarity cutoff. Each OTU was classified, at the genus level, based on the consensus taxonomy of the sequences in the cluster with default bootstrapping confidence threshold. Statistical analysis Dataset consisting of the abundance table of OTUs in the different samples, the OTUs taxonomy and patient metadata were analysed using R v4.3.2, phyloseq v1.46.0 and microbiome v1.24.0 R libraries. The composition of the mock community positive control was evaluated and checked for correspondence with the expected theoretical composition. Initial data pre-processing was carried out: OTUs present only in the mock community were excluded, as were OTUs with a relative abundance of 0%. This filtered dataset was used for the core microbiota analysis and the linear discriminant analysis. The core microbiota was defined as bacterial genera present in > 90% of the samples. Different additional filtrations were then applied before the alpha diversity analysis and the full microbiota analysis. After a rarefaction step to 44,000 sequences per sample, to avoid biases related to sequencing depth, alpha diversity of breast milk samples was measured using Shannon and inverse Simpson indices. Only one sample per mother was selected for alpha diversity analysis and differential abundance analysis: the sample with the highest Shannon index > 0.5. For each maternal factor, alpha diversity differences between groups were assessed using the Wilcoxon-Mann-Whitney rank sum test for two-groups factors and the Kruskal-Wallis test for factors with more than two groups. Results with a p-value ≤ 0.1 or 0.05 were accepted as relative significant or significant respectively. For analysis of full microbiota, OTUs with relative abundance ≤ 0.05% were filtered out. For each factor, differences in the relative abundance of bacterial taxa between sample groups were assessed using the LEfSe method [ 23 ] with default parameters and logarithmic discriminant analysis (LDA) score threshold of three. Maternal factors studied in the analyses described above were maternal and gestational age, population and mother’s blood group, mode of delivery, parity, pregnancy number, postpartum BMI, lactation stage, infant gender and birth weight, and use of antibiotic prophylaxis. Longitudinal analysis Mothers who provided at least three samples were included for longitudinal analysis of alpha diversity and TAF over time. Results Sample inclusion criteria In this longitudinal, observational, monocentric study, we collected 204 samples (Fig. 1 ), 67 (32.8%) were excluded, including 39 after the DNA extraction step due their degraded bacterial DNA profiles, 18 after PCR amplification step because of an insufficient DNA concentration, and 10 after the sequencing step according to the criteria specified in the methods section. Clinical characteristics of study cohort The study was performed from January 06 to November 06, 2022. We analysed 137 (67.2%) samples representing 40 mothers. Population characteristics are summarized in Table 1 (more details in Additional file 1: Table S2). The mean maternal age was 32.4 ± 6.2 years, and the gestational age was 30.8 ± 2.9 weeks. There were 25 mothers (62.5%) who had delivered by cesarean section and 26 (65%) had antibiotic prophylaxis during delivery. Table 1 Clinical characteristics of mother-infant pairs (n = 40) Demographics of mother-infant pairs Mean ± SD or n (%) Maternal age (years) 32.4 ± 6.2 Mode of delivery Vaginal 15 (37.5) Cesarean 25 (62.5) Length of pregnancy (weeks) 30.8 ± 2.9 Maternal antibiotic treatment Cefazoline 20 (50) Amoxicilline 5 (12.5) Clindamycine 1 (2.5) Missing data 3 (7.5) None 11 (27.5) Parity Primiparous 20 (50) Multiparous 18 (45) Missing data 2 (5) Pregnancy number 2.3 ± 1.7 Maternal BMI after delivery (kg/m 2 ) 27.0 ± 3.4 Maternal blood group A + 13 (32.5) A - 1 (2.5) B + 7 (17.5) O + 14 (35) O - 1 (2.5) Missing data 4 (10) Population Caribbean 4 (10) European 12 (30) Middle Eastern 1 (2.5) North African 6 (15) Sub Saharan African 17 (42.5) Infant gender Female 21 (52.5) Male 19 (47.5) Infant birth weight (kg) 1.4 ± 0.4 Each mother provided one or more samples on different days postpartum and the mean lactation period (colostrum, transitional or mature milk) at collection was 26 ± 18 days (Table 2 ) Table 2 Sample(s) distribution per mother Mother_number Amount of samples per mother Days of postpartum collection M01 15 29/31/32/35/37/42/44/46/51/53/57/58/63/71/84 M02 2 10/15 M03 1 11 M04 1 11 M05 7 22/27/34/49/50/55/62 M06 3 9/18/30 M07 1 10 M08 3 6/11/27 M09 1 21 M10 2 20/34 M11 4 4/6/27/37 M12 13 6/7/9/14/15/21/24/42/44/48/51/69/72 M13 1 6 M14 6 13/15/26/32/36/53 M15 5 10/16/23/37/39 M16 3 5/9/14 M17 3 12/19/33 M18 1 3 M19 1 9 M20 4 6/19/21/34 M21 1 21 M22 1 6 M23 2 27/30 M24 1 3 M25 3 12/19/23 M26 2 9/15 M27 10 4/15/21/25/32/41/43/47/54/90 M28 2 24/29 M29 6 11/18/27/30/34/45 M30 8 5/9/11/16/18/23/25/30 M31 4 4/6/11/27 M32 1 9 M33 3 13/16/48 M34 6 6/13/15/27/34/36 M35 1 6 M36 3 6/20/27 M37 3 12/19/26 M38 1 11 M39 1 18 M40 1 14 Bioinformatics processing After the poor-quality sequence removal steps, a mean of 218,049 reads was generated for all samples analysed versus 578,700 reads for the mock community. As expected, sequence sizes ranged from 450 to 500 base pairs. Analysis of the milk microbiota composition A total of 89,394 OTUs were assembled across all samples, corresponding to 31 phyla and 983 genera. After removing OTUs specific to mock samples, a total of 76,130 OTUs were kept across all 137 breast milk samples. These could be annotated as 31 phyla and 981 genera. After deletion of taxa whose relative abundance was ≤ 0.05%, 66 OTUs were included in the final analysis. These could be annotated as 4 phyla and 39 genera. The phylum Firmicutes was predominant (78 ± 22%) in 119 (87%) samples followed by Proteobacteria (13 ± 22%), Actinobacteria (8 ± 11%) and Bacteroidetes (1 ± 3%) (Fig. 2 ). Relative abundance profiles were heterogeneous between each mother. A phyla inversion in towards of Proteobacteria was observed in 14 samples (10.2%), corresponding to 10 mothers (25%). In addition, significantly more Proteobacteria were found in seven mothers (17.5%). Mother M23 had a completely atypical profile, with Proteobacteria dominating (> 97%), corresponding to Acinetobacter dominating (> 60%). The same phenomenon was observed for Actinobacteria in two samples (1.5%), corresponding to two mothers (5%) (more details in Additional file 1: Table S3). For the genera, Staphylococcus dominated (69 ± 26%) in 105 (77%) samples, followed by Acinetobacter (5 ± 15%) and Corynebacterium (4 ± 6%). Firmicutes were mainly represented by the genera Staphylococcus , Streptococcus (2 ± 7%) and Enterococcus (2 ± 7%), Actinobacteria by the genera Corynebacterium (4 ± 6%), Cutibacterium (2 ± 5%) and Bifidobacterium (1 ± 4%), Bacteroidetes by the genus Prevotella (1 ± 3%), and Proteobacteria by the genera Acinetobacter (5 ± 15%) and Pseudomonas (2 ± 4%) (more details in Additional file 1: Table S4). An analysis of the microbial community membership across all the 137 samples from the 40 mothers suggests that a core milk microbiota was present. This core was defined as genera present in over 90% of the 137 samples. The phylum Firmicutes was predominant (72 ± 24%) in 114 (83%) samples, followed by Proteobacteria (7 ± 16%) and Actinobacteria (6 ± 8%). Bacteroidetes were no longer represented (more details in Additional file 1: Table S5). The core milk microbiota was made up of seven genera: Staphylococcus (67 ± 26%), Acinetobacter (5 ± 14%), Corynebacterium (4 ± 6%), Pseudomonas (2 ± 4%), Cutibacterium (2 ± 4%), Enterococcus (2 ± 7%) and Streptococcus (2 ± 7%) (Fig. 3 and more details in Additional file 1: Table S6). Alpha diversity: longitudinal analysis Mothers who provided at least three samples were selected for longitudinal analysis of bacterial diversity over time. A total of 20 mothers (50%) representing 112 samples (81.8%) were included in the final analysis (Fig. 4 ). This graph shows the bacterial diversity of breast milk samples provided by different mothers. Each colour represents a mother. The y-axis shows the Shannon index: the higher the numerical value, the greater the bacterial diversity in a sample. On the x-axis, this is the mother identification (Mx) associated with the day on which the post-delivery milk sample was taken (Dx). Alpha diversity varies over time in all mothers, with an average value of 0.98 ± 0.57 (0.08–2.41). In general, this diversity tends to increase. There was intra-individual variability (heterogeneity of alpha diversity over time) and inter-individual variability. Bacterial diversity seems to be a parameter intrinsic to each mother. Indeed, irrespective of the lactation stage, some mothers had a lower bacterial diversity at a later lactation stage (M01), while others had a higher bacterial diversity at an earlier lactation stage, from colostrum onwards (M27). Alpha diversity and maternal factors The relationship between alpha diversity and several factors was investigated. One sample per mother, with a Shannon threshold > 0.5 and the highest alpha diversity, was analysed. A total of 37 samples were analysed. No statistically significant differences were observed for maternal and gestational age, parity, population and blood group, mode of delivery, infant gender, and infant weight, and use of antibiotic prophylaxis during delivery (Fig. 5 ). Conversely, alpha diversity was significantly modified by pregnancy number, lactation stage and BMI after delivery, based on several measures including Shannon Index and Inverse Simpson (Fig. 6 ). Bacterial markers of maternal factors The relationship between bacterial taxa and several maternal factors was investigated. For each factor, LEfSe reported features differentially abundant between groups, with a LDA score ≥ 3. Bacterial markers were distinct between the different groups (Fig. 7 ). We found that bacterial genera Aeromonas and Rhizobium were enriched in the BMI ≥ 25 group while breast milk from mothers with BMI < 25 were composed of Anaeroglobus , Amaricoccus , Prevotella , Synergistaceae and Campylobacter (Fig. 7 A). Streptococcus , Enterococcus , Cutibacterium , Bacillus , Limosilactobacillus , Salmonella and Veillonella were enriched in the primiparous group (Fig. 7 B). In colostrum, Staphylococcus , Actinobacillus , PLTA13 were identified (Fig. 7 C). Mothers who have had more than one pregnancy have milk enriched with Bifidobacterium , while those with a first pregnancy produce breast milk rich in the Veillonella and Streptococcus genera (Fig. 7 D). Sub-Saharan African mothers had a microbiota enriched in Corynebacterium , North African with Chryseobacterium , Empedobacter , Aeromonas , Middle Eastern with Lactobacillus , Alloscardovia , Atopobium , Peptostreptococcus , Lactobacillales , European with Campylobacter and Caribbean with Cutibacterium , Listeria (Fig. 7 E). Pseudomonas , Streptococcus , Rothia were enriched for babies having a weight greater or equal to 1.5 kg, while Rhizobium , Delftia , Finegoldia for those with a lower weight (Fig. 7 F). Streptococcus , Chryseobacterium were enriched for mothers with age ≥ 30 yrs, whereas Prevotella , Finegoldia , Corynebacterium were associated for the 20–29 yrs maternal age (Fig. 7 G). In the case of maternal blood group, Bifidobacterium , Prevotellaceae , Anaerococcus , Actinomyces were enriched for mothers O- blood group, Prevotella , Haemophilus , Varibaculum with mothers O + blood group, Empedobacter with mothers A- blood group (Fig. 7 H). Mothers who had a baby boy were given milk enriched with Streptococcus , while those who had a girl had milk enriched with Anaerococcus and Acinetobacter (Fig. 7 I). Mothers who have not received antibiotic prophylaxis had milk enriched with Anaerococcus , Corynebacterium and Prevotella (Fig. 7 J). In the case of gestational age, Streptococcus was enriched for mothers withgestational age ≥ 32 weeks, whereas Prevotellaceae for < 32 SA (Fig. 7 K). Vaginal deliveries were associated with Corynebacterium and Pseudomonas genera, and cesarean deliveries were characterized by Anaerococcus (Fig. 7 L). Bacterial culture: longitudinal analysis of Total Aerobic Flora Mothers who provided at least three samples were selected for longitudinal analysis of TAF over time. A total of 20 mothers (50%) representing 112 samples (81.8%) were included in the final analysis (Fig. 8 ). This is a graph representing the bacterial TAF of breast milk samples provided by different mothers. Each colour represents a mother. The y-axis shows the counting of TAF: the higher the numerical value, the greater the bacterial TAF in a sample. On the x-axis, this is the mother identification (Mx) associated with the day on which the post-delivery milk sample was taken (Dx). TAF varied considerably from one mother to the next, with an average of 442,998 (420; 9,700,000) Colony Forming Unit (CFU)/mL for all samples analysed but was fairly constant for samples from the same mother. However, these variations did not appear to be as marked for samples from the same mother, where stability was observed over time. TAF seemed to be a parameter intrinsic to each mother. Indeed, irrespective of the lactation stage, some mothers had a lower TAF like M01 (median at 6,500), while others had a higher TAF like M06 (median at 8,400,000). Comparative metagenomics analysis and bacterial culture Bacterial culture analysis identified one or more bacterial species in 23 mother’s breast milk (58%) representing 68 samples (49.6%). Staphylococcus spp ( Staphylococcus aureus , Staphylococcus lugdunensis , Staphylococcus epidermidis and Staphylococcus haemolyticus ) and Enterobacterales ( Escherichia coli , Enterobacter bugandensis , Enterobacter cloacae , Klebsiella aerogenes , Klebsiella oxytoca and Kosakonia cowanii ) were in a predominant place, with 21% and 22% respectively. Bacterial species identified by bacterial culture were identified to genus taxonomic level by metagenomics in 61 (89.7%) of 68 samples. The unidentified bacteria by metagenomics were Enterobacter bugandensis , Enterobacter cloacae , Kosakonia cowanii and Klebsiella aerogenes . Conversely, the Acinetobacter , Corynebacterium and Pseudomonas genera were identified by metagenomics in higher-than-normal quantities. However, none of these bacteria were identified by bacterial culture in the corresponding samples (more details in Additional file 1. Table S2). Discussion Of the 204 initial samples, 67 (32,8%) were excluded after the DNA extraction step, PCR amplification step and the sequencing step. This type of exclusion is already documented in literature where in some case up to 25% of the mother-infant pairs were eliminated due to insufficient quantity of qPCR-recovered milk bacteria [ 24 ]. Metagenomic DNA extraction from breast milk is a challenge due to its low biomass [ 25 ]. Of the excluded samples, eight (12%) were provided by the same mother. None of the samples from this mother could be analysed. The exclusion of the eight samples may be caused by inadequate sample storage [ 26 , 27 ] or related to an inherent maternal factor associated with an already reduced TAF prior to the analysis of the milk samples. Indeed, the TAF counts for these 67 excluded samples were relatively low with a median of 12,000 CFU/mL versus 30,000 CFU/mL for the 137 samples in our study. Furthermore, this was confirmed with the eight samples provided by the same mother, which had a median of 138 CFU/mL. It appears to correlate with TAF counts obtained by bacterial culture and samples excluded from DNA extraction, PCR amplification and sequencing steps. Indeed, the median TAF counts for our hospital ranged from 19,000 to 33,000 CFU/mL between 2017 and 2019. Examination of the communities on a sample-by-sample basis within the mothers suggested that the stability and membership of bacterial communities present were less variable within a single mother than from mother to mother, which has already been observed in other studies [ 13 ]. Four main phyla were identified. The phylum Firmicutes was predominant and more specifically Staphylococcus : the majority of performed studies have identified Staphylococcus as the most consistently and frequently observed genera in human milk [ 28 ]. Only phylum Bacteroidetes was not observed in all 137 samples and was predominantly represented by the genus Prevotella whose species are mainly found in oral, vaginal, skin and intestinal microbiota [ 29 ]. Its presence in some samples could indicate either breastfeeding of the infant with a migration of bacteria from the oral cavity to the mother's mammary ducts [ 30 , 31 , 32 ], or a transfer of intestinal bacteria from the mother to the mammary glands via the entero-mammary pathway [ 24 , 33 , 34 ]. Studies have highlighted retrograde back flow of milk due to infant suckling using ultrasound imaging [ 30 ]. This back flow into the mammary ducts is a possible mechanism for the transfer of bacteria from the infant's mouth into the mother's mammary gland. Moreover, the discovery of anaerobic species (e.g. Bifidobacterium ) associated with gut environments and unable to exist in aerobic environments has raised questions about the origin of these bacteria in breast milk. Live bacteria from the maternal gut could travel by an endogenous route to the mammary gland via the presence of an entero-mammary pathway [ 24 ]. This translocation of bacteria from maternal gut to mammary gland involves complex interactions between epithelial cells, immune cells and bacteria [ 33 ]. One piece of evidence of the entero-mammary pathway includes the presence of bacterial communities in colostrum collected before the infant's first feeding [ 35 ]. The genus Streptococcus is poorly represented in this study. It is a genus observed in studies where mothers mainly breastfeed because it is a germ predominant in the oral cavity of infants [ 13 , 24 ]. In our study, mothers used a breast pump to collect their milk due to the relatively high prematurity of the children, which explains the low occurrence of the genus Streptococcus . Although maternal factors are thought to significantly influence the microbiota present in breast milk, a few taxa are consistently observed in studies. The seven bacterial genera that make up the core microbiota of our study belong to the genera most frequently detected by sequencing of breast milk [ 36 ]. The conventional relative abundance profile found in our study was dominated by Firmicutes followed by Proteobacteria and Actinobacteria . However, samples from the M23 mother showed a predominance of Proteobacteria > 97%, represented by the genus Acinetobacter . It is an environmental (soil and water) bacterium that is generally transmitted by hands [ 37 ]. It is therefore probably a case of contamination at the time of sampling by the mother. Furthermore, our hypothesis is confirmed by the fact that the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium genus is the second most common bacterial genus found in this mother's samples. Populations of Rhizobium are bacteria specialized in nitrogen fixation in close association with plant roots, suggesting Rhizobium was likely derived from soil. These observations appeared to be consistent with patterns of accidental contamination from the environment [ 38 ]. Presuming that it is a contamination, it is however very unusual to find such an abundance. In addition, no species of the genus Acinetobacter were isolated in bacterial culture in these samples. This could be due to phenotypic confusion during microbiological analysis at the Department of HPIC. Although Acinetobacter is easily cultivated on standard media, the phenotypic characteristics of the different species make them difficult to identify. Indeed, the Acinetobacter genus can be phenotypically confused with coagulase-negative Staphylococcus or Enterobacterales , meaning that the results obtained are operator-dependent. It is therefore possible that the Acinetobacter genus of samples from the M23 mother have not been identified due to an erroneous phenotypic determination. The genus Corynebacterium is frequently isolated from breast milk samples [ 13 , 14 , 15 , 39 , 40 , 41 ] and considered as contaminant [ 41 ]. Species are generally considered commensal in nature found on the skin microbiota of humans [42 - Grice EA]. Its occurrence in the samples is probably related to contamination through the mother's skin during milk collection. The genus Bifidobacterium has already been isolated from breast milk samples in several studies [ 14 , 15 , 16 , 24 , 32 , 39 , 41 , 43 ], although it is a strict anaerobic bacterium present in the human gut. In our study, the 16S rRNA gene approach did not allow us to reach the taxonomic rank of the species by metagenomics. It is consequently difficult to determine the origin of this bacterium: maternal intestine supporting the hypothesis of the entero-mammary pathway [ 33 , 34 , 44 ] or contamination during milk collection due to poor hand hygiene. However, thus far, little is known about the occurrence and the ecological relevance of rare Bifidobacterium phylotypes in human body environments. The findings of Wenli et al clearly supported the hypothesis that breast milk can contain commensal bacteria, in particular endosymbiotic Bifidobacterium species. On the other hand, certain exogenous phylotypes of Bifidobacterium insensitive to oxygen and cosmopolitan in lifestyle ( B. animalis and B. adolescentis ) may be indirectly transferred to breast milk via environmental contamination [ 45 ]. Longitudinal analysis findings suggest that there are intra and inter-individual variations in alpha diversity among different mothers over time. This may be explained by certain parameters not considered in our study, such as maternal diet may differ from one mother to the next. Regardless of dietary choices, nutrition has a significant impact on breast milk composition: a higher intake of saturated fatty acids and monounsaturated fatty acids is associated with a lower relative abundance of Corynebacterium in breast milk. Similarly, higher levels of Rhizobium are found in the milk of women who eat a legume-based diet [ 46 ]. There must also be genetic aspects specific to each mother that have not yet been studied. Intra-individual variations highlight the importance of collecting several milk samples per mother, as a single sample is not sufficiently representative and can lead to major biases in the researchers' analyses and conclusions. Our results highlighted links between alpha diversity, predominance of bacterial genera and several maternal factors. About maternal age, Williams et al found no association between maternal age and the composition of breast milk [ 47 ]. However, studies that analysed gut microbiota compositions from people of advanced age concluded that there is a general decrease in microorganism diversity, together with an increase in opportunistic agents. Indeed, differences in the gut microbiota composition were found with the predominance of the phylum Bacteroidetes , more prevalent in the elderly than in younger adults where the phylum Firmicutes is more abundant. Numerous microorganisms, mainly opportunistic pathogens, increase during aging, including Akkermansia , Lactobacillus , Streptococcus , Prevotella , Paraprevotella , Helicobacter , Coprobacillus , and Peptoniphilus [ 48 ]. These various studies are therefore in line with our results on breast milk. Indeed, we have identified the genera Streptococcus and Chryseobacterium (belonging to the phylum Bacteroidetes ) for mothers with age ≥ 30 yrs. Like ourselves, Urbaniak et al found no association between gestational age and the composition of breast milk [ 49 ]. However, Mantziari et al have reported significant differences in the microbiota of breast milk correlated to the gestational age, with higher levels of Enterococcus and lower levels of Bifidobacterium observed in the milk of preterm-delivered mothers compared to those who gave birth on the expected birth date [ 28 ]. About parity, no statistically significant differences in bacterial diversity were observed on breast milk microbiota, but the primiparous mothers had milk samples enriched with Streptococcus , Enterococcus , Cutibacterium , Bacillus , Limosilactobacillus , Salmonella and Veillonella . Two studies did not find an effect of parity on the composition of the breast milk microbiota [ 31 , 47 ]. However, parity has been shown to affect the microbiota composition of cow milk: primiparous cows have a milk more diversified compared to multiparous cows [ 50 ]. About population, studies have explored the impact of geographic origin on breast milk composition. This could be due to the different dietary and environmental exposure patterns. For example, population differences were also found in Iran, with total counts of Lactobacillus being higher in the milk of mothers residing in rural areas compared to those in urban areas. Meehan and colleagues examined the breast milk of hunter-gatherer and horticulturalist women in the Central African Republic, reporting lower levels of Lactobacillus in the milk of hunter-gatherers than horticulturalists. Lower relative abundances of Bifidobacterium, Propionibacterium, Veillonella and Serratia were reported in the breast milk of Central African Republic mothers compared to United states or Swiss mothers. Lackey and colleagues showed that the composition of breast milk varies significantly within and across cohorts from international sites (Ethiopia, Gambia, United States, Kenya, Peru, Spain, and Sweden) after analysis of 394 breast milk samples and assume that bacterial compositions of breast milk and feces of breastfed infants are likely specific within a culture and location [ 51 ]. When the breast milk composition was studied in mothers who had delivered vaginally, Spanish mothers had the highest relative abundance of Bacteroidetes in breast milk when compared to mothers from Finland, China or South Africa [ 28 ]. In our study, we observed that five distinct geographical origins did not share any bacterial genera. Each group seemed to get its own microbiota enrichment. Additional research is needed to understand environmental, behavioral, dietary and genetic factors driving these variations and associations. Our results showed each blood group seemed to have their own functional bacterial markers that should hypothesize that blood group shape in part the milk microbiota as it was already described for intestinal microbiota [ 52 ]. To better understand the microbiome, we need to consider the blood type antigens and their influence on the intestinal microbiota and correlate with the possible incidence on microbiota breast milk considering that a set of breast milk bacteria come from the mother’s intestine via the entero-mammary pathway [ 24 , 33 , 34 ]. Indeed, these surface-associated markers not only serve as antigens, but they are also a food source for intestinal bacteria. These antigens found on cell surfaces and in body fluids and secretions are not identical. There are two glycoside “precursor” chains with slightly different structures, known as Type 1 and Type 2 chains. Type 1 chains and their terminal antigens are detectable in breast milk of individuals who are known as “secretors”, but these antigens are missing in the body fluids of about 20% of people of European descent, who are known as “non-secretors”. Secretor status influences susceptibility to pathogens such as Vibrio cholerae , Campylobacter jejuni , Helicobacter pylori , and could therefore have an influence on the composition of microbiota, including that of breast milk [ 52 ]. It would be relevant to look at the secretor status of the mothers in a future study. Overall, three bacterial genera discriminated the milk samples from women who gave birth vaginally and via cesarean. Lyons et al have described that Peptoniphilus and Anaerococcus were associated with vaginal deliveries [ 53 ], in contrario to our results showing rather an association between Anaerococcus and cesarean mode of delivery. Urbaniak et al did not find significant differences in microbial profiles in the breast milk microbiota based on mode of delivery [ 49 ]. Hermansson et al found no significant differences related to the mode of delivery in the relative abundance of bacteria on the phylum and family level. However, the alpha diversity and richness of the breast milk microbiota was significantly higher in mothers who had delivered vaginally as compared with the mothers who had delivered by cesarean section [ 54 ]. Contrasting results have been reported for infant gender, which in some studies seemed to affect breast milk composition, but in others, no correlation was observed. For instance, a higher relative abundance of Rothia was detected in the milk from mothers of female infants compared with mothers of male infants [ 55 ], in contrario to our results showing rather an association between Anaerococcus and Acinetobacter for female infants and Streptococcus for male infants. However, in Simpson's study, the women were breastfeeding, whereas in ours they were using a breast pump, which could explain these discrepancies. Moossavi et al observed differences in milk microbiota alpha diversity and overall community structure according to infant gender [ 31 ]. The mechanism for the observed sex differences in milk microbiota remains to be determined. Sex differences in gut microbiota have been reported and are usually attributed to hormonal differences between male and female hosts [ 31 ]. Analysis of neonates’ stool samples during the first 30 days revealed for example that male infants had a lower alpha diversity as compared to females, which showed also higher abundance of Clostridiales , and lower abundance of Enterobacterales . Another study showed that boys had higher Bifidobacterium level in their intestinal microbiota at the first day of life as compared to girls [ 56 ]. In contrast, Urbaniak et al found no differences in microbial profiles of breast milk based on infant gender [ 49 ]. Our analyses have shown that bacterial markers are correlated with infant weight at birth. In contrast, Moossavi et al, found no differences in microbial profiles based on infant birth weight [ 31 ]. This is a parameter that has been little explored in the previous studies on breast milk microbiota. Our findings underline the need to take this into account in future studies. Our results show that the presence or absence of antibiotic prophylaxis during delivery did not influence bacterial diversity. Few studies on the impact of antibiotics on the microbiota of breast milk have been described to date. Results regarding the use of antibiotics are contradictory. For instance, in a study of women living in Germany and Austria abundance of lactobacilli or bifidobacteria was lower in women who had received antibiotics during pregnancy or lactation [ 57 ]. However, another study reported that the effect of antibiotic exposure on breast milk microbiota at one-month postpartum increased bacterial richness and diversity [ 54 ]. The impact of the consumption of antibiotics during pregnancy and lactation on the milk microbiome needs to be more extensively explored. According to some studies on the impact of antibiotics on the gut microbiota, it would have been expected that the presence of antibiotic prophylaxis during delivery would decrease bacterial diversity [ 58 ]. Our findings may be related to a lack of power due to a small number of patients or to the pharmacokinetics of the drugs used. Indeed, only CEFAZOLIN and AMOXICILLIN were administered intravenously. These antibiotics belong to the beta-lactam family of the 1st generation cephalosporins (CEFAZOLIN) and penicillins (AMOXICILLIN), which pass into breast milk at very low concentrations. The use of these antibiotics is possible and permitted during breastfeeding. It is mainly eliminated in the urine and a small percentage is eliminated in the bile. When administered intravenously, the half-life of CEFAZOLIN is approximately 100 minutes compared to 60 minutes for AMOXICILLIN. It is possible to consider that the elimination of a drug occurs in five half-lives, i.e. 500 minutes, i.e. 8.3 hours for CEFAZOLIN as well as five hours for AMOXICILLIN [59, 60]. In addition, these antibiotics were administered at a single dose, which could explain a more rapid resilience of the bacterial microbiota in breast milk and a stable diversity in milk samples collected several days after administration. Thus, it seems unlikely that the administration of these antibiotics at the time of delivery would have had a sufficiently significant impact on the microbiota of breast milk as opposed to a conventional treatment over several days. A small percentage of the antibiotics are excreted in the bile, which means that their enterohepatic cycle should have a minor impact on the gut microbiota and therefore ultimately on the microbiota of breast milk via the entero-mammary pathway [59, 60]. However, it is interesting to note that only samples from mothers who had received antibiotic prophylaxis had no bacterial markers. The microbiota in breast milk therefore seems to be slightly affected. It seems appropriate to pursue this area of research. In our study, mothers who have had more than one pregnancy have milk enriched with Bifidobacterium , while those with a first pregnancy produce breast milk rich in the Veillonella and Streptococcus genera. It was important to distinguish between pregnancy number and parity. Indeed, most of the mothers in our study come from Sub-Saharan Africa, a population where the number of pregnancies that are not carried to term is high [61]. There are therefore differences between pregnancy number and parity in our cohort of mothers. Bearing in mind that the microbiota of the mother's milk begins to form in the last trimester of pregnancy, some primiparous mothers may have a more diverse breast milk microbiota due to several previous pregnancies that were not carried to term. All these hypotheses may be confirmed by our results, as we observed significant differences between pregnancy number and parity in terms of diversity via alpha diversity and bacterial composition via LEfSe analysis. Our study showed that the alpha diversity of the samples analysed varied over the lactation period and increased with the maturity of the breast milk. In contrast, another study did not show changes in the number of bacteria in breast milk during the first month of life [62]. Two studies reported higher total bacterial loads in colostrum compared with mature milk [63, 64]. Two other studies reported an increase in total bacterial loads throughout the lactation period [65, 66]. The microbiota present in colostrum has already demonstrated broad bacterial diversity, with the dominant bacteria mainly belonging to the genera Weissella , Leuconostoc , Staphylococcus , Streptococcus , and Lactococcus [67, 68], which is partly confirmed by our LEfSe analysis, as we also identified the genus Staphylococcus like bacterial markers correlated with colostrum. About BMI, overweight and obese mothers have decreased bacterial diversity impacting the composition of the breast milk which could affect and delay a right establishment of the neonatal gut microbiota. It is in concordance with was already described in the literature [ 15 , 46 ]. Moreover, it is also described in studies on the gut microbiota [69]. It is therefore possible to establish a link between the intestinal microbiota and that of breast milk, supporting the hypothesis of an entero-mammary pathway linking the two microbiotas. Nonetheless, two studies did not find any influence of BMI on the composition of the breast milk microbiota [ 31 , 40 ]. The Prevotella enterotype has already been described to be associated with a lower BMI, in accordance with our LEfSe analysis [70]. Our results concerning the bacterial markers Rhizobium and Aeromonas associated with a BMI ≥ 25 post-delivery are correlated with a study recently described in case of these two bacterial markers were found in mother’s gut which had an excessive pregnancy weight gain, as well as in their offspring neonatal gut too [71]. It's worth noting that presence of the genera Rhizobium and Aeromonas in the mother's gut microbiota may come from the diet during pregnancy and should be transferred to the neonatal gut by the mother's milk in case of breastfeeding, which could support the entero-mammary theory. Our findings on longitudinal analysis of TAF counts are not correlated with those obtained from the analysis of bacterial diversity. This confirms that bacterial diversity is not only influenced by aerobic bacteria, but also by anaerobic bacteria, most likely derived from the maternal gut microbiota. Most bacterial species identified in the samples by bacterial culture were identified to the taxonomic genus by metagenomic analysis except in some bacteria. These are four bacteria from the Enterobacterales family ( Enterobacter bugandensis , Enterobacter cloacae , Kosakonia cowanii and Klebsiella aerogenes ). This is probably a taxonomic assignment error in the SILVA database. The 97% similarity threshold for defining an OTU is limiting for discriminating between genera in the Enterobacterales family such as Enterobacter or Kosakonia [72]. 16S rRNA gene sequencing cannot identify all Enterobacterales and seems to be that the accuracy of 16S rRNA gene sequencing, regardless of analytical approach (OTU), is poor for Enterobacterales . Nevertheless, OTU-based analysis remains the more accurate approach for detecting Enterobacterales [73]. On the contrary, some genera ( Bifidobacterium , Prevotella ) were identified in certain breast milk samples by metagenomics analysis but not in bacterial culture. But the genus Bifidobacterium and most species of the genus Prevotella are strict anaerobic bacteria, and bacterial culture analysis could only identify aerobic bacteria in our study. A comparison between bacterial culture and metagenomics sequencing for the same samples constitutes one of the originalities of our research. It limits the comparison bias between two different studies and allows to compare the strengths and limitations of each of these identification methods. It is important to combine the two identification methods which are not antagonistic but complementary. Indeed, bacterial culture makes it possible to determine the viability of bacteria, essential in the clinical practices of a hospital. Metagenomics sequencing does not provide such information. However, a non-pathogenic species can grow predominantly on agar and prevent the identification of another pathogenic species (even in small quantities), which constitutes one of the limits of bacterial culture. Metagenomics makes it possible to overcome the difficulties of identifying anaerobic species and can identify bacteria that cannot yet be cultivated. Finally, the longitudinal analysis enabled us to observe variation over time in bacterial diversity and TAF counts throughout the lactation period, which could not have been performed with a single breast milk sample per mother. Very few studies have analysed all three stages of lactation and several samples per mother. Moreover, our LEfSe analysis allowed us to make associations between bacteria and certain maternal factors down to the genus level unlike other studies limited to the phylum or family level [ 54 ]. The post-partum collection day of the samples was different from one mother to another, making comparison more difficult. The first PCR amplified the V3-V4 variable segment of the 16S rRNA gene, but studies have shown that some bacterial genera may be over or underrepresented depending on the regions amplified [74], which could create an interpretation bias. Metagenomic analysis has several limitations: 16S rRNA gene targeted analysis allowed us to access to the taxonomic rank of the genus but without precision of the species, the viability of the identified bacteria is unknown and the methodology must be sufficiently to lyse both Gram negative and Gram positive cells, preventing distortion of the relative abundance of the Gram positive species to provide sufficient DNA yield to limit the contribution of signal from reagent contaminants [75]. To counter limitations of amplicon-based sequencing, there is increasing use of whole genome sequencing (WGS) [76]. WGS approaches can serve to resolve issues related to 16S rRNA gene sequencing and carry out bacterial identifications down to the strain level. 16S rRNA gene targeted analysis and WGS approaches do not provide the same biological answers, but these two approaches are complementary. Initially, our aim was to obtain a global view of bacterial diversity and its variations as a function of different factors, and 16S rRNA gene targeted enabled us to obtain such results. In a subsequent study future, WGS approaches will enable us to confirm our data and provide more precise identification of genes, metabolic pathways and characterization of bacteria down to the taxonomic rank of the strain. Finally, the number of mothers could be insufficient and did not allow significant results to be obtained for certain maternal factors for which a trend towards significance was observed (e.g. maternal age). Our findings need to be investigated with larger cohorts. Conclusions In this study, we have explored many features and potential determinants of human breast milk composition. Our results have confirmed that breast milk bacterial communities are highly diverse and complex. Breast milk is influenced by several maternal factors, which impact its bacterial composition and diversity from mother to mother. Metagenomic analysis should not substitute for bacterial culture, but rather complement it. Longitudinal analyses should be supported, as many changes occur during the various lactation stages. In-depth knowledge of the factors influencing the bacterial composition of mother's milk and of bacterial markers associated with maternal factors opens the way to possible intervention on these factors with a view to rebalancing the bacterial composition of mother's milk, thereby enabling the optimal development of the newborn's intestinal microbiota. A logical next step will be to investigate with WGS approaches a larger population to determine and confirm characteristics of the breast milk microbiota are associated with enhanced health outcomes for women and their babies. Abbreviations Hygiene, Prevention and Infection Control (HPIC) Neonatal Intensive Care Unit (NICU) Centre Hospitalier Sud Francilien (CHSF) Centre National de Recherche en Génomique Humaine (CNRGH) body mass index (BMI) Total Aerobic Flora (TAF) operational taxonomic units (OTU) linear discriminant analysis effect size (LEfSe) logarithmic discriminant analysis (LDA) Colony Forming Unit (CFU) whole genome sequencing (WGS) Declarations Ethics approval and consent to participate The French Ethics Committee South Mediterranean I (2023-A01731-44) approved the study. Written informed consent was obtained from each participant prior to enrolment. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article [and its supplementary information files]. Competing interests The authors declare that they have no competing interests. Funding This work was financially supported by the Centre Hospitalier Sud Francilien, the Centre National de Recherche en Génomique Humaine and the Genopole. Authors' contributions BQ is the guarantor of the study from conception and design to conduct of the study and acquisition of data, data analysis, and interpretation of data. EL contributed with analysis and interpretation of the data. CC carried out the molecular studies, sample preparation for the analysis, directed the molecular sequencing. VM oversaw the analysis and interpretation of the data. LC led the design and cohort selection. HR participated in the conception of the study, oversaw the design and cohort selection. JFD oversaw the molecular studies, sample preparation for the analysis, the molecular sequencing, analysis and interpretation of the data. DL and BS oversaw the study from conception and design to conduct of the study and acquisition of data, data analysis, and interpretation of data. All authors read and approved the final manuscript. Acknowledgements The authors wish to acknowledge the clinical and paramedical staff, as well as patients and families, of the Department of Hygiene, Prevention and Infection Control, and the Neonatal Intensive Care Unit of the Centre Hospitalier Sud Francilien. We would also like to thank Steven Mcginn for his help with the English version. Authors' information Not applicable. Endnotes Not applicable. References Eidelman AI, Schanler RJ, Johnston M, Landers S, Noble L, Szucs K, et al. Breastfeeding and the use of human milk. Pediatrics. 2012;129 (3): e827–e841. Klopp A, Vehling L, Becker AB, Subbarao P, Mandhane PJ, Turvey SE, et al. Modes of Infant Feeding and the Risk of Childhood Asthma: A Prospective Birth Cohort Study. J Pediatr. 2017;190:192-199.e2. Xu L, Lochhead P, Ko Y, Claggett B, Leong RW, Ananthakrishnan AN. Systematic review with meta-analysis: breastfeeding and the risk of Crohn's disease and ulcerative colitis. Aliment Pharmacol Ther. 2017;46(9):780-789. Kim KM, Choi JW. Associations between breastfeeding and cognitive function in children from early childhood to school age: a prospective birth cohort study. Int Breastfeed J. 2020;15(1):83. Ibrahim SH, El-Ghany A, El Shafi M, El Hady M. Cognitive Functions in Breastfed versus Artificially Fed in Preschool Children. Egypt. J. Hosp. Med. 2019;77(5):5742-5751. Gregory KE, Samuel BS, Houghteling P, Shan G, Ausubel FM, Sadreyev RI, et al. Influence of maternal breast milk ingestion on acquisition of the intestinal microbiome in preterm infants. Microbiome. 2016 Dec 30;4(1):68. Ryder RW, Crosby-Ritchie A, McDonough B, Hall WJ. Human milk contaminated with Salmonella kottbus : A cause of nosocomial illness in infants. JAMA. 1977;238, 1533–1534. Heikkilä MP, Saris P. Inhibition of Staphylococcus aureus by the commensal bacteria of human milk. J. Appl. Microbiol. 2003;95, 471–478. Martin R, Langa S, Reviriego C, Jiminez E, Marin ML, Xaus J, et al. Human milk is a source of lactic acid bacteria for the infant gut. J. Pediatr. 2003;143, 754–758. Lyons KE, Ryan CA, Dempsey EM, Ross RP, Stanton C. Breast Milk, a Source of Beneficial Microbes and Associated Benefits for Infant Health. Nutrients. 2020;12, 1039. Sinkiewicz G, Nordström EA. 353 occurrence of Lactobacillus reuteri, lactobacilli and bifidobacteria in human breast milk. Pediatric Res. 2005;58, 415. Diene SM, Bertelli C, Pillonel T, Schrenzel J, Greub G. Génomique et métagénomique bactériennes : applications cliniques et importance médicale. Rev. Med. Suisse. 2014;10: 2155-61. Hunt KM, Foster JA, Forney LJ, Schütte UME, Beck DL, Abdo Z, et al. Characterization of the diversity and temporal stability of bacterial communities in human milk. PLoS One. 2011;6(6):e21313. Browne PD, Aparicio M, Alba C, Hechler C, Beijers R, Rodríguez JM, et al. Human milk microbiome and maternal postnatal psychosocial distress. Front. Microbiol. 2019;10:2333. Cabrera-Rubio R, Collado MC, Laitinen K, Salminen S, Isolauri E, Mira A. The human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. Am. J. Clin. Nutr. 2012;96(3):544-51. Olivares M, Albrecht S, De Palma G, Ferrer MD, Castillejo G, Schols HA, et al. Human milk composition differs in healthy mothers and mothers with celiac disease. Eur. J. Nutr. 2015;54(1):119-28. Walani SR. Global burden of preterm birth. Int J Gynaecol Obstet. 2020 Jul;150(1):31-33. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17(1), pp. 10-12. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460-1. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75(23):7537-41. MiSeq SOP. https://mothur.org/wiki/miseq_sop/ . Accessed 30 July 2024. Silva reference files. https://mothur.org/wiki/silva_reference_files/ . Accessed 30 July 2024. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. Kordy K, Gaufin T, Mwangi M, Li F, Cerini C, Lee DJ, et al. Contributions to human breast milk microbiome and enteromammary transfer of Bifidobacterium breve. PLoS ONE. 2020;15(1):e0219633. Stinson LF, Ma J, Sindi AS, Geddes DT. Methodological approaches for studying the human milk microbiome. 2023;81(6):705-715. Fouhy F, Deane J, Rea MC, O'Sullivan Ó, Ross RP, O'Callaghan G, et al. The effects of freezing on faecal microbiota as determined using MiSeq sequencing and culture-based investigations. PLoS One. 2015;10(3):e0119355. Lackey KA, Williams JE, Price WJ, Carrothers JM, Brooker SL, Shafii B, et al. Comparison of commercially-available preservatives for maintaining the integrity of bacterial DNA in human milk. J Microbiol Methods. 2017;141:73-81. Mantziari A, Rautava S. Factors influencing the microbial composition of human milk. Semin Perinatol. 2021;45(8):151507. Tett A, Pasolli E, Masetti G, Ercolini D, Segata N. Prevotella diversity, niches and interactions with the human host. Nat Rev Microbiol. 2021;19(9):585-599. Geddes DT. The use of ultrasound to identify milk ejection in women–tips and pitfalls. Int. Breastfeed. J. 2009;4:5. Moossavi S, Sepehri S, Robertson B, Bode L, Goruk S, Field CJ, et al. Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors. Cell Host Microbe. 2019;25(2):324-335.e4. Moossavi S, Azad MB. Origins of human milk microbiota: new evidence and arising questions. Gut Microbes. 2020;12(1):1667722. Rodríguez JM. The origin of human milk bacteria: Is there a bacterial entero-mammary pathway during late pregnancy and lactation? Adv. Nutr. 2014;5(6):779-84. Perez PF, Doré J, Leclerc M, Levenez F, Benyacoub J, Serrant P, et al. Bacterial imprinting of the neonatal immune system: lessons from maternal cells? Pediatrics. 2007;119(3):e724-32. Jeurink PV, Van Bergenhenegouwen J, Jiménez E, Knippels LMJ, Fernández L, Garssen J, et al. Human milk: a source of more life than we imagine. Beneficial Microbes. 2013;4(1):17-30. Lopez Leyva L, Brereton NJB, Koski KG. Emerging frontiers in human milk microbiome research and suggested primers for 16S rRNA gene analysis. Comput Struct Biotechnol J. 2020;19:121-133. Wong D, Nielsen TB, Bonomo RA, Pantapalangkoor P, Luna B, Spellberg B. Clinical and Pathophysiological Overview of Acinetobacter Infections: a Century of Challenges. Clin Microbiol Rev. 2017;30(1):409-447. Swanson CS, Dhand R, Cao L, Ferris J, Elder CS, He Q. Microbiome Profiles of Nebulizers in Hospital Use. J Aerosol Med Pulm Drug Deliv. 2022;35(4):212-222. Jost T, Lacroix C, Braegger C, Chassard C. Assessment of bacterial diversity in breast milk using culture-dependent and culture-independent approaches. Br J Nutr. 2013;110(7):1253-62. Li SW, Watanabe K, Hsu CC, Chao SH, Yang ZH, Lin YJ, et al. Bacterial Composition and Diversity in Breast Milk Samples from Mothers Living in Taiwan and Mainland China. Front Microbiol. 2017;8:965. Gonzalez E, Brereton NJB, Li C, Lopez Leyva L, Solomons NW, Agellon LB, et al. Distinct Changes Occur in the Human Breast Milk Microbiome Between Early and Established Lactation in Breastfeeding Guatemalan Mothers. Front Microbiol. 2021;12:557180. Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324(5931):1190-2. Murphy K, Curley D, O'Callaghan TF, O'Shea CA, Dempsey EM, O'Toole PW, et al. The Composition of Human Milk and Infant Faecal Microbiota Over the First Three Months of Life: A Pilot Study. Sci Rep.2017;7:40597. Greiner AK, Papineni RV, Umar S. Chemoprevention in gastrointestinal physiology and disease. Natural products and microbiome. Am J Physiol Gastrointest Liver Physiol. 2014;307(1):G1-15. Yan W, Luo B, Zhang X, Ni Y, Tian F. Association and Occurrence of Bifidobacterial Phylotypes Between Breast Milk and Fecal Microbiomes in Mother-Infant Dyads During the First 2 Years of Life. Front Microbiol. 2021;12:669442. Zimmermann P, Curtis N. Breast milk microbiota: A review of the factors that influence composition. J Infect. 2020;81(1):17-47. Williams JE, Carrothers JM, Lackey KA, Beatty NF, York MA, Brooker SL, et al. Human Milk Microbial Community Structure Is Relatively Stable and Related to Variations in Macronutrient and Micronutrient Intakes in Healthy Lactating Women. J Nutr. 2017;147(9):1739-1748. Salazar J, Durán P, Díaz MP, Chacín M, Santeliz R, Mengual E, et al. Exploring the Relationship between the Gut Microbiota and Ageing: A Possible Age Modulator. Int J Environ Res Public Health. 2023;20(10):5845. Urbaniak C, Angelini M, Gloor GB, Reid G. Human milk microbiota profiles in relation to birthing method, gestation and infant gender. Microbiome. 2016;4:1. Lima SF, Teixeira AGV, Lima FS, Ganda EK, Higgins CH, Oikonomou G, et al. The bovine colostrum microbiome and its association with clinical mastitis. J Dairy Sci. 2017;100(4):3031-3042. Lackey KA, Williams JE, Meehan CL, Zachek JA, Benda ED, Price WJ, et al. What's Normal? Microbiomes in Human Milk and Infant Feces Are Related to Each Other but Vary Geographically: The INSPIRE Study. Front Nutr. 2019;6:45. Ewald DR, Sumner SCJ. Human microbiota, blood group antigens, and disease. Wiley Interdiscip Rev Syst Biol Med. 2018;10(3):e1413. Lyons KE, Shea CAO, Grimaud G, et al. The human milk microbiome aligns with lactation stage and not birth mode. Sci Rep. 2022;12, 5598. Hermansson H, Kumar H, Collado MC, Salminen S, Isolauri E, Rautava S. Breast Milk Microbiota Is Shaped by Mode of Delivery and Intrapartum Antibiotic Exposure. Front Nutr. 2019;6:4. Simpson MR, Avershina E, Storrø O, Johnsen R, Rudi K, Øien T. Breastfeeding-associated microbiota in human milk following supplementation with Lactobacillus rhamnosus GG, Lactobacillus acidophilus La-5, and Bifidobacterium animalis ssp. lactis Bb-12. J Dairy Sci. 2018;101(2):889-899. Valeri F, Endres K. How biological sex of the host shapes its gut microbiota. Front Neuroendocrinol. 2021;61:100912. Soto A, Martín V, Jiménez E, Mader I, Rodríguez JM, Fernández L. Lactobacilli and bifidobacteria in human breast milk: influence of antibiotherapy and other host and clinical factors. J Pediatr Gastroenterol Nutr. 2014;59(1):78-88. Dethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 2008;6(11):e280. Summary of product characteristics – CLAMOXYL, powder for injectable solution (I.M.-I.V.) - National Agency for the Safety of Medicines and Health Products - Last updated on August 21, 2017. http://agence-prd.ansm.sante.fr/php/ecodex/rcp/R0305509.htm . Accessed 15 November 2023. Summary of product characteristics – CEFAZOLINE, powder for injectable solution/infusion - National Agency for the Safety of Medicines and Health Products - Last updated on December 6, 2019. http://agence-prd.ansm.sante.fr/php/ecodex/rcp/R0347277.htm . Accessed 15 November 2023. Quenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD, Fisher J, et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. Lancet. 2021;397(10285):1658-1667. Boix-Amorós A, Collado MC, Mira A. Relationship between Milk Microbiota, Bacterial Load, Macronutrients, and Human Cells during Lactation. Front Microbiol. 2016;7:492. Damaceno QS, Souza JP, Nicoli JR, Paula RL, Assis GB, Figueiredo HC, et al. Evaluation of Potential Probiotics Isolated from Human Milk and Colostrum. Probiotics Antimicrob Proteins. 2017;9(4):371-379. Solís G, de Los Reyes-Gavilan CG, Fernández N, Margolles A, Gueimonde M. Establishment and development of lactic acid bacteria and bifidobacteria microbiota in breast-milk and the infant gut. Anaerobe. 2010;16(3):307-10. Khodayar-Pardo P, Mira-Pascual L, Collado MC, Martínez-Costa C. Impact of lactation stage, gestational age and mode of delivery on breast milk microbiota. J Perinatol. 2014;34(8):599-605. Dahaban NM, Romli MF, Roslan NR, Kong SS, Cheah FC. Bacteria in expressed breast milk from mothers of premature infants and maternal hygienic status. Breastfeed Med. 2013;8(4):422-3. Cabrera-Rubio R, Mira-Pascual L, Mira A, Collado MC. Impact of mode of delivery on the milk microbiota composition of healthy women. J Dev Orig Health Dis. 2016;7(1):54-60. Consales A, Cerasani J, Sorrentino G, Morniroli D, Colombo L, Mosca F, et al. The hidden universe of human milk microbiome: origin, composition, determinants, role, and future perspectives. Eur J Pediatr. 2022;181(5):1811-1820. Boscaini S, Leigh SJ, Lavelle A, García-Cabrerizo R, Lipuma T, Clarke G, et al. Microbiota and body weight control: Weight watchers within? Mol Metab. 2022;57:101427. van der Vossen EWJ, de Goffau MC, Levin E, Nieuwdorp M. Recent insights into the role of microbiome in the pathogenesis of obesity. Therap Adv Gastroenterol. 2022;15:17562848221115320. Song Z, Liu H. The investigation of the association of pregnancy weight gain on maternal and neonatal gut microbiota composition and abundance using 16sRNA sequencing. BMC Pregnancy Childbirth. 2023;23(1):109. Edgar R. Taxonomy annotation and guide tree errors in 16S rRNA databases. PeerJ. 2018;6:e5030. Martinson JNV, Pinkham NV, Peters GW, Cho H, Heng J, Rauch M, et al. Rethinking gut microbiome residency and the Enterobacteriaceae in healthy human adults. ISME J. 2019;13(9):2306-2318. Meyer KM, Pace RM, Mohammad M, Haymond M, Aagaard KM. Composition of the breast milk microbiome is influenced by the method of 16S-amplicon sequencing used. American Journal of Obstetrics & Gynecology. 2019;220(1):S607-S608. Douglas CA, Ivey KL, Papanicolas LE, Best KP, Muhlhausler BS, Rogers GB. DNA extraction approaches substantially influence the assessment of the human breast milk microbiome. Sci Rep. 2020;10(1):123. Wright ML, Podnar J, Longoria KD, Nguyen TC, Lim S, Garcia S, et al. Comparison of commercial DNA extraction kits for whole metagenome sequencing of human oral, vaginal, and rectal microbiome samples. bioRxiv. 2023;2023.02.01.526597. Additional Declarations No competing interests reported. Supplementary Files Supplementary.xlsx Supplementary information Additional file 1. Table S1. Composition of the standard mock community representing a mixed microbial community of well-defined composition, including three Gram negative bacteria, five Gram positive bacteria and two yeasts (not used in the analysis). Additional file 1. Table S2. All clinical data concerning mother-child pairs. Each line represents a sample of breast milk provided by a mother and designated in the column entitled “Sample” bold character. The last four columns on the right of the table correspond to the bacterial culture and metagenomic analysis results for each breast milk sample. Additional file 1. Table S3. Relative abundance at phylum taxonomic rank for the 137 breast milk samples. Each line represents a sample of breast milk with relative abundances for each phylum (column). The last two lines in bold red correspond to the mean and standard deviation of each bacterial phylum. Additional file 1. Table S4. Relative abundance at genus taxonomic rank for the 137 breast milk samples. Each line represents a sample of breast milk with relative abundances for each genus (column). All bacterial genera have been assigned to the corresponding phylum. The last two lines in bold red correspond to the mean and standard deviation of each bacterial genus. Additional file 1. Table S5. Relative abundance at the genus taxonomic rank of the core microbiota. Each line represents a sample of breast milk and the detail of relative abundances of phyla contained in the sample corresponds to column. The last two lines in bold red correspond to the mean and standard deviation of each bacterial phylum. Additional file 1. Table S6. Relative abundance at the genus taxonomic rank of the core microbiota. The seven bacterial genera have been assigned to the corresponding phylum. The last two lines in bold red correspond to the mean and standard deviation of each bacterial genus. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5348794","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371765917,"identity":"ef37abcc-e4eb-4806-a06c-063cd2421d5d","order_by":0,"name":"Benjamin 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criteria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/8e213e3dbbde12e240dfcee0.png"},{"id":68384535,"identity":"46a6008e-b52e-4888-bfc7-a7c66e227be3","added_by":"auto","created_at":"2024-11-06 17:16:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall distribution of bacteria in 137 breast milk samples (phylum taxonomic rank)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/3a09fbcd60e7d575d95a913c.png"},{"id":68384538,"identity":"d562646b-476d-4b18-9236-635d06e089c6","added_by":"auto","created_at":"2024-11-06 17:16:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of the seven bacterial genera making up the core milk microbiota\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/a651198734b6e15c420605e8.png"},{"id":68384537,"identity":"e42747b7-8974-428b-9c16-8f45cb8706a2","added_by":"auto","created_at":"2024-11-06 17:16:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges over time in bacterial diversity of breast milk samples (n = 112 samples, 20 mothers)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/8212c93d455ae157828660a2.png"},{"id":68386536,"identity":"8863be79-d585-4dfc-a5cf-282a9e33e20c","added_by":"auto","created_at":"2024-11-06 17:40:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaternal factors not associated with bacterial diversity in breast milk microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Maternal age: 20 - 29 (n = 14), ≥ 30 (n = 23); (B) Gestational age: \u0026lt; 32 (n = 21), ≥ 32 (n = 16); (C) Parity: primiparous (n = 18), multiparous (n = 17), Not available (NA) (n = 2); (D) Population: Caribbean (n =4), European (n = 10), Middle Eastern (n = 1), North African (n = 5), Sub-Saharan African (n = 17); (E) Maternal blood group: A- (n = 1), A+ (n = 11), B+ (n = 7), O- (n = 1), O+ (n = 13), NA (n = 4); (F) Mode of delivery: cesarean (n = 24), vaginal (n = 13); (G) Infant gender: female (n = 20), male (n = 17); (H) Infant weight at birth: \u0026lt; 1.5 kg (n = 18), ≥ 1.5 kg (n = 15), NA (n = 4); (I) Antibiotic prophylaxis: no (n = 10), yes (n = 24), NA (n = 3).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/c5e6b45290e5899693df5cd4.png"},{"id":68384541,"identity":"feffed90-59a7-4524-8b21-a7c4b362a68e","added_by":"auto","created_at":"2024-11-06 17:16:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaternal factors associated with bacterial diversity in breast milk microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Pregnancy number: \u0026gt; 1 (n = 22), 1 (n = 13), Not available (NA) (n = 2); (B) Lactation stage: colostrum (n = 2), mature (n = 21), transient (n = 14); (C) BMI after delivery (2 ranges): ≥ 25 (n = 29), 18-24.9 (n = 7), NA (n = 1); (D) BMI after delivery (3 ranges): ≥ 30 (n = 11), 18-24.9 (n = 7), 25-29.9 (n = 18), NA (n = 1). The Inverse Simpson index demonstrated a relative significant higher diversity for mothers with a single pregnancy than those with several pregnancies (Inverse Simpson – p = 0.067; p \u0026lt; 0.1) (Figure 6A) and a gradual increase in diversity from colostrum to mature milk (Inverse Simpson – p = 0.065; p \u0026lt; 0.1) (Figure 6B). Alpha diversity for mothers overweight and obese (BMI ≥ 25) was lower than in mothers with BMI between 18-24.9, confirmed across all measures (Shannon – p = 0.032; p \u0026lt; 0.05, and Inverse Simpson – p = 0.065; p \u0026lt; 0.1) (Figure 6C). This last outcome was confirmed, using the Shannon Index, showing a decrease in alpha diversity with increasing BMI (Shannon - p = 0.062; p \u0026lt; 0.1): obese mothers had a lower diversity than overweight mothers (Figure 6D).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/a230ab040627d4c59cc3fe55.png"},{"id":68385753,"identity":"67df585a-d394-41f3-ac54-34d1f3a7b794","added_by":"auto","created_at":"2024-11-06 17:32:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":58260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacteria associated with different maternal factors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/05a90a28f7dbc634cf82298a.png"},{"id":68384543,"identity":"37af167b-ea16-402d-a085-a343303f747d","added_by":"auto","created_at":"2024-11-06 17:16:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges over time in bacterial on the Total Aerobic Flora (n = 112 samples, 20 mothers)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/81f2e85c9bac3dff974aa77b.png"},{"id":71166314,"identity":"9c77c324-4514-4438-a789-81a64c0f7845","added_by":"auto","created_at":"2024-12-11 17:38:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/4501f446-21a5-40fb-913e-334e86e88c3e.pdf"},{"id":68384544,"identity":"129ddde7-9ce2-46fa-91a2-33a68780af22","added_by":"auto","created_at":"2024-11-06 17:16:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":137965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1. \u003cstrong\u003eTable S1\u003c/strong\u003e. Composition of the standard mock community representing a mixed microbial community of well-defined composition, including three Gram negative bacteria, five Gram positive bacteria and two yeasts (not used in the analysis).\u003c/p\u003e\n\u003cp\u003eAdditional file 1. \u003cstrong\u003eTable S2\u003c/strong\u003e. All clinical data concerning mother-child pairs. Each line represents a sample of breast milk provided by a mother and designated in the column entitled “Sample” bold character. The last four columns on the right of the table correspond to the bacterial culture and metagenomic analysis results for each breast milk sample.\u003c/p\u003e\n\u003cp\u003eAdditional file 1. \u003cstrong\u003eTable S3\u003c/strong\u003e. Relative abundance at phylum taxonomic rank for the 137 breast milk samples. Each line represents a sample of breast milk with relative abundances for each phylum (column). The last two lines in bold red correspond to the mean and standard deviation of each bacterial phylum.\u003c/p\u003e\n\u003cp\u003eAdditional file 1. \u003cstrong\u003eTable S4.\u003c/strong\u003e Relative abundance at genus taxonomic rank for the 137 breast milk samples. Each line represents a sample of breast milk with relative abundances for each genus (column). All bacterial genera have been assigned to the corresponding phylum. The last two lines in bold red correspond to the mean and standard deviation of each bacterial genus.\u003c/p\u003e\n\u003cp\u003eAdditional file 1. \u003cstrong\u003eTable S5\u003c/strong\u003e. Relative abundance at the genus taxonomic rank of the core microbiota. Each line represents a sample of breast milk and the detail of relative abundances of phyla contained in the sample corresponds to column. The last two lines in bold red correspond to the mean and standard deviation of each bacterial phylum.\u003c/p\u003e\n\u003cp\u003eAdditional file 1. \u003cstrong\u003eTable S6\u003c/strong\u003e. Relative abundance at the genus taxonomic rank of the core microbiota. The seven bacterial genera have been assigned to the corresponding phylum. The last two lines in bold red correspond to the mean and standard deviation of each bacterial genus.\u003c/p\u003e","description":"","filename":"Supplementary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/d6eddc9b0af6bfa1dcc4858f.xlsx"},{"id":68385360,"identity":"b9b37d4f-4e12-4149-88b4-03055e5647ad","added_by":"auto","created_at":"2024-11-06 17:24:00","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":93204,"visible":true,"origin":"","legend":"","description":"","filename":"GraaphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5348794/v1/742ea147f14eb470790045ef.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic and bacterial culture studies of human breast milk microbiota: factors influencing its bacterial composition and diversity","fulltext":[{"header":"Background","content":"\u003cp\u003eHuman breast milk is considered the optimum feeding regime for newborn infants. It is a dynamic biological fluid that provides critical nutrients (human milk oligosaccharides, lipids, proteins, minerals...) and bioactive compounds such as antibodies, cytokines, defensins and certain bacteria that promote the establishment of the gut microbiome in newborns and support growth and immune development during early childhood. Breast milk feeding confers protection to the infant against several diseases such as gastrointestinal tract infections, necrotizing enterocolitis, respiratory tract infections and reduces the incidence of sudden infant death syndrome [1- Eidelman AI]. Infants fed with breast milk have reduced risk of chronic diseases such as allergies, asthma, diabetes, obesity, irritable bowel syndrome and Crohn's disease in both childhood and adult life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Breastfeeding has also been associated with higher cognitive development in children, with a dose-dependent effect (exclusive breastfeeding is more beneficial than partial breastfeeding) and a time-dependent effect (breastfeeding\u0026thinsp;\u0026gt;\u0026thinsp;3 months is more beneficial than \u0026le;\u0026thinsp;3 months) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Breast milk promotes intestinal health in the preterm infant but also a dynamic interplay of host and dietary factors that stimulate the colonization of and enrichment for specific bacteria during establishment of the preterm infant microbiota [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Initially, bacteria isolated from breast milk were considered as contaminants from the mother's skin and infant oral cavity or from incorrect handling methods [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Indeed, breast milk has long been considered a sterile biological fluid, but several studies have identified it as an integral source of microorganisms for the developing infant [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Breast milk has its own microbiota, including beneficial, commensal and potentially probiotic bacteria (\u003cem\u003eBifidobacterium spp.\u003c/em\u003e, \u003cem\u003eLactobacillus spp\u003c/em\u003e.). Breastfed infants consume on average up to 8 x 10\u003csup\u003e5\u003c/sup\u003e bacteria every day, with a breast milk considered the second most important integral source of microorganisms for the infant after the birth canal in vaginally born infants [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence of breast milk containing its own microbiota has been demonstrated by studies based on bacterial culture and metagenomics analysis. Some studies based only on aerobic and anaerobic culture have identified and isolated bacterial genera from breast milk, such as \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, this approach has many limitations as some bacterial are strict anaerobes (e.g. \u003cem\u003eBifidobacterium\u003c/em\u003e) and therefore more difficult to isolate. One of the most comprehensive methods for studying microbiota is metagenomics which enabled a more exhaustive identification of non-cultivable bacteria in breast milk. Unlike genomics, which usually consists in sequencing a single genome of a single bacterial strain, metagenomics simultaneously sequences genomes of several different strains from the same ecosystem. A metagenomic analysis provides the overall composition of microbiota: all the bacteria present with their respective abundances and diversities. A metagenomic approach by sequencing makes it possible to study the microbiota according to two complementary methodologies. Either by sequencing the whole genomes of the organisms present in the environment (shotgun metagenomics), or by sequencing one or more genes specific to each bacterial species (targeted 16S rRNA gene) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Using this culture-independent method, Hunt et al. identified eight bacterial genera (\u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003ePropionibacterium\u003c/em\u003e and \u003cem\u003eSphingomonas\u003c/em\u003e) in breast milk samples from 16 women [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the different published studies analysing milk microbiota, there are variations that can be attributed to many factors such as mode of delivery and health status of the mother [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, the bacterial composition and diversity of breast milk changes throughout the lactation period, from colostrum to transitional milk, and then to mature milk. Colostrum, the first liquid produced by mothers when their babies are born, is present from the first to the fourth day. Transitional milk is produced between five days and two weeks after birth and from the 15th day after delivery, milk is considered mature [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe conventional methods of bacterial culture (streaking by using calibrated sterile loops and plating by using calibrated micropipettes following serial dilutions) for quantifying and identifying the bacteria composing the microbiota of breast milk are not standardized, which makes it difficult to carry out an accurate assessment of its composition. Strictly anaerobic bacteria can only be cultured in the absence of O2, requiring more complex bacteriological methods for sampling, transport and subsequent culture and/or isolation, especially in hospital environments, where time is limited and samples are abundant. These technical constraints may explain why only a few laboratories master these analyses. It is therefore important to be able to use an alternative method. A study of the composition of the bacterial microbiota of breast milk using standard methods of bacterial culture on agar and metagenomics analysis methods would allow for more precise and comprehensive knowledge.\u003c/p\u003e \u003cp\u003e It is in this scientific and medical context that we decided to carry out a collaborative study between two expert teams, the Department of Hygiene, Prevention and Infection Control (HPIC) and Neonatal Intensive Care Unit (NICU) of the Centre Hospitalier Sud Francilien (CHSF), which has milk samples from the hospital's lactarium and the Centre National de Recherche en G\u0026eacute;nomique Humaine (CNRGH), which enables a response to scientific questions necessitating high throughput sequencing and bioinformatics analysis thanks to the development and deployment of innovative integrated technologies.\u003c/p\u003e \u003cp\u003eThe study of breast milk by bacterial culture on agar and metagenomics sequencing (targeted 16S rRNA gene) on the same samples constitutes one of the originalities of our research, with the aim of determining the factors influencing its bacterial composition and diversity. The analysis of numerous samples per mother enabled us to carry out a longitudinal study to investigate intra-individual variations in bacterial composition and diversity during the three lactation stages. Moreover, linear discriminant analysis effect size (LEfSe) allowed us to explore many clinical parameters (e.g. mode of delivery, lactation stage...) to identify bacterial markers related to clinical conditions.\u003c/p\u003e \u003cp\u003eWe report the outcomes of a cohort of 40 women who delivered premature and extremely premature infants, therefore with a breast milk microbiota highly specific. The challenge of breast milk nutrition for premature infants is increasingly significant. Indeed, preterm birth rates are rising in most countries. A study examining the trends of birth rates found that the global preterm birth rate rose from 9.8% in 2000 to 10.6% in 2014 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Birth prematurity condition of the 40 newborns have prevented them from breastfeeding directly - a hospital grade electric breast pump was an effective option to express milk and establish and maintain an adequate nutrition supply.\u003c/p\u003e \u003cp\u003eThus, to determine the factors influencing bacterial composition and diversity we studied breast milk from 40 mothers along the three lactation phases. We explored a wide range of clinical parameters to highlight the association of bacterial markers with clinical conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThe CHSF has approximately 1,200 beds across medicine, surgery and obstetrics, including three intensive care units, with over 80,000 admissions per year. The NICU has 58 beds, including 16 in the resuscitation department, 12 in the intensive care unit, 18 in medicine and 12 in the kangaroo unit, with 1,500 admissions per year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and inclusion criteria\u003c/h3\u003e\n\u003cp\u003e A prospective study was carried out on a cohort of 40 mothers who had given birth in our hospital or another in the surrounding area, and whose newborns were cared for in the NICU of our establishment. All mothers who delivered premature and extremely premature infants, with available milk samples who had decided to pump their milk without breastfeeding, were eligible for this study. Mothers who had received an antibiotic in the three months prior to delivery were excluded from the study, except for those who received antibiotic prophylaxis during delivery by injection. Informed consent was obtained from all mothers enrolled in the study.\u003c/p\u003e\n\u003ch3\u003ePatient data\u003c/h3\u003e\n\u003cp\u003eMaternal and gestational age (weeks of amenorrhea), geographical origin, blood group, mode of delivery, parity, pregnancy number, postpartum body mass index (BMI), lactation stage (colostrum, transitional and mature milk), infant gender and birth weight, and use of antibiotic prophylaxis during delivery were obtained from hospital records.\u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eEach mother provided one or more milk samples collected a few days to a few weeks postpartum in a sterile milk container. All collections were performed using a breast pump either at NICU or at home. Before sample collection, the breast pump was decontaminated with a detergent and disinfectant solution. The mother cleaned her hands and breasts with a mild liquid soap. The breast area was wiped while leaving the areolas and nipples wet without putting any clothes on them. If the collection was done in the NICU, a nurse collected the samples directly and immediately stored them at +\u0026thinsp;4\u0026deg;C. If collection was done at home, the samples were immediately stored at +\u0026thinsp;4\u0026deg;C for a maximum of 48 h before being transported in an isotherm container with a cold system and then stored in the NICU at +\u0026thinsp;4\u0026deg;C. Samples were partitioned into five mL tubes prior to transfer directly by the nurses with a cold system, from the NICU to the HPIC, and stored at +\u0026thinsp;4\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eCulture dependent bacterial analysis\u003c/h3\u003e\n\u003cp\u003eBreast milk was screened by culture on Columbia\u0026thinsp;+\u0026thinsp;5% sheep blood agar (COS, bioM\u0026eacute;rieux SA, Marcy-l\u0026rsquo;Etoile, France) for the Total Aerobic Flora (TAF) counts, and Chapman agar (MSA2, bioM\u0026eacute;rieux SA) for the \u003cem\u003eStaphylococcus aureus\u003c/em\u003e counts. Four plates of blood agar were inoculated by depositing with a calibrated pipette 100 \u0026micro;L of pure samples or diluted at 1/21th, 7/441th and 1/9261th with NaCl 0.85% Medium and then spread out with a rake. Two plates of Chapman agar were inoculated by depositing 100 \u0026micro;L of pure samples or diluted at 1/21th with NaCl 0.85% Medium. After 48 h incubation at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, TAF and \u003cem\u003eS. aureus\u003c/em\u003e were enumerated. All bacterial species were identified using standard bacteriological methods based on colony morphology, hemolytic activity, catalase production, coagulase test reactions and Gram staining. All enumerations were performed with an automatic colonies counter Scan 1200\u0026reg; (Interscience, St Nom, France). Finally, these same milk samples were stored at -80\u0026deg;C until transported to the CNRGH, for metagenomics analysis with a cold system, and stored at -20\u0026deg;C.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA gene sequencing of the milk microbiota\u003c/h2\u003e \u003cp\u003eDNA extraction was performed from whole breast milk (including lipids) using the ZymoBIOMICS\u0026trade; DNA Miniprep Kit according to the manufacturer's instructions (Zymo Research\u0026reg;, Irvine, California, USA). Briefly, 950 \u0026micro;L aliquots of each milk sample were submitted to a chemical lysis. Mechanical lysis was then performed by bead-beating with 0.1- and 0.5-mm diameter ceramic beads (Zymo Research\u0026reg;, Irvine, California, USA) using the FastPrep-24\u0026trade; machine (MP Biomedicals\u0026reg;, Illkirch-Graffenstaden, France). The samples were centrifuged (11,000 g at room temperature for 1 min), and the supernatant, containing the metagenomic DNA, was recovered in a volume of 500 \u0026micro;L. After several filtration/purification steps, the bacterial DNA was ready for quantitative and qualitative analyses. PCR reactions were performed using primers to amplify the V3-V4 variable segment of the 16S rRNA gene, which yielded approximately 550 bp. The primer sequences were as follows:\u003c/p\u003e \u003cp\u003e16S Amplicon PCR Forward Primer:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3'\u003c/h3\u003e\n\u003cp\u003e16S Amplicon PCR Reverse Primer:\u003c/p\u003e \u003cp\u003e5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3'.\u003c/p\u003e \u003cp\u003eNegative control was carried out alongside for each PCR performed. A mock community consisting of DNA extracted from 10 species with known relative abundances (Zymo Research\u0026reg; D6300, Irvine, California, USA) was also run as a positive control (more details in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEach 25 \u0026micro;L PCR reaction included 10.5 \u0026micro;L DNA template, 1 \u0026micro;L of both forward and reverse primers (5 \u0026micro;M) and 12.5 \u0026micro;L of dNTP mix (KAPA HiFi HotStart ReadyMix PCR Kit, Roche). Thermal cycler settings included a 3 min denaturation step at 95\u0026deg;C followed by 28 cycles of 95\u0026deg;C, 55\u0026deg;C, and 72\u0026deg;C at each temperature for 30 seconds. A final elongation step at 72\u0026deg;C for 5 min was then performed to complete each reaction before storing PCR products at -20\u0026deg;C until further use. A second series of PCR reactions was performed to integrate the Illumina\u0026reg; indexes and adapters, resulting in fragments of about 630 bp. Reactions were initially carried out adding 5 \u0026micro;L of DNA template. Thermal cycler settings included a 3 min denaturation step at 95\u0026deg;C followed by 8 cycles of 95\u0026deg;C, 55\u0026deg;C, and 72\u0026deg;C at each temperature for 30 seconds. A final elongation step at 72\u0026deg;C for 5 min was then performed to complete each reaction before storing PCR products at -20\u0026deg;C until further use. Purification steps were performed after each PCR using AMPure XP\u0026reg; paramagnetic beads. Finally, the 16S libraries were then normalized to 4 nM to form an equimolar pool. The DNA libraries were sequenced on an Illumina\u0026reg; MiSeq sequencer using the kit (MiSeq\u0026reg; Reagent Kit v3 600 Cycles PE, Illumina\u0026reg;) which sequenced 2x300 bp at the ends of the 16S amplicons. A sample of sterile water (5 Prime\u0026reg;, Germany) was used as a negative control. Demultiplexed sequencing data were deposited into the European Nucleotide Archive under accession number PRJEB73593.\u003c/p\u003e\n\u003ch3\u003eBioinformatics processing\u003c/h3\u003e\n\u003cp\u003eRaw sequences were trimmed to remove poor-quality tails (potentially clustering nucleotides associated to a Phred quality score\u0026thinsp;\u0026lt;\u0026thinsp;30), PCR primers, indexes and adapters by using \u003cem\u003ecutadapt v3.5 software\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Trimmed reads shorter than 100 bp were discarded. Then, the sequences were assembled into a single fragment per 16S amplicon (referred to hereafter as merged sequences) with the \u003cem\u003eusearch\u003c/em\u003e v11.0.667 \u003cem\u003efastq_mergepairs\u003c/em\u003e command [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The size of the conserved fragments was between 350 and 500 bp. Samples with less than 50,000 assembled sequences or less than 40% assembled sequences were excluded from the study. A downsampling to 150,000 sequences per sample with \u003cem\u003eseqtk v1.4.130 software\u003c/em\u003e was then performed to reduce the memory usage and the computation time of \u003cem\u003emothur software\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] used afterwards. Further sequence processing was performed with \u003cem\u003emothur v1.48.0\u003c/em\u003e following the MiSeq standard operating procedures [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Specifically, sequences were filtered to remove those with a homopolymer longer than 7 bp, those with more than 4 ambiguous bases and those shorter than 400 bp or longer than 500 bp. Sequences were de-replicated. Unique sequences were aligned to the SILVA v138.1 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and unaligned sequences were removed. To account for PCR and sequencing errors, sequences were binned together at 98% identity and chimeras were removed. Quality filtered reads were taxonomically classified by using the k-Nearest Neighbor algorithm and the SILVA v138.1 reference database. Sequences belonging to the taxa \u003cem\u003eEukaryota\u003c/em\u003e, \u003cem\u003eChloroplast\u003c/em\u003e, \u003cem\u003eMitochondria\u003c/em\u003e and \u003cem\u003eArchaea\u003c/em\u003e were discarded from the dataset. Sequences were subsequently binned into operational taxonomic units (OTU) by using a 97% similarity cutoff. Each OTU was classified, at the genus level, based on the consensus taxonomy of the sequences in the cluster with default bootstrapping confidence threshold.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDataset consisting of the abundance table of OTUs in the different samples, the OTUs taxonomy and patient metadata were analysed using R v4.3.2, phyloseq v1.46.0 and microbiome v1.24.0 R libraries. The composition of the mock community positive control was evaluated and checked for correspondence with the expected theoretical composition. Initial data pre-processing was carried out: OTUs present only in the mock community were excluded, as were OTUs with a relative abundance of 0%. This filtered dataset was used for the core microbiota analysis and the linear discriminant analysis. The core microbiota was defined as bacterial genera present in \u0026gt;\u0026thinsp;90% of the samples. Different additional filtrations were then applied before the alpha diversity analysis and the full microbiota analysis. After a rarefaction step to 44,000 sequences per sample, to avoid biases related to sequencing depth, alpha diversity of breast milk samples was measured using Shannon and inverse Simpson indices. Only one sample per mother was selected for alpha diversity analysis and differential abundance analysis: the sample with the highest Shannon index\u0026thinsp;\u0026gt;\u0026thinsp;0.5. For each maternal factor, alpha diversity differences between groups were assessed using the Wilcoxon-Mann-Whitney rank sum test for two-groups factors and the Kruskal-Wallis test for factors with more than two groups. Results with a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.1 or 0.05 were accepted as relative significant or significant respectively. For analysis of full microbiota, OTUs with relative abundance\u0026thinsp;\u0026le;\u0026thinsp;0.05% were filtered out. For each factor, differences in the relative abundance of bacterial taxa between sample groups were assessed using the LEfSe method [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] with default parameters and logarithmic discriminant analysis (LDA) score threshold of three. Maternal factors studied in the analyses described above were maternal and gestational age, population and mother\u0026rsquo;s blood group, mode of delivery, parity, pregnancy number, postpartum BMI, lactation stage, infant gender and birth weight, and use of antibiotic prophylaxis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLongitudinal analysis\u003c/h2\u003e \u003cp\u003eMothers who provided at least three samples were included for longitudinal analysis of alpha diversity and TAF over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eSample inclusion criteria\u003c/h2\u003e\n \u003cp\u003eIn this longitudinal, observational, monocentric study, we collected 204 samples (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), 67 (32.8%) were excluded, including 39 after the DNA extraction step due their degraded bacterial DNA profiles, 18 after PCR amplification step because of an insufficient DNA concentration, and 10 after the sequencing step according to the criteria specified in the \u003cspan class=\"InternalRef\"\u003emethods\u003c/span\u003e section.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical characteristics of study cohort\u003c/h2\u003e\n \u003cp\u003eThe study was performed from January 06 to November 06, 2022. We analysed 137 (67.2%) samples representing 40 mothers. Population characteristics are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (more details in Additional file 1: Table S2). The mean maternal age was 32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2 years, and the gestational age was 30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 weeks. There were 25 mothers (62.5%) who had delivered by cesarean section and 26 (65%) had antibiotic prophylaxis during delivery.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical characteristics of mother-infant pairs (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographics of mother-infant pairs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMode of delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of pregnancy (weeks)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal antibiotic treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCefazoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmoxicilline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClindamycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePregnancy number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal BMI after delivery (kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal blood group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub Saharan African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfant gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfant birth weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eEach mother provided one or more samples on different days postpartum and the mean lactation period (colostrum, transitional or mature milk) at collection was 26\u0026thinsp;\u0026plusmn;\u0026thinsp;18 days (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample(s) distribution per mother\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMother_number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmount of samples\u003c/p\u003e\n \u003cp\u003eper mother\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDays of postpartum collection\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/31/32/35/37/42/44/46/51/53/57/58/63/71/84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22/27/34/49/50/55/62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9/18/30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/11/27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/6/27/37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/7/9/14/15/21/24/42/44/48/51/69/72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/15/26/32/36/53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/16/23/37/39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/9/14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/19/33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/19/21/34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27/30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/19/23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9/15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/15/21/25/32/41/43/47/54/90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11/18/27/30/34/45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/9/11/16/18/23/25/30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/6/11/27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/16/48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/13/15/27/34/36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/20/27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/19/26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eBioinformatics processing\u003c/h2\u003e\n \u003cp\u003eAfter the poor-quality sequence removal steps, a mean of 218,049 reads was generated for all samples analysed \u003cem\u003eversus\u003c/em\u003e 578,700 reads for the mock community. As expected, sequence sizes ranged from 450 to 500 base pairs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of the milk microbiota composition\u003c/h2\u003e\n \u003cp\u003eA total of 89,394 OTUs were assembled across all samples, corresponding to 31 phyla and 983 genera. After removing OTUs specific to mock samples, a total of 76,130 OTUs were kept across all 137 breast milk samples. These could be annotated as 31 phyla and 981 genera. After deletion of taxa whose relative abundance was \u0026le;\u0026thinsp;0.05%, 66 OTUs were included in the final analysis. These could be annotated as 4 phyla and 39 genera. The phylum \u003cem\u003eFirmicutes\u003c/em\u003e was predominant (78\u0026thinsp;\u0026plusmn;\u0026thinsp;22%) in 119 (87%) samples followed by \u003cem\u003eProteobacteria\u003c/em\u003e (13\u0026thinsp;\u0026plusmn;\u0026thinsp;22%), \u003cem\u003eActinobacteria\u003c/em\u003e (8\u0026thinsp;\u0026plusmn;\u0026thinsp;11%) and \u003cem\u003eBacteroidetes\u003c/em\u003e (1\u0026thinsp;\u0026plusmn;\u0026thinsp;3%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRelative abundance profiles were heterogeneous between each mother. A phyla inversion in towards of \u003cem\u003eProteobacteria\u003c/em\u003e was observed in 14 samples (10.2%), corresponding to 10 mothers (25%). In addition, significantly more \u003cem\u003eProteobacteria\u003c/em\u003e were found in seven mothers (17.5%). Mother M23 had a completely atypical profile, with \u003cem\u003eProteobacteria\u003c/em\u003e dominating (\u0026gt;\u0026thinsp;97%), corresponding to \u003cem\u003eAcinetobacter\u003c/em\u003e dominating (\u0026gt;\u0026thinsp;60%). The same phenomenon was observed for \u003cem\u003eActinobacteria\u003c/em\u003e in two samples (1.5%), corresponding to two mothers (5%) (more details in Additional file 1: Table S3).\u003c/p\u003e\n \u003cp\u003eFor the genera, \u003cem\u003eStaphylococcus\u003c/em\u003e dominated (69\u0026thinsp;\u0026plusmn;\u0026thinsp;26%) in 105 (77%) samples, followed by \u003cem\u003eAcinetobacter\u003c/em\u003e (5\u0026thinsp;\u0026plusmn;\u0026thinsp;15%) and \u003cem\u003eCorynebacterium\u003c/em\u003e (4\u0026thinsp;\u0026plusmn;\u0026thinsp;6%). \u003cem\u003eFirmicutes\u003c/em\u003e were mainly represented by the genera \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;7%) and \u003cem\u003eEnterococcus\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;7%), \u003cem\u003eActinobacteria\u003c/em\u003e by the genera \u003cem\u003eCorynebacterium\u003c/em\u003e (4\u0026thinsp;\u0026plusmn;\u0026thinsp;6%), \u003cem\u003eCutibacterium\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;5%) and \u003cem\u003eBifidobacterium\u003c/em\u003e (1\u0026thinsp;\u0026plusmn;\u0026thinsp;4%), \u003cem\u003eBacteroidetes\u003c/em\u003e by the genus \u003cem\u003ePrevotella\u003c/em\u003e (1\u0026thinsp;\u0026plusmn;\u0026thinsp;3%), and \u003cem\u003eProteobacteria\u003c/em\u003e by the genera \u003cem\u003eAcinetobacter\u003c/em\u003e (5\u0026thinsp;\u0026plusmn;\u0026thinsp;15%) and \u003cem\u003ePseudomonas\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;4%) (more details in Additional file 1: Table S4).\u003c/p\u003e\n \u003cp\u003eAn analysis of the microbial community membership across all the 137 samples from the 40 mothers suggests that a core milk microbiota was present. This core was defined as genera present in over 90% of the 137 samples. The phylum \u003cem\u003eFirmicutes\u003c/em\u003e was predominant (72\u0026thinsp;\u0026plusmn;\u0026thinsp;24%) in 114 (83%) samples, followed by \u003cem\u003eProteobacteria\u003c/em\u003e (7\u0026thinsp;\u0026plusmn;\u0026thinsp;16%) and \u003cem\u003eActinobacteria\u003c/em\u003e (6\u0026thinsp;\u0026plusmn;\u0026thinsp;8%). \u003cem\u003eBacteroidetes\u003c/em\u003e were no longer represented (more details in Additional file 1: Table S5). The core milk microbiota was made up of seven genera: \u003cem\u003eStaphylococcus\u003c/em\u003e (67\u0026thinsp;\u0026plusmn;\u0026thinsp;26%), \u003cem\u003eAcinetobacter\u003c/em\u003e (5\u0026thinsp;\u0026plusmn;\u0026thinsp;14%), \u003cem\u003eCorynebacterium\u003c/em\u003e (4\u0026thinsp;\u0026plusmn;\u0026thinsp;6%), \u003cem\u003ePseudomonas\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;4%), \u003cem\u003eCutibacterium\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;4%), \u003cem\u003eEnterococcus\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;7%) and \u003cem\u003eStreptococcus\u003c/em\u003e (2\u0026thinsp;\u0026plusmn;\u0026thinsp;7%) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and more details in Additional file 1: Table S6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eAlpha diversity: longitudinal analysis\u003c/h2\u003e\n \u003cp\u003eMothers who provided at least three samples were selected for longitudinal analysis of bacterial diversity over time. A total of 20 mothers (50%) representing 112 samples (81.8%) were included in the final analysis (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThis graph shows the bacterial diversity of breast milk samples provided by different mothers. Each colour represents a mother. The y-axis shows the Shannon index: the higher the numerical value, the greater the bacterial diversity in a sample. On the x-axis, this is the mother identification (Mx) associated with the day on which the post-delivery milk sample was taken (Dx). Alpha diversity varies over time in all mothers, with an average value of 0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 (0.08\u0026ndash;2.41). In general, this diversity tends to increase. There was intra-individual variability (heterogeneity of alpha diversity over time) and inter-individual variability. Bacterial diversity seems to be a parameter intrinsic to each mother. Indeed, irrespective of the lactation stage, some mothers had a lower bacterial diversity at a later lactation stage (M01), while others had a higher bacterial diversity at an earlier lactation stage, from colostrum onwards (M27).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eAlpha diversity and maternal factors\u003c/h2\u003e\n \u003cp\u003eThe relationship between alpha diversity and several factors was investigated. One sample per mother, with a Shannon threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and the highest alpha diversity, was analysed. A total of 37 samples were analysed. No statistically significant differences were observed for maternal and gestational age, parity, population and blood group, mode of delivery, infant gender, and infant weight, and use of antibiotic prophylaxis during delivery (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConversely, alpha diversity was significantly modified by pregnancy number, lactation stage and BMI after delivery, based on several measures including Shannon Index and Inverse Simpson (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eBacterial markers of maternal factors\u003c/h2\u003e\n \u003cp\u003eThe relationship between bacterial taxa and several maternal factors was investigated. For each factor, LEfSe reported features differentially abundant between groups, with a LDA score\u0026thinsp;\u0026ge;\u0026thinsp;3. Bacterial markers were distinct between the different groups (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWe found that bacterial genera \u003cem\u003eAeromonas\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e were enriched in the BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 group while breast milk from mothers with BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 were composed of \u003cem\u003eAnaeroglobus\u003c/em\u003e, \u003cem\u003eAmaricoccus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eSynergistaceae\u003c/em\u003e and \u003cem\u003eCampylobacter\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eCutibacterium\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eLimosilactobacillus\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e were enriched in the primiparous group (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). In colostrum, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eActinobacillus\u003c/em\u003e, \u003cem\u003ePLTA13\u003c/em\u003e were identified (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). Mothers who have had more than one pregnancy have milk enriched with \u003cem\u003eBifidobacterium\u003c/em\u003e, while those with a first pregnancy produce breast milk rich in the \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e genera (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD). Sub-Saharan African mothers had a microbiota enriched in \u003cem\u003eCorynebacterium\u003c/em\u003e, North African with \u003cem\u003eChryseobacterium\u003c/em\u003e, \u003cem\u003eEmpedobacter\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, Middle Eastern with \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eAlloscardovia\u003c/em\u003e, \u003cem\u003eAtopobium\u003c/em\u003e, \u003cem\u003ePeptostreptococcus\u003c/em\u003e, \u003cem\u003eLactobacillales\u003c/em\u003e, European with \u003cem\u003eCampylobacter\u003c/em\u003e and Caribbean with \u003cem\u003eCutibacterium\u003c/em\u003e, \u003cem\u003eListeria\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE). \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e were enriched for babies having a weight greater or equal to 1.5 kg, while \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eDelftia\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e for those with a lower weight (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF). \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eChryseobacterium\u003c/em\u003e were enriched for mothers with age\u0026thinsp;\u0026ge;\u0026thinsp;30 yrs, whereas \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e were associated for the 20\u0026ndash;29 yrs maternal age (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eG). In the case of maternal blood group, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003ePrevotellaceae\u003c/em\u003e, \u003cem\u003eAnaerococcus\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e were enriched for mothers O- blood group, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eVaribaculum\u003c/em\u003e with mothers O\u0026thinsp;+\u0026thinsp;blood group, \u003cem\u003eEmpedobacter\u003c/em\u003e with mothers A- blood group (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eH). Mothers who had a baby boy were given milk enriched with \u003cem\u003eStreptococcus\u003c/em\u003e, while those who had a girl had milk enriched with \u003cem\u003eAnaerococcus\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eI). Mothers who have not received antibiotic prophylaxis had milk enriched with \u003cem\u003eAnaerococcus\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eJ). In the case of gestational age, \u003cem\u003eStreptococcus\u003c/em\u003e was enriched for mothers withgestational age\u0026thinsp;\u0026ge;\u0026thinsp;32 weeks, whereas \u003cem\u003ePrevotellaceae\u003c/em\u003e for \u0026lt;\u0026thinsp;32 SA (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eK). Vaginal deliveries were associated with \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e genera, and cesarean deliveries were characterized by \u003cem\u003eAnaerococcus\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eL).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eBacterial culture: longitudinal analysis of Total Aerobic Flora\u003c/h2\u003e\n \u003cp\u003eMothers who provided at least three samples were selected for longitudinal analysis of TAF over time. A total of 20 mothers (50%) representing 112 samples (81.8%) were included in the final analysis (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThis is a graph representing the bacterial TAF of breast milk samples provided by different mothers. Each colour represents a mother. The y-axis shows the counting of TAF: the higher the numerical value, the greater the bacterial TAF in a sample. On the x-axis, this is the mother identification (Mx) associated with the day on which the post-delivery milk sample was taken (Dx). TAF varied considerably from one mother to the next, with an average of 442,998 (420; 9,700,000) Colony Forming Unit (CFU)/mL for all samples analysed but was fairly constant for samples from the same mother. However, these variations did not appear to be as marked for samples from the same mother, where stability was observed over time. TAF seemed to be a parameter intrinsic to each mother. Indeed, irrespective of the lactation stage, some mothers had a lower TAF like M01 (median at 6,500), while others had a higher TAF like M06 (median at 8,400,000).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eComparative metagenomics analysis and bacterial culture\u003c/h2\u003e\n \u003cp\u003eBacterial culture analysis identified one or more bacterial species in 23 mother\u0026rsquo;s breast milk (58%) representing 68 samples (49.6%). \u003cem\u003eStaphylococcus\u003c/em\u003e spp (\u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eStaphylococcus lugdunensis\u003c/em\u003e, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e and \u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e) and \u003cem\u003eEnterobacterales\u003c/em\u003e (\u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eEnterobacter bugandensis\u003c/em\u003e, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e, \u003cem\u003eKlebsiella aerogenes\u003c/em\u003e, \u003cem\u003eKlebsiella oxytoca\u003c/em\u003e and \u003cem\u003eKosakonia cowanii\u003c/em\u003e) were in a predominant place, with 21% and 22% respectively. Bacterial species identified by bacterial culture were identified to genus taxonomic level by metagenomics in 61 (89.7%) of 68 samples. The unidentified bacteria by metagenomics were \u003cem\u003eEnterobacter bugandensis\u003c/em\u003e, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e, \u003cem\u003eKosakonia cowanii\u003c/em\u003e and \u003cem\u003eKlebsiella aerogenes\u003c/em\u003e. Conversely, the \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e genera were identified by metagenomics in higher-than-normal quantities. However, none of these bacteria were identified by bacterial culture in the corresponding samples (more details in Additional file 1. Table S2).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOf the 204 initial samples, 67 (32,8%) were excluded after the DNA extraction step, PCR amplification step and the sequencing step. This type of exclusion is already documented in literature where in some case up to 25% of the mother-infant pairs were eliminated due to insufficient quantity of qPCR-recovered milk bacteria [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Metagenomic DNA extraction from breast milk is a challenge due to its low biomass [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Of the excluded samples, eight (12%) were provided by the same mother. None of the samples from this mother could be analysed. The exclusion of the eight samples may be caused by inadequate sample storage [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] or related to an inherent maternal factor associated with an already reduced TAF prior to the analysis of the milk samples. Indeed, the TAF counts for these 67 excluded samples were relatively low with a median of 12,000 CFU/mL \u003cem\u003eversus\u003c/em\u003e 30,000 CFU/mL for the 137 samples in our study. Furthermore, this was confirmed with the eight samples provided by the same mother, which had a median of 138 CFU/mL. It appears to correlate with TAF counts obtained by bacterial culture and samples excluded from DNA extraction, PCR amplification and sequencing steps. Indeed, the median TAF counts for our hospital ranged from 19,000 to 33,000 CFU/mL between 2017 and 2019.\u003c/p\u003e \u003cp\u003eExamination of the communities on a sample-by-sample basis within the mothers suggested that the stability and membership of bacterial communities present were less variable within a single mother than from mother to mother, which has already been observed in other studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Four main phyla were identified. The phylum \u003cem\u003eFirmicutes\u003c/em\u003e was predominant and more specifically \u003cem\u003eStaphylococcus\u003c/em\u003e: the majority of performed studies have identified \u003cem\u003eStaphylococcus\u003c/em\u003e as the most consistently and frequently observed genera in human milk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Only phylum \u003cem\u003eBacteroidetes\u003c/em\u003e was not observed in all 137 samples and was predominantly represented by the genus \u003cem\u003ePrevotella\u003c/em\u003e whose species are mainly found in oral, vaginal, skin and intestinal microbiota [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Its presence in some samples could indicate either breastfeeding of the infant with a migration of bacteria from the oral cavity to the mother's mammary ducts [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], or a transfer of intestinal bacteria from the mother to the mammary glands via the entero-mammary pathway [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies have highlighted retrograde back flow of milk due to infant suckling using ultrasound imaging [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This back flow into the mammary ducts is a possible mechanism for the transfer of bacteria from the infant's mouth into the mother's mammary gland. Moreover, the discovery of anaerobic species (e.g. \u003cem\u003eBifidobacterium\u003c/em\u003e) associated with gut environments and unable to exist in aerobic environments has raised questions about the origin of these bacteria in breast milk. Live bacteria from the maternal gut could travel by an endogenous route to the mammary gland via the presence of an entero-mammary pathway [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This translocation of bacteria from maternal gut to mammary gland involves complex interactions between epithelial cells, immune cells and bacteria [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. One piece of evidence of the entero-mammary pathway includes the presence of bacterial communities in colostrum collected before the infant's first feeding [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The genus \u003cem\u003eStreptococcus\u003c/em\u003e is poorly represented in this study. It is a genus observed in studies where mothers mainly breastfeed because it is a germ predominant in the oral cavity of infants [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study, mothers used a breast pump to collect their milk due to the relatively high prematurity of the children, which explains the low occurrence of the genus \u003cem\u003eStreptococcus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAlthough maternal factors are thought to significantly influence the microbiota present in breast milk, a few taxa are consistently observed in studies. The seven bacterial genera that make up the core microbiota of our study belong to the genera most frequently detected by sequencing of breast milk [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe conventional relative abundance profile found in our study was dominated by \u003cem\u003eFirmicutes\u003c/em\u003e followed by \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e. However, samples from the M23 mother showed a predominance of \u003cem\u003eProteobacteria\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;97%, represented by the genus \u003cem\u003eAcinetobacter\u003c/em\u003e. It is an environmental (soil and water) bacterium that is generally transmitted by hands [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It is therefore probably a case of contamination at the time of sampling by the mother. Furthermore, our hypothesis is confirmed by the fact that the \u003cem\u003eAllorhizobium-Neorhizobium-Pararhizobium-Rhizobium\u003c/em\u003e genus is the second most common bacterial genus found in this mother's samples. Populations of \u003cem\u003eRhizobium\u003c/em\u003e are bacteria specialized in nitrogen fixation in close association with plant roots, suggesting \u003cem\u003eRhizobium\u003c/em\u003e was likely derived from soil. These observations appeared to be consistent with patterns of accidental contamination from the environment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Presuming that it is a contamination, it is however very unusual to find such an abundance. In addition, no species of the genus \u003cem\u003eAcinetobacter\u003c/em\u003e were isolated in bacterial culture in these samples. This could be due to phenotypic confusion during microbiological analysis at the Department of HPIC. Although \u003cem\u003eAcinetobacter\u003c/em\u003e is easily cultivated on standard media, the phenotypic characteristics of the different species make them difficult to identify. Indeed, the \u003cem\u003eAcinetobacter\u003c/em\u003e genus can be phenotypically confused with coagulase-negative \u003cem\u003eStaphylococcus\u003c/em\u003e or \u003cem\u003eEnterobacterales\u003c/em\u003e, meaning that the results obtained are operator-dependent. It is therefore possible that the \u003cem\u003eAcinetobacter\u003c/em\u003e genus of samples from the M23 mother have not been identified due to an erroneous phenotypic determination.\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eCorynebacterium\u003c/em\u003e is frequently isolated from breast milk samples [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and considered as contaminant [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Species are generally considered commensal in nature found on the skin microbiota of humans [42 - Grice EA]. Its occurrence in the samples is probably related to contamination through the mother's skin during milk collection.\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eBifidobacterium\u003c/em\u003e has already been isolated from breast milk samples in several studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], although it is a strict anaerobic bacterium present in the human gut. In our study, the 16S rRNA gene approach did not allow us to reach the taxonomic rank of the species by metagenomics. It is consequently difficult to determine the origin of this bacterium: maternal intestine supporting the hypothesis of the entero-mammary pathway [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] or contamination during milk collection due to poor hand hygiene. However, thus far, little is known about the occurrence and the ecological relevance of rare \u003cem\u003eBifidobacterium\u003c/em\u003e phylotypes in human body environments. The findings of Wenli et al clearly supported the hypothesis that breast milk can contain commensal bacteria, in particular endosymbiotic \u003cem\u003eBifidobacterium\u003c/em\u003e species. On the other hand, certain exogenous phylotypes of \u003cem\u003eBifidobacterium\u003c/em\u003e insensitive to oxygen and cosmopolitan in lifestyle (\u003cem\u003eB. animalis\u003c/em\u003e and \u003cem\u003eB. adolescentis\u003c/em\u003e) may be indirectly transferred to breast milk via environmental contamination [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLongitudinal analysis findings suggest that there are intra and inter-individual variations in alpha diversity among different mothers over time. This may be explained by certain parameters not considered in our study, such as maternal diet may differ from one mother to the next. Regardless of dietary choices, nutrition has a significant impact on breast milk composition: a higher intake of saturated fatty acids and monounsaturated fatty acids is associated with a lower relative abundance of \u003cem\u003eCorynebacterium\u003c/em\u003e in breast milk. Similarly, higher levels of \u003cem\u003eRhizobium\u003c/em\u003e are found in the milk of women who eat a legume-based diet [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. There must also be genetic aspects specific to each mother that have not yet been studied. Intra-individual variations highlight the importance of collecting several milk samples per mother, as a single sample is not sufficiently representative and can lead to major biases in the researchers' analyses and conclusions.\u003c/p\u003e \u003cp\u003eOur results highlighted links between alpha diversity, predominance of bacterial genera and several maternal factors.\u003c/p\u003e \u003cp\u003eAbout maternal age, Williams et al found no association between maternal age and the composition of breast milk [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, studies that analysed gut microbiota compositions from people of advanced age concluded that there is a general decrease in microorganism diversity, together with an increase in opportunistic agents. Indeed, differences in the gut microbiota composition were found with the predominance of the phylum \u003cem\u003eBacteroidetes\u003c/em\u003e, more prevalent in the elderly than in younger adults where the phylum \u003cem\u003eFirmicutes\u003c/em\u003e is more abundant. Numerous microorganisms, mainly opportunistic pathogens, increase during aging, including \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eParaprevotella\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003eCoprobacillus\u003c/em\u003e, and \u003cem\u003ePeptoniphilus\u003c/em\u003e [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These various studies are therefore in line with our results on breast milk. Indeed, we have identified the genera \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eChryseobacterium\u003c/em\u003e (belonging to the phylum \u003cem\u003eBacteroidetes\u003c/em\u003e) for mothers with age\u0026thinsp;\u0026ge;\u0026thinsp;30 yrs.\u003c/p\u003e \u003cp\u003eLike ourselves, Urbaniak et al found no association between gestational age and the composition of breast milk [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, Mantziari et al have reported significant differences in the microbiota of breast milk correlated to the gestational age, with higher levels of \u003cem\u003eEnterococcus\u003c/em\u003e and lower levels of \u003cem\u003eBifidobacterium\u003c/em\u003e observed in the milk of preterm-delivered mothers compared to those who gave birth on the expected birth date [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbout parity, no statistically significant differences in bacterial diversity were observed on breast milk microbiota, but the primiparous mothers had milk samples enriched with \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eCutibacterium\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eLimosilactobacillus\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e. Two studies did not find an effect of parity on the composition of the breast milk microbiota [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, parity has been shown to affect the microbiota composition of cow milk: primiparous cows have a milk more diversified compared to multiparous cows [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbout population, studies have explored the impact of geographic origin on breast milk composition. This could be due to the different dietary and environmental exposure patterns. For example, population differences were also found in Iran, with total counts of \u003cem\u003eLactobacillus\u003c/em\u003e being higher in the milk of mothers residing in rural areas compared to those in urban areas. Meehan and colleagues examined the breast milk of hunter-gatherer and horticulturalist women in the Central African Republic, reporting lower levels of \u003cem\u003eLactobacillus\u003c/em\u003e in the milk of hunter-gatherers than horticulturalists. Lower relative abundances of \u003cem\u003eBifidobacterium, Propionibacterium, Veillonella\u003c/em\u003e and \u003cem\u003eSerratia\u003c/em\u003e were reported in the breast milk of Central African Republic mothers compared to United states or Swiss mothers. Lackey and colleagues showed that the composition of breast milk varies significantly within and across cohorts from international sites (Ethiopia, Gambia, United States, Kenya, Peru, Spain, and Sweden) after analysis of 394 breast milk samples and assume that bacterial compositions of breast milk and feces of breastfed infants are likely specific within a culture and location [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. When the breast milk composition was studied in mothers who had delivered vaginally, Spanish mothers had the highest relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e in breast milk when compared to mothers from Finland, China or South Africa [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In our study, we observed that five distinct geographical origins did not share any bacterial genera. Each group seemed to get its own microbiota enrichment. Additional research is needed to understand environmental, behavioral, dietary and genetic factors driving these variations and associations.\u003c/p\u003e \u003cp\u003eOur results showed each blood group seemed to have their own functional bacterial markers that should hypothesize that blood group shape in part the milk microbiota as it was already described for intestinal microbiota [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. To better understand the microbiome, we need to consider the blood type antigens and their influence on the intestinal microbiota and correlate with the possible incidence on microbiota breast milk considering that a set of breast milk bacteria come from the mother\u0026rsquo;s intestine via the entero-mammary pathway [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Indeed, these surface-associated markers not only serve as antigens, but they are also a food source for intestinal bacteria. These antigens found on cell surfaces and in body fluids and secretions are not identical. There are two glycoside \u0026ldquo;precursor\u0026rdquo; chains with slightly different structures, known as Type 1 and Type 2 chains. Type 1 chains and their terminal antigens are detectable in breast milk of individuals who are known as \u0026ldquo;secretors\u0026rdquo;, but these antigens are missing in the body fluids of about 20% of people of European descent, who are known as \u0026ldquo;non-secretors\u0026rdquo;. Secretor status influences susceptibility to pathogens such as \u003cem\u003eVibrio cholerae\u003c/em\u003e, \u003cem\u003eCampylobacter jejuni\u003c/em\u003e, \u003cem\u003eHelicobacter pylori\u003c/em\u003e, and could therefore have an influence on the composition of microbiota, including that of breast milk [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It would be relevant to look at the secretor status of the mothers in a future study.\u003c/p\u003e \u003cp\u003eOverall, three bacterial genera discriminated the milk samples from women who gave birth vaginally and via cesarean. Lyons et al have described that \u003cem\u003ePeptoniphilus\u003c/em\u003e and \u003cem\u003eAnaerococcus\u003c/em\u003e were associated with vaginal deliveries [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], \u003cem\u003ein contrario\u003c/em\u003e to our results showing rather an association between \u003cem\u003eAnaerococcus\u003c/em\u003e and cesarean mode of delivery. Urbaniak et al did not find significant differences in microbial profiles in the breast milk microbiota based on mode of delivery [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Hermansson et al found no significant differences related to the mode of delivery in the relative abundance of bacteria on the phylum and family level. However, the alpha diversity and richness of the breast milk microbiota was significantly higher in mothers who had delivered vaginally as compared with the mothers who had delivered by cesarean section [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContrasting results have been reported for infant gender, which in some studies seemed to affect breast milk composition, but in others, no correlation was observed. For instance, a higher relative abundance of \u003cem\u003eRothia\u003c/em\u003e was detected in the milk from mothers of female infants compared with mothers of male infants [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], \u003cem\u003ein contrario\u003c/em\u003e to our results showing rather an association between \u003cem\u003eAnaerococcus\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e for female infants and \u003cem\u003eStreptococcus\u003c/em\u003e for male infants. However, in Simpson's study, the women were breastfeeding, whereas in ours they were using a breast pump, which could explain these discrepancies. Moossavi et al observed differences in milk microbiota alpha diversity and overall community structure according to infant gender [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The mechanism for the observed sex differences in milk microbiota remains to be determined. Sex differences in gut microbiota have been reported and are usually attributed to hormonal differences between male and female hosts [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Analysis of neonates\u0026rsquo; stool samples during the first 30 days revealed for example that male infants had a lower alpha diversity as compared to females, which showed also higher abundance of \u003cem\u003eClostridiales\u003c/em\u003e, and lower abundance of \u003cem\u003eEnterobacterales\u003c/em\u003e. Another study showed that boys had higher \u003cem\u003eBifidobacterium\u003c/em\u003e level in their intestinal microbiota at the first day of life as compared to girls [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In contrast, Urbaniak et al found no differences in microbial profiles of breast milk based on infant gender [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analyses have shown that bacterial markers are correlated with infant weight at birth. In contrast, Moossavi et al, found no differences in microbial profiles based on infant birth weight [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This is a parameter that has been little explored in the previous studies on breast milk microbiota. Our findings underline the need to take this into account in future studies.\u003c/p\u003e \u003cp\u003eOur results show that the presence or absence of antibiotic prophylaxis during delivery did not influence bacterial diversity. Few studies on the impact of antibiotics on the microbiota of breast milk have been described to date. Results regarding the use of antibiotics are contradictory. For instance, in a study of women living in Germany and Austria abundance of lactobacilli or bifidobacteria was lower in women who had received antibiotics during pregnancy or lactation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. However, another study reported that the effect of antibiotic exposure on breast milk microbiota at one-month postpartum increased bacterial richness and diversity [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The impact of the consumption of antibiotics during pregnancy and lactation on the milk microbiome needs to be more extensively explored. According to some studies on the impact of antibiotics on the gut microbiota, it would have been expected that the presence of antibiotic prophylaxis during delivery would decrease bacterial diversity [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Our findings may be related to a lack of power due to a small number of patients or to the pharmacokinetics of the drugs used. Indeed, only CEFAZOLIN and AMOXICILLIN were administered intravenously. These antibiotics belong to the beta-lactam family of the 1st generation cephalosporins (CEFAZOLIN) and penicillins (AMOXICILLIN), which pass into breast milk at very low concentrations. The use of these antibiotics is possible and permitted during breastfeeding. It is mainly eliminated in the urine and a small percentage is eliminated in the bile. When administered intravenously, the half-life of CEFAZOLIN is approximately 100 minutes compared to 60 minutes for AMOXICILLIN. It is possible to consider that the elimination of a drug occurs in five half-lives, i.e. 500 minutes, i.e. 8.3 hours for CEFAZOLIN as well as five hours for AMOXICILLIN [59, 60]. In addition, these antibiotics were administered at a single dose, which could explain a more rapid resilience of the bacterial microbiota in breast milk and a stable diversity in milk samples collected several days after administration. Thus, it seems unlikely that the administration of these antibiotics at the time of delivery would have had a sufficiently significant impact on the microbiota of breast milk as opposed to a conventional treatment over several days. A small percentage of the antibiotics are excreted in the bile, which means that their enterohepatic cycle should have a minor impact on the gut microbiota and therefore ultimately on the microbiota of breast milk via the entero-mammary pathway [59, 60]. However, it is interesting to note that only samples from mothers who had received antibiotic prophylaxis had no bacterial markers. The microbiota in breast milk therefore seems to be slightly affected. It seems appropriate to pursue this area of research.\u003c/p\u003e \u003cp\u003eIn our study, mothers who have had more than one pregnancy have milk enriched with \u003cem\u003eBifidobacterium\u003c/em\u003e, while those with a first pregnancy produce breast milk rich in the \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e genera. It was important to distinguish between pregnancy number and parity. Indeed, most of the mothers in our study come from Sub-Saharan Africa, a population where the number of pregnancies that are not carried to term is high [61]. There are therefore differences between pregnancy number and parity in our cohort of mothers. Bearing in mind that the microbiota of the mother's milk begins to form in the last trimester of pregnancy, some primiparous mothers may have a more diverse breast milk microbiota due to several previous pregnancies that were not carried to term. All these hypotheses may be confirmed by our results, as we observed significant differences between pregnancy number and parity in terms of diversity via alpha diversity and bacterial composition via LEfSe analysis.\u003c/p\u003e \u003cp\u003eOur study showed that the alpha diversity of the samples analysed varied over the lactation period and increased with the maturity of the breast milk. In contrast, another study did not show changes in the number of bacteria in breast milk during the first month of life [62]. Two studies reported higher total bacterial loads in colostrum compared with mature milk [63, 64]. Two other studies reported an increase in total bacterial loads throughout the lactation period [65, 66]. The microbiota present in colostrum has already demonstrated broad bacterial diversity, with the dominant bacteria mainly belonging to the genera \u003cem\u003eWeissella\u003c/em\u003e, \u003cem\u003eLeuconostoc\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e [67, 68], which is partly confirmed by our LEfSe analysis, as we also identified the genus \u003cem\u003eStaphylococcus\u003c/em\u003e like bacterial markers correlated with colostrum.\u003c/p\u003e \u003cp\u003eAbout BMI, overweight and obese mothers have decreased bacterial diversity impacting the composition of the breast milk which could affect and delay a right establishment of the neonatal gut microbiota. It is in concordance with was already described in the literature [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Moreover, it is also described in studies on the gut microbiota [69]. It is therefore possible to establish a link between the intestinal microbiota and that of breast milk, supporting the hypothesis of an entero-mammary pathway linking the two microbiotas. Nonetheless, two studies did not find any influence of BMI on the composition of the breast milk microbiota [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The \u003cem\u003ePrevotella\u003c/em\u003e enterotype has already been described to be associated with a lower BMI, in accordance with our LEfSe analysis [70]. Our results concerning the bacterial markers \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003eAeromonas\u003c/em\u003e associated with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 post-delivery are correlated with a study recently described in case of these two bacterial markers were found in mother\u0026rsquo;s gut which had an excessive pregnancy weight gain, as well as in their offspring neonatal gut too [71]. It's worth noting that presence of the genera \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003eAeromonas\u003c/em\u003e in the mother's gut microbiota may come from the diet during pregnancy and should be transferred to the neonatal gut by the mother's milk in case of breastfeeding, which could support the entero-mammary theory.\u003c/p\u003e \u003cp\u003eOur findings on longitudinal analysis of TAF counts are not correlated with those obtained from the analysis of bacterial diversity. This confirms that bacterial diversity is not only influenced by aerobic bacteria, but also by anaerobic bacteria, most likely derived from the maternal gut microbiota.\u003c/p\u003e \u003cp\u003eMost bacterial species identified in the samples by bacterial culture were identified to the taxonomic genus by metagenomic analysis except in some bacteria. These are four bacteria from the \u003cem\u003eEnterobacterales\u003c/em\u003e family (\u003cem\u003eEnterobacter bugandensis\u003c/em\u003e, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e, \u003cem\u003eKosakonia cowanii\u003c/em\u003e and \u003cem\u003eKlebsiella aerogenes\u003c/em\u003e). This is probably a taxonomic assignment error in the SILVA database. The 97% similarity threshold for defining an OTU is limiting for discriminating between genera in the \u003cem\u003eEnterobacterales\u003c/em\u003e family such as \u003cem\u003eEnterobacter\u003c/em\u003e or \u003cem\u003eKosakonia\u003c/em\u003e [72]. 16S rRNA gene sequencing cannot identify all \u003cem\u003eEnterobacterales\u003c/em\u003e and seems to be that the accuracy of 16S rRNA gene sequencing, regardless of analytical approach (OTU), is poor for \u003cem\u003eEnterobacterales\u003c/em\u003e. Nevertheless, OTU-based analysis remains the more accurate approach for detecting \u003cem\u003eEnterobacterales\u003c/em\u003e [73]. On the contrary, some genera (\u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e) were identified in certain breast milk samples by metagenomics analysis but not in bacterial culture. But the genus \u003cem\u003eBifidobacterium\u003c/em\u003e and most species of the genus \u003cem\u003ePrevotella\u003c/em\u003e are strict anaerobic bacteria, and bacterial culture analysis could only identify aerobic bacteria in our study.\u003c/p\u003e \u003cp\u003eA comparison between bacterial culture and metagenomics sequencing for the same samples constitutes one of the originalities of our research. It limits the comparison bias between two different studies and allows to compare the strengths and limitations of each of these identification methods. It is important to combine the two identification methods which are not antagonistic but complementary. Indeed, bacterial culture makes it possible to determine the viability of bacteria, essential in the clinical practices of a hospital. Metagenomics sequencing does not provide such information. However, a non-pathogenic species can grow predominantly on agar and prevent the identification of another pathogenic species (even in small quantities), which constitutes one of the limits of bacterial culture. Metagenomics makes it possible to overcome the difficulties of identifying anaerobic species and can identify bacteria that cannot yet be cultivated. Finally, the longitudinal analysis enabled us to observe variation over time in bacterial diversity and TAF counts throughout the lactation period, which could not have been performed with a single breast milk sample per mother. Very few studies have analysed all three stages of lactation and several samples per mother. Moreover, our LEfSe analysis allowed us to make associations between bacteria and certain maternal factors down to the genus level unlike other studies limited to the phylum or family level [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe post-partum collection day of the samples was different from one mother to another, making comparison more difficult. The first PCR amplified the V3-V4 variable segment of the 16S rRNA gene, but studies have shown that some bacterial genera may be over or underrepresented depending on the regions amplified [74], which could create an interpretation bias. Metagenomic analysis has several limitations: 16S rRNA gene targeted analysis allowed us to access to the taxonomic rank of the genus but without precision of the species, the viability of the identified bacteria is unknown and the methodology must be sufficiently to lyse both Gram negative and Gram positive cells, preventing distortion of the relative abundance of the Gram positive species to provide sufficient DNA yield to limit the contribution of signal from reagent contaminants [75]. To counter limitations of amplicon-based sequencing, there is increasing use of whole genome sequencing (WGS) [76]. WGS approaches can serve to resolve issues related to 16S rRNA gene sequencing and carry out bacterial identifications down to the strain level. 16S rRNA gene targeted analysis and WGS approaches do not provide the same biological answers, but these two approaches are complementary. Initially, our aim was to obtain a global view of bacterial diversity and its variations as a function of different factors, and 16S rRNA gene targeted enabled us to obtain such results. In a subsequent study future, WGS approaches will enable us to confirm our data and provide more precise identification of genes, metabolic pathways and characterization of bacteria down to the taxonomic rank of the strain. Finally, the number of mothers could be insufficient and did not allow significant results to be obtained for certain maternal factors for which a trend towards significance was observed (e.g. maternal age). Our findings need to be investigated with larger cohorts.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we have explored many features and potential determinants of human breast milk composition. Our results have confirmed that breast milk bacterial communities are highly diverse and complex. Breast milk is influenced by several maternal factors, which impact its bacterial composition and diversity from mother to mother. Metagenomic analysis should not substitute for bacterial culture, but rather complement it. Longitudinal analyses should be supported, as many changes occur during the various lactation stages. In-depth knowledge of the factors influencing the bacterial composition of mother's milk and of bacterial markers associated with maternal factors opens the way to possible intervention on these factors with a view to rebalancing the bacterial composition of mother's milk, thereby enabling the optimal development of the newborn's intestinal microbiota.\u003c/p\u003e \u003cp\u003eA logical next step will be to investigate with WGS approaches a larger population to determine and confirm characteristics of the breast milk microbiota are associated with enhanced health outcomes for women and their babies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eHygiene, Prevention and Infection Control (HPIC)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNeonatal Intensive Care Unit (NICU)\u003c/li\u003e\n \u003cli\u003eCentre Hospitalier Sud Francilien (CHSF)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCentre National de Recherche en G\u0026eacute;nomique Humaine (CNRGH)\u003c/li\u003e\n \u003cli\u003ebody mass index (BMI)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eTotal Aerobic Flora (TAF)\u003c/li\u003e\n \u003cli\u003eoperational taxonomic units (OTU)\u003c/li\u003e\n \u003cli\u003elinear discriminant analysis effect size (LEfSe)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003elogarithmic discriminant analysis (LDA)\u003c/li\u003e\n \u003cli\u003eColony Forming Unit (CFU)\u003c/li\u003e\n \u003cli\u003ewhole genome sequencing (WGS)\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe French Ethics Committee South Mediterranean I (2023-A01731-44) approved the study. Written informed consent was obtained from each participant prior to enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the\u0026nbsp;Centre Hospitalier Sud Francilien, the Centre National de Recherche en Génomique Humaine and the Genopole.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBQ is the guarantor of the study from conception and design to conduct of the study and acquisition of data, data analysis, and interpretation of data. EL contributed with analysis and interpretation of the data. CC carried out the molecular studies, sample preparation for the analysis, directed the molecular sequencing. VM oversaw the analysis and interpretation of the data. LC led the design and cohort selection. HR participated in the conception of the study, oversaw the design and cohort selection. JFD oversaw the molecular studies, sample preparation for the analysis, the molecular sequencing, analysis and interpretation of the data. DL and BS oversaw the study from conception and design to conduct of the study and acquisition of data, data analysis, and interpretation of data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the clinical and paramedical staff, as well as patients and families, of the\u0026nbsp;Department of Hygiene, Prevention and Infection Control, and the\u0026nbsp;Neonatal Intensive Care Unit of the Centre Hospitalier Sud Francilien. We would also like to thank Steven Mcginn for his help with the English version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eEidelman AI, Schanler RJ, Johnston M, Landers S, Noble L, Szucs K, et al. Breastfeeding and the use of human milk. Pediatrics. 2012;129 (3): e827\u0026ndash;e841.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKlopp A, Vehling L, Becker AB, Subbarao P, Mandhane PJ, Turvey SE, et al. Modes of Infant Feeding and the Risk of Childhood Asthma: A Prospective Birth Cohort Study. J Pediatr. 2017;190:192-199.e2.\u003c/li\u003e\n \u003cli\u003eXu L, Lochhead P, Ko Y, Claggett B, Leong RW, Ananthakrishnan AN.\u0026nbsp;Systematic review with meta-analysis: breastfeeding and the risk of Crohn\u0026apos;s disease and ulcerative colitis. Aliment Pharmacol Ther. 2017;46(9):780-789.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKim KM, Choi JW. Associations between breastfeeding and cognitive function in children from early childhood to school age: a prospective birth cohort study. Int Breastfeed J.\u0026nbsp;2020;15(1):83.\u003c/li\u003e\n \u003cli\u003eIbrahim SH, El-Ghany A, El Shafi M, El Hady\u0026nbsp;M. Cognitive Functions in Breastfed versus Artificially Fed in Preschool Children. Egypt. J. Hosp. Med. 2019;77(5):5742-5751.\u003c/li\u003e\n \u003cli\u003eGregory KE, Samuel BS, Houghteling P, Shan G, Ausubel FM, Sadreyev RI, et al. Influence of maternal breast milk ingestion on acquisition of the intestinal microbiome in preterm infants. Microbiome. 2016 Dec 30;4(1):68.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRyder RW, Crosby-Ritchie A, McDonough B, Hall WJ. Human milk contaminated with\u0026nbsp;\u003cem\u003eSalmonella kottbus\u003c/em\u003e: A cause of nosocomial illness in infants. JAMA. 1977;238, 1533\u0026ndash;1534.\u003c/li\u003e\n \u003cli\u003eHeikkil\u0026auml; MP, Saris P. Inhibition of Staphylococcus aureus by the commensal bacteria of human milk. J. Appl. Microbiol.\u0026nbsp;2003;95, 471\u0026ndash;478.\u003c/li\u003e\n \u003cli\u003eMartin R, Langa S, Reviriego C, Jiminez E, Marin ML, Xaus J, et al.\u0026nbsp;Human milk is a source of lactic acid bacteria for the infant gut. J. Pediatr. 2003;143, 754\u0026ndash;758.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLyons KE, Ryan CA, Dempsey EM, Ross RP, Stanton C. Breast Milk, a Source of Beneficial Microbes and Associated Benefits for Infant Health. Nutrients. 2020;12, 1039.\u003c/li\u003e\n \u003cli\u003eSinkiewicz G, Nordstr\u0026ouml;m EA. 353 occurrence of Lactobacillus reuteri, lactobacilli and bifidobacteria in human breast milk. Pediatric Res. 2005;58, 415.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDiene SM, Bertelli C, Pillonel T, Schrenzel J, Greub G. G\u0026eacute;nomique et m\u0026eacute;tag\u0026eacute;nomique bact\u0026eacute;riennes : applications cliniques et importance m\u0026eacute;dicale.\u0026nbsp;Rev. Med. Suisse. 2014;10: 2155-61.\u003c/li\u003e\n \u003cli\u003eHunt KM, Foster JA, Forney LJ, Sch\u0026uuml;tte UME, Beck DL, Abdo Z, et al. Characterization of the diversity and temporal stability of bacterial communities in human milk. PLoS One. 2011;6(6):e21313.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBrowne PD, Aparicio M, Alba C, Hechler C, Beijers R, Rodr\u0026iacute;guez JM, et al. Human milk microbiome and maternal postnatal psychosocial distress. Front. Microbiol. 2019;10:2333.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCabrera-Rubio R, Collado MC, Laitinen K, Salminen S, Isolauri E, Mira A. The human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. Am. J. Clin. Nutr. 2012;96(3):544-51.\u003c/li\u003e\n \u003cli\u003eOlivares M, Albrecht S, De Palma G, Ferrer MD, Castillejo G, Schols HA, et al. Human milk composition differs in healthy mothers and mothers with celiac disease. Eur. J. Nutr.\u0026nbsp;2015;54(1):119-28.\u003c/li\u003e\n \u003cli\u003eWalani SR. Global burden of preterm birth. Int J Gynaecol Obstet. 2020 Jul;150(1):31-33.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17(1), pp. 10-12.\u003c/li\u003e\n \u003cli\u003eEdgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics.\u0026nbsp;2010;26(19):2460-1.\u003c/li\u003e\n \u003cli\u003eSchloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75(23):7537-41.\u003c/li\u003e\n \u003cli\u003eMiSeq SOP.\u0026nbsp;\u003ca href=\"https://mothur.org/wiki/miseq_sop/\"\u003ehttps://mothur.org/wiki/miseq_sop/\u003c/a\u003e. Accessed 30 July 2024.\u003c/li\u003e\n \u003cli\u003eSilva reference files.\u0026nbsp;\u003ca href=\"https://mothur.org/wiki/silva_reference_files/\"\u003ehttps://mothur.org/wiki/silva_reference_files/\u003c/a\u003e. Accessed 30 July 2024.\u003c/li\u003e\n \u003cli\u003eSegata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.\u003c/li\u003e\n \u003cli\u003eKordy K, Gaufin T, Mwangi M, Li F, Cerini C, Lee DJ, et al.\u0026nbsp;Contributions to human breast milk microbiome and enteromammary transfer of Bifidobacterium breve. PLoS ONE.\u0026nbsp;2020;15(1):e0219633.\u003c/li\u003e\n \u003cli\u003eStinson LF, Ma J, Sindi AS, Geddes DT. Methodological approaches for studying the human milk microbiome. 2023;81(6):705-715.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFouhy F, Deane J, Rea MC, O\u0026apos;Sullivan \u0026Oacute;, Ross RP, O\u0026apos;Callaghan G, et al. The effects of freezing on faecal microbiota as determined using MiSeq sequencing and culture-based investigations. PLoS One.\u0026nbsp;2015;10(3):e0119355.\u003c/li\u003e\n \u003cli\u003eLackey KA, Williams JE, Price WJ, Carrothers JM, Brooker SL, Shafii B, et al. Comparison of commercially-available preservatives for maintaining the integrity of bacterial DNA in human milk. J Microbiol Methods.\u0026nbsp;2017;141:73-81.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMantziari A, Rautava S. Factors influencing the microbial composition of human milk. Semin Perinatol. 2021;45(8):151507.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTett A, Pasolli E, Masetti G, Ercolini D, Segata N. Prevotella diversity, niches and interactions with the human host. Nat Rev Microbiol. 2021;19(9):585-599.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGeddes DT. The use of ultrasound to identify milk ejection in women\u0026ndash;tips and pitfalls. Int. Breastfeed. J.\u0026nbsp;2009;4:5.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMoossavi S, Sepehri S, Robertson B, Bode L, Goruk S, Field CJ, et al. Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors. Cell Host Microbe. 2019;25(2):324-335.e4.\u003c/li\u003e\n \u003cli\u003eMoossavi S, Azad MB. Origins of human milk microbiota: new evidence and arising questions. Gut Microbes. 2020;12(1):1667722.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRodr\u0026iacute;guez JM. The origin of human milk bacteria: Is there a bacterial entero-mammary pathway during late pregnancy and lactation? Adv. Nutr. 2014;5(6):779-84.\u003c/li\u003e\n \u003cli\u003ePerez PF, Dor\u0026eacute; J, Leclerc M, Levenez F, Benyacoub J, Serrant P, et al.\u0026nbsp;Bacterial imprinting of the neonatal immune system: lessons from maternal cells? Pediatrics. 2007;119(3):e724-32.\u003c/li\u003e\n \u003cli\u003eJeurink PV, Van Bergenhenegouwen J, Jim\u0026eacute;nez E, Knippels LMJ, Fern\u0026aacute;ndez L, Garssen J, et al. Human milk: a source of more life than we imagine. Beneficial Microbes. 2013;4(1):17-30.\u003c/li\u003e\n \u003cli\u003eLopez Leyva L, Brereton NJB, Koski KG.\u0026nbsp;Emerging frontiers in human milk microbiome research and suggested primers for 16S rRNA gene analysis. Comput Struct Biotechnol J. 2020;19:121-133.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWong D, Nielsen TB, Bonomo RA, Pantapalangkoor P, Luna B, Spellberg B. Clinical and Pathophysiological Overview of Acinetobacter Infections: a Century of Challenges. Clin Microbiol Rev. 2017;30(1):409-447.\u003c/li\u003e\n \u003cli\u003eSwanson CS, Dhand R, Cao L, Ferris J, Elder CS, He Q. Microbiome Profiles of Nebulizers in Hospital Use. J Aerosol Med Pulm Drug Deliv. 2022;35(4):212-222.\u003c/li\u003e\n \u003cli\u003eJost T, Lacroix C, Braegger C, Chassard C. Assessment of bacterial diversity in breast milk using culture-dependent and culture-independent approaches. Br J Nutr. 2013;110(7):1253-62.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi SW, Watanabe K, Hsu CC, Chao SH, Yang ZH, Lin YJ, et al. Bacterial Composition and Diversity in Breast Milk Samples from Mothers Living in Taiwan and Mainland China. Front Microbiol. 2017;8:965.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGonzalez E, Brereton NJB, Li C, Lopez Leyva L, Solomons NW, Agellon LB, et al.\u0026nbsp;Distinct Changes Occur in the Human Breast Milk Microbiome Between Early and Established Lactation in Breastfeeding Guatemalan Mothers. Front Microbiol.\u0026nbsp;2021;12:557180.\u0026nbsp;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGrice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al.\u0026nbsp;Topographical and temporal diversity of the human skin microbiome. Science.\u0026nbsp;2009;324(5931):1190-2.\u003c/li\u003e\n \u003cli\u003eMurphy K, Curley D, O\u0026apos;Callaghan TF, O\u0026apos;Shea CA, Dempsey EM, O\u0026apos;Toole PW, et al. The Composition of Human Milk and Infant Faecal Microbiota Over the First Three Months of Life: A Pilot Study. Sci Rep.2017;7:40597.\u003c/li\u003e\n \u003cli\u003eGreiner AK, Papineni RV, Umar S. Chemoprevention in gastrointestinal physiology and disease. Natural products and microbiome. Am J Physiol Gastrointest Liver Physiol.\u0026nbsp;2014;307(1):G1-15.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYan W, Luo B, Zhang X, Ni Y, Tian F. Association and Occurrence of Bifidobacterial Phylotypes Between Breast Milk and Fecal Microbiomes in Mother-Infant Dyads During the First 2 Years of Life. Front Microbiol. 2021;12:669442.\u003c/li\u003e\n \u003cli\u003eZimmermann P, Curtis N. Breast milk microbiota: A review of the factors that influence composition. J Infect. 2020;81(1):17-47.\u003c/li\u003e\n \u003cli\u003eWilliams JE, Carrothers JM, Lackey KA, Beatty NF, York MA, Brooker SL, et al. Human Milk Microbial Community Structure Is Relatively Stable and Related to Variations in Macronutrient and Micronutrient Intakes in Healthy Lactating Women. J Nutr. 2017;147(9):1739-1748.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSalazar J, Dur\u0026aacute;n P, D\u0026iacute;az MP, Chac\u0026iacute;n M, Santeliz R, Mengual E, et al.\u0026nbsp;Exploring the Relationship between the Gut Microbiota and Ageing: A Possible Age Modulator. Int J Environ Res Public Health. 2023;20(10):5845.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUrbaniak C, Angelini M, Gloor GB, Reid G. Human milk microbiota profiles in relation to birthing method, gestation and infant gender. Microbiome. 2016;4:1.\u003c/li\u003e\n \u003cli\u003eLima SF, Teixeira AGV, Lima FS, Ganda EK, Higgins CH, Oikonomou G, et al.\u0026nbsp;The bovine colostrum microbiome and its association with clinical mastitis. J Dairy Sci. 2017;100(4):3031-3042.\u003c/li\u003e\n \u003cli\u003eLackey KA, Williams JE, Meehan CL, Zachek JA, Benda ED, Price WJ, et al. What\u0026apos;s Normal? Microbiomes in Human Milk and Infant Feces Are Related to Each Other but Vary Geographically: The INSPIRE Study. Front Nutr. 2019;6:45.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEwald DR, Sumner SCJ. Human microbiota, blood group antigens, and disease. Wiley Interdiscip Rev Syst Biol Med. 2018;10(3):e1413.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLyons KE, Shea CAO, Grimaud G, et al. The human milk microbiome aligns with lactation stage and not birth mode. Sci Rep. 2022;12, 5598.\u003c/li\u003e\n \u003cli\u003eHermansson H, Kumar H, Collado MC, Salminen S, Isolauri E, Rautava S. Breast Milk Microbiota Is Shaped by Mode of Delivery and Intrapartum Antibiotic Exposure. Front Nutr. 2019;6:4.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSimpson MR, Avershina E, Storr\u0026oslash; O, Johnsen R, Rudi K, \u0026Oslash;ien T. Breastfeeding-associated microbiota in human milk following supplementation with Lactobacillus rhamnosus GG, Lactobacillus acidophilus La-5, and Bifidobacterium animalis ssp. lactis Bb-12. J Dairy Sci. 2018;101(2):889-899.\u003c/li\u003e\n \u003cli\u003eValeri F, Endres K. How biological sex of the host shapes its gut microbiota. Front Neuroendocrinol. 2021;61:100912.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSoto A, Mart\u0026iacute;n V, Jim\u0026eacute;nez E, Mader I, Rodr\u0026iacute;guez JM, Fern\u0026aacute;ndez L.\u0026nbsp;Lactobacilli and bifidobacteria in human breast milk: influence of antibiotherapy and other host and clinical factors. J Pediatr Gastroenterol Nutr.\u0026nbsp;2014;59(1):78-88.\u003c/li\u003e\n \u003cli\u003eDethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol.\u0026nbsp;2008;6(11):e280.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSummary of product characteristics \u0026ndash; CLAMOXYL, powder for injectable solution (I.M.-I.V.) - National Agency for the Safety of Medicines and Health Products - Last updated on August 21, 2017.\u0026nbsp;\u003ca href=\"http://agence-prd.ansm.sante.fr/php/ecodex/rcp/R0305509.htm\"\u003ehttp://agence-prd.ansm.sante.fr/php/ecodex/rcp/R0305509.htm\u003c/a\u003e.\u0026nbsp;Accessed 15 November 2023.\u003c/li\u003e\n \u003cli\u003eSummary of product characteristics \u0026ndash; CEFAZOLINE, powder for injectable solution/infusion - National Agency for the Safety of Medicines and Health Products - Last updated on December 6, 2019.\u0026nbsp;\u003ca href=\"http://agence-prd.ansm.sante.fr/php/ecodex/rcp/R0347277.htm\"\u003ehttp://agence-prd.ansm.sante.fr/php/ecodex/rcp/R0347277.htm\u003c/a\u003e.\u0026nbsp;Accessed 15 November 2023.\u003c/li\u003e\n \u003cli\u003eQuenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD, Fisher J, et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. Lancet. 2021;397(10285):1658-1667.\u003c/li\u003e\n \u003cli\u003eBoix-Amor\u0026oacute;s A, Collado MC, Mira A. Relationship between Milk Microbiota, Bacterial Load, Macronutrients, and Human Cells during Lactation. Front Microbiol. 2016;7:492.\u003c/li\u003e\n \u003cli\u003eDamaceno QS, Souza JP, Nicoli JR, Paula RL, Assis GB, Figueiredo HC, et al.\u0026nbsp;Evaluation of Potential Probiotics Isolated from Human Milk and Colostrum. Probiotics Antimicrob Proteins. 2017;9(4):371-379.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSol\u0026iacute;s G, de Los Reyes-Gavilan CG, Fern\u0026aacute;ndez N, Margolles A, Gueimonde M. Establishment and development of lactic acid bacteria and bifidobacteria microbiota in breast-milk and the infant gut. Anaerobe. 2010;16(3):307-10.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKhodayar-Pardo P, Mira-Pascual L, Collado MC, Mart\u0026iacute;nez-Costa C. Impact of lactation stage, gestational age and mode of delivery on breast milk microbiota. J Perinatol. 2014;34(8):599-605.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDahaban NM, Romli MF, Roslan NR, Kong SS, Cheah FC. Bacteria in expressed breast milk from mothers of premature infants and maternal hygienic status. Breastfeed Med. 2013;8(4):422-3.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCabrera-Rubio R, Mira-Pascual L, Mira A, Collado MC.\u0026nbsp;Impact of mode of delivery on the milk microbiota composition of healthy women. J Dev Orig Health Dis. 2016;7(1):54-60. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eConsales A, Cerasani J, Sorrentino G, Morniroli D, Colombo L, Mosca F, et al.\u0026nbsp;The hidden universe of human milk microbiome: origin, composition, determinants, role, and future perspectives. Eur J Pediatr. 2022;181(5):1811-1820.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBoscaini S, Leigh SJ, Lavelle A, Garc\u0026iacute;a-Cabrerizo R, Lipuma T, Clarke G, et al.\u0026nbsp;Microbiota and body weight control: Weight watchers within? Mol Metab. 2022;57:101427.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003evan der Vossen EWJ, de Goffau MC, Levin E, Nieuwdorp M. Recent insights into the role of microbiome in the pathogenesis of obesity. Therap Adv Gastroenterol. 2022;15:17562848221115320.\u003c/li\u003e\n \u003cli\u003eSong Z, Liu H. The investigation of the association of pregnancy weight gain on maternal and neonatal gut microbiota composition and abundance using 16sRNA sequencing. BMC Pregnancy Childbirth. 2023;23(1):109.\u003c/li\u003e\n \u003cli\u003eEdgar R. Taxonomy annotation and guide tree errors in 16S rRNA databases. PeerJ. 2018;6:e5030.\u003c/li\u003e\n \u003cli\u003eMartinson JNV, Pinkham NV, Peters GW, Cho H, Heng J, Rauch M, et al. Rethinking gut microbiome residency and the Enterobacteriaceae in healthy human adults. ISME J. 2019;13(9):2306-2318.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMeyer KM, Pace RM, Mohammad M, Haymond M, Aagaard KM. Composition of the breast milk microbiome is influenced by the method of 16S-amplicon sequencing used. American Journal of Obstetrics \u0026amp; Gynecology. 2019;220(1):S607-S608.\u003c/li\u003e\n \u003cli\u003eDouglas CA, Ivey KL, Papanicolas LE, Best KP, Muhlhausler BS, Rogers GB. DNA extraction approaches substantially influence the assessment of the human breast milk microbiome. Sci Rep. 2020;10(1):123.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWright ML, Podnar J, Longoria KD, Nguyen TC, Lim S, Garcia S, et al. Comparison of commercial DNA extraction kits for whole metagenome sequencing of human oral, vaginal, and rectal microbiome samples. bioRxiv. 2023;2023.02.01.526597. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"human breast milk, bacteria microbiota, metagenomic, 16S rRNA gene, bacterial culture, bacterial diversity, bacterial composition","lastPublishedDoi":"10.21203/rs.3.rs-5348794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5348794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eHuman breast milk is used as the optimum feeding regime for newborn infants. In hospitals, it is critical for premature infants, protecting them from illnesses such as necrotizing enterocolitis. It has its own companion microbiota, whose bacterial composition and diversity are affected by several factors linked to the mother and her baby. However, little is known about the breast milk microbiota of mothers who delivered premature infants. The goal of the present study was to examine the breast milk microbiota and determine the factors influencing its composition and diversity over the time.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eWe analysed 137 breast milk samples collected from 40 women who delivered premature and extremely premature infants. Each sample was both analysed by bacterial culture and the V3-V4 region of the bacterial 16S rRNA gene was sequenced using an Illumina platform to characterize microbial communities. For the samples analysed, a diverse community of breast milk bacteria was found with the most dominant phyla being \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e. At the genus level, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eCorynebacterium \u003c/em\u003ewere the most prevalent. A core milk microbiota has been defined, composed of seven genera. The pregnancy number, lactation stage and mother’s body mass index were shown to influence bacterial diversity. In contrast, no statistically significant differences were observed for maternal age, gestational age, parity, population, mother’s blood group, mode of delivery, infant gender, infant weight, and use of antibiotic prophylaxis during delivery. Most of the bacterial species detected by bacterial culture were also identified to genus taxonomic level by 16S rRNA gene sequencing. Longitudinal analyses showed that bacterial diversity tended to increase over time, while the aerobic bacterial flora was rather stable over time. Moreover, LEfSe analysis allowed us to make associations between bacteria in breast milk with certain maternal conditions or factors.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eOur study showed that the bacterial composition and diversity of breast milk are influenced by several maternal factors. We have observed a variability in bacterial diversity and composition during the 3 stages of lactation, which differs between mothers. We have identified bacterial signatures associated with different clinical conditions in the mother and child.\u003c/p\u003e","manuscriptTitle":"Metagenomic and bacterial culture studies of human breast milk microbiota: factors influencing its bacterial composition and diversity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 17:15:55","doi":"10.21203/rs.3.rs-5348794/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"920a9e3d-2aa9-4513-8165-0c0657de8c85","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-27T12:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 17:15:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5348794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5348794","identity":"rs-5348794","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}