https://orcid.org/0000-0002-0305-8262
Figures
Abstract
Very low birth weight (VLBW) infants, mostly preterm, have many barriers to feeding directly from the mother’s breast, and need to be fed alternatively. Feeding is a major influencer in oral microbial colonization, and this colonization in early life is crucial for the promotion of human health. Therefore, this research aimed to observe the establishment of oral microbiome in VLBW infants during their first month of life through hospitalization, and to verify the impact caused by the implementation of oral diet on the colonization of these newborns. We included 23 newborns followed during hospitalization and analyzed saliva samples collected weekly, using 16S rRNA gene sequencing. We observed a significant decrease in richness and diversity and an increase in dominance over time (q-value < 0.05). The oral microbiome is highly dynamic during the first weeks of life, and beta diversity suggests a microbial succession in early life. The introduction of oral diet does not change the community structure, but affects the abundance, especially of Streptococcus. Our results indicate that although time is related to significant changes in the oral microbial profile, oral feeding benefits genera that will remain colonizers throughout the host’s life.
Citation: Vanzele PAR, Sparvoli LG, de Camargo PP, Tragante CR, Beozzo GPNS, Krebs VLJ, et al. (2023) Establishment of oral microbiome in very low birth weight infants during the first weeks of life and the impact of oral diet implementation. PLoS ONE 18(12): e0295962. https://doi.org/10.1371/journal.pone.0295962
Editor: Farah Al-Marzooq, UAE University: United Arab Emirates University, UNITED ARAB EMIRATES
Received: August 30, 2023; Accepted: December 2, 2023; Published: December 15, 2023
Copyright: © 2023 Vanzele et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets presented in this study can be found in the National Center for Biotechnology Information (NCBI) repository: https://www.ncbi.nlm.nih.gov/genbank/, under BioProject ID PRJNA894175.
Funding: This work was supported by The São Paulo Research Foundation (FAPESP), under the grant number #2018/10612-7 for author C.R.Taddei and by Coordination for the Improvement of Higher Education Personnel (CAPES - Finance Code 001) under the grant number #88887.508504/2020-00 for author P.A.R.V. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Low birth weight (LBW, <2500 g) is one of the main causes of neonatal mortality and morbidity in low- and middle-income countries, along with preterm birth, infections, and asphyxia at birth [1]. This term also includes very low birth weight (VLBW, <1500 g) infants, and it is described that the lower the birth weight and gestational age, the greater the chances of death in early life [2].
Most VLBW infants are premature, and enteral feeding is often a challenge, due to their immature physiological system and neurological development [3, 4]. Thus, these newborns need to be initially fed by alternative routes, such as gastric tubes, which lead the milk directly to the intestine. Therefore, there is no direct contact of the milk with oral cavity, until the baby reaches maturity for implementation of oral diet [5].
The oral microbiome colonization begins from species able to adhere to epithelial cells, such as Streptococcus salivarius [6], and these will serve as an adhesion site for subsequent colonizers, through co-aggregation and co-adhesion mechanisms [7]. The presence of some microorganisms can create a niche for the establishment of others [8], as seen with several Streptococcus species (S. gordonii, S. mitis, S. oralis, S. sanguinis [7], and S. salivarius [6]), which start the colonization in oral biofilm, to ensure an adequate pathway for this process in this crucial window of time. Furthermore, the oral microbiome is referred as a potential biomarker for health in a local and systemic manner [9], and once initial colonization can persist over time, the microbiome preservation early in life is essential for promoting human health. The occurrence of dysbiosis, mediated by the presence of Porphyromonas gingivalis or an increased abundance of Fusobacterium nucleatum [10], can induce or predispose certain diseases, such as periodontitis, which causes a deregulated inflammatory response that potentiates many damages. This process is already related to the emergence of autoimmune reactions [11], oral cancer [12], and systemic diseases [13].
In addition to all the challenges preterm newborns face, they are also more susceptible to being colonized by oral pathogens, such as methicillin-resistant Staphylococcus aureus, as described in the study by Underwood and Sohn [14], which compared full-term neonates to extremely low birth weight babies. This is because these babies are restricted to a colonization by microorganisms often found in Neonatal Intensive Care Units (NICUs), since they remain hospitalized in these NICUs for a considerable time and lack important factors that help their initial microbial colonization [15, 16], such as the contact with their parents [17] and exposure to a healthier and more diverse environment [18].
After birth, some variables can affect the oral microbiome in very low birth weight infants, for example, the delivery mode [19]. Also, preterm infants present an oral microbiome composition different from term infants in early life, but within three months their microbiome stabilizes and reflects a similar pattern [20]. Although some studies have already evaluated the oral microbiome of preterm and/or VLBW infants [21], we barely know how oral diet implementation can influence the colonization of the oral microbiome in these infants during the early life period.
Due to their prematurity, VLBW infants stay in NICUs for a long time, and need to be fed by alternative routes until oral feeding is possible. Therefore, the purpose of this study was to evaluate the establishment of oral microbiome in VLBW infants during their first month of life through hospitalization in a NICU, considering the role of oral diet implementation.
Materials and methods
Ethics statement
The study was approved by the Research Ethics Committee of the Hospital das Clínicas of the University of São Paulo (Sao Paulo, Brazil) (CAAE: 09673619.7.0000.0068) and the newborns’ parents were informed about the study during recruitment and signed a written informed consent form stating their agreement with the research.
Study design and subjects
This is a longitudinal, observational study, which included VLBW infants hospitalized in the NICU of Hospital das Clínicas of the University of São Paulo (Sao Paulo, Brazil). We collected weekly samples from 23 preterm babies born with very low birth weight, who were hospitalized in a NICU for at least four weeks. All newborns included were delivered by cesarean section and received oral diet at some point during hospitalization, which consisted of expressed human milk or milk-based infant formula for preterm. The chances of death in early life increases as lower the birth weight and gestational age of the infant [2], what demonstrate the needed to study this group. Also, we only included infants delivered by cesarian section due to most of the preterm babies with very low birth weight are delivered through this method [22]. The samples collection was conducted between February 11th–August 6th, 2019.
Sample collection and DNA extraction
All saliva samples were collected using two sterile swabs, rubbing them in the baby’s oral cavity for about 30 seconds, covering the cheeks and tongue. The first sample was collected within the first 24 hours after birth, and subsequent collections were carried out weekly. For this study, we selected samples collected in the first, third and fourth postpartum weeks, and in the weeks immediately before and after the implementation of oral diet. After collection, the cotton swabs were transferred to a 15-ml tube containing 500 μl of Phosphate-Buffered Saline (PBS, pH 7.4) and stored at -20°C until shipped, within 24 hours after collection, to the Molecular Microbiology Laboratory, where they were stored at -80°C until DNA extraction was performed.
Initially, the samples were pre-treated as described by Cortez et al. [23], which basically consisted of adding 500 μl of 0.1% Dithiothreitol (DTT) solution to the samples, vortexing for 1 minute and incubating for 10 minutes at room temperature. The suspension was then centrifuged, the supernatant was discarded, and the pellet was resuspended in 200 μl of TELS buffer (20mg/mL lysozyme, 1M Tris-HCl (pH 7.5), 0.5 M EDTA (pH 8.0), 20% sucrose). Then, DNA extraction was performed with the QIAamp DNA blood mini kit (Qiagen), according to the manufacturer’s protocol. The extracted DNA was eluted in 80 μl of autoclaved ultrapure type I water (Milli-Q®) and stored at -80°C until use. As a negative control, we used a sterile swab opened at the laboratory and kept in air for 30 seconds, then transferred to a 1.5-ml tube with 500 μl of PBS solution, and DNA extraction was performed using the same procedures described for samples. The amount of DNA was quantified using Qubit 4 Fluorometer (ThermoFisher).
16S rRNA library preparation and sequencing
The 16S rRNA region amplified by PCR technique was V3-V4, with the primers 5′- CCTACGGGNGGCWGCAG-3′ forward and 5′-GACTACHVGGGTATCTAATCC-3 reverse to the described region [24], with Illumina adapters. The amplicons were pooled and loaded onto Illumina MiSeq clamshell style cartridge kit V2 with 500 cycles, for paired-end 250 sequencing at a final concentration of 10 pM. The library was clustered to a density of approximately 820 k/mm2. The MiSeq platform was used for image analysis, base calling, and data quality assessment.
Bioinformatic analysis
16S rRNA amplicons were analyzed using software QIIME2 version 2021.11 (Quantitative Insights Into Microbial Ecology) [25]. Chimeric artifacts removal, sequencing alignment, and sequence quality control were executed using DADA2 plugin [26], with both forward and reverse sequences truncated at 245 nucleotides. Taxonomic assignment of Amplicon Sequence Variants (ASVs) was performed using the q2-feature-classifier resource [27] and the Bayes naive classify-sklearn taxonomy classifier against SILVA database version 138, adopting a 99% similarity [28, 29]. Diversity analyses and community comparisons were performed using R packages qiime2R [30], phyloseq [31], microbiome [32], and vegan [33] in the R software (version 4.1.2). For relative abundance analysis and graphical representation, we selected the ten most abundant bacterial genera, and the less representative genera were grouped into “others”. Alpha diversity is a numeric value that summarize the structure of the community for a single sample, with respect to the number of different organisms (richness), their abundance (evenness), or both [34]. In this research, it was determined by the indices Chao1 for richness [35] (with values converted to log10 to provide a better graphical representation), Shannon for diversity [36], and Simpson for dominance [37]. Beta diversity analysis is a way to quantifies similarity or distance among microbiomes [38], and it was determined observing communities’ unique fractions through unweighted and weighted UniFrac metrics.
Statistical analyses
Statistical analyses were performed using R software (version 4.1.2), by observing samples over time (first, third, and fourth postpartum weeks) and in relation to the introduction of oral feeding (before and after). Relative abundance of the representative genera and alpha diversity were analyzed by linear model regression using MASS [39] and jtools [40] R packages. To select which variable was included in the adjusted models, they were individually tested and those with p-value < 0.05 were selected, both for alpha (S1 Table) and beta diversity (S2 Table). The adjusted model of alpha diversity and weeks after birth included oral feeding (yes/no) as covariate. Regarding oral diet, sepsis (yes/no) and gestational antibiotic use (yes/no) were selected as covariates for alpha diversity analysis. False Discovery Rate (FDR) correction was applied, and we used a q-value ≤ 0.10 as a significant level. To compare beta diversity over time and by oral feeding, permutation test PERMANOVA (adonis2 test; vegan R package [33]) was performed, with adjustment for confounding variables oral feeding (yes/no), time without oral feeding (yes/no), and sepsis (yes/no). Principal Coordinate Analysis (PCoA) graphics were created to visualize interaction of the communities, and a p-value ≤ 0.05 was considered significant for beta diversity. The heatmap of the ASVs abundance was constructed with the R package ComplexHeatmap [41], and the ASVs were selected to be included in the plot based on the ASVs significantly associated with the postpartum weeks (q-value < 0.25) using generalized linear models (Maaslin2 R package [42]) with a filter for the ASVs with a minimum prevalence of 25%. The shared ASVs diagram was constructed using the VennDiagram R package [43]. All graphical representations were performed using the ggplot2 package in R [44].
Results
The newborns were assigned to a single group for analysis, which consisted of observing the effect of time on microbial colonization, and how diet could affect the oral microbiome of these babies. For this purpose, a total of 23 VLBW infants were included, with samples collected in the first (n = 19), third (n = 21) and fourth (n = 18) postpartum weeks, and also in the week before (n = 22) and the week after (n = 23) the implementation of oral diet, totaling 89 samples. For some babies, the sample from weeks before and after the implementation of oral diet coincided with the third or fourth hospitalization week. The variation in the number of samples during follow-up is due to some loss during the experiment conduction, since some samples did not have sufficient DNA concentration or did not amplify in the PCR step.
Clinical data
All newborns enrolled in this study were preterm, with a mean gestational age of 30.52 weeks (± 2.98; 24–35 weeks) (Table 1), and a mean weight of 1134.30 grams (± 257.46; 612–1500 grams) at birth. These babies had to stay hospitalized for about 7.27 weeks (± 3.38; 3.14–17.86 weeks) and waited around 4.70 weeks (± 3.17; 1–15 weeks) for the introduction of oral diet. Therefore, the sample collection interval from birth to before oral diet implementation was about 28.50 days (± 20.25; 7–94 days), and for samples collected between before and after oral diet introduction, it was around 14.90 days (± 11.01; 6–38 days). Of the total diet administered orally to these babies, only about 24.90% (± 24.12%; 0–76.32%) was mother’s breast milk, while the remainder was formula. The mothers of these babies had a mean age of 33.48 (± 5.16; 23–42 years), and only 21.74% of them used antibiotics during pregnancy. More than half of the newborns (69.57%) included in the study underwent antibiotic therapy, although the rate of sepsis occurrence was 43.48%. The other infants received antibiotics as a prophylaxis. Among the 16 babies who underwent antibiotic therapy, more than half (56.25%) received the combination of penicillin and aminoglycoside, and the vast majority of babies (62.50%) underwent only one antibiotic therapy cycle, administered mainly at the first week of life. The S3 Table shows all the data on antibiotic therapy used by the newborns.
Microbiome composition and relative abundance
During the first four weeks of the baby’s life, the oral microbiome showed an adaptation over time (Fig 1A). There was a marked decrease in microbiome diversity within the first two weeks of life, and the individual’s microbiome community was dominated by one or two genera, most of them members of Streptococcus, Staphylococcus and Enterobacteriaceae members, by the third week of life. In the fourth week of life, when most babies had already started oral diet, the dominance of Enterobacteriaceae decreased and Streptococcus and Staphylococcus remained dominant (Fig 1A). The average relative abundance of genera corroborated individual data, demonstrating that the oral microbiome was mostly composed of less abundant genera, herein represented as “others”. In the first week of life, the greatest abundances were Streptococcus (10.86%), Staphylococcus (9.59%), Escherichia-Shigella (6.23%), Enterobacter (5.74%), and Acinetobacter (3.84%). Over time, there was an increase in the relative abundance of Staphylococcus (32.64%), Streptococcus (23.62%), Enterobacter (9.00%), Klebsiella (6.38%), Haemophilus (4.86%), Gemella (3.93%), and Neisseria (3.33%), and a slightly decrease in the abundance of Escherichia-Shigella (5.35%) and Acinetobacter (3.63%). In the fourth postpartum week, Streptococcus (33.90%) became the most abundant genus in babies’ oral microbiome, followed by Staphylococcus (23.07%), Veillonella (10.91%), Enterobacter (9.36%), Haemophilus (7.47%), Klebsiella (4.02%), Neisseria (3.00%), and Gemella (2.17%). The genera Acinetobacter (0.69%) and Escherichia-Shigella (0.13%) along with Staphylococcus, had a greater decrease when compared to the third postpartum week (Fig 1C). However, none of these data showed a statistically significant value related to relative abundance changes over time (S4 Table).
Mean relative abundance of the main genera observed in the samples by (A) postpartum week (first, third, and fourth weeks, as shown in the rows) and (B) oral diet (before and after introduction, as shown in the rows), depicted per infant (columns). (C) Stacked bar graphs showing the mean relative abundance of genera (%) in the infants’ oral microbiome in the first, third and fourth postpartum weeks. (D) Stacked bar graphs showing the mean relative abundance of genera (%) in the infants’ oral microbiome before and after the implementation of oral diet.
By assessing the oral microbiome of VLBW infants right after the implementation of oral diet, an increase in Streptococcus dominance (Fig 1B) was found in the analysis of the individual microbiome. The mean values of genera abundance before and after the implementation of oral diet were not statistically significant different; however, there was a marked increase in the abundance of Streptococcus (36.19% to 66.39%). Other genera showed a decrease in abundance after introduction of oral diet, as Staphylococcus (28.68% to 12.38%), Neisseria (10.18% to 2.69%), Haemophilus (6.70% to 2.16%), Klebsiella (3.79% to 3.73%), Gemella (5.58% to 1.70%), Enterobacter (3.33% to 0.09%), and Clostridium_sensu_stricto_1 (1.26% to 0.01%), as shown in Fig 1D (S5 Table).
When observed the taxa composition at Amplicon Sequencing Variants (ASV) level, we found ASVs that were significantly associated with the postpartum weeks. In addition to the genus abundance results, there is a decrease of the diversity over time, mainly from the first to the further weeks of life, with a slightly increase in the abundance of the ASVs of bacteria described as oral eobiont colonizers (Fig 2), but this is not associated with the oral diet implementation.
Alpha diversity
The alpha diversity analysis, regarding postpartum weeks, showed that the oral microbiome is richer and more diverse in the first week, and that richness and diversity, as measured by Chao1 (2.50 ± 0.16 to 1.38 ± 0.08 to 1.43 ± 0.05; Fig 3A) and Shannon (3.96 ± 0.4 to 1.47 ± 0.21 to 1.39 ± 0.14; Fig 3B) indices, respectively, decrease over the weeks. Linear model testing showed that these decreases were significantly associated with weeks of life (β = -0.404; β = -0.856, respectively; q-value < 0.001 for both). Furthermore, there was an increase in dominance, as assessed by the Simpson index (0.15 ± 0.06 to 0.40 ± 0.05 to 0.39 ± 0.06; Fig 3C), confirmed as statistically significant using linear model testing (β = 0.062; q-value = 0.041). These results are shown in the S6 Table.
Boxplots showing the alpha diversity indices of Chao1(log10), Shannon, and Simpson, according to (A-C) postpartum weeks and (D-F) introduction of oral diet. Linear regression model testing was used to calculate p-value, and q-value results were confirmed with False Discovery Rate (FDR) post-hoc. The q-values were considered significant when ≤ 0.10. *q-value < 0.10; **q-value < 0.05.
After oral feeding, the oral microbiome diversity is decreased (1.40 ± 0.11 to 1.04 ± 0.15; Fig 3E), in contrast with an increase in dominance (0.41 ± 0.05 to 0.56 ± 0.06; Fig 3F), both statistically significant (β = -0.362 and β = 0.150, respectively; q-value = 0.095 for both) (S7 Table). There was no difference in richness before and after the implementation of oral diet (1.52 ± 0.05 to 1.5 ± 0.04; Fig 3D), which means that there is no association of Chao1 index with oral diet (β = -0.022; q-value = 0.707).
Beta diversity
Significant differences in beta diversity (based on unweighted Unifrac ‐ Fig 4A) were observed over time, with the babies’ first week samples clustered further apart from the others (R² = 0.23; p-value = 0.001). This was also found on beta diversity according to the weighted Unifrac metric, as shown in Fig 4B (R² = 0.20; p-value = 0.001).
Principal coordinate analysis (PCoA) for (A) unweighted (R² = 0.23, p-value = 0.001) and (B) weighted (R² = 0.20, p-value = 0.001) Unifrac distance metrics over time (first, third and fourth week postpartum); (C) unweighted (R² = 0.01, p-value = 0.907) and (D) weighted (R² = 0.08, p-value = 0.008) Unifrac distance metrics, before and after implementation of oral diet.
Concerning the implementation of oral diet, no significant differences were found in the babies’ microbiome by unweighted Unifrac metric (R² = 0.01; p-value = 0.907) (Fig 4C). However, the weighted Unifrac demonstrates that there are statistical differences between the samples collected before and after oral diet (R² = 0.08; p-value = 0.008), with oral communities clustering closer after diet implementation than before (Fig 4D). Considering the impact of both postpartum weeks and oral diet status during the first weeks of life, we noticed that the time has greater effect in beta diversity than diet (S1 Fig).
Shared ASVs
The shared ASVs diagram (Fig 5) shows which ASVs were consistent in the oral microbiome during the first weeks of life. The microbiome composition of the first postpartum week has the greater richness and diversity, with 5393 exclusive ASVs at this point, and without a dominance of specific ASVs. In contrast, the third week has only 334 exclusive ASVs, with the main ASVs being Enterobacteriaceae members (73.43%). The fourth week has the lowest number of exclusive ASVs, but the greater abundance was of Streptococcus’ ASVs (19.00%). In addition, 121 ASVs were shared among all the first four weeks of life, and the most prevalent ASVs were of oral eobiont microorganisms, such as Streptococcus, Staphylococcus, Neisseria and Haemophilus. The main ASV shared in the first month (from all the weeks enrolled in this study) is from the Streptococcus genus, with ASVs of the Streptococcus salivarius species being the most prevalent among them.
Concerning the shared ASVs between the samples before and after the oral diet implementation, we observe that the 277 exclusive ASVs before the oral diet were consisted of Enterobacteriaceae members (71.21%), similar to the third postpartum week. After the oral diet, there is the introduction of 233 ASVs, mostly of oral eobiont colonizers such as Veillonella and Streptococcus. 164 ASVs were common both before and after the oral diet introduction, and it was also consisted of microorganism described as oral colonizers, mainly from the Streptococcus genus (S2 Fig).
Discussion
This research evaluated the process of colonization of the oral microbiome in preterm VLBW infants in a NICU environment at different times, from birth to the implementation of oral diet, to observe the establishment of the oral microbiome. We emphasize here the decrease of genera associated with healthcare-associated infections through the weeks, and an increase in oral eobiont colonizers, especially after oral diet introduction. Also, there is a decrease in diversity and richness over time in contrast with an increase in dominance, according to alpha diversity indices, and the same was observed after oral diet implementation, except for richness. Structural changes in microbial communities were also observed, mainly driven by the decrease of abundance of some dominant genera, as seen in weighted Unifrac analysis, in both oral diet introduction and time variables.
In the delivery room, the newborn’s mouth is exposed to a diverse load of microorganisms, which are provided by breathing, and interaction with parents and hospital staff [6]. During the first weeks of life, we observed that the oral cavity is colonized by a highly diverse microbial community, especially in the first week, as evidenced by the high diversity in both relative abundance and alpha diversity data. The main genera observed in VLBW infants were Streptococcus, Staphylococcus and Enterobacteriaceae members, while Acinetobacter, Haemophilus and Neisseria showed lower relative abundance. In hospitalized premature newborns, microbes present in the hospital environment were also found as oral colonizers, such as Escherichia, Staphylococcus, and Streptococcus [45]. The authors also identified Acinetobacter, Haemophilus, and Neisseria in lower relative abundance colonizing the environment. In our study, we found similar bacteria in the babies’ mouth, suggesting that these genera may be acquired from an environmental source. However, to state this more categorically, a sampling of the environment would be necessary.
In the relative abundance transition between the first and the third weeks of life, there was a decrease in microbial diversity and richness, and this data is supported by the significant differences observed in Shannon and Chao1 indices over the weeks. This finding is associated to a microbiome shift related to the increase of oral eobiont genera, such as Streptococcus, Veillonella, and Haemophilus. In addition, there was a decrease in genera such as Staphylococcus, Acinetobacter, Escherichia-Shigella, and Klebsiella, described as colonizers of the hospital environment [46, 47]. This result suggests that there is a microbial succession occurring in the oral microbiome of hospitalized VLBW infants over time, regardless of oral feeding, also evidenced in beta diversity results. A previous publication by our group looking at the effect of oropharyngeal colostrum administration in premature newborns found similar results [23].
Newborns’ microbial communities evolve as the baby grows [48] and this dynamicity was observed in the relative abundance of genera during hospitalization. Despite the changes observed over time in the oral microbiome of early life, oral feeding also plays a role in establishing the microbiome. We found a decrease in the abundance of genera associated to health-related infections, such as Staphylococcus, Klebsiella, and Enterobacter, and an increase in the abundance of Veillonella and, mostly Streptococcus, which becomes the dominant genus when evaluating the genera relative abundance before and after the implementation of oral diet. The individual graph of genera relative abundance over time and in relation to diet status shows that oral microbiome in early life is widely dynamic and with greater inter-individual variations. However, the oral microbiome tends to acquire a certain stability over time, shifting to a more similar pattern between individuals after the implementation of oral diet.
Most of the oral diet given to the babies was preterm infant formula, in which maltodextrin is one of the most abundant carbohydrates. It is described that some species of the Streptococcus genus have genes encoding proteins associated with carbohydrate metabolism, specifically maltosaccharides [49]. For example, the gene malP encodes the enzyme maltodextrin phosphorylase [50], which performs a catalytic reaction of phosphorolysis of an alpha-1,4-glycosidic bond in maltodextrins [51]. Also, there is a putative maltodextrin-binding protein encoded by the malE gene described by the authors as an important factor for oropharyngeal colonization by Streptococcus [49].
Over the weeks, genera described as colonizers of the human oral cavity appear and stand out in relation to the others [48, 52], mostly Streptococcus. Cortez et al. [23], observed, in hospitalized newborns receiving oropharyngeal colostrum administration, higher levels of Streptococcus relative abundance around the 21st day of life, slightly higher compared to the standard care group. Although we also observed an increased relative abundance of Streptococcus, this occurred around the 5th week of life, suggesting that colostrum administration favors an early increase in the abundance of this genus.
Oral microbiome colonization begins with species able to adhere to epithelial cells and salivary proteins, such as Streptococcus salivarius [6], also known as the most abundant specie from the Streptococcus genus in newborns’ oral cavity [53]. We found that 121 ASV were shared in oral babies’ microbiome over time, and 168 ASV shared before and after oral diet introduction. Interestingly, most of them were Streptococcus salivarius in both analyzes. This specie will serve as an adhesion site for subsequent colonizers [7], which suggests that the presence of some microorganisms can create a niche for the establishment of others [8], thus providing an increased diversity and formation of more stable and complex communities. This genus has another important role in oral microbiome, since the commensal Streptococcus species can compete with pathogenic species for substrate, as Porphyromonas gingivalis, to prevent their adhesion and inhibit their growth [54, 55].
Since most babies in our study were not breastfed, we cannot state how human milk could impact the formation of the oral microbiome in these VLBW infants. However, we found a lack of important microorganisms, such as Lactobacillus, a genus associated with oral colonization in neonates fed exclusively or partially with human milk. This genus has properties that can inhibit the growth of pathogenic Streptococcus species, such as cariogenic Streptococcus mutans [56]. Additionally, human milk provides many elements for babies, due to its composition of macronutrients (proteins, lipids, and carbohydrates), micronutrients (vitamins), and bioactive components (cells, immunoglobulins, cytokines, chemokines, growth factors, hormones, and antimicrobial proteins) [57], besides its own source of probiotics (human milk microbiota) and prebiotics (human milk oligosaccharides (HMOs)) [58].
As limitations of our study, we can point out that the small sample size, along with the loss of some follow-up samples, impacted the statistical results. The sequence methodology used (16s rRNA gene sequencing) also is a limitation, as it does not allow us to go beyond the genus level for many microorganisms. In addition, the low number of babies fed with human milk did not allow us to attribute differences in the microbial profile to the intake of human milk. Also, all included infants were caesarean born, therefore our results cannot be generalized to the entire population, especially vaginally born VLBW infants. However, as strengths of our study, we can point the follow-up of the infants in different time points and the sampling before and after the oral diet implementation, which allowed us to compare how this type of diet could impact in the microbiome establishment in the oral cavity.
All these findings highlight the importance of stimulating oral feeding as early as possible in VLBW infants, mainly through an oropharyngeal colostrum administration protocol, which is very well described for its safety, simplicity and potential beneficial effects, with no additional risk to infants [59]. Many studies [48, 52, 60] shows that the oral microbiome fluctuates in early life along with the introduction of new colonizing factors, like solid food or tooth eruption. We could not specify how our findings can influence the short- and long-term health, but it is not possible to exclude that a colonization at this moment of life can determine a worse prognosis for the infants. Therefore, as perspectives, studies comparing the oral microbiome of VLBW preterm infants and full-term infants over the first year of life are needed to bring new and stronger hypotheses about how it could affect the establishment of the oral microbiome.
Conclusions
In conclusion, our results suggest that, although time is related to significant changes in the oral microbial profile of very low birth weight babies, the implementation of oral diet also plays a major role in the initial establishment of microbiome, favoring specific genera that will remain as oral eobiont colonizers throughout the host’s life, as shown with Streptococcus relative abundance.
Supporting information
S1 Fig.
Principal coordinate analysis (PCoA) for (A) unweighted (Postpartum Week: R² = 0.23, p-value = 0.001; Oral Diet: R² = 0.01, p-value = 0.923;) and (B) weighted (Postpartum Week: R² = 0.20, p-value = 0.001; Oral Diet: R² = 0.01, p-value = 0.365;) Unifrac distance metrics over time (first, third and fourth week postpartum) and according to the oral diet status (before and after).
https://doi.org/10.1371/journal.pone.0295962.s001
(TIF)
S2 Fig. Shared and exclusive Amplicon Sequence Variants (ASVs) before and after the oral diet implementation.
https://doi.org/10.1371/journal.pone.0295962.s002
(TIF)
S1 Table. Covariates tested to be included in the adjusted model for alpha diversity analysis (p-value < 0.05 were selected).
https://doi.org/10.1371/journal.pone.0295962.s003
(DOCX)
S2 Table. Covariates tested to be included in the adjusted model for beta diversity analysis (p-value < 0.05 were selected).
https://doi.org/10.1371/journal.pone.0295962.s004
(DOCX)
S3 Table. Data on the use of antibiotic therapy by the study subjects.
https://doi.org/10.1371/journal.pone.0295962.s005
(DOCX)
S4 Table. Distribution of the main observed bacterial genera according to postpartum weeks.
#p-value was based on linear model test, and q-value results were confirmed with False Discovery Rate (FDR) post-hoc.
https://doi.org/10.1371/journal.pone.0295962.s006
(DOCX)
S5 Table. Distribution of the main observed bacterial genera according to oral diet introduction.
#p-value was based on linear model test, and q-value results were confirmed with False Discovery Rate (FDR) post-hoc.
https://doi.org/10.1371/journal.pone.0295962.s007
(DOCX)
S6 Table. Alpha diversity metrics considering the postpartum weeks.
#p-value was based on a linear model test, and q-value results were confirmed with False Discovery Rate (FDR) post-hoc. * q-value was considered significant when ≤ 0.10.
https://doi.org/10.1371/journal.pone.0295962.s008
(DOCX)
S7 Table. Alpha diversity indices considering oral diet (before and after implementation).
#p-value was based on a linear model test, and q-value results were confirmed with False Discovery Rate (FDR) post-hoc. *q-value was considered significant when ≤ 0.10.
https://doi.org/10.1371/journal.pone.0295962.s009
(DOCX)
Acknowledgments
The authors would like to thank all the study parents and participants, staff at the NICU of Hospital das Clínicas of the University of São Paulo, and the Department of Clinical and Toxicological Analyses of the School of Pharmaceutical Sciences–USP.
References
- 1. Azad K, Mathews J. Preventing newborn deaths due to prematurity. Best Pract Res Clin Obstet Gynaecol. 2016;36: 131–144. pmid:27545716
- 2. Watkins WJ, Kotecha SJ, Kotecha S. All-Cause Mortality of Low Birthweight Infants in Infancy, Childhood, and Adolescence: Population Study of England and Wales. PLoS Med. 2016;13: 1–19. pmid:27163787
- 3. Buckley KM, Charles GE. Benefits and challenges of transitioning preterm infants to at-breast feedings. Int Breastfeed J. 2006;1: 1–7. pmid:16945150
- 4. Lima AH, Côrtes MG, Bouzada MCF, Friche AA de L. Preterm newborn readiness for oral feeding: Systematic review and meta-analysis. Codas. 2015;27: 101–107. pmid:25885204
- 5. Morlacchi L, Roggero P, Giannì ML, Bracco B, Porri D, Battiato E, et al. Protein use and weight-gain quality in very-low-birth-weight preterm infants fed human milk or formula. American Journal of Clinical Nutrition. 2018;107: 195–200. pmid:29529139
- 6. Sampaio-Maia B, Monteiro-Silva F. Acquisition and maturation of oral microbiome throughout childhood: An update. Dent Res J (Isfahan). 2014;11: 291–301. pmid:25097637
- 7. Kolenbrander PE, Palmer RJ, Periasamy S, Jakubovics NS. Oral multispecies biofilm development and the key role of cell-cell distance. Nat Rev Microbiol. 2010;8: 471–480. pmid:20514044
- 8. Rickard AH, Gilbert P, High NJ, Kolenbrander PE, Handley PS. Bacterial coaggregation: An integral process in the development of multi-species biofilms. Trends Microbiol. 2003;11: 94–100. pmid:12598132
- 9. Arishi RA, Lai CT, Geddes DT, Stinson LF. Impact of breastfeeding and other early-life factors on the development of the oral microbiome. Front Microbiol. 2023;14: 1–15. pmid:37744908
- 10. Lenartova M, Tesinska B, Janatova T, Hrebicek O, Mysak J, Janata J, et al. The Oral Microbiome in Periodontal Health. Front Cell Infect Microbiol. 2021;11: 1–16. pmid:33828997
- 11. Suárez LJ, Garzón H, Arboleda S, Rodríguez A. Oral Dysbiosis and Autoimmunity: From Local Periodontal Responses to an Imbalanced Systemic Immunity. A Review. Front Immunol. 2020;11: 1–24. pmid:33363538
- 12. la Rosa GRM, Gattuso G, Pedullà E, Rapisarda E, Nicolosi D, Salmeri M. Association of oral dysbiosis with oral cancer development (Review). Oncol Lett. 2020;19: 3045–3058. pmid:32211076
- 13. Sudhakara P, Gupta A, Bhardwaj A, Wilson A. Oral dysbiotic communities and their implications in systemic diseases. Dent J (Basel). 2018;6: 1–14. pmid:29659479
- 14. Underwood MA, Sohn K. The Microbiota of the Extremely Preterm Infant. Clin Perinatol. 2017;44: 407–427. pmid:28477669
- 15. Hartz LE, Bradshaw W, Brandon DH. Potential NICU Environmental Influences on the Neonate’s Microbiome: A Systematic Review. Adv Neonatal Care. 2015;15: 324–335. pmid:26340035
- 16. Hewitt KM, Mannino FL, Gonzalez A, Chase JH, Caporaso JG, Knight R, et al. Bacterial Diversity in Two Neonatal Intensive Care Units (NICUs). PLoS One. 2013;8. pmid:23372757
- 17. Azevedo MJ, Garcia A, Costa CFFA, Ferreira AF, Falcão-Pires I, Brandt BW, et al. The contribution of maternal factors to the oral microbiota of the child: Influence from early life and clinical relevance. Japanese Dental Science Review. 2023;59: 191–202. pmid:37415593
- 18. Adler CJ, Cao KAL, Hughes T, Kumar P, Austin C. How does the early life environment influence the oral microbiome and determine oral health outcomes in childhood? BioEssays. 2021;43: 1–13. pmid:34151446
- 19. Li H, Zhang Y, Xiao B, Xiao S, Wu J, Huang W. Impacts of delivery mode on very low birth weight infants’ oral microbiome. Pediatr Neonatol. 2020;61: 201–209. pmid:31810597
- 20. Selway CA, Collins CT, Makrides M, Sullivan TR, Gibson RA, Penttila I, et al. Variable preterm oral microbiome stabilizes and reflects a full-term infant profile within three months. Pediatr Res. 2023; 1–9. pmid:36859444
- 21. Young GR, Van Der Gast CJ, Smith DL, Berrington JE, Embleton ND, Lanyon C. Acquisition and Development of the Extremely Preterm Infant Microbiota Across Multiple Anatomical Sites. J Pediatr Gastroenterol Nutr. 2020;70: 12–19. pmid:31714477
- 22. Chen Y, Wu L, Zhang W, Zou L, Li G, Fan L. Delivery modes and pregnancy outcomes of low birth weight infants in China. Journal of Perinatology. 2016;36: 41–46. pmid:26540243
- 23. Cortez R v., Fernandes A, Sparvoli LG, Padilha M, Feferbaum R, Neto CM, et al. Impact of oropharyngeal administration of colostrum in preterm newborns’ oral microbiome. Nutrients. 2021;13: 1–14. pmid:34959775
- 24. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41: 1–11. pmid:22933715
- 25. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37: 852–857. pmid:31341288
- 26. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13: 581–583. pmid:27214047
- 27. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6: 1–17. pmid:29773078
- 28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41: D590–D596. pmid:23193283
- 29. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42: 643–648. pmid:24293649
- 30. Bisanz JE. qiime2R: Importing QIIME2 artifacts and associated data into R sessions. 2018. Available online: https://github.com/jbisanz/qiime2R (accessed on 10 July 2022).
- 31. McMurdie PJ, Holmes S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One. 2013;8. pmid:23630581
- 32. Lahti L, Shetty S. microbiome R package. 2017. Available online: http://microbiome.github.com/microbiome (accessed on 10 July 2022).
- 33. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR, et al. vegan: Community Ecology Package. 2022. Available online: https://github.com/vegandevs/vegan (accessed on 10 July 2022).
- 34. Willis AD. Rarefaction, alpha diversity, and statistics. Front Microbiol. 2019;10: 1–5. pmid:31708888
- 35. Chao A, Bunge J. Estimating the number of species in a stochastic abundance model. Biometrics. 2002;58: 531–539. pmid:12229987
- 36. Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. 1948;27: 379–423.
- 37. Simpson EH. Measurement of diversity. Nature. 1949;163: 688.
- 38. Su X. Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment. mSystems. 2021;6: 363–384. pmid:34402645
- 39.
Venables WN, Ripley BD. Modern Applied Statistics with S. New York: Springer, 2002. Available online: http://www.stats.ox.ac.uk/pub/MASS4 (accessed on 10 July 2022).
- 40. Long JA. jtools: Analysis and Presentation of Social Scientific Data. 2022. Available online: https://github.com/jacob-long/jtools/issues (accessed on 10 July 2022).
- 41. Gu Z. Complex heatmap visualization. iMeta. 2022;1: 1–15.
- 42. Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol. 2021;17: 1–27. pmid:34784344
- 43. Chen H, Boutros PC. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 2011;12: 1–7. pmid:21269502
- 44. Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2016. Available online: https://ggplot2.tidyverse.org (accessed on 10 July 2022).
- 45. Younge NE, Araújo-Pérez F, Brandon D, Seed PC. Early-life skin microbiota in hospitalized preterm and full-term infants. Microbiome. 2018;6: 98. pmid:29855335
- 46. Worku T, Derseh D, Kumalo A. Bacterial profile and antimicrobial susceptibility pattern of the isolates from stethoscope, thermometer, and inanimate surfaces of mizan-tepi university teaching hospital, southwest Ethiopia. Int J Microbiol. 2018;2018: 1–7. pmid:30050575
- 47. Cason C, D’accolti M, Campisciano G, Soffritti I, Ponis G, Mazzacane S, et al. Microbial contamination in hospital environment has the potential to colonize preterm newborns’ nasal cavities. Pathogens. 2021;10: 1–17. pmid:34067889
- 48. Dashper SG, Mitchell HL, Lê Cao KA, Carpenter L, Gussy MG, Calache H, et al. Temporal development of the oral microbiome and prediction of early childhood caries. Sci Rep. 2019;9: 1–12. pmid:31874981
- 49. Shelburne SA, Sumby P, Sitkiewicz I, Okorafor N, Granville C, Patel P, et al. Maltodextrin utilization plays a key role in the ability of group A Streptococcus to colonize the oropharynx. Infect Immun. 2006;74: 4605–4614. pmid:16861648
- 50. Nieto C, Espinosa M, Puyet A. The maltose/maltodextrin regulon of Streptococcus pneumoniae: DIFFERENTIAL PROMOTER REGULATION BY THE TRANSCRIPTIONAL REPRESSOR MalR. Journal of Biological Chemistry. 1997;272: 30860–30865. pmid:9388231
- 51. Geremia S, Campagnolo M, Schinzel R, Johnson LN. Enzymatic catalysis in crystals of Escherichia coli maltodextrin phosphorylase. J Mol Biol. 2002;322: 413–423. pmid:12217700
- 52. Lif Holgerson P, Esberg A, Sjödin A, West CE, Johansson I. A longitudinal study of the development of the saliva microbiome in infants 2 days to 5 years compared to the microbiome in adolescents. Sci Rep. 2020;10: 1–14. pmid:32541791
- 53. Chaffanel F, Charron-Bourgoin F, Soligot C, Kebouchi M, Bertin S, Payot S, et al. Surface proteins involved in the adhesion of Streptococcus salivarius to human intestinal epithelial cells. Appl Microbiol Biotechnol. 2018;102: 2851–2865. pmid:29442170
- 54. van Essche M, Loozen G, Godts C, Boon N, Pauwels M, Quirynen M, et al. Bacterial Antagonism Against Periodontopathogens. J Periodontol. 2013;84: 801–811. pmid:22897652
- 55. van Hoogmoed CG, Geertsema-Doornbusch GI, Teughels W, Quirynen M, Busscher HJ, van der Mei HC. Reduction of periodontal pathogens adhesion by antagonistic strains. Oral Microbiol Immunol. 2008;23: 43–48. pmid:18173797
- 56. Lif Holgerson P, Vestman NR, Claesson R, Öhman C, Domellöf M, Tanner ACR, et al. Oral Microbial Profile Discriminates Breastfed from Formula-Fed Infants. J Pediatr Gastroenterol Nutr. 2013;56: 127–136. pmid:22955450
- 57. Ballard O, Morrow AL. Human Milk Composition: nutrients and bioactive factors. Pediatr Clin North Am. 2013;60: 49–74. pmid:23178060
- 58. Moossavi S, Miliku K, Sepehri S, Khafipour E, Azad MB. The prebiotic and probiotic properties of human milk: Implications for infant immune development and pediatric asthma. Front Pediatr. 2018;6: 1–7. pmid:30140664
- 59. OuYang X, Yang CY, Xiu WL, Hu YH, Mei SS, Lin Q. Oropharyngeal administration of colostrum for preventing necrotizing enterocolitis and late-onset sepsis in preterm infants with gestational age ≤ 32 weeks: a pilot single-center randomized controlled trial. Int Breastfeed J. 2021;16: 1–15. pmid:34419090
- 60. Dzidic M, Collado MC, Abrahamsson T, Artacho A, Stensson M, Jenmalm MC, et al. Oral microbiome development during childhood: an ecological succession influenced by postnatal factors and associated with tooth decay. ISME Journal. 2018;12: 2292–2306. pmid:29899505