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Article

Dynamic Alternations in Mother–Infant Dyad’s Gut Microbiota over the Period of Six Months

by
Lan Yang
1,2,3,
Zhihong Sun
1,2,3 and
Heping Zhang
1,2,3,*
1
Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot 010018, China
2
Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot 010018, China
3
Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(6), 3239; https://doi.org/10.3390/app15063239
Submission received: 7 February 2025 / Revised: 13 March 2025 / Accepted: 14 March 2025 / Published: 16 March 2025
Browse Figures
Versions Notes

Abstract

:
Since birth, the human microbiome plays a pivotal role in health. It is intriguing to understand the establishing roles of intestinal microbiota in infants. This study aimed to investigate the dynamic changes in maternal and infant gut microbiota, the interaction between intestinal microbiota, and the function of intestinal microbiota. This study recruited one pregnant woman >35 weeks gestational age. Faeces of the mother–infant dyad were regularly collected until the infant was six months old for metagenomic analysis. The results showed that the infant’s gut microbiota was significantly different from the mother’s, encompassing Bacteroides fragilis, Ruminococcus gnavus, Klebsiella pneumoniae, and Klebsiella michiganensis. Infant- or mother-specific differential metabolic pathways were found between the mother and infant’s gut microbiome, implicating differences in the intestinal metagenomic potential/function. In conclusion, the gut microbes and functions were gradually established as the infant grew.

1. Introduction

The human gut harbours trillions of symbiotic bacteria [1]. Everyone has hundreds of different gut microbial species. To date, about 1500 different microbial species have been identified from the human gut. Since birth, the human microbiome plays a crucial role in the maturing immune system and regulates health. Various maternal factors, vertical microbial transmission from the mother, horizontal environmental transmission, and internal factors associated with the infant play an indispensable role in regulating the gut microbiome [2].
Gut microbiome ecology and function are dynamic and highly unstable during infancy, especially during the first year of life. The gut microbiota changes dramatically through interactions with the developing immune system in the gut [3]. The healthy microbiome maintains the health of the host. The interaction between the microbiome and the host is substantially critical in early life because the abundance and composition of the microbiome undergo major changes in early life, and these changes become stable and remain constant throughout life, thereby determining the healthy adult life [4]. In early life, the gut microbiome is relatively active, and these initial inhabitants have a key impact on the health of the host throughout the life cycle [5]. Understanding the composition and structure of the microbiome in the intestine and the factors that influence them in early life could provide a platform for developing strategies to maintain the host’s health by restoring the gut microbiota.
High-throughput sequencing is a powerful method that unfolds both the cultivable and uncultivable microbial population in samples. Metagenomic analysis has been used to describe the taxonomic composition and functional potential of microbial communities and to assemble the entire genome sequence of a specific strain [6]. Metagenomic data analysis has also enabled us to gain a comprehensive understanding of microbial diversity, composition, community function, and their relationship with the colonic environment. Cost-effective and high-throughput metagenomic techniques have been widely used, and deep phylogenetic characteristics of intestinal microbiome have been successfully analysed. It could be used as a nifty approach to analyse the structure and function of intestinal microflora in infants.
In this study, microbial metagenomic DNA was extracted from one mother–infant dyad’s faecal samples using a DNA kit, and a metagenomic library containing genomic DNA was constructed and sequenced by Illumina sequencer. The dynamic changes in the intestinal microbiota of the mother before and after delivery were analysed, along with the infant’s microbiota over 6 months. Furthermore, the relationship between the structure and function of the intestinal microflora of mother and infant was analysed to explore the influencing factors of intestinal microflora composition and establishment in early life.

2. Materials and Methods

2.1. Ethics Statement

This work was approved by the Ethics Committee of Inner Mongolia Medical University (under the registration number ChiCTR2100044607, Chinese Clinical Trial Registry, 24 March 2021). The participant provided the written informed consent prior to starting the study. All methods were performed in accordance with the relevant guidelines.

2.2. Subject Recruitment

Recruitment took place in Hohhot, China. The recruitment period for this study was from 1 January to 1 April 2021. Participants provided informed consent through oral questions and answers. One normal pregnant woman at 35th week of gestational age, with normal BMI, no periodontal disease, no type II diabetes, no gestational diabetes, no previous pregnancy and miscarriage history, no vaginitis, and without other history of serious/chronic diseases, Asian, was recruited for this study. During pregnancy and lactation, the mother did not take additional antibiotics, nutritional supplements, or probiotics; did not carry out special work outside the office; had no exposure to cigarette smoke and additional psychological stress; and did not carry out additional physical activity. The foetus was delivered by natural labour at full term and fed by a mixture of breast milk and infant formula milk powder. The participant underwent ultrasound examination to assess the state of foetal well-being.
The research was reporting a retrospective study of archived samples. The data were accessed for research purposes on 1 June 2021. During or after data collection, authors were able to obtain information that could identify individual participants. Since the minor volunteer in this study was an infant, he did not have the ability to speak or think independently. Consent was not obtained from the minor. Only guardian consent was obtained.

2.3. Study Design and Sample Collection

The participant was requested not to take antibiotics and probiotic-containing foods during the trial period and to notify her doctor of any abnormalities. The participant was also asked to return the probiotic packaging materials for compliance assessment.
The first sample was collected 30 days before the expected date of confinement until the day of delivery (D-30) and was performed by trained professionals under strict aseptic conditions using standard procedures. Meanwhile, the participant was taught the standard sampling method. About 10 g of faecal samples were collected each time. From the first sample collection to the day of delivery, faecal samples were collected every seven days. Sampling after the delivery was continued by the mother and the infant. For the first week after delivery, samples were collected on the day of delivery (D0) and days 1, 2, 3, and 7 after delivery (D1, D2, D3, and D7, respectively). From the second week to two months after delivery (D14 to D56), faecal samples were collected every seven days. From the third to sixth month after delivery (D70 to D182), sampling was performed every 14 days. The complete sampling lasted 212 days; however, as the participants did not defecate at certain time points, only a total of 33 faecal samples were collected from the mother (18 samples) and the infant (15 samples).
Faecal samples were collected into sterile tubes provided by our laboratory beforehand. Collected samples were stored temporarily in a household refrigerator (−20 °C) and were transported to the laboratory by our staff within 24 h of sample collection. They were stored at −80 °C until total DNA was extracted.

2.4. DNA Extraction

Metagenomic DNA was extracted from faecal samples using QIAamp Fast DNA Stool Mini-kit (QIAGEN, Hilden, Germany). The quality of extracted DNA was evaluated by agarose gel electrophoresis and Nanodrop spectrophotometry (optical density ratio at 260 nm/280 nm). All DNA samples were stored at −20 °C until further processing.

2.5. Whole Genome Metagenomics Sequencing and Quality Control

Metagenomic libraries containing 2 μg of genomic DNA were constructed according to the Illumina TruSeq DNA Sample Prep V2 Guide. The qualities of all libraries were assessed by an Agilent bioanalyzer and the DNA LabChip 1000 Kit. Sequencing was performed on an Illumina HiSeq XTEN sequencer (Illumina, San Diego, CA, USA). The quality control filters were set according to Liu et al. [7]. Briefly, (1) reads with adaptor sequences were removed by the software Seqprep (Ver. 1.2.0). (2) Reads were trimmed from the 3’ end using a quality threshold of 30. (3) Low-quality (Q30) reads that comprised more than 50% of the bases were removed. (4) Reads less than 70 bp were removed by Sickle software (Ver. 1.33). (5) Reads of host genome sequences were removed. High-quality reads (a total of 363.45 Gb, average of 11.01 Gb per sample) were retained and used for further analysis.

2.6. Bioinformatic Analysis

MetaPhlAn3 (Ver. 3.0) was used for species-level taxonomic assignment of faecal microbiota using default settings via the search engine Bowtie2 (Ver. 2.2.9) [8,9]. PanPhlAn3 (Ver. 3.0.1) was used for species-level analysis [10]. If a strain was identified by PanPhlAn3, the abundance of the strain would be regarded as the species abundance. High-quality Illumina sequencing-generated metagenomic datasets were assembled by MegaHit [11]. QUAST (Ver. 5.0.0) was used to evaluate the metagenomic assembly results. MetaBAT2 (Ver. 2.12.1) and Maxbin2 (Ver. 2.0) were used for binning the assemblies using a minimum scaffold length threshold of 1500 bp [12,13]. Das Tool (Ver. 1.1.2) was used to connect the contigs assembled by MetaBAT2 and Maxbin2 [14]. Metagenome-assembled genomes (MAGs) in conformity with quality requirements (integrity > 80%, contamination < 10%) were selected. CheckM (Ver. 1.0.18) was then used to evaluate the integrity and degree of contamination of each MAG [15], and high-quality MAGs were used for further analysis.
Each MAG was annotated by BLASTn using the Non-redundant Nucleotide Sequence Database (NT) of National Center for Biotechnology Information (NCBI), phylogenetic trees were constructed using 400 generic PhyloPhlAn markers, and the MAGs were clustered by dRep (Ver. 2.2.4) to define species-level genome bins [16].

2.7. Statistical Analysis

R package vegan was used for alpha and beta diversity analyses. Statistical differences in alpha diversity were assessed by Wilcoxon rank-sum tests. Multivariate analysis, such as principal coordinate analysis (PCoA), was performed by R software (Ver. 4.0.3; https://www.rproject.org/ (accessed on 31 December 2021)). Differential metabolic pathways between mother’s and infant’s gut microbiome were identified by t-tests. Correlations between different biological indicators were assessed with Spearman’s rank correlation coefficient and visualised by Cytoscape 3.5.1. Spearman’s correlation analysis was also performed to find significant correlations between the microbial communities (r > 0.6, p < 0.05).

3. Data Availability

The datasets generated and analysed during the current study are available in the NCBI Short Read Archive (SRA) repository, the persistent web link was http://www.ncbi.nlm.nih.gov/bioproject/770101 (accessed on 31 December 2021), and the accession number was PRJNA770101.

4. Results

4.1. Microbial Dynamics of Mother’s and Infant’s Faecal Microbiomes

The dynamics of the faecal microbiome composition and diversity of the mother–infant dyad were tracked over a period of 212 days. Species-level taxonomic annotation was performed with MetaPhlAn3. In total, the faecal microbiota of mother and infant contained sequences representing 310 species that belonged to 10 phyla and 55 families. There were 18 major species in the complete dataset (average relative abundance > 1.0%; Figure 1A). Generally, the microbial complexity was rather low in the infant’s faeces, having four major species, including Bacteroides fragilis, Ruminococcus gnavus, Klebsiella pneumoniae, and Klebsiella michiganensis. Comparatively, the mother’s faecal microbiota was relatively complex, comprising a number of major species, e.g., Bacteroides vulgatus, Bacteroides stercoris, Faecalibacterium prausnitzii, Bacteroides uniformis, Eubacterium rectale, Bifidobacterium pseudocatenulatum, and Alistipes putredinis.
The metagenomic data were subjected to PCoA (Bray–Curtis distance; Figure 1B). Symbols representing the mother and infant’s microbiota showed distinct clustering patterns on the PCoA score plot, confirming great differences in the faecal microbiota between the two individuals and little variation among samples of the same person. The correlation between the gut microbiota of the mother and the infant was worth exploring.

4.2. Correlation Network of Faecal Microbiota of Mother and Infant

The infant was fed a mixture of breast milk and infant formula milk powder. To explore the effects of this feeding pattern on the gut microbiota of infants and the relationship between mother and infant intestinal microbiota, a correlation network of faecal microbiota of the mother and infant was constructed (Figure 2). The top 30 species were included in the correlation analysis. Interestingly, most species showed a significantly positive correlation (r = 0.8, p < 0.05), while a few species exhibited a significantly negative correlation (r = 0.8, p < 0.05). Bacteroides fragilis (predominantly present in the infant’s sample) showed a significant positive correlation with Methanobrevibacter smithii, Anaerostipes hadrus, and Klebsiella pneumoniae with Klebsiella variicola. Some bacteria from the mother, such as Bacteroides vulgatus with Bacteroides stercoris, Eubacterium rectale, Bifidobacterium pseudocatenulatum, and Bacteroides uniformis; Faecalibacterium prausnitzii with Bifidobacterium pseudocatenulatum, Bacteroides vulgatus, Eubacterium rectale, Bacteroides uniformis, and Bacteroides stercoris; Bacteroides stercoris with Bifidobacterium pseudocatenulatum, Eubacterium rectale, and Bacteroides uniformis; and Eubacterium rectale with Bifidobacterium pseudocatenulatum, showed a significantly positive correlation. These results showed that there was a complex correlation between the mother’s and infant’s gut microbiota.

4.3. Metagenomic Potential of Mother’s and Infant’s Microbiota

The metagenomic potential of the mother’s and infant’s gut microbiota was annotated, and differential metabolic pathways were identified between the mother and infant’s data subsets (Figure 3). In this study, a Hierarchical Cluster Analysis was performed on the metabolic pathways of all samples (Figure 3A).
Interestingly, the gene abundance of these metabolic pathways was relatively stable in the mother’s microbiome but not in the infant’s microbiome over time. The results of the cluster heatmap broadly classified these metabolic pathways into three groups. The first group comprised nine metabolic pathways, including PWY-7204, PWY0-1241, and PWY-7269. Genes coding these metabolic pathways were mainly detected in the infant’s faecal samples. The second group contained eight metabolic pathways, including P562-PWY, PWY-6863, and P162-PWY. Genes coding these metabolic pathways were mainly observed in the maternal samples but with a relatively low amount. The third category possessed 11 metabolic pathways, including PWY-5676, GALACT-GLUCUROCAT-PWY, and PRPP-PWY. These pathways were also mainly encoded in the maternal faecal microbiome. However, their relative contents were relatively high compared with those in the second group. These results indicated obvious differences existed in the metagenomic potential between the mother and infant’s gut microbiota, suggesting different physiological conditions within the mother and infant’s gut, which might in turn shape the functions of their microbial communities, respectively.

5. Discussion

As one of the highest densities of microecology on earth, intestinal microbiota is a community of key contributors to human nutrition. The gut microbiota is closely related to human health and nutrition. Since birth, the human microbiome plays a crucial role in influencing the health of the host. Understanding the changes in the human gut microbiome helps to construct the intestinal microecosystem. Metagenomic sequencing analyses have been performed to explore the structure and function of human gut microbiota. This study used metagenomic sequencing to investigate the dynamic variation in the mother’s and infant’s gut microbiota, the interaction between intestinal microbiota, and the function of intestinal microbiota.
The development of intestinal microbiota during the first 1000 days of life will affect a person’s health throughout life [17,18]. Many factors determine the composition of intestinal microbiota in newborns. The method of delivery plays an important role in the initial establishment of infant gut microbes [19]. Arboleya and colleagues proposed that vaginal delivery and exclusive reliance on breastfeeding in early life are the gold standards for the establishment and development of healthy gut microbiota in infants [20]. Bifidobacterium, Bacteroides, and Clostridium proliferate and become the dominant genera associated with early life [21]. In the data we collected from our experiment, although Bifidobacterium was not found in the intestine of the newborn, Bacteroides was the dominant genus in the intestine. This was consistent with previous research. With the growth of the infant, the intestinal microbiota was more abundant, such as Ruminococcus gnavus and Klebsiella Pneumoniae, among others. Nevertheless, before six months, Bacteroides fragilis was still the highest relative abundance species in the infant’s intestine, which was beneficial to maintaining the healthy environment of the intestine and worthy of continuous attention and exploration. Our data also showed that the gut microbiota composition of the infant was much less complicated than that of the mother, which was in line with the findings of previous studies [22].
Interestingly, we also observed differences in the richly differentiated taxonomic symbiotic network between the mother and infant. For example, in the infant’s intestine, Bacteroides fragilis, Methanobrevibacter smithii, etc., were significantly positively correlated (p < 0.05), while Methanobrevibacter smithii generally appeared in the intestine of adults [23]. After obtaining this result, we repeated the investigation on the mother and learned that the infant was fed a mixture of breast milk and infant formula milk powder. In the study by Melsaether C. et al., breastfeeding was one of the main factors affecting the early development of intestinal microbiota in infants [24], which confirmed the important influence of breastfeeding on the development of intestinal microbiota in infants. For mixed infants, the composition of infant formula milk powder was very different from breast milk. For example, infant formula milk powder lacked key compounds found in human breast milk, such as HMOs [25], which caused the intestinal microbiota of infants fed formula milk powder and breast milk to be very different [26]. Some studies have shown that formula milk powder might lead to a lack of Bifidobacteria in the intestinal microbial composition of infants [27,28], which was consistent with our findings.
The composition and activity of the intestinal microbiota co-develop with the host from birth and are influenced by complex interactions with the host’s genome, nutrition, and lifestyle. Intestinal microbiota is involved in the regulation of multiple host metabolic pathways, mediating interactions between the host and bacterial metabolism, signal transduction, and immune and inflammatory response. These responses connect the gut, liver, muscle, and brain physiologically. An in-depth understanding of these responses is the prerequisite for optimising therapeutic strategies to manipulate the gut microbiome to fight disease and improve health [29]. Within these metabolic responses, multiple bacterial genomes could sequentially regulate metabolic responses, leading to combined metabolism of microbiome and host genomes to substrates [30]. The gut microbiota communicates metabolically with the host in a coordinated manner. After clustering analysis of metabolic pathways in the samples, we found that PWY-5676, GALACT-GLUCUROCAT-PWY, PRPP-PWY, and others were consistent metabolic pathways in the infant and mother. Among them, PWY-5676: acetyl-CoA fermentation to the butanoate II pathway regulated the synthesis of butyric acid. Acetic acid, propionic acid, and butyric acid were the main short-chain fatty acids (SCFAs) in the intestine. As the main energy source of intestinal cells, SCFAs regulate the absorption of a variety of nutrients in the intestinal tract, are widely involved in energy metabolism, and are one of the most important products of intestinal microorganisms [31]. This is also evidence that the infant’s intestinal metabolic pathway is closer to that of adults. We speculated this might be due to the large differences in protein composition, lactose, fat, etc., between breast milk and infant formula milk powder. As a result, the structure and function of the microbiota in mixed diet-fed infants were different from those in breastfed infants.
In conclusion, we found that the gut microbes were gradually established and perfected as the infant grew. Natural childbirth could promote the colonisation of beneficial species in the infant’s intestine, bringing positive effects on the infant’s intestinal microflora. For the mixed diet feeding infant, the structure and function of intestinal microbiota were more similar to adults. Intestinal microbiota affects human development. Effectively using the information about intestinal microbiota could augment human health. A better understanding of establishing mechanisms of intestinal microbiota in infants and the prediction of infant’s intestinal microbiota could provide a theoretical basis and guidance to promote the intestinal health of mothers and infants. However, this study was a scientific phenomenon discovered accidentally in the experiment; further large-scale population experiments are needed.

Author Contributions

Data curation, Z.S.; Funding acquisition, H.Z.; Methodology, L.Y.; Project administration, H.Z.; Supervision, H.Z.; Validation, Z.S.; Writing—original draft, L.Y.; Writing—review and editing, Z.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Inner Mongolia Agricultural University high-level/excellent doctoral talent introduction research project (NDYB2022-5), University Basic Scientific Research Business Expenses Project—Young Teachers Research Ability Enhancement Fund Project, 2023 Financial Funds (BR230118), and Discipline Project of College of Veterinary Medicine, Inner Mongolia Agricultural University (SYKJZD202302).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and was authorised by the Ethics Committee of Inner Mongolia Medical University (under the registration number ChiCTR2100044607, Chinese Clinical Trial Registry).

Informed Consent Statement

The participant provided the written informed consent prior to starting the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Applsci 15 03239 g001
Figure 1. Faecal microbiota composition and diversity of the mother–infant pair. (A) Faecal microbiota composition. Non-major species of relative abundance < 1% of the total sequences are grouped under ‘others’. (B) Principal coordinates analysis (PCoA; Bray–Curtis distance) of mother and infant faecal microbiota. The codes, BC and BM, represent samples of the infant and the mother, respectively.
Figure 1. Faecal microbiota composition and diversity of the mother–infant pair. (A) Faecal microbiota composition. Non-major species of relative abundance < 1% of the total sequences are grouped under ‘others’. (B) Principal coordinates analysis (PCoA; Bray–Curtis distance) of mother and infant faecal microbiota. The codes, BC and BM, represent samples of the infant and the mother, respectively.
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Figure 2. Correlation network between faecal microbiota of the mother and the infant. The top 30 most abundant features were used to construct the network. The size of the circle represents the relative abundance of the specific species. Positive and negative Spearman’s correlations were represented by purple and black lines, respectively. The line thickness represents the strength of correlation as illustrated by the colour scheme. The colour scheme represents Spearman’s rho, ranking between 0.8 and −0.8. P value was less than 0.05. A value greater than 0 indicates positive correlation, and vice versa.
Figure 2. Correlation network between faecal microbiota of the mother and the infant. The top 30 most abundant features were used to construct the network. The size of the circle represents the relative abundance of the specific species. Positive and negative Spearman’s correlations were represented by purple and black lines, respectively. The line thickness represents the strength of correlation as illustrated by the colour scheme. The colour scheme represents Spearman’s rho, ranking between 0.8 and −0.8. P value was less than 0.05. A value greater than 0 indicates positive correlation, and vice versa.
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Figure 3. The clustering analysis of metabolic pathways of all samples. (A) Hierarchical Clustering Analysis of microbial metabolic pathways in faecal samples from mothers and infants. Hierarchical Clustering Analysis was performed based on R package cluster. *: P value was less than 0.05. (B) Cluster heatmap of differential metabolic pathways between the maternal and infant’s faecal microbiota. The 28 significant differential metabolic pathways were identified by t-test (cut-off: p < 0.05) and fold difference (>1 [log2]) between metabolites of the mother–infant pair. The codes, BC and BM, represent samples of the infant and the mother, respectively.
Figure 3. The clustering analysis of metabolic pathways of all samples. (A) Hierarchical Clustering Analysis of microbial metabolic pathways in faecal samples from mothers and infants. Hierarchical Clustering Analysis was performed based on R package cluster. *: P value was less than 0.05. (B) Cluster heatmap of differential metabolic pathways between the maternal and infant’s faecal microbiota. The 28 significant differential metabolic pathways were identified by t-test (cut-off: p < 0.05) and fold difference (>1 [log2]) between metabolites of the mother–infant pair. The codes, BC and BM, represent samples of the infant and the mother, respectively.
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Yang, L.; Sun, Z.; Zhang, H. Dynamic Alternations in Mother–Infant Dyad’s Gut Microbiota over the Period of Six Months. Appl. Sci. 2025, 15, 3239. https://doi.org/10.3390/app15063239

AMA Style

Yang L, Sun Z, Zhang H. Dynamic Alternations in Mother–Infant Dyad’s Gut Microbiota over the Period of Six Months. Applied Sciences. 2025; 15(6):3239. https://doi.org/10.3390/app15063239

Chicago/Turabian Style

Yang, Lan, Zhihong Sun, and Heping Zhang. 2025. "Dynamic Alternations in Mother–Infant Dyad’s Gut Microbiota over the Period of Six Months" Applied Sciences 15, no. 6: 3239. https://doi.org/10.3390/app15063239

APA Style

Yang, L., Sun, Z., & Zhang, H. (2025). Dynamic Alternations in Mother–Infant Dyad’s Gut Microbiota over the Period of Six Months. Applied Sciences, 15(6), 3239. https://doi.org/10.3390/app15063239

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