Parity influences postpartum adaptations in the maternal gut microbiota

Parity influences postpartum adaptations in the maternal gut microbiota | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Parity influences postpartum adaptations in the maternal gut microbiota Zharkyn Jarmukhanov, Elizaveta Vinogradova, Nurislam Mukhanbetzhanov, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5972536/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The gut microbiome undergoes substantial modifications during pregnancy, yet its postpartum adaptations remain poorly understood, particularly with respect to the influence of parity. Here, we investigated the impact of childbirth history on maternal gut microbiome composition and function one month postpartum. By conducting metagenomic sequencing analysis on 60 participants (34 postpartum mothers and 26 controls), we demonstrated significant differences in microbial diversity and community structure between postpartum mothers and control, as well as subtle differences between first-time mothers and multiple-birth mothers. We identified parity-specific signatures, with first-time mothers showing enrichment in Dysosmobacter welbionis, Candidatus Saccharibacteria , and Anaerotruncus species . Functional analysis revealed distinct metabolic reprogramming patterns, including increased amino acid biosynthesis and modified fermentation pathways supporting postpartum recovery. We observed significant correlations between specific bacterial taxa and metabolic pathways, particularly in energy metabolism and immune modulation. Notably, the enhanced capacity for short-chain fatty acid production in primiparous mothers, mediated by Anaerotruncus and D. welbionis , suggests a potential role in shaping breast milk composition, which may influence neonatal development. These findings establish the concept of parity-dependent microbiome programming and provide insights into the biological mechanisms underlying maternal adaptation to pregnancy and childbirth. Biological sciences/Microbiology/Bacteria/Metagenomics Biological sciences/Microbiology/Communities/Microbiome Gut microbiome Postpartum Infants Pregnancy Figures Figure 1 Figure 2 Figure 3 Introduction The human gut microbiome exhibits remarkable plasticity in response to physiological changes, with pregnancy representing one of the most profound periods of host-microbiome adaptation 1 . While pregnancy-associated microbiome alterations have been extensively documented, the postpartum period remains a critically understudied phase of maternal-microbiome interactions 2 . This oversight is particularly significant given the emerging evidence suggesting that pregnancy and childbirth may induce lasting changes in the maternal gut ecosystem, potentially establishing what we term a "microbiome memory of motherhood" 3 – 5 . Postpartum adaptation encompasses fundamental physiological restructuring, including metabolic recalibration, immune system reconstitution, and extensive tissue remodeling 3 – 5 . Understanding the alterations occurring in the maternal gut microbiome during the postpartum period is critical for several reasons. First, microbial metabolites can influence breast milk composition, directly impacting neonatal health 6 . Second, the microbiome participates in regulating post-gestational metabolic processes, including the restoration of insulin sensitivity and normalization of lipid metabolism 7 . Third, the gut microbiome plays a key role in immune system recovery following gestational immunosuppression 8 . Recent advances in microbiome research have revealed that pregnancy-induced changes in the gut microbiome extend well beyond parturition, indicating the occurence of long-term maternal adaptations 9 . These modifications appear particularly pronounced during first pregnancies, potentially establishing a "metabolic memory" that influences subsequent gestations 2 . This phenomenon aligns with the emerging concept of parity-dependent microbiome programming, where the first pregnancy may create a lasting imprint on the maternal gut ecosystem. This model suggests that primary pregnancy and childbirth trigger a fundamental reorganization of the gut microbiota, which may have long-term physiological implications for maternal health and subsequent pregnancies 2 . Evidence for this phenomenon comes from observations of distinct microbial signatures in multiparous women, suggesting that the maternal microbiome retains aspects of its pregnancy-adapted state 10 . However, the current understanding of how these processes differ between primiparous and multiparous women remains fragmentary. This knowledge gap is particularly relevant given the emerging evidence that first-time pregnancies may induce persistent alterations in the maternal gut ecosystem, potentially influencing long-term maternal health trajectories 7 . Here, we present a comprehensive analysis of the postpartum gut microbiome, examining how parity influences microbial community structure and function. By integrating taxonomic profiling with metabolic pathway analysis, we revealed distinct parity-specific signatures in the maternal microbiome one month postpartum. Our findings provide novel insights into the biological mechanisms underlying maternal adaptation to pregnancy and childbirth, with implications for understanding the long-term impact of reproductive history on maternal health. Methods Study Design and Participants This study is a cross-sectional observational study with a case-control design, that investigated the impact of parity on maternal gut microbiome composition and function one month postpartum. Participants were recruited from the Сity perinatal center in Astana, Kazakhstan, between April 2021 and April 2022. The inclusion criteria included healthy postpartum women with a history of pregnancy and full-term delivery, while the exclusion criteria encompassed antibiotic use within the last three months, gastrointestinal disorders, and metabolic conditions. Women who had not delivered in the prior three years were chosen as controls to account for potential long-term postpartum effects on the microbiome while still maintaining biological comparability with the study group. This approach minimizes variability associated with major life-stage differences, such as pregnancy-induced metabolic and immunological adaptations, while allowing us to focus on parity-specific microbiome signatures. Additionally, this selection criterion helps control for potential confounding factors related to early-life microbiome establishment, which may differ between never-pregnant individuals and those who have previously experienced pregnancy. Ethical approval for this study was obtained from the Local Bioethics Commission of the Private Institution National Laboratory of Astana (Approval №05-2023). Informed consent was obtained from all participants and/or their legal guardians. All methods were performed in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. Sample Collection and processing Stool samples were collected from participants at approximately one month postpartum using sterile, DNA/RNA-free Zymo DNA/RNA Shield collection kits (Cat. No: R1101-1). Participants were instructed to collect samples at home following standardized guidelines to minimize contamination. The samples were immediately stored at – 20°C upon collection to preserve microbial integrity. For transportation to the laboratory, temperature-controlled containers were used to maintain stability, with a transport duration of approximately 1–1.5 hours. Upon arrival, samples were either stored at – 20°C for short-term preservation or at – 80°C for long-term storage, as needed. DNA Extraction and Sequencing Microbial DNA was extracted via the ZymoBiomics DNA Microprep Kit (Cat. No: D4301) following the manufacturer’s protocol. The kit utilizes bead beating with high-density BashingBeads for efficient mechanical lysis, ensuring uniform disruption of resilient microbial cells, including Gram-positive bacteria and fungi. A high-speed cell disruptor was used to enhance homogenization. The DNA concentration was assessed via a Nanodrop 2000/2000c (Thermo Fisher Scientific). To ensure quality control, sterile water was included as a negative control. Sequencing was performed at Novogene (Beijing, China) using the Illumina NovaSeq 6000 platform following standard Illumina protocols. Each metagenomic sample generated an average of 6 Gb of raw sequencing data. Bioinformatic Analysis The raw sequencing reads were processed and analyzed on a computing system. Data preprocessing was conducted via KneadData v0.12.0, which removes contaminating sequences such as ribosomal RNAs and host DNA to improve data quality. Functional profiling was performed with the HUMAnN 3.0 pipeline, whereas the taxonomic composition was determined via MetaPhlAn 4 with the mpa_vJan21_CHOCOPhlAnSGB202103 database, ensuring high-resolution microbial classification. UniRef90 was used for protein sequence annotation and retrieval. Default parameters were applied throughout the pipeline to maintain standardization and ensure compatibility across analytical stages. Statistical Analysis Data analysis was performed in Python 3.9 using the numpy v1.26.4, pandas v1.5.3, scipy v.13.1, statsmodels v0.14.2, scikit-bio v0.5.6, matplotlib v3.9.2 and seaborn v0.13.2 libraries. Only taxa present in at least 25% of the samples were included in the analysis. Demographic comparisons were made using independent t-tests, mann-whitiney u-tests, or binomial tests where appropriate. Alpha diversity was assessed using richness and evenness indices at the species level, namely the Observed and Pielou indices were used. Beta diversity was assessed using Bray-Curtis and Jaccard distances aiming to assess distributional and presence/absence differences, respectively. Significance of grouping was assessed on PCoA ordination and using ANOSIM and PERMANOVA grouping tests with 9999 permutations. Taxonomic comparisons between control (Cntrl) and postpartum (MM) groups were performed using LEfSe utils, with significance level set to effect size LDA > 2&p ≤ 0.05. Association between taxa and parity was performed using generalized linear models (GLM) on center-log-ratio (clr) transformed abundance data, significance level set to p ≤ 0.05&q ≤ 0.25, FDR, while adjusting for age as a covariate. Pathway comparison was based on evaluation of differences between means, as significant changes were considered those that showed statistical significance p ≤ 0.05 and had non-overlapping 95% confidence intervals (CI) of the difference between means. Correlation analysis between identified taxonomic and functional (pathway) markers was performed on clr-transformed data using pearson's r coefficient, significance level set was set to p ≤ 0.05 11 . Results Obstetric characteristics of the study and control groups The study included 60 participants, who were divided into a study group (MM, n = 34) and a control group ( n = 26). The mean age in the study group was 30.0 ± 5.2 years, whereas it was 32.1 ± 4.79 years in the age-matched control group, which included individuals with no history of deliveries or abortions in the preceding three years. The participants in the study group reported an average of 3.41 ± 1.71 pregnancies and 2.53 ± 1.26 deliveries. A significant proportion of participants had experienced multiple pregnancies (P = 0.0004) and deliveries (P = 0.004), with a notable increase in those with more than two pregnancies (P = 0.02). Alpha diversity analysis Our findings reveal that childbirth affects the composition and diversity of the gut microbiota. As shown in Fig. 1 (A), we detected a significant increase in alpha diversity between the control and MM groups one month postpartum (Observed, p = 0.02, Fig. 1 (A), above) in terms of observed species, but not in terms of taxonomic evenness (Pielou, P = 0.49). Currently, mixed evidence is presented regarding postpartum changes in the microbiome, and some studies report minimal changes, whereas others note significant changes in the community 12 , 13 . We investigated the relationships between childbirth history and microbial community characteristics by comparing first-time mothers with those who had multiple births. Significant differences were not observed in community richness but evenness (Pielou's index, p = 0.043, Fig. 1 (A), below) were detected between the study groups. Compared with the first-time mothers, women with multiple births (≥ 2) demonstrated increased community evenness. The Pielou evenness index was positively correlated with childbirth frequency ( r = 0.4, p = 0.037), whereas age was less significantly associated with childbirth frequency ( r = 0.3, p = 0.08). These findings suggest a potential link between multiple childbirths and increased microbial diversity. Beta diversity analysis Beta diversity analyses, as shown in Fig. 1 (B), revealed significant differences in community structure between the MM and control groups across multiple distance metrics. The Ordination plots generated via the Bray-Curtis and Jaccard indices demonstrated consistent patterns of community differentiation, with ANOSIM revealing weak but significant clustering ( r = 0.09–0.1, p < 0.05 for all the metrics). These findings were corroborated by PERMANOVAs (F = 2.22–2.73, p < 0.05), indicating that while there was substantial overlap between groups, there were detectable differences in community composition. These results provide evidence for altered community composition in MM, highlighting the considerable difference between the MM and control groups. The gut microbiome composition one month postpartum A total of 322/958 (33.6%) microbial species were prevalent in at least 25% of all samples from which 307/322 were present in the 25% MM, the most abundant taxa of the control and MM groups at the class and genus are shown in Fig. 1 (C). As shown in Fig. 1 (D), (E) we identified significant (LDA ≥ 2, p ≤ 0.05) enrichment in carbohydrate-metabolizing species ( Bacteroides stercoris, Bifidobacterium angulatum ) and SCFA producers ( Eubacterium siraeum, Clostridium leptum, Harryflintia acetispora, Eubacterium sp. AF15-50 ), corresponding with elevated energy demands during lactation. The increased abundance of immunomodulatory species ( Tyzzerella nexilis, Romboutsia timonensis, and Eisenbergiella tayi ) and barrier-supporting bacteria ( Alistipes ihumii, Anaerotruncus rubiinfantis , and Christensenellaceae bacterium ) aligns with postpregnancy immune recalibration. Notably, we observed significant (LDA ≥ 2, p ≤ 0.05) increases in nitrogen-fixing species ( Afipia broomeae, Bradyrhizobium elkanii ), potentially contributing to increased protein metabolism and amino acid biosynthesis, which are crucial for tissue repair and milk protein production. Concurrent increases in anaerobic metabolizers, including Clostridium sp. Marseille-P3244 , Oscillospiraceae bacterium NSJ-64 , and specialized fermenters ( Anaeromassilibacillus sp. An250, Candidatus Geddesella stercoravicola , and Candidatus Pararuminococcus gallinarum ), suggest the optimization of energy harvesting through enhanced anaerobic fermentation pathways. A selective depletion pattern is observed in specific butyrate producers ( Roseburia hominis, Lachnospira eligens, Gemmiger formicilis ), possibly indicating a metabolic shift towards alternative SCFA production pathways that better support postpartum recovery. Similarly, the reductions in the levels of certain polysaccharide metabolizers ( Bacteroides caccae and Parabacteroides distasonis ) and fermentative species ( Clostridium sp. AF34-10BH, Clostridium sp. AF15-49, Clostridium sp. AM33-3 ) suggest the remodeling of carbohydrate metabolism pathways. The decreased abundance of Ruminococcus sp. NSJ-71, Faecalicatena fissicatena , and Lacrimispora amygdalina indicates a potential metabolic reprogramming favoring more efficient energy extraction and nutrient utilization patterns specific to postpartum needs. We also revealed the features of the original taxonomic profile in the original in comparison with those of two or more births, as shown in Fig. 2 . First-time mothers presented consistently increased abundances of Dysosmobacter welbionis, Candidatus Saccharibacteria , and Anaerotruncus. These findings suggest fundamental differences in maternal microbiome adaptation between first and subsequent childbirths, potentially reflecting different metabolic and immunological requirements for primary versus subsequent postpartum recoveries. Functional profiling of the gut microbiome one month postpartum Total of 20/385 (5%) derived pathways differed significantly ( p < 0.05&non-overlaping 95%CI) between the groups, of which 14 (70%) were significantly associated with the Cntrl and 6 (30%) with the MM group. These results are presented in Fig. 3 (A). Our findings revealed significant modifications in key metabolic processes ( p < 0.05), indicating that a coordinated reprogramming of cellular metabolism is associated with postpartum adaptation. Central carbon metabolism showed marked changes, with significant downregulation of gluconeogenesis (GLUCONEO-PWY, p = 3.3e-05) and glycolysis pathways (GLYCOLYSI, p = 4.3e-04; PWY-5484, p = 3.7e-04), reflecting the transition from pregnancy-associated insulin resistance to restored glucose homeostasis. Nucleotide metabolism was associated with reduced activity in pyrimidine deoxyribonucleotide pathways (PWY-7199, p = 4.9e-04; PWY-7210, p = 5.2e-05) and NAD salvage pathways (PWY-7761, p = 6.7e-04), corresponding to the shift from gestational proliferation to postpartum tissue recovery and uterine involution. Notably, we observed enhanced amino acid biosynthesis, including the upregulation of L-histidine (HISTSYN-PWY, p = 1.8e-02), aromatic amino acid (COMPLETE-ARO-PWY, p = 6.4e-03), and L-alanine pathways (PWY0-1061, p = 2.1e-02), supporting protein synthesis for lactation (requiring 500–700 additional kcal/day), tissue repair, and immune system restoration. Fermentation pathways were significantly elevated, particularly in pyruvate fermentation to acetate and lactate (PWY-5100, p = 1.2e-02), indicating adaptation to increased energy demands and tissue remodeling during uterine involution. We conducted a correlation analysis between significant marker metabolic pathways ( p ≤ 0.05&non-overlapping 95%CI) and bacterial species abundance (LDA ≥ 2& p ≤ 0.05), as shown in Fig. 3 (B). Significant positive correlations were identified between Clostridia bacterium and aromatic amino acid biosynthesis (COMPLETE-ARO-PWY, r = 0.6, p < 0.0001) and both gluconeogenesis I and gluconeogenesis III were elevated in cntrl, aligning with increased energy demands and protein synthesis requirements during lactation. Significant positive correlations were identified between Candidatus Geddesella, Ruminococcus genera and aromatic amino acid biosynthesis (COMPLETE-ARO-PWY, r = 0.3–0.4, p < 0.01 − 0.001). Positive correlations were also observed for tetrapyrrole biosynthesis (PWY-5188) and most of the postpartum marker genera: Romboutsia, Desulfovibrio, Candidatus_Geddesell, Ruminococcus, Anaeromassilibacillus, Eisenbergiella (r = 0.3–0.5, p < 0.05 − 0.001), as well as Anaerotruncus genera (r = 0.3, p < 0.01), which is a specific marker of first parity. Interestingly, Dysosmobacter genera demonstrated a negative correlation with this pathway (r=-0.3, p < 0.05). A strong positive correlation was also observed between Bifidobacterium and the superpathway of L-alanine biosynthesis (PWY0-1061, r = 0.6, p < 0.0001). These correlations correspond to the physiological transitions of the postpartum period, including metabolic adjustment, immune system recalibration, and tissue regeneration 3 – 5 . The observed relationships between specific bacterial species and metabolic pathways provide insights into the metabolic reprogramming that occurs during postpartum recovery, particularly in terms of supporting lactation demands, tissue repair, and immune system restoration. These findings enhance our understanding of the complex metabolic adaptations in the maternal gut microbiome during the critical first month postpartum. Discussion The postpartum period represents a critical phase of physiological adaptation during which maternal systems return to their antepartum state 14 . The first postpartum month encompasses complex, coordinated physiological changes across multiple organ systems 15 . These changes follow a predictable pattern but vary in duration and intensity among individuals. The immune system undergoes reconstitution, a complex physiological process that determines the restoration of the normal immune response following the period of gestational immunosuppression 16 . Research has established that immune reconstitution in the postpartum period occurs through three sequential phases. The early phase (0–7 days) is characterized by physiological leukocytosis and the activation of proinflammatory cytokines. The recovery phase (1–2 weeks) is characterized by the redistribution of lymphocyte subpopulations and enhanced NK cell activity. The stabilization phase (2–4 weeks) culminates in the formation of novel immunological patterns, including immunological tolerance to lactation and the restoration of local immunity. By the end of the first month, most systems demonstrate substantial progress in returning to their antepartum state, although complete recovery may require up to 6 months 16 . The postpartum intestinal microbiome was enriched with representatives of the Actinobacteria class. While previous studies 17 reported elevated Actinobacteria levels in the third trimester, our data revealed sustained elevation in the early postpartum period, particularly in pathways supporting amino acid biosynthesis and protein synthesis for lactation (PWY0-1061: superpathway of L-alanine biosynthesis, r = 0.6, p < 0.0001). Specific differences between primiparous and multiparous women have been identified. In the work of Berry et al. demonstrated that, compared with first-time mothers, women with multiple pregnancies (higher parity) have faster microbiome adaptations during pregnancy compared to first-time mothers 2 . One of the key findings is the significant enrichment of specific bacterial taxa in primiparous mothers, particularly Dysosmobacter welbionis, Candidatus Saccharibacteria , and Anaerotruncus (q < 0.25, FDR, adjusted for age). These differences align with the emerging concept of parity-dependent microbiome programming, suggesting that the first pregnancy induces persistent changes in the maternal gut ecosystem. The observed patterns extend beyond simple taxonomic changes, reflecting fundamental differences in metabolic capabilities and host-microbiota interactions. The differential abundance of these key taxa between primiparous and multiparous women suggests the formation of a "microbiome memory of motherhood." Our findings expand the current understanding of the role of Dysosmobacter welbionis in metabolic adaptation, suggesting its particular importance in primary postpartum recovery 18 – 20 . This concept is substantiated by the observed correlations between bacterial species abundance and specific metabolic pathways, particularly those involved in energy metabolism and immune modulation. The enhanced capacity for SCFA production in primiparous mothers, mediated by Anaerotruncus and D. welbionis , may influence breast milk composition through metabolite profile modifications, potentially affecting neonatal gut colonization and immune system development. We hypothesize that the maternal gut microbiota undergoes parity-specific adaptations that may influence postpartum recovery trajectories, with the crucial observation that primary pregnancy and childbirth irreversibly alter the maternal microbiome landscape. Future investigations should examine the long-term persistence of these parity-specific signatures and their potential impact on subsequent pregnancies. Longitudinal multi-omics approaches could track the persistence of parity-specific microbiome signatures, while animal models could help validate host-microbe interactions underlying these differences. Furthermore, mechanistic studies investigating the molecular interactions between these key bacterial species and host metabolism may provide valuable insights for developing targeted interventions to support postpartum recovery. Our investigation has several significant limitations that we have considered when interpreting the results. These limitations include a relatively small sample size, which limits the statistical power for detecting subtle differences in microbial communities and metabolic pathways. The single data collection point at one month postpartum precludes tracking the temporal dynamics of microbiome changes throughout pregnancy and the extended postpartum period. Additionally, although the groups were age-matched, other potential confounding factors, including dietary patterns, lifestyle habits, and mode of delivery, were not accounted for in our analysis. The lack of dietary control may have influenced microbial composition, potentially confounding the observed parity-specific differences. Similarly, variations in lifestyle habits and mode of delivery could have contributed to differences in microbiome profiles, highlighting the need for future studies to incorporate these factors to ensure a more comprehensive understanding of maternal microbiome adaptations. We identified distinct parity-dependent signatures manifested in both the taxonomic composition and functional profiles, providing evidence for the existence of a "microbiome memory of motherhood". Prospective longitudinal investigations examining the retention of these parity-specific signatures are essential for the development of targeted therapeutic interventions designed to optimize maternal-infant health trajectories. Declarations Competing interests The authors declare that they have no competing interests. Ethics approval This study was approved by the Local Bioethics Commission of the Private Institution National Laboratory of Astana (Approval №05-2023). All study procedures and subsequent analyses complied with relevant institutional guidelines and regulations. Participants meeting the eligibility criteria provided verbal and written informed consent before enrollment. Funding This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23489538 «Maternal Microbiota Contribution to Antibiotic Resistance Development in Infants», Grant No. AP19575153 and Grant No. BR21882152). Author Contribution AK and ZJ drafted the original manuscript, prepared figures and tables, and contributed to project administration and investigation. SK and AK were involved in funding acquisition and conceptualization. SK also participated in manuscript review and editing, as well as investigations. EV and ZJ were responsible for data analysis, data curation, and conceptualization. NM and DK played key roles in methodology development. AK further contributed by reviewing and editing the manuscript and providing supervision. All the authors have read and approved the final version of the manuscript for publication. Acknowledgement The authors would like to thank all volunteer participants in the study who gave their consent and provided their samples for this study, and the nursing staff for their assistance with specimen collection. Data Availability The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI BioProject [accession numbers PRJNA1167438, PRJNA949528, and PRJNA973824]. References Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 19 , 55–71 (2021). Berry, A. S. F. et al. Remodeling of the maternal gut microbiome during pregnancy is shaped by parity. Microbiome 9 , 146 (2021). Miyoshi, J. et al. Peripartum Antibiotics Promote Gut Dysbiosis, Loss of Immune Tolerance, and Inflammatory Bowel Disease in Genetically Prone Offspring. Cell Rep 20 , 491–504 (2017). Gomez de Agüero, M. et al. The maternal microbiota drives early postnatal innate immune development. Science (1979) 351 , 1296–1302 (2016). Thorburn, A. N. et al. Evidence that asthma is a developmental origin disease influenced by maternal diet and bacterial metabolites. Nat Commun 6 , 7320 (2015). Moossavi, S. et al. Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors. Cell Host Microbe 25 , 324-335.e4 (2019). Crusell, M. K. W. et al. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome 6 , 89 (2018). Al Nabhani, Z. & Eberl, G. Imprinting of the immune system by the microbiota early in life. Mucosal Immunol 13 , 183–189 (2020). Ferretti, P. et al. Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome. Cell Host Microbe 24 , 133-145.e5 (2018). Wang, J. et al. Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut 67 , 1614–1625 (2018). Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 17 , e1009442 (2021). Sinha, T., Brushett, S., Prins, J. & Zhernakova, A. The maternal gut microbiome during pregnancy and its role in maternal and infant health. Curr Opin Microbiol 74 , 102309 (2023). Lu, X., Shi, Z., Jiang, L. & Zhang, S. Maternal gut microbiota in the health of mothers and offspring: from the perspective of immunology. Front Immunol 15 , (2024). Chauhan G, T. P. Physiology, Postpartum Changes . (Treasure Island (FL): StatPearls Publishing, 2022). Pascual ZN, L. M. Physiology, Pregnancy . (Treasure Island (FL): StatPearls Publishing, 2023). Singh, N. & Perfect, J. R. Immune Reconstitution Syndrome and Exacerbation of Infections after Pregnancy. Clinical Infectious Diseases 45 , 1192–1199 (2007). Koren, O. et al. Host Remodeling of the Gut Microbiome and Metabolic Changes during Pregnancy. Cell 150 , 470–480 (2012). Le Roy, T. et al. Dysosmobacter welbionis is a newly isolated human commensal bacterium preventing diet-induced obesity and metabolic disorders in mice. Gut 71 , 534–543 (2022). Moens de Hase, E. et al. Dysosmobacter welbionis effects on glucose, lipid, and energy metabolism are associated with specific bioactive lipids. J Lipid Res 64 , 100437 (2023). Moens de Hase, E. et al. Impact of metformin and Dysosmobacter welbionis on diet-induced obesity and diabetes: from clinical observation to preclinical intervention. Diabetologia 67 , 333–345 (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1Demographiccharacteristicsofallsubjects.xlsx Supplementarytable2Correlationanalysisbetweenmetabolicpathwaysandbacterialspecies.xlsx Cite Share Download PDF Status: Published Journal Publication published 21 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 26 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5972536","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":438396736,"identity":"f3a9ee55-4433-457a-8b2a-bbc6e8c53fbc","order_by":0,"name":"Zharkyn Jarmukhanov","email":"data:image/png;base64,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","orcid":"","institution":"Laboratory of Microbiome, Center for Life Sciences, National Laboratory Astana, Nazarbayev University","correspondingAuthor":true,"prefix":"","firstName":"Zharkyn","middleName":"","lastName":"Jarmukhanov","suffix":""},{"id":438396737,"identity":"054a0c0c-1ad3-4985-9a53-cd5b8134febe","order_by":1,"name":"Elizaveta Vinogradova","email":"","orcid":"","institution":"Laboratory of Microbiome, Center for Life Sciences, National Laboratory Astana, Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Elizaveta","middleName":"","lastName":"Vinogradova","suffix":""},{"id":438396738,"identity":"56b1b8db-9157-43d4-b283-ec0230f74b95","order_by":2,"name":"Nurislam Mukhanbetzhanov","email":"","orcid":"","institution":"Laboratory of Microbiome, Center for Life Sciences, National Laboratory Astana, Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Nurislam","middleName":"","lastName":"Mukhanbetzhanov","suffix":""},{"id":438396739,"identity":"a6937e47-08f3-483d-8a9e-71e6e6848616","order_by":3,"name":"Samat Kozhakhmetov","email":"","orcid":"","institution":"Laboratory of Microbiome, Center for Life Sciences, National Laboratory Astana, Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Samat","middleName":"","lastName":"Kozhakhmetov","suffix":""},{"id":438396740,"identity":"28401caa-5843-47f0-bf2b-fbb7b19bc1e5","order_by":4,"name":"Deniza Khassenbekova","email":"","orcid":"","institution":"Nazarbayev Intellectual School of Physics and Mathematics","correspondingAuthor":false,"prefix":"","firstName":"Deniza","middleName":"","lastName":"Khassenbekova","suffix":""},{"id":438396741,"identity":"2b7e1e17-3615-4ca7-bfc6-e74c9e66d810","order_by":5,"name":"Almagul Kushugulova","email":"","orcid":"","institution":"Laboratory of Microbiome, Center for Life Sciences, National Laboratory Astana, Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Almagul","middleName":"","lastName":"Kushugulova","suffix":""}],"badges":[],"createdAt":"2025-02-06 10:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5972536/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5972536/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-02013-y","type":"published","date":"2025-05-21T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80043248,"identity":"029bb531-55e7-4ef8-a5e5-d61a243ed864","added_by":"auto","created_at":"2025-04-07 09:34:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1792376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe first boxplot shows a significant increase in microbial richness (\u003cem\u003ep=\u003c/em\u003e0.02) in the MM group one month postpartum. The second indicates greater microbial evenness (\u003cem\u003ep=\u003c/em\u003e 0.043) in women with multiple childbirths, suggesting parity influences diversity and distribution. \u003cstrong\u003e(B)\u003c/strong\u003e Ordination plots (Bray-Curtis and Jaccard) showing significant microbial differences between the MM and control groups. ANOSIM (\u003cem\u003er\u003c/em\u003e = 0.09-0.1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) and PERMANOVA (F = 2.24-2.73, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) confirmed detectable compositional shifts. \u003cstrong\u003e(C)\u003c/strong\u003e The bar plots show microbial differences between the MM and control groups, with \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eBacteroidia\u003c/em\u003ediffering at the class level (LDA ≥ 2, \u003cem\u003ep\u003c/em\u003e ≤ 0.05) and \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e enriched in the MM group at the genus level.\u003cstrong\u003e(D) \u003c/strong\u003eLEfSe analysis revealedincreased \u003cem\u003eActinobacteria\u003c/em\u003e in MM groups and higher \u003cem\u003eBacteroidetes\u003c/em\u003ein controls. At the family level, MM was enriched in \u003cem\u003eBifidobacteriaceae, Christensenellaceae\u003c/em\u003e, and \u003cem\u003eBradyrhizobiaceae\u003c/em\u003e, whereas \u003cem\u003eClostridiaceae\u003c/em\u003e and \u003cem\u003eOscillospiraceae\u003c/em\u003e were more abundant in the controls. The LDA score plot shows species-level differences, with MM enriched in carbohydrate-metabolizing (\u003cem\u003eB. stercoris, B. angulatum\u003c/em\u003e), SCFA-producing (\u003cem\u003eE. siraeum, C. leptum\u003c/em\u003e), and immunomodulatory species (\u003cem\u003eT. nexilis, E. tayi\u003c/em\u003e), whereas butyrate producers (\u003cem\u003eR. hominis, L. eligens\u003c/em\u003e) and polysaccharide metabolizers (\u003cem\u003eB. caccae, P. distasonis\u003c/em\u003e) are depleted. \u003cstrong\u003e(E) \u003c/strong\u003eThe phylogenetic tree illustrates the hierarchical taxonomic differences between the MM (red) and control (blue) groups, highlighting enriched bacterial taxa across different taxonomic levels.\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5972536/v1/0fc3fe8218d18eef27279537.jpeg"},{"id":80043251,"identity":"05eaa1ce-7b77-42e0-8c36-8d90dc7e84bd","added_by":"auto","created_at":"2025-04-07 09:34:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":260153,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic differences in the gut microbiome based on parity.\u003cstrong\u003e \u003c/strong\u003eThese boxplots illustrate differences in the relative abundance of key microbial taxa between first-time mothers (blue) and those with two or more childbirths (red).\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5972536/v1/fda1641bfe65a8b2bb9b804b.jpeg"},{"id":80043257,"identity":"e3507b12-a9c1-4ab9-98a6-6b6fd00002d8","added_by":"auto","created_at":"2025-04-07 09:34:26","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":313721,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional profiling of the gut microbiome one month postpartum.\u003cstrong\u003e \u003c/strong\u003eThe figure presents the functional pathway analysis of the gut microbiome in the MM and control groups one month postpartum. The bar plot compares the relative abundances of metabolic pathways between groups, with the MM (red) and control (blue) groups. The 95% confidence intervals of the differences in means are shown alongside the corresponding \u003cem\u003ep\u003c/em\u003e values\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5972536/v1/397eae03cb7a7ad01d815d3e.jpeg"},{"id":83459970,"identity":"8f881307-a8a8-44c6-be17-987079da584d","added_by":"auto","created_at":"2025-05-26 16:07:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3024435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5972536/v1/ed2a7f61-dddf-440a-ba5f-061df263e2b2.pdf"},{"id":80046259,"identity":"6b951c2b-8f3f-49ae-82a2-d45a631d6c0c","added_by":"auto","created_at":"2025-04-07 09:50:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10477,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1Demographiccharacteristicsofallsubjects.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5972536/v1/ddccee6da769de28fd5bda00.xlsx"},{"id":80046256,"identity":"5ea590ab-acbd-43bb-b9f0-1f513bbb0ca0","added_by":"auto","created_at":"2025-04-07 09:50:26","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30593,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2Correlationanalysisbetweenmetabolicpathwaysandbacterialspecies.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5972536/v1/79d56fa81388949a61270f9d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Parity influences postpartum adaptations in the maternal gut microbiota","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human gut microbiome exhibits remarkable plasticity in response to physiological changes, with pregnancy representing one of the most profound periods of host-microbiome adaptation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While pregnancy-associated microbiome alterations have been extensively documented, the postpartum period remains a critically understudied phase of maternal-microbiome interactions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This oversight is particularly significant given the emerging evidence suggesting that pregnancy and childbirth may induce lasting changes in the maternal gut ecosystem, potentially establishing what we term a \"microbiome memory of motherhood\"\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Postpartum adaptation encompasses fundamental physiological restructuring, including metabolic recalibration, immune system reconstitution, and extensive tissue remodeling\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Understanding the alterations occurring in the maternal gut microbiome during the postpartum period is critical for several reasons. First, microbial metabolites can influence breast milk composition, directly impacting neonatal health\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Second, the microbiome participates in regulating post-gestational metabolic processes, including the restoration of insulin sensitivity and normalization of lipid metabolism\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Third, the gut microbiome plays a key role in immune system recovery following gestational immunosuppression\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent advances in microbiome research have revealed that pregnancy-induced changes in the gut microbiome extend well beyond parturition, indicating the occurence of long-term maternal adaptations\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These modifications appear particularly pronounced during first pregnancies, potentially establishing a \"metabolic memory\" that influences subsequent gestations\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This phenomenon aligns with the emerging concept of parity-dependent microbiome programming, where the first pregnancy may create a lasting imprint on the maternal gut ecosystem.\u003c/p\u003e \u003cp\u003eThis model suggests that primary pregnancy and childbirth trigger a fundamental reorganization of the gut microbiota, which may have long-term physiological implications for maternal health and subsequent pregnancies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Evidence for this phenomenon comes from observations of distinct microbial signatures in multiparous women, suggesting that the maternal microbiome retains aspects of its pregnancy-adapted state\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the current understanding of how these processes differ between primiparous and multiparous women remains fragmentary. This knowledge gap is particularly relevant given the emerging evidence that first-time pregnancies may induce persistent alterations in the maternal gut ecosystem, potentially influencing long-term maternal health trajectories\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we present a comprehensive analysis of the postpartum gut microbiome, examining how parity influences microbial community structure and function. By integrating taxonomic profiling with metabolic pathway analysis, we revealed distinct parity-specific signatures in the maternal microbiome one month postpartum. Our findings provide novel insights into the biological mechanisms underlying maternal adaptation to pregnancy and childbirth, with implications for understanding the long-term impact of reproductive history on maternal health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional observational study with a case-control design, that investigated the impact of parity on maternal gut microbiome composition and function one month postpartum. Participants were recruited from the Сity perinatal center in Astana, Kazakhstan, between April 2021 and April 2022. The inclusion criteria included healthy postpartum women with a history of pregnancy and full-term delivery, while the exclusion criteria encompassed antibiotic use within the last three months, gastrointestinal disorders, and metabolic conditions. Women who had not delivered in the prior three years were chosen as controls to account for potential long-term postpartum effects on the microbiome while still maintaining biological comparability with the study group. This approach minimizes variability associated with major life-stage differences, such as pregnancy-induced metabolic and immunological adaptations, while allowing us to focus on parity-specific microbiome signatures. Additionally, this selection criterion helps control for potential confounding factors related to early-life microbiome establishment, which may differ between never-pregnant individuals and those who have previously experienced pregnancy.\u003c/p\u003e \u003cp\u003eEthical approval for this study was obtained from the Local Bioethics Commission of the Private Institution National Laboratory of Astana (Approval №05-2023). Informed consent was obtained from all participants and/or their legal guardians. All methods were performed in accordance with relevant guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection and processing\u003c/h3\u003e\n\u003cp\u003eStool samples were collected from participants at approximately one month postpartum using sterile, DNA/RNA-free Zymo DNA/RNA Shield collection kits (Cat. No: R1101-1). Participants were instructed to collect samples at home following standardized guidelines to minimize contamination. The samples were immediately stored at \u0026ndash; 20\u0026deg;C upon collection to preserve microbial integrity. For transportation to the laboratory, temperature-controlled containers were used to maintain stability, with a transport duration of approximately 1\u0026ndash;1.5 hours. Upon arrival, samples were either stored at \u0026ndash; 20\u0026deg;C for short-term preservation or at \u0026ndash; 80\u0026deg;C for long-term storage, as needed.\u003c/p\u003e\n\u003ch3\u003eDNA Extraction and Sequencing\u003c/h3\u003e\n\u003cp\u003eMicrobial DNA was extracted via the ZymoBiomics DNA Microprep Kit (Cat. No: D4301) following the manufacturer\u0026rsquo;s protocol. The kit utilizes bead beating with high-density BashingBeads for efficient mechanical lysis, ensuring uniform disruption of resilient microbial cells, including Gram-positive bacteria and fungi. A high-speed cell disruptor was used to enhance homogenization. The DNA concentration was assessed via a Nanodrop 2000/2000c (Thermo Fisher Scientific). To ensure quality control, sterile water was included as a negative control. Sequencing was performed at Novogene (Beijing, China) using the Illumina NovaSeq 6000 platform following standard Illumina protocols. Each metagenomic sample generated an average of 6 Gb of raw sequencing data.\u003c/p\u003e\n\u003ch3\u003eBioinformatic Analysis\u003c/h3\u003e\n\u003cp\u003eThe raw sequencing reads were processed and analyzed on a computing system. Data preprocessing was conducted via KneadData v0.12.0, which removes contaminating sequences such as ribosomal RNAs and host DNA to improve data quality. Functional profiling was performed with the HUMAnN 3.0 pipeline, whereas the taxonomic composition was determined via MetaPhlAn 4 with the mpa_vJan21_CHOCOPhlAnSGB202103 database, ensuring high-resolution microbial classification. UniRef90 was used for protein sequence annotation and retrieval. Default parameters were applied throughout the pipeline to maintain standardization and ensure compatibility across analytical stages.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed in Python 3.9 using the numpy v1.26.4, pandas v1.5.3, scipy v.13.1, statsmodels v0.14.2, scikit-bio v0.5.6, matplotlib v3.9.2 and seaborn v0.13.2 libraries. Only taxa present in at least 25% of the samples were included in the analysis. Demographic comparisons were made using independent t-tests, mann-whitiney u-tests, or binomial tests where appropriate. Alpha diversity was assessed using richness and evenness indices at the species level, namely the Observed and Pielou indices were used. Beta diversity was assessed using Bray-Curtis and Jaccard distances aiming to assess distributional and presence/absence differences, respectively. Significance of grouping was assessed on PCoA ordination and using ANOSIM and PERMANOVA grouping tests with 9999 permutations. Taxonomic comparisons between control (Cntrl) and postpartum (MM) groups were performed using LEfSe utils, with significance level set to effect size LDA\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026amp;p\u0026thinsp;\u0026le;\u0026thinsp;0.05. Association between taxa and parity was performed using generalized linear models (GLM) on center-log-ratio (clr) transformed abundance data, significance level set to p\u0026thinsp;\u0026le;\u0026thinsp;0.05\u0026amp;q\u0026thinsp;\u0026le;\u0026thinsp;0.25, FDR, while adjusting for age as a covariate. Pathway comparison was based on evaluation of differences between means, as significant changes were considered those that showed statistical significance p\u0026thinsp;\u0026le;\u0026thinsp;0.05 and had non-overlapping 95% confidence intervals (CI) of the difference between means. Correlation analysis between identified taxonomic and functional (pathway) markers was performed on clr-transformed data using pearson's r coefficient, significance level set was set to p\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eObstetric characteristics of the study and control groups\u003c/h2\u003e \u003cp\u003eThe study included 60 participants, who were divided into a study group (MM, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34) and a control group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26). The mean age in the study group was 30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 years, whereas it was 32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79 years in the age-matched control group, which included individuals with no history of deliveries or abortions in the preceding three years.\u003c/p\u003e \u003cp\u003eThe participants in the study group reported an average of 3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 pregnancies and 2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 deliveries. A significant proportion of participants had experienced multiple pregnancies (P\u0026thinsp;=\u0026thinsp;0.0004) and deliveries (P\u0026thinsp;=\u0026thinsp;0.004), with a notable increase in those with more than two pregnancies (P\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAlpha diversity analysis\u003c/h3\u003e\n\u003cp\u003eOur findings reveal that childbirth affects the composition and diversity of the gut microbiota. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (A), we detected a significant increase in alpha diversity between the control and MM groups one month postpartum (Observed, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.02, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(A), above) in terms of observed species, but not in terms of taxonomic evenness (Pielou, P\u0026thinsp;=\u0026thinsp;0.49). Currently, mixed evidence is presented regarding postpartum changes in the microbiome, and some studies report minimal changes, whereas others note significant changes in the community\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe investigated the relationships between childbirth history and microbial community characteristics by comparing first-time mothers with those who had multiple births. Significant differences were not observed in community richness but evenness (Pielou's index, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(A), below) were detected between the study groups. Compared with the first-time mothers, women with multiple births (\u0026ge;\u0026thinsp;2) demonstrated increased community evenness. The Pielou evenness index was positively correlated with childbirth frequency (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), whereas age was less significantly associated with childbirth frequency (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08). These findings suggest a potential link between multiple childbirths and increased microbial diversity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBeta diversity analysis\u003c/h2\u003e \u003cp\u003eBeta diversity analyses, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(B), revealed significant differences in community structure between the MM and control groups across multiple distance metrics. The Ordination plots generated via the Bray-Curtis and Jaccard indices demonstrated consistent patterns of community differentiation, with ANOSIM revealing weak but significant clustering (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09\u0026ndash;0.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all the metrics). These findings were corroborated by PERMANOVAs (F\u0026thinsp;=\u0026thinsp;2.22\u0026ndash;2.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that while there was substantial overlap between groups, there were detectable differences in community composition. These results provide evidence for altered community composition in MM, highlighting the considerable difference between the MM and control groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe gut microbiome composition one month postpartum\u003c/h2\u003e \u003cp\u003eA total of 322/958 (33.6%) microbial species were prevalent in at least 25% of all samples from which 307/322 were present in the 25% MM, the most abundant taxa of the control and MM groups at the class and genus are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (C).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (D), (E) we identified significant (LDA\u0026thinsp;\u0026ge;\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) enrichment in carbohydrate-metabolizing species (\u003cem\u003eBacteroides stercoris, Bifidobacterium angulatum\u003c/em\u003e) and SCFA producers (\u003cem\u003eEubacterium siraeum, Clostridium leptum, Harryflintia acetispora, Eubacterium sp. AF15-50\u003c/em\u003e), corresponding with elevated energy demands during lactation. The increased abundance of immunomodulatory species (\u003cem\u003eTyzzerella nexilis, Romboutsia timonensis, and Eisenbergiella tayi\u003c/em\u003e) and barrier-supporting bacteria (\u003cem\u003eAlistipes ihumii, Anaerotruncus rubiinfantis\u003c/em\u003e, and \u003cem\u003eChristensenellaceae bacterium\u003c/em\u003e) aligns with postpregnancy immune recalibration. Notably, we observed significant (LDA\u0026thinsp;\u0026ge;\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) increases in nitrogen-fixing species (\u003cem\u003eAfipia broomeae, Bradyrhizobium elkanii\u003c/em\u003e), potentially contributing to increased protein metabolism and amino acid biosynthesis, which are crucial for tissue repair and milk protein production. Concurrent increases in anaerobic metabolizers, including \u003cem\u003eClostridium sp. Marseille-P3244\u003c/em\u003e, \u003cem\u003eOscillospiraceae bacterium NSJ-64\u003c/em\u003e, and specialized fermenters (\u003cem\u003eAnaeromassilibacillus sp. An250, Candidatus Geddesella stercoravicola\u003c/em\u003e, and \u003cem\u003eCandidatus Pararuminococcus gallinarum\u003c/em\u003e), suggest the optimization of energy harvesting through enhanced anaerobic fermentation pathways. A selective depletion pattern is observed in specific butyrate producers (\u003cem\u003eRoseburia hominis, Lachnospira eligens, Gemmiger formicilis\u003c/em\u003e), possibly indicating a metabolic shift towards alternative SCFA production pathways that better support postpartum recovery. Similarly, the reductions in the levels of certain polysaccharide metabolizers (\u003cem\u003eBacteroides caccae\u003c/em\u003e and \u003cem\u003eParabacteroides distasonis\u003c/em\u003e) and fermentative species (\u003cem\u003eClostridium sp. AF34-10BH, Clostridium sp. AF15-49, Clostridium sp. AM33-3\u003c/em\u003e) suggest the remodeling of carbohydrate metabolism pathways. The decreased abundance of \u003cem\u003eRuminococcus sp. NSJ-71, Faecalicatena fissicatena\u003c/em\u003e, and \u003cem\u003eLacrimispora amygdalina\u003c/em\u003e indicates a potential metabolic reprogramming favoring more efficient energy extraction and nutrient utilization patterns specific to postpartum needs.\u003c/p\u003e \u003cp\u003eWe also revealed the features of the original taxonomic profile in the original in comparison with those of two or more births, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. First-time mothers presented consistently increased abundances of \u003cem\u003eDysosmobacter welbionis, Candidatus Saccharibacteria\u003c/em\u003e, and \u003cem\u003eAnaerotruncus.\u003c/em\u003e These findings suggest fundamental differences in maternal microbiome adaptation between first and subsequent childbirths, potentially reflecting different metabolic and immunological requirements for primary versus subsequent postpartum recoveries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional profiling of the gut microbiome one month postpartum\u003c/h2\u003e \u003cp\u003eTotal of 20/385 (5%) derived pathways differed significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026amp;non-overlaping 95%CI) between the groups, of which 14 (70%) were significantly associated with the Cntrl and 6 (30%) with the MM group. These results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (A). Our findings revealed significant modifications in key metabolic processes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that a coordinated reprogramming of cellular metabolism is associated with postpartum adaptation. Central carbon metabolism showed marked changes, with significant downregulation of gluconeogenesis (GLUCONEO-PWY, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.3e-05) and glycolysis pathways (GLYCOLYSI, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.3e-04; PWY-5484, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.7e-04), reflecting the transition from pregnancy-associated insulin resistance to restored glucose homeostasis. Nucleotide metabolism was associated with reduced activity in pyrimidine deoxyribonucleotide pathways (PWY-7199, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.9e-04; PWY-7210, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.2e-05) and NAD salvage pathways (PWY-7761, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.7e-04), corresponding to the shift from gestational proliferation to postpartum tissue recovery and uterine involution.\u003c/p\u003e \u003cp\u003eNotably, we observed enhanced amino acid biosynthesis, including the upregulation of L-histidine (HISTSYN-PWY, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8e-02), aromatic amino acid (COMPLETE-ARO-PWY, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.4e-03), and L-alanine pathways (PWY0-1061, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.1e-02), supporting protein synthesis for lactation (requiring 500\u0026ndash;700 additional kcal/day), tissue repair, and immune system restoration. Fermentation pathways were significantly elevated, particularly in pyruvate fermentation to acetate and lactate (PWY-5100, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2e-02), indicating adaptation to increased energy demands and tissue remodeling during uterine involution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted a correlation analysis between significant marker metabolic pathways (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05\u0026amp;non-overlapping 95%CI) and bacterial species abundance (LDA\u0026thinsp;\u0026ge;\u0026thinsp;2\u0026amp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (B). Significant positive correlations were identified between \u003cem\u003eClostridia bacterium\u003c/em\u003e and aromatic amino acid biosynthesis (COMPLETE-ARO-PWY, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and both gluconeogenesis I and gluconeogenesis III were elevated in cntrl, aligning with increased energy demands and protein synthesis requirements during lactation.\u003c/p\u003e \u003cp\u003eSignificant positive correlations were identified between \u003cem\u003eCandidatus Geddesella, Ruminococcus\u003c/em\u003e genera and aromatic amino acid biosynthesis (COMPLETE-ARO-PWY, r\u0026thinsp;=\u0026thinsp;0.3\u0026ndash;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u0026thinsp;\u0026minus;\u0026thinsp;0.001). Positive correlations were also observed for tetrapyrrole biosynthesis (PWY-5188) and most of the postpartum marker genera: \u003cem\u003eRomboutsia, Desulfovibrio, Candidatus_Geddesell, Ruminococcus, Anaeromassilibacillus, Eisenbergiella\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.3\u0026ndash;0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026thinsp;\u0026minus;\u0026thinsp;0.001), as well as \u003cem\u003eAnaerotruncus\u003c/em\u003e genera (r\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which is a specific marker of first parity. Interestingly, \u003cem\u003eDysosmobacter\u003c/em\u003e genera demonstrated a negative correlation with this pathway (r=-0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A strong positive correlation was also observed between Bifidobacterium and the superpathway of L-alanine biosynthesis (PWY0-1061, r\u0026thinsp;=\u0026thinsp;0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eThese correlations correspond to the physiological transitions of the postpartum period, including metabolic adjustment, immune system recalibration, and tissue regeneration\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The observed relationships between specific bacterial species and metabolic pathways provide insights into the metabolic reprogramming that occurs during postpartum recovery, particularly in terms of supporting lactation demands, tissue repair, and immune system restoration. These findings enhance our understanding of the complex metabolic adaptations in the maternal gut microbiome during the critical first month postpartum.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe postpartum period represents a critical phase of physiological adaptation during which maternal systems return to their antepartum state\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The first postpartum month encompasses complex, coordinated physiological changes across multiple organ systems\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These changes follow a predictable pattern but vary in duration and intensity among individuals. The immune system undergoes reconstitution, a complex physiological process that determines the restoration of the normal immune response following the period of gestational immunosuppression\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Research has established that immune reconstitution in the postpartum period occurs through three sequential phases. The early phase (0\u0026ndash;7 days) is characterized by physiological leukocytosis and the activation of proinflammatory cytokines. The recovery phase (1\u0026ndash;2 weeks) is characterized by the redistribution of lymphocyte subpopulations and enhanced NK cell activity. The stabilization phase (2\u0026ndash;4 weeks) culminates in the formation of novel immunological patterns, including immunological tolerance to lactation and the restoration of local immunity. By the end of the first month, most systems demonstrate substantial progress in returning to their antepartum state, although complete recovery may require up to 6 months\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe postpartum intestinal microbiome was enriched with representatives of the \u003cem\u003eActinobacteria\u003c/em\u003e class. While previous studies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e reported elevated \u003cem\u003eActinobacteria\u003c/em\u003e levels in the third trimester, our data revealed sustained elevation in the early postpartum period, particularly in pathways supporting amino acid biosynthesis and protein synthesis for lactation (PWY0-1061: superpathway of L-alanine biosynthesis, r\u0026thinsp;=\u0026thinsp;0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eSpecific differences between primiparous and multiparous women have been identified. In the work of Berry et al. demonstrated that, compared with first-time mothers, women with multiple pregnancies (higher parity) have faster microbiome adaptations during pregnancy compared to first-time mothers\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. One of the key findings is the significant enrichment of specific bacterial taxa in primiparous mothers, particularly \u003cem\u003eDysosmobacter welbionis, Candidatus Saccharibacteria\u003c/em\u003e, and \u003cem\u003eAnaerotruncus\u003c/em\u003e (q\u0026thinsp;\u0026lt;\u0026thinsp;0.25, FDR, adjusted for age). These differences align with the emerging concept of parity-dependent microbiome programming, suggesting that the first pregnancy induces persistent changes in the maternal gut ecosystem. The observed patterns extend beyond simple taxonomic changes, reflecting fundamental differences in metabolic capabilities and host-microbiota interactions. The differential abundance of these key taxa between primiparous and multiparous women suggests the formation of a \"microbiome memory of motherhood.\"\u003c/p\u003e \u003cp\u003eOur findings expand the current understanding of the role of \u003cem\u003eDysosmobacter welbionis\u003c/em\u003e in metabolic adaptation, suggesting its particular importance in primary postpartum recovery\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis concept is substantiated by the observed correlations between bacterial species abundance and specific metabolic pathways, particularly those involved in energy metabolism and immune modulation. The enhanced capacity for SCFA production in primiparous mothers, mediated by \u003cem\u003eAnaerotruncus\u003c/em\u003e and \u003cem\u003eD. welbionis\u003c/em\u003e, may influence breast milk composition through metabolite profile modifications, potentially affecting neonatal gut colonization and immune system development.\u003c/p\u003e \u003cp\u003eWe hypothesize that the maternal gut microbiota undergoes parity-specific adaptations that may influence postpartum recovery trajectories, with the crucial observation that primary pregnancy and childbirth irreversibly alter the maternal microbiome landscape. Future investigations should examine the long-term persistence of these parity-specific signatures and their potential impact on subsequent pregnancies. Longitudinal multi-omics approaches could track the persistence of parity-specific microbiome signatures, while animal models could help validate host-microbe interactions underlying these differences. Furthermore, mechanistic studies investigating the molecular interactions between these key bacterial species and host metabolism may provide valuable insights for developing targeted interventions to support postpartum recovery.\u003c/p\u003e \u003cp\u003eOur investigation has several significant limitations that we have considered when interpreting the results. These limitations include a relatively small sample size, which limits the statistical power for detecting subtle differences in microbial communities and metabolic pathways. The single data collection point at one month postpartum precludes tracking the temporal dynamics of microbiome changes throughout pregnancy and the extended postpartum period. Additionally, although the groups were age-matched, other potential confounding factors, including dietary patterns, lifestyle habits, and mode of delivery, were not accounted for in our analysis. The lack of dietary control may have influenced microbial composition, potentially confounding the observed parity-specific differences. Similarly, variations in lifestyle habits and mode of delivery could have contributed to differences in microbiome profiles, highlighting the need for future studies to incorporate these factors to ensure a more comprehensive understanding of maternal microbiome adaptations.\u003c/p\u003e \u003cp\u003eWe identified distinct parity-dependent signatures manifested in both the taxonomic composition and functional profiles, providing evidence for the existence of a \"microbiome memory of motherhood\". Prospective longitudinal investigations examining the retention of these parity-specific signatures are essential for the development of targeted therapeutic interventions designed to optimize maternal-infant health trajectories.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e This study was approved by the Local Bioethics Commission of the Private Institution National Laboratory of Astana (Approval №05-2023). All study procedures and subsequent analyses complied with relevant institutional guidelines and regulations. Participants meeting the eligibility criteria provided verbal and written informed consent before enrollment.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23489538 \u0026laquo;Maternal Microbiota Contribution to Antibiotic Resistance Development in Infants\u0026raquo;, Grant No. AP19575153 and Grant No. BR21882152).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAK and ZJ drafted the original manuscript, prepared figures and tables, and contributed to project administration and investigation. SK and AK were involved in funding acquisition and conceptualization. SK also participated in manuscript review and editing, as well as investigations. EV and ZJ were responsible for data analysis, data curation, and conceptualization. NM and DK played key roles in methodology development. AK further contributed by reviewing and editing the manuscript and providing supervision. All the authors have read and approved the final version of the manuscript for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank all volunteer participants in the study who gave their consent and provided their samples for this study, and the nursing staff for their assistance with specimen collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI BioProject [accession numbers PRJNA1167438, PRJNA949528, and PRJNA973824].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFan, Y. \u0026amp; Pedersen, O. Gut microbiota in human metabolic health and disease. \u003cem\u003eNat Rev Microbiol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 55\u0026ndash;71 (2021).\u003c/li\u003e\n\u003cli\u003eBerry, A. S. F. \u003cem\u003eet al.\u003c/em\u003e Remodeling of the maternal gut microbiome during pregnancy is shaped by parity. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 146 (2021).\u003c/li\u003e\n\u003cli\u003eMiyoshi, J. \u003cem\u003eet al.\u003c/em\u003e Peripartum Antibiotics Promote Gut Dysbiosis, Loss of Immune Tolerance, and Inflammatory Bowel Disease in Genetically Prone Offspring. \u003cem\u003eCell Rep\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 491\u0026ndash;504 (2017).\u003c/li\u003e\n\u003cli\u003eGomez de Ag\u0026uuml;ero, M. \u003cem\u003eet al.\u003c/em\u003e The maternal microbiota drives early postnatal innate immune development. \u003cem\u003eScience (1979)\u003c/em\u003e \u003cstrong\u003e351\u003c/strong\u003e, 1296\u0026ndash;1302 (2016).\u003c/li\u003e\n\u003cli\u003eThorburn, A. N. \u003cem\u003eet al.\u003c/em\u003e Evidence that asthma is a developmental origin disease influenced by maternal diet and bacterial metabolites. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 7320 (2015).\u003c/li\u003e\n\u003cli\u003eMoossavi, S. \u003cem\u003eet al.\u003c/em\u003e Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors. \u003cem\u003eCell Host Microbe\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 324-335.e4 (2019).\u003c/li\u003e\n\u003cli\u003eCrusell, M. K. W. \u003cem\u003eet al.\u003c/em\u003e Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 89 (2018).\u003c/li\u003e\n\u003cli\u003eAl Nabhani, Z. \u0026amp; Eberl, G. Imprinting of the immune system by the microbiota early in life. \u003cem\u003eMucosal Immunol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 183\u0026ndash;189 (2020).\u003c/li\u003e\n\u003cli\u003eFerretti, P. \u003cem\u003eet al.\u003c/em\u003e Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome. \u003cem\u003eCell Host Microbe\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 133-145.e5 (2018).\u003c/li\u003e\n\u003cli\u003eWang, J. \u003cem\u003eet al.\u003c/em\u003e Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. \u003cem\u003eGut\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, 1614\u0026ndash;1625 (2018).\u003c/li\u003e\n\u003cli\u003eMallick, H. \u003cem\u003eet al.\u003c/em\u003e Multivariable association discovery in population-scale meta-omics studies. \u003cem\u003ePLoS Comput Biol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e1009442 (2021).\u003c/li\u003e\n\u003cli\u003eSinha, T., Brushett, S., Prins, J. \u0026amp; Zhernakova, A. The maternal gut microbiome during pregnancy and its role in maternal and infant health. \u003cem\u003eCurr Opin Microbiol\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 102309 (2023).\u003c/li\u003e\n\u003cli\u003eLu, X., Shi, Z., Jiang, L. \u0026amp; Zhang, S. Maternal gut microbiota in the health of mothers and offspring: from the perspective of immunology. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eChauhan G, T. P. \u003cem\u003ePhysiology, Postpartum Changes\u003c/em\u003e. (Treasure Island (FL): StatPearls Publishing, 2022).\u003c/li\u003e\n\u003cli\u003ePascual ZN, L. M. \u003cem\u003ePhysiology, Pregnancy\u003c/em\u003e. (Treasure Island (FL): StatPearls Publishing, 2023).\u003c/li\u003e\n\u003cli\u003eSingh, N. \u0026amp; Perfect, J. R. Immune Reconstitution Syndrome and Exacerbation of Infections after Pregnancy. \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 1192\u0026ndash;1199 (2007).\u003c/li\u003e\n\u003cli\u003eKoren, O. \u003cem\u003eet al.\u003c/em\u003e Host Remodeling of the Gut Microbiome and Metabolic Changes during Pregnancy. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e150\u003c/strong\u003e, 470\u0026ndash;480 (2012).\u003c/li\u003e\n\u003cli\u003eLe Roy, T. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eDysosmobacter welbionis\u003c/em\u003e is a newly isolated human commensal bacterium preventing diet-induced obesity and metabolic disorders in mice. \u003cem\u003eGut\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 534\u0026ndash;543 (2022).\u003c/li\u003e\n\u003cli\u003eMoens de Hase, E. \u003cem\u003eet al.\u003c/em\u003e Dysosmobacter welbionis effects on glucose, lipid, and energy metabolism are associated with specific bioactive lipids. \u003cem\u003eJ Lipid Res\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 100437 (2023).\u003c/li\u003e\n\u003cli\u003eMoens de Hase, E. \u003cem\u003eet al.\u003c/em\u003e Impact of metformin and Dysosmobacter welbionis on diet-induced obesity and diabetes: from clinical observation to preclinical intervention. \u003cem\u003eDiabetologia\u003c/em\u003e\u003cstrong\u003e67\u003c/strong\u003e, 333\u0026ndash;345 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiome, Postpartum, Infants, Pregnancy","lastPublishedDoi":"10.21203/rs.3.rs-5972536/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5972536/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe gut microbiome undergoes substantial modifications during pregnancy, yet its postpartum adaptations remain poorly understood, particularly with respect to the influence of parity. Here, we investigated the impact of childbirth history on maternal gut microbiome composition and function one month postpartum.\u003c/p\u003e \u003cp\u003eBy conducting metagenomic sequencing analysis on 60 participants (34 postpartum mothers and 26 controls), we demonstrated significant differences in microbial diversity and community structure between postpartum mothers and control, as well as subtle differences between first-time mothers and multiple-birth mothers. We identified parity-specific signatures, with first-time mothers showing enrichment in \u003cem\u003eDysosmobacter welbionis, Candidatus Saccharibacteria\u003c/em\u003e, and \u003cem\u003eAnaerotruncus species\u003c/em\u003e. Functional analysis revealed distinct metabolic reprogramming patterns, including increased amino acid biosynthesis and modified fermentation pathways supporting postpartum recovery. We observed significant correlations between specific bacterial taxa and metabolic pathways, particularly in energy metabolism and immune modulation. Notably, the enhanced capacity for short-chain fatty acid production in primiparous mothers, mediated by \u003cem\u003eAnaerotruncus\u003c/em\u003e and \u003cem\u003eD. welbionis\u003c/em\u003e, suggests a potential role in shaping breast milk composition, which may influence neonatal development.\u003c/p\u003e \u003cp\u003eThese findings establish the concept of parity-dependent microbiome programming and provide insights into the biological mechanisms underlying maternal adaptation to pregnancy and childbirth.\u003c/p\u003e","manuscriptTitle":"Parity influences postpartum adaptations in the maternal gut microbiota","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 09:34:21","doi":"10.21203/rs.3.rs-5972536/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T07:02:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T02:44:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T09:35:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103498715447497457781647543811148978674","date":"2025-04-07T02:23:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T10:12:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168177663209504948847983506102533116534","date":"2025-04-04T03:41:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27301458257386300452818525724719324746","date":"2025-04-03T23:05:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T15:31:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T08:58:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-26T12:40:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9679a90-d04c-4144-a858-c4202131d18b","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46683616,"name":"Biological sciences/Microbiology/Bacteria/Metagenomics"},{"id":46683617,"name":"Biological sciences/Microbiology/Communities/Microbiome"}],"tags":[],"updatedAt":"2025-05-26T15:59:53+00:00","versionOfRecord":{"articleIdentity":"rs-5972536","link":"https://doi.org/10.1038/s41598-025-02013-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-21 15:57:07","publishedOnDateReadable":"May 21st, 2025"},"versionCreatedAt":"2025-04-07 09:34:21","video":"","vorDoi":"10.1038/s41598-025-02013-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-02013-y","workflowStages":[]},"version":"v1","identity":"rs-5972536","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5972536","identity":"rs-5972536","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}