Abstract Background While most clinical microbiome research has investigated gut microbiota, less is known about extracellular vesicles (EVs) produced by microbiota. Microbiota-derived EVs form a distinct taxonomic entity from the gut microbiota. Recently, microbiota-derived EVs from maternal microbiota have been shown to reach the fetus, which could be an important mechanism of microbiota–host interaction during the fetal period. We hypothesized that maternal factors could change the composition of microbiota-derived EVs during pregnancy. Methods We compared the influence of antibiotics and maternal weight on microbiota-derived EVs secreted by maternal microbiota during pregnancy. We collected fecal samples from 60 pregnant women and 18 amniotic fluid samples from those undergoing C-section. Microbiota-derived EVs were isolated from the samples using size-exclusion chromatography and density-gradient ultracentrifugation and characterized using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). RNA was isolated from the microbiota-derived EVs and converted to complementary DNA (cDNA), and 16 S rRNA gene sequencing was performed. Results Altogether, 18 (30%) women had received antibiotics during pregnancy and 37 (62%) during delivery. Twenty (33%) women were lean, 27 (45%) had overweight and 13 had obesity (22%) during the first trimester. Taxonomic diversity of microbiota-secreted microbiota-derived EVs was lower in women exposed to antibiotics during pregnancy. When women exposed to antibiotics during delivery were excluded, the differences were not statistically significant. The microbiota-derived EVs differed in the amniotic fluid and maternal feces in women with overweight and obesity, gestational diabetes (GDM), and those who gained > 15 kg during pregnancy, as many bacterial origins of microbiota-derived EVs were depleted in these women. A beta diversity analysis of microbiota-derived EVs from fecal samples showed significant differences between the overweight and obesity groups. Diversity analysis showed no differences in various health factors during pregnancy, including asthma, allergies, or smoking. Conclusions Maternal factors influence the composition of microbiota-derived extracellular vesicles in feces and amniotic fluid of pregnant women, which may change host-microbiota interaction in the fetal period.

The gut microbiota contributes to the pathophysiology of Parkinson's disease (PD), but the mechanism is not known. Extracellular vesicles (EVs), membrane-enclosed nanosized particles, contain various biomolecules, cross biological barriers, and reach sterile body compartments. We hypothesized that EVs released by the gut microbiota might influence PD pathophysiology. We isolated gut microbiota-derived EVs from the fecal samples of 20 patients with PD and 19 healthy controls. Patients with PD had higher concentrations of fecal EVs, and the mean particle size of EVs was larger. The 16S ribosomal RNA analysis of gut microbiota and microbiota-derived EVs showed taxonomic differences at both the phylum and genus levels between patients with PD and healthy controls. Proteomic analysis of EVs identified unique protein signatures in patients with PD. These findings suggest that gut microbiota-derived EVs constitute a disease-relevant compartment in PD, with their molecular cargo representing a potential mechanistic link between gut microbiota and PD.

Human gut microbiota is associated with obesity. Gut microbiota-derived extracellular vesicles (EVs), lipid coated nanoparticles secreted by bacteria, have been suggested as a communication mechanism between gut microbiota and the host. This study characterized the effect of Roux-en-Y gastric bypass (RYGB) on gut microbiota and gut microbiota-derived EVs in patients with obesity. Fecal samples were collected from 30 recruited patients at baseline and 6 months after surgery. EVs were isolated from fecal samples, and their origin and protein content were analyzed. The number of unique proteins was increased in gut microbiota-derived EVs after the surgery as compared to baseline. A significant difference in both microbiota composition (p = 0.001; PERMANOVA) and microbiota-derived EVs (p = 0.001; PERMANOVA) was observed in response to surgery. Based on 16S rRNA gene sequencing data, a random forest classifier accurately classified both gut microbiota (AUC = 0.93) and EVs (AUC = 0.80) to baseline and after surgery groups. This study found that gastric bypass surgery altered both the composition and characteristics of gut microbiota and gut microbiota-derived EVs in patients with obesity. Thus, gut microbiota-derived EVs may play a role in obesity and influence the health effects of bariatric surgery beyond the gut. Trial Registration: ClinicalTrials.gov identifier: NCT00950003.

Extracellular vesicles (EVs), nanoparticles secreted by both gram-negative and gram-positive bacteria, carry various biomolecules and cross biological barriers. Gut microbiota-derived EVs are currently being investigated as a communication mechanism between the microbiota and the host. Few clinical studies, however, have investigated gut microbiota-derived EVs. Here, we show that machine learning models were able to accurately distinguish gut microbiota and respective microbiota-derived EV samples according to their taxonomic composition both within each data set (area under the curve [AUC] 0.764-1.00) and in a cross-study setting (AUC 0.701-0.997). These results show that gut microbiota-derived EVs form a distinct taxonomic entity from gut microbiota. Thus, conventional gut microbiota composition may not correctly reflect communication between the gut microbiota and the host unless microbiota-derived EVs are reported separately.IMPORTANCEGut microbiota-derived extracellular vesicles (EVs) have been suggested to be a communication mechanism between the gut microbiota and the human body. However, the data on EV secretion from the gut microbiota remain limited. To investigate and compare the composition of gut microbiota-derived EVs to gut microbiota composition, we used a machine learning approach to classify 16S rRNA gene sequencing data in seven clinical data sets incorporating both gut microbiota and gut microbiota-derived EV samples. The results of the study show that microbiota-derived EVs form a separate taxonomic entity from the gut microbiota. Gut microbiota-derived EVs should be included in clinical studies that investigate gut microbiota to gain more comprehensive insight into gut microbiota-host communication.

STUDY QUESTION How does the gut bacteriome differ based on mood disorders (MDs) in women with polycystic ovary syndrome (PCOS), and how can the gut bacteriome contribute to the associations between these two conditions? SUMMARY ANSWER Women with PCOS who also have MDs exhibited a distinct gut bacteriome with reduced alpha diversity and a significantly lower abundance of Butyricicoccus compared to women with PCOS but without MDs. WHAT IS KNOWN ALREADY Women with PCOS have a 4- to 5-fold higher risk of having MDs compared to women without PCOS. The gut bacteriome has been suggested to influence the pathophysiology of both PCOS and MDs. STUDY DESIGN, SIZE, DURATION This population-based cohort study was derived from the Northern Finland Birth Cohort 1966 (NFBC1966), which includes all women born in Northern Finland in 1966. Women with PCOS who donated a stool sample at age 46 years (n = 102) and two BMI-matched controls for each case (n = 205), who also responded properly to the MD criteria scales, were included. PARTICIPANTS/MATERIALS, SETTING, METHODS A total of 102 women with PCOS and 205 age- and BMI-matched women without PCOS were included. Based on the validated MD criteria, the subjects were categorized into MD or no-MD groups, resulting in the following subgroups: PCOS no-MD (n = 84), PCOS MD (n = 18), control no-MD (n = 180), and control MD (n = 25). Clinical characteristics were assessed at age 31 years and age 46 years, and stool samples were collected from the women at age 46 years, followed by the gut bacteriome analysis using 16 s rRNA sequencing. Alpha diversity was assessed using observed features and Shannon's index, with a focus on genera, and beta diversity was characterized using principal components analysis (PCA) with Bray-Curtis Dissimilarity at the genus level. Associations between the gut bacteriome and PCOS-related clinical features were explored by Spearman's correlation coefficient. A P-value for multiple testing was adjusted with the Benjamini-Hochberg false discovery rate (FDR) method. MAIN RESULTS AND THE ROLE OF CHANCE We observed changes in the gut bacteriome associated with MDs, irrespective of whether the women also had PCOS. Similarly, PCOS MD cases showed a lower alpha diversity (Observed feature, PCOS no-MD, median 272; PCOS MD, median 208, FDR = 0.01; Shannon, PCOS no-MD, median 5.95; PCOS MD, median 5.57, FDR = 0.01) but also a lower abundance of Butyricicoccus (log-fold changeAnalysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC)=-0.90, FDRANCOM-BC=0.04) compared to PCOS no-MD cases. In contrast, in the controls, the gut bacteriome did not differ based on MDs. Furthermore, in the PCOS group, Sutterella showed positive correlations with PCOS-related clinical parameters linked to obesity (BMI, r(2)=0.31, FDR = 0.01; waist circumference, r(2)=0.29, FDR = 0.02), glucose metabolism (fasting glucose, r(2)=0.46, FDR < 0.001; fasting insulin, r(2)=0.24, FDR = 0.05), and gut barrier integrity (zonulin, r(2)=0.25, FDR = 0.03). LIMITATIONS, REASONS FOR CAUTION Although this was the first study to assess the link between the gut bacteriome and MDs in PCOS and included the largest PCOS dataset for the gut microbiome analysis, the number of subjects stratified by the presence of MDs was limited when contrasted with previous studies that focused on MDs in a non-selected population. WIDER IMPLICATIONS OF THE FINDINGS The main finding is that gut bacteriome is associated with MDs irrespective of the PCOS status, but PCOS may also modulate further the connection between the gut bacteriome and MDs. STUDY FUNDING/COMPETING INTEREST(S) This research was funded by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement (MATER, No. 813707), the Academy of Finland (project grants 315921, 321763, 336449), the Sigrid Juselius Foundation, Novo Nordisk Foundation (NNF21OC0070372), grant numbers PID2021-12728OB-100 (Endo-Map) and CNS2022-135999 (ROSY) funded by MCIN/AEI/10.13039/501100011033 and ERFD A Way of Making Europe. The study was also supported by EU QLG1-CT-2000-01643 (EUROBLCS) (E51560), NorFA (731, 20056, 30167), USA/NIH 2000 G DF682 (50945), the Estonian Research Council (PRG1076, PRG1414), EMBO Installation (3573), and Horizon 2020 Innovation Grant (ERIN, No. EU952516). The funders did not participate in any process of the study. We have no conflicts of interest to declare. TRIAL REGISTRATION NUMBER N/A.

Gut bacterial alterations have been previously linked to several non-communicable diseases in adults, while the association of mycobiome is not well understood in these diseases, especially in infants and children. Few studies have been conducted on the association between gut mycobiome and non-communicable diseases in children. We investigated gut mycobiome composition using 194 faecal samples collected at birth, 6 months after birth, and 18 months after birth in relation to atopic dermatitis (AD) and overweight diagnoses at the age of 18 or 36 months. The mycobiome exhibited distinct patterns, with Truncatella prevalent in the meconium samples of both overweight and non-overweight groups. Saccharomyces took precedence in overweight cases at 6 and 18 months, while Malassezia dominated non-overweight samples at 6 months. Saccharomyces emerged as a consistent high-abundance taxon across groups that had dermatitis and were overweight. We found a weak association between gut mycobiome and AD at birth and overweight at 18 months when using machine learning (ML) analyses. In ML, unidentified fungi, Alternaria, Rhodotorula, and Saccharomyces, were important for classifying AD, while Saccharomyces, Thelebolus, and Dothideomycetes were important for classifying overweight. Gut mycobiome might be associated with the development of AD and overweight in children.

Background The first-pass meconium has been suggested as a proxy for the fetal gut microbiota because it is formed in utero . This systematic review and cohort study investigated how pre- and perinatal factors influence the composition of the meconium microbiota. Methods We performed the systematic review using Covidence by searching PubMed, Scopus, and Web of Science databases with the search terms “meconium microbiome” and “meconium microbiota”. In the cohort study, we performed 16 S rRNA gene sequencing on 393 meconium samples and analyzed the sequencing data using QIIME2. Results Our systematic review identified 69 studies exploring prenatal factors, immediate perinatal factors, and microbial composition in relation to subsequent health of infants but gave only limited comparative evidence regarding factors related to the composition of the meconium microbiota. The cohort study pointed to a low-biomass microbiota consisting of the phyla Firmicutes, Proteobacteria and Actinobacteriota and the genera Staphylococcus , Escherichia-Shigella and Lactobacillus , and indicated that immediate perinatal factors affected the composition of the meconium microbiota more than did prenatal factors. Conclusions This finding supports the idea that the meconium microbiota mostly starts developing during delivery. Impact It is unclear when the first-pass meconium microbiota develops, and what are the sources of the colonization. In this systematic review, we found 69 studies exploring prenatal factors, immediate perinatal factors, and microbial composition relative to subsequent health of infants, but there was no consensus on the factors affecting the meconium microbiota development. In this cohort study, immediate perinatal factors markedly affected the meconium microbiota development while prenatal factors had little effect on it. As the meconium microbiota composition was influenced by immediate perinatal factors, the present study supports the idea that the initial gut microbiota develops mainly during delivery.

Reports regarding the presence of bacteria in the fetal environment remain limited and controversial. Recently, extracellular vesicles secreted by the human gut microbiota have emerged as a novel mechanism for host-microbiota interaction. We aimed to investigate the presence of bacterial extracellular vesicles in the fetal environment during healthy pregnancies and determine whether extracellular vesicles derived from the gut microbiota can cross biological barriers to reach the fetus. Bacterial extracellular vesicles were detectable in the amniotic fluid of healthy pregnant women, exhibiting similarities to extracellular vesicles found in the maternal gut microbiota. In pregnant mice, extracellular vesicles derived from human maternal gut microbiota were found to reach the intra-amniotic space. Our findings reveal maternal microbiota-derived extracellular vesicles as an interaction mechanism between the maternal microbiota and fetus, potentially playing a pivotal role in priming the prenatal immune system for gut colonization after birth.

Delivery mode and perinatal antibiotics influence gut microbiome composition in children. Most microbiome studies have used the sequencing of the bacterial 16S marker gene but have not reported the metabolic function of the gut microbiome, which may mediate biological effects on the host. Here, we used the PICRUSt2 bioinformatics tool to predict the functional profiles of the gut microbiome based on 16S sequencing in two child cohorts. Both Caesarean section and perinatal antibiotics markedly influenced the functional profiles of the gut microbiome at the age of 1 year. In machine learning analysis, bacterial fatty acid, phospholipid, and biotin biosynthesis were the most important pathways that differed according to delivery mode. Proteinogenic amino acid biosynthesis, carbohydrate degradation, pyrimidine deoxyribonucleotide and biotin biosynthesis were the most important pathways differing according to antibiotic exposure. Our study shows that both Caesarean section and perinatal antibiotics markedly influence the predicted metabolic profiles of the gut microbiome at the age of 1 year.

The mechanism by which cranberry-lingonberry juice (CLJ) prevents urinary tract infections (UTI) in children remains unknown. We hypothesized that it alters the composition of the gut or urinary microbiome. Altogether, 113 children with UTIs were randomly allocated to drink either CLJ or a placebo juice for 6 months. We collected urinary samples at 3 months and fecal samples at 3, 6 and 12 months and used next-generation sequencing of the bacterial 16S gene. The children who consumed CLJ had a lower abundance of Proteobacteria (p = 0.03) and a higher abundance of Firmicutes phylum (p = 0.04) in their urinary microbiome at 3 months than did those in the placebo group. The abundance of Escherichia coli in the urinary microbiome was 6% in the CLJ group and 13% in the placebo group (p = 0.42). In the gut microbiome the abundance of Actinobacteria at 3 and 12 months was higher in the children receiving CLJ. The diversity of the urinary and gut microbiome did not differ between the groups. The children drinking CLJ had a different urinary and gut microbiome from those receiving a placebo juice. A healthy urinary microbiome may be important in preventing UTIs in children.

Introduction Knowledge on the human gut microbiota in health and disease continues to rapidly expand. In recent years, changes in the gut microbiota composition have been reported as a part of the pathology in numerous neurodegenerative diseases. Bacterial extracellular vesicles (EVs) have been suggested as a novel mechanism for the crosstalk between the brain and gut microbiota, physiologically connecting the observed changes in the brain to gut microbiota dysbiosis. Methods Publications reporting findings on bacterial EVs passage through the blood–brain barrier were identified in PubMed and Scopus databases. Results The literature search yielded 138 non-duplicate publications, from which 113 records were excluded in title and abstract screening step. From 25 publications subjected to full-text screening, 8 were excluded. The resulting 17 publications were considered for the review. Discussion Bacterial EVs have been described with capability to cross the blood–brain barrier, but the mechanisms behind the crossing remain largely unknown. Importantly, very little data exists in this context on EVs secreted by the human gut microbiota. This systematic review summarizes the present evidence of bacterial EVs crossing the blood–brain barrier and highlights the importance of future research on gut microbiota-derived EVs in the context of gut-brain communication across the blood–brain barrier.

ABSTRACT Combining and comparing microbiome data from distinct infant cohorts has been challenging because such data are inherently multidimensional and complex. Here, we used an ensemble of machine-learning (ML) models and studied 16S rRNA amplicon sequencing data from 4,099 gut microbiome samples representing 12 prospectively collected infant cohorts. We chose the childbirth delivery mode as a starting point for such analysis because it has previously been associated with alterations in the gut microbiome in infants. In cross-study ensemble models, Bacteroides was the most important feature in all machine-learning models. The predictive capacity by taxonomy varied with age. At the age of 1–2 months, gut microbiome data were able to predict delivery mode with an area under the curve of 0.72 to 0.83. In contrast, ML models trained on taxa were not able to differentiate between the modes of delivery, in any of the cohorts, when the infants were between 3 and 12 months of age. Moreover, no ML model, alternately trained on the functional pathways of the infant gut microbiome, could consistently predict mode of delivery at any infant age. This study shows that infant gut microbiome data sets can be effectively combined with the application of ML analysis across different study populations. IMPORTANCE There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

The mechanism by which cranberry-lingonberry juice (CLJ) prevents urinary tract infections (UTI) in children remains unknown. We hypothesized that it alters the composition of the gut or urinary microbiome. Altogether, 113 children with UTIs were randomly allocated to drink either CLJ or a placebo juice for 6 months. We collected urinary samples at 3 months and fecal samples at 3, 6 and 12 months and used next-generation sequencing of the bacterial 16S gene. The children who consumed CLJ had a lower abundance of Proteobacteria (p = 0.03) and a higher abundance of Firmicutes phylum (p = 0.04) in their urinary microbiome at 3 months than did those in the placebo group. The abundance of Escherichia coli in the urinary microbiome was 6% in the CLJ group and 13% in the placebo group (p = 0.42). In the gut microbiome the abundance of Actinobacteria at 3 and 12 months was higher in the children receiving CLJ. The diversity of the urinary and gut microbiome did not differ between the groups. The children drinking CLJ had a different urinary and gut microbiome from those receiving a placebo juice. A healthy urinary microbiome may be important in preventing UTIs in children.

Both exposure to antibiotics at birth and delivery via Caesarean section influence the gut bacteriome's development in infants. Using 16S rRNA and internal transcribed spacer sequencing on the Ion Torrent platform, we employed network analysis to investigate the bacterial and fungal interkingdom relationships in the gut microbiome from birth to age 18 months in a prospective cohort study of 140 infants. The gut microbiome at ages six and 18 months revealed distinctive microbial interactions, including both positive and negative associations between bacterial and fungal genera in the gut ecosystem. Perinatal factors, delivery mode and intrapartum antibiotic exposure affected the associations between bacterial and fungal species. In infants exposed and unexposed to perinatal antibiotics, the gut microbiome formed distinct networks for the bacteriome and mycobiome. The fungi Saccharomyces, Trichosporon, Pezoloma, Cystofilobasidium, Rigidoporus and Fomitopsis were strongly associated with exposure to antibiotics at birth. Hyaloscypha, Trichosporon, Fomitopsis and Vishniacozyma were strongly associated with the control group that was not exposed to antibiotics. Five distinct networks were formed according to delivery mode. The present study confirms that bacteria and fungi clearly interact in the infant gut ecosystem. Furthermore, perinatal factors appear to influence the relationships between bacteria and fungi in the developing gut microbiome.

Background The composition of the gut fungal microbiome, mycobiome, is likely associated with human health. Yet, the development of gut mycobiome is poorly understood in infants and children. Here we investigate how perinatal events influence the development of gut mycobiome. Methods In this prospective cohort study of 140 infants, we used ITS gene sequencing of fecal samples from birth to the age of 18 months. We compared gut mycobiome composition according to delivery mode and exposure to intrapartum antibiotics during vaginal delivery. Results At birth, gut mycobiome were dominated by the genus Candida , at 6-month stool samples by Malassezia and Cystofilobasidium , and the 18-month stool samples by Trichosporon and unidentified fungi. Perinatal factors altered mycobiome. At 18 months, gut mycobiome of infants born vaginally consisted mostly of Trichosporon (32%) and unidentified fungi (31%), while those born via Cesarean section delivery samples had mycobiome dominated by Saccharomyces (50%). At the age of 18 months, those exposed to intrapartum antibiotics had mycobiome dominated by Trichosporon (66%) not seen in those unexposed to antibiotics. Conclusions Delivery mode and exposure to intrapartum antibiotic prophylaxis were markedly associated with gut mycobiome composition from birth to 18 months of age. Impact The composition of the gut mycobiome is likely associated with human health. Yet, the development of gut mycobiome is poorly understood in infants and children. In this prospective cohort study, delivery mode and exposure to intrapartum antibiotic prophylaxis were markedly associated with gut mycobiome composition from birth to 18 months of age. The impact of intrapartum antibiotic prophylaxis on fungal microbiome in vaginally born infants, previously shown to influence gut bacteriome composition, may be explained by the interaction between bacteria and fungi. Gut mycobiome composition likely deserves further investigation in relation to gut microbiome and health in children.

Mother vaginal microbes contribute to microbiome of vaginally delivered neonates. Child microbiome can be associated with autoimmune diseases, such as type 1 diabetes (T1D). We collected vaginal DNA samples from 25 mothers with a vaginally delivered child diagnosed with T1D and samples from 24 control mothers who had vaginally delivered a healthy child and analyzed bacteriome and mycobiome of the samples. The total DNA of the samples was extracted, and ribosomal DNA regions (16S for bacteria, ITS2 for fungi) were amplified, followed by next-generation sequencing and machine learning. We found that alpha-diversity of bacteriome was increased (P < 0.002), whereas alpha-diversity of mycobiome was decreased (P < 0.001) in mothers with a diabetic child compared to the control mothers. Beta-diversity analysis suggested differences in mycobiomes between the mother groups (P = 0.001). Random forest models were able to effectively predict diabetes and control status of unknown samples (bacteria: 0.86 AUC, fungi: 0.96 AUC). Our data indicate several fungal genera and bacterial metabolic pathways of mother vaginal microbiome to be associated with child T1D. We suggest that early onset of T1D in a child has a relationship with altered mother vaginal microbiome and that both bacteriome and mycobiome contribute to this shift.

Some cohort studies have suggested that gut microbiota composition is associated with allergic diseases in children. The microbiota of the first‐pass meconium, which forms before birth, represents the first gut microbiota that is easily available for research and little is known about any relationship with allergic disease development.

Antimicrobial resistance (AMR) is a major global public health problem. Human gut microbiome plays an important role in modulating AMR. On one hand, the microbiome itself can serve as a reservoir of AMR genes, i.e. resistome. On the other hand, the microbiome performs colonization resistance, preventing invasive microbes from colonizing the gastrointestinal tract. In this study, we investigated how antibiotic treatment affects the resistome and colonization resistance of the gut microbiome in children receiving amoxicillin, amoxicillin-clavulanate, or no treatment for acute otitis media in a randomized clinical trial. Fecal samples from children receiving an antibiotic or no treatment before and after the treatment were analyzed using deep metagenomic sequencing. We used a flow cytometry-based approach to quantify the bacterial load in the fecal samples. Both metagenomic sequencing-based relative abundance and flow cytometry-based absolute abundance of the microbial species were analyzed. We found that the resistome fluctuated over time and in a small fraction (∼10%) of subjects, AMR genes increased rapidly due to colonization by AMR species, even in the control group without any antibiotic treatment. Amoxicillin significantly increased the risk for invasive species, especially pathogenic species carrying AMR genes, to colonize the gut. We also found that children lacking Blautia, Ruminococcus, Faecalibacterium, Roseburia , or Faecalitalea were more vulnerable to colonization by invasive AMR species in their gut microbiome. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT02935374 ### Funding Statement The work was supported by federal funds from the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Department of Health and Human Services under Award Number [1R21AI151730 to WL] ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Regional Ethics Committee of the Northern Ostrobothnia Hospital District, Oulu University Hospital, Oulu, Finland, reviewed and approved the study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The raw metagenomic reads were deposited at NCBI and are available from SRA under bioproject access number PRJNA800433.

BACKGROUND:Bacterial extracellular vesicles (EVs) are more likely to cross biological barriers than whole-cell bacteria. We previously observed EV-sized particles by electron microscopy in the first-pass meconium of newborn infants. We hypothesized that EVs may be of bacterial origin and represent a novel entity in the human microbiome during fetal and perinatal periods.METHODS:We extracted EVs from first-pass meconium samples of 17 newborn infants and performed bacterial 16S rRNA gene sequencing of the vesicles. We compared the EV content from the meconium samples of infants based on the delivery mode, and in vaginal delivery samples, based on the usage of intrapartum antibiotics.RESULTS:We found bacterial EVs in all first-pass meconium samples. All EV samples had bacterial RNA. Most of the phyla present in the samples were Firmicutes (62%), Actinobacteriota (18%), Proteobacteria (10%), and Bacteroidota (7.3%). The most abundant genera were Streptococcus (21%) and Staphylococcus (17%). The differences between the delivery mode and exposure to antibiotics were not statistically significant.CONCLUSIONS:Bacterial EVs were present in the first-pass meconium of newborn infants. Bacterial EVs may represent an important novel feature of the gut microbiome during fetal and perinatal periods.IMPACT:We show that bacterial extracellular vesicles are present in the microbiome of first-pass meconium in newborn infants. This is a novel finding. To our knowledge, this is the first study to report the presence of bacterial extracellular vesicles in the gut microbiome during fetal and perinatal periods. This finding is important because bacterial extracellular vesicles are more likely to cross biological barriers than whole-cell bacteria. Thus, the early gut microbiome may potentially interact with the host through bacterial EVs.

Abstract Background The gut mycobiome may have important effects on health. Yet, there is limited data on the mycobiome. Previously, we have shown that antibiotic exposure at birth markedly alters the gut bacteriome in children. As fungi and bacteria may have important interactions in the gut microbiome, we hypothesized that perinatal antibiotics may influence the composition of the gut mycobiome. Methods For the study, 434 fecal samples were collected at birth, 6 months, and 18 months of age. We extracted DNA from samples using DNeasy PowerSoil Pro kit. We sequenced the ITS2 gene and analyzed the results using QIIME2. The taxonomy was assigned using UNITE database. Results At birth, newborn infants had a low-biomass gut mycobiome, which developed over time until the age of 18 months. At the genus level, there was a transition from Candida to Trichosporon and Saccharomyces at 6 and 18 months respectively. Unidentified fungi were found in greater numbers in 18-month stool samples than in earlier samples. Alpha and beta diversity analyses showed statistically significant differences depending on the age of infants. Both delivery mode and intrapartum antibiotics were associated with significant differences in the gut mycobiome composition. Infants born vaginally had mycobiome with abundant Candida at birth and their mycobiome progressed towards composition dominated by Trichosporon and Saccharomyces. Samples from infants born via C-section had more Malassezia, Hyalotiella and Saccharomyces. Vaginally delivered infants exposed to antibiotics at birth had more Candida sp. in the gut mycobiome than those unexposed to antibiotics. By 18 months of age, half of the fungi in no intrapartum antibiotic group were unidentified fungi, but more than half of the fungi in the intrapartum antibiotic group were of genus Trichosporon.Figure 1.Taxonomic figures of each sample type. Taxa have been drawn from phylum (innermost level) to species (outermost level). Figure 2. Taxonomic figures of each sample type are based on the delivery mode. Taxa have been drawn from phylum (innermost level) to species (outermost level). Figure 3. Taxonomic figures of each vaginal delivery sample are based on the usage of intrapartum antibiotics. Taxa have been drawn from phylum (innermost level) to species (outermost level). Conclusion In this prospective cohort study, the gut mycobiome composition of newborn infants and young children was markedly influenced both by delivery mode and exposure to antibiotics at birth. The effects were observed from birth until 18 months of age. Disclosures All Authors: No reported disclosures.