Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts.

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.

Microbiota of the first-pass meconium and subsequent atopic and allergic disorders in children.

BACKGROUND: 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. OBJECTIVE: We investigated whether the bacterial composition of the first-pass meconium is associated with the development of allergic diseases before 4 years of age. METHODS: Prospective birth cohort study. Bacterial composition of first-pass meconium was analysed using bacterial 16S rRNA gene amplicon sequencing. Atopic and allergic diseases were evaluated via online survey or telephone to age 4 years, based on the International Study of Asthma and Allergies in Childhood questionnaire. RESULTS: During a 6-week period in 2014, 312 children were born at the Central Finland Central Hospital. Meconium was collected from 212 at a mean of 8-hour age. Outcome data at 4 years were available for 177 (83%) children, and 159 of these had sufficient amplification of bacterial DNA in meconium. Meconium microbiota composition, including diversity indices and relative abundances of the main phyla and genera, was not associated with subsequent atopic eczema, wheezing or cow's milk allergy. Principal components analysis did not identify any clustering of the meconium microbiomes of children with respect to wheezing or cow's milk allergy. CONCLUSIONS: We found no evidence that gut microbiota composition of first-pass meconium is associated with atopic manifestations to age 4 years. However, larger studies are needed to fully exclude a relationship.

Antibiotics at birth and later antibiotic courses: effects on gut microbiota.

BACKGROUND: Intrapartum antibiotic prophylaxis (IAP) is widely used, but the evidence of the long-term effects on the gut microbiota and subsequent health of children is limited. Here, we compared the impacts of perinatal antibiotic exposure and later courses of antibiotic courses on gut microbiota. METHODS: This was a prospective, controlled cohort study among 100 vaginally delivered infants with different perinatal antibiotic exposures: control (27), IAP (27), postnatal antibiotics (24), and IAP and postnatal antibiotics (22). At 1 year of age, we performed next-generation sequencing of the bacterial 16S ribosomal RNA gene of fecal samples. RESULTS: Exposure to the perinatal antibiotics had a clear impact on the gut microbiota. The abundance of the Bacteroidetes phylum was significantly higher in the control group, whereas the relative abundance of Escherichia coli was significantly lower in the control group. The impact of the perinatal antibiotics on the gut microbiota composition was greater than exposure to later courses of antibiotics (28% of participants). CONCLUSIONS: Perinatal antibiotic exposure had a marked impact on the gut microbiota at the age of 1 year. The timing of the antibiotic exposure appears to be the critical factor for the changes observed in the gut microbiota. IMPACT: Infants are commonly exposed to IAP and postnatal antibiotics, and later to courses of antibiotics during the first year of life. Perinatal antibiotics have been associated with an altered gut microbiota during the first months of life, whereas the evidence regarding the long-term impact is more limited. Perinatal antibiotic exposure had a marked impact on the infant's gut microbiota at 1 year of age. Impact of the perinatal antibiotics on the gut microbiota composition was greater than that of the later courses of antibiotics at the age of 1 year.

Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome.

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.

Impact of Streptococcus salivarius K12 on Nasopharyngeal and Saliva Microbiome: A Randomized Controlled Trial.

BACKGROUND: Probiotic lactobacilli have been ineffective in preventing acute otitis media. In contrast to lactobacilli, alpha-hemolytic streptococci belong to the core microbiome of nasopharynx. METHODS: We investigated the effects of Streptococcus salivarius K12 probiotic on the saliva and nasopharyngeal microbiome in 121 children attending daycare. Children were randomly allocated to receive oral K12 product for 1 month or no treatment. We obtained saliva and nasopharyngeal samples at study entry, at 1 and 2 months. The next-generation sequencing of the bacterial 16S gene was performed. RESULTS: After the intervention, the diversity of saliva or nasopharyngeal microbiome did not differ between groups. The proportion of children with any otopathogen did not differ between the groups. At 1 month, the abundance of otopathogens in nasopharynx was lower in K12 group compared with that in control children (34% vs. 55%, P = 0.037). When we compared each otopathogen separately, Moraxella was the only group lower in the treatment group. We could not verify the reduction of Moraxella when an alternative Human Oral Microbiome Database taxonomy database was used. In children receiving K12 product, the mean abundance of S. salivarius was greater in saliva after the intervention (0.9% vs. 2.0%, P = 0.009). CONCLUSIONS: The use of S. salivarius K12 probiotic appeared to be safe because it did not disrupt the normal microbiome in young children. Even though a short-term colonization of S. salivarius was observed in the saliva, the impact of S. salivarius K12 probiotic on the otopathogens in nasopharyngeal microbiome remained uncertain.

Microbiome of the first stool and overweight at age 3 years: A prospective cohort study.

BACKGROUND: Several reports have revealed that the first-pass meconium hosts a diverse microbiome, but its clinical significance is not known. OBJECTIVE: We designed a prospective population-based cohort study to evaluate whether the meconium microbiome predicts subsequent growth in children. METHODS: The study comprised 212 consecutive newborns with a meconium sample and a follow-up sample at 1 year of age. Trained nurses measured the children for weight and length using standardized techniques. We used next-generation sequencing of bacterial 16S rRNA gene and machine-learning approach for the analysis. RESULTS: The children with overweight at 3 years of age differed in their meconium microbiome from those with normal weight, having a higher proportion of Bacteroidetes phylum (29% vs 15%, P = .013). Using the machine-learning approach, the gut microbiome at birth predicted subsequent overweight with area under the curve 0.70 (SD 0.04). A lower proportion of Staphylococcus at birth was associated with greater length/height at 1 year (ß = -.68, P = .029) and 2 years of age (ß = -.74, P = .030). CONCLUSIONS: The microbiome of the first-pass meconium predicted subsequent overweight at the age of 3 years. The association between the gut microbiome and overweight appears to start already during pregnancy and at birth.

Microbiome of the first stool after birth and infantile colic.

BACKGROUND: Recent studies have shown a diverse microbiome in the first stool after birth. The clinical significance of the microbiome of the first stool is not known. Infantile colic has earlier been associated with the composition of the intestinal microbiome. METHODS: We set out to test whether the microbiome of the first stool is associated with subsequent infantile colic in a prospective, population-based cohort study of 212 consecutive newborn infants. We used next-generation sequencing of the bacterial 16S rRNA gene. RESULTS: The newborns who later developed infantile colic (n = 19) had a lower relative abundance of the genus Lactobacillus and the phylum Firmicutes in the first stool than those who remained healthy (n = 139). By using all microbiome data, random forest algorithm classified newborn with subsequent colic and those who remained healthy with area under the curve of 0.66 (SD 0.03) as compared to that of shuffled samples (P value <0.001). CONCLUSIONS: In this prospective, population-based study, the microbiome of the first-pass meconium was associated with subsequent infantile colic. Our results suggest that the pathogenesis of infantile colic is closely related to the intestinal microbiome at birth.

Tonsil Mycobiome in PFAPA (Periodic Fever, Aphthous Stomatitis, Pharyngitis, Adenitis) Syndrome: A Case-Control Study.

Periodic fever, aphthous stomatitis, pharyngitis and adenitis syndrome (PFAPA) is the most common periodic fever syndrome in children with unknown etiology, effectively treated with tonsillectomy. Earlier we have shown that tonsil microbiome is different in patients with PFAPA as compared to that in controls. Recently, fungal microbiome, mycobiome, has been linked to the pathogenesis of inflammatory diseases. We now investigated the role of mycobiome of tonsils in PFAPA. Random forest classification, a machine learning approach, was used for the analysis of mycobiome data. We examined tonsils from 30 children with PFAPA and 22 control children undergoing tonsillectomy for non-infectious reasons. We identified 103 amplicon sequence variants, mainly from two fungal phyla, Ascomycota and Basidiomycota. The mean relative abundance of Candida albicans in the tonsil mycobiome was 11% (95% CI: 19 to 27%) in cases and 3.4 % (95% CI: -0.8% to 8%) in controls, p =0.104. Mycobiome data showed no statistical difference in differentiating between PFAPA cases and controls compared to a random chance classifier (area under the curve (AUC) = 0.47, SD = 0.05, p = 0.809). In conclusion, in this controlled study, tonsillar mycobiome in children with PFAPA syndrome did not differ from that of the controls.

Microbial orchestra in juvenile idiopathic arthritis: Sounds of disarray?

The role of the microbiota in multiple autoimmune diseases, including juvenile idiopathic arthritis (JIA) has earned substantial attention in the last 10 years. Increasing evidence suggests that the microbiota's link to JIA begins in early childhood, as early life events that influence the nature of the microbiota also appear to influence disease risk. In this review, we discuss these early life events including mode of delivery, infant feeding practice, antibiotics exposure, and other events and their impacts on the microbiota and on disease risk; reported abnormalities of the microbiota in children with JIA; mechanisms by which an altered microbiota at birth and later on in childhood may influence disease risk; and the prospects for therapeutic alteration of the microbiota in children with JIA.