Dataset Information

Interactions between the Gut Microbiome and Mucosal Immunoglobulins A, M, and G in the Developing Infant Gut.

ABSTRACT: Interactions between the gut microbiome and immunoglobulin A (IgA) in the gut during infancy are important for future health. IgM and IgG are also present in the gut; however, their interactions with the microbiome in the developing infant remain to be characterized. Using stool samples sampled 15 times in infancy from 32 healthy subjects at 4 locations in 3 countries, we characterized patterns of microbiome development in relation to fecal levels of IgA, IgG, and IgM. For 8 infants from a single location, we used fluorescence-activated cell sorting of microbial cells from stool by Ig-coating status over 18?months. We used 16S rRNA gene profiling on full and sorted microbiomes to assess patterns of antibody coating in relation to age and other factors. All antibodies decreased in concentration with age but were augmented by breastmilk feeding regardless of infant age. Levels of IgA correlated with relative abundances of operational taxonomic units (OTUs) belonging to the Bifidobacteria and Enterobacteriaceae, which dominated the early microbiome, and IgG levels correlated with Haemophilus The diversity of Ig-coated microbiota was influenced by breastfeeding and age. IgA and IgM coated the same microbiota, which reflected the overall diversity of the microbiome, while IgG targeted a different subset. Blautia generally evaded antibody coating, while members of the Bifidobacteria and Enterobacteriaceae were high in IgA/M. IgA/M displayed similar dynamics, generally coating the microbiome proportionally, and were influenced by breastfeeding status. IgG only coated a small fraction of the commensal microbiota and differed from the proportion targeted by IgA and IgM.IMPORTANCE Antibodies are secreted into the gut and attach to roughly half of the trillions of bacterial cells present. When babies are born, the breastmilk supplies these antibodies until the baby's own immune system takes over this task after a few weeks. The vast majority of these antibodies are IgA, but two other types, IgG and IgM, are also present in the gut. Here, we ask if these three different antibody types target different types of bacteria in the infant gut as the infant develops from birth to 18?months old and how patterns of antibody coating of bacteria change with age. In this study of healthy infant samples over time, we found that IgA and IgM coat the same bacteria, which are generally representative of the diversity present, with a few exceptions that were more or less antibody coated than expected. IgG coated a separate suite of bacteria. These results provide a better understanding of how these antibodies interact with the developing infant gut microbiome.

SUBMITTER: Janzon A

PROVIDER: S-EPMC6880043 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interactions between the Gut Microbiome and Mucosal Immunoglobulins A, M, and G in the Developing Infant Gut.

Janzon Anders A Goodrich Julia K JK Koren Omry O Waters Jillian L JL Ley Ruth E RE

mSystems 20191126 6

Interactions between the gut microbiome and immunoglobulin A (IgA) in the gut during infancy are important for future health. IgM and IgG are also present in the gut; however, their interactions with the microbiome in the developing infant remain to be characterized. Using stool samples sampled 15 times in infancy from 32 healthy subjects at 4 locations in 3 countries, we characterized patterns of microbiome development in relation to fecal levels of IgA, IgG, and IgM. For 8 infants from a singl ...[more]

PMID: 31771976

Similar Datasets

Project description:The gut microbiome is a complex microbial community that has a significant influence on the host. Microbial interactions in the gut are mediated by dietary substrates, especially complex polysaccharides. In this environment, breakdown products from larger carbohydrates and short chain fatty acids are commonly shared among gut microbes. Understanding the forces that guide microbiome development and composition is important to determine its role in health and in the intervention of the gut microbiome as a therapeutic tool. Recently, modeling approaches such as genome-scale models and time-series analyses have been useful to predict microbial interactions. In this study, a bottom-up approach was followed to develop a mathematical model based on microbial growth equations that incorporate metabolic sharing and inhibition. The model was developed using experimental in vitro data from a system comprising four microorganisms of the infant gut microbiome (Bifidobacterium longum subsp. infantis, Lactobacillus acidophilus, Escherichia coli, and Bacteroides vulgatus), one substrate (fructooligosaccharides, FOS), and evaluating two metabolic products (acetate and lactate). After parameter optimization, the model accurately predicted bacterial abundance in co-cultures from mono-culture data. In addition, a good correlation was observed between the experimental data with predicted FOS consumption and acid production. B. infantis and L. acidophilus were dominant under these conditions. Further model validation included cultures with the four-species in a bioreactor using FOS. The model was able to predict the predominance of the two aforementioned species, as well as depletion of acetate and lactate. Finally, the model was tested for parameter identifiability and sensitivity. These results suggest that variations in microbial abundance and activities in the infant gut were mainly explained by metabolic interactions, and could be properly modeled using Monod kinetics with metabolic interactions. The model could be scaled to include data from larger consortia, or be applied to microbial communities where sharing metabolic resources is important in shaping bacterial abundance. Moreover, the model could be useful in designing microbial consortia with desired properties such as higher acid production.

Project description:Background - Prepregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, during pregnancy the impact of overweight on immune cell gene expression and its association with maternal and infant outcomes is not well explored. Methods – Blood samples were collected from healthy normal weight (NW, BMI 18.5-24.9) or overweight (OW, BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. Results - Gene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions – These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Further, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.

Project description:BackgroundThe neonatal intensive care unit (NICU) contains a unique cohort of patients with underdeveloped immune systems and nascent microbiome communities. Patients often spend several months in the same room, and it has been previously shown that the gut microbiomes of these infants often resemble the microbes found in the NICU. Little is known, however, about the identity, persistence, and absolute abundance of NICU room-associated bacteria over long stretches of time. Here, we couple droplet digital PCR (ddPCR), 16S rRNA gene surveys, and recently published metagenomics data from infant gut samples to infer the extent to which the NICU microbiome is shaped by its room occupants.ResultsOver 2832 swabs, wipes, and air samples were collected from 16 private-style NICU rooms housing very low birth weight (< 1500 g), premature (< 31 weeks' gestation) infants. For each infant, room samples were collected daily, Monday through Friday, for 1 month. The first samples from the first infant and the last samples from the last infant were collected 383 days apart. Twenty-two NICU locations spanning room surfaces, hands, electronics, sink basins, and air were collected. Results point to an incredibly simple room community where 5-10 taxa, mostly skin-associated, account for over 50% of the amplicon reads. Biomass estimates reveal four to five orders of magnitude difference between the least to the most dense microbial communities, air, and sink basins, respectively. Biomass trends from bioaerosol samples and petri dish dust collectors suggest occupancy to be a main driver of suspended biological particles within the NICU. Using a machine learning algorithm to classify the origin of room samples, we show that each room has a unique microbial fingerprint. Several important taxa driving this model were dominant gut colonizers of infants housed within each room.ConclusionsDespite regular cleaning of hospital surfaces, bacterial biomass was detectable at varying densities. A room-specific microbiome signature was detected, suggesting microbes seeding NICU surfaces are sourced from reservoirs within the room and that these reservoirs contain actively dividing cells. Collectively, the data suggests that hospitalized infants, in combination with their caregivers, shape the microbiome of NICU rooms.

Project description:Broad-spectrum antibiotics for suspected early-onset neonatal sepsis (sEONS) may have pronounced effects on gut microbiome development and selection of antimicrobial resistance when administered in the first week of life, during the assembly phase of the neonatal microbiome. Here, 147 infants born at ≥36 weeks of gestational age, requiring broad-spectrum antibiotics for treatment of sEONS in their first week of life were randomized 1:1:1 to receive three commonly prescribed intravenous antibiotic combinations, namely penicillin + gentamicin, co-amoxiclav + gentamicin or amoxicillin + cefotaxime (ZEBRA study, Trial Register NL4882). Average antibiotic treatment duration was 48 hours. A subset of 80 non-antibiotic treated infants from a healthy birth cohort served as controls (MUIS study, Trial Register NL3821). Rectal swabs and/or faeces were collected before and immediately after treatment, and at 1, 4 and 12 months of life. Microbiota were characterized by 16S rRNA-based sequencing and a panel of 31 antimicrobial resistance genes was tested using targeted qPCR. Confirmatory shotgun metagenomic sequencing was executed on a subset of samples. The overall gut microbial community composition and antimicrobial resistance gene profile majorly shift directly following treatment (R2 = 9.5%, adjusted p-value = 0.001 and R2 = 7.5%, adjusted p-value = 0.001, respectively) and normalize over 12 months (R2 = 1.1%, adjusted p-value = 0.03 and R2 = 0.6%, adjusted p-value = 0.23, respectively). We find a decreased abundance of Bifidobacterium spp. and increased abundance of Klebsiella and Enterococcus spp. in the antibiotic treated infants compared to controls. Amoxicillin + cefotaxime shows the largest effects on both microbial community composition and antimicrobial resistance gene profile, whereas penicillin + gentamicin exhibits the least effects. These data suggest that the choice of empirical antibiotics is relevant for adverse ecological side-effects.

Dataset Information

Interactions between the Gut Microbiome and Mucosal Immunoglobulins A, M, and G in the Developing Infant Gut.

Publications

Interactions between the Gut Microbiome and Mucosal Immunoglobulins A, M, and G in the Developing Infant Gut.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets