Dataset Information

Modeling Metabolic Interactions in a Consortium of the Infant Gut Microbiome.

ABSTRACT: 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.

SUBMITTER: Pinto F

PROVIDER: S-EPMC5735223 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Modeling Metabolic Interactions in a Consortium of the Infant Gut Microbiome.

Pinto Francisco F Medina Daniel A DA Pérez-Correa José R JR Garrido Daniel D

Frontiers in microbiology 20171214

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 microbi ...[more]

PMID: 29312209

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Project description:BackgroundIntestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.Methodology/principal findingsIn this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.Conclusion/significanceThe model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

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: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.

Dataset Information

Modeling Metabolic Interactions in a Consortium of the Infant Gut Microbiome.

Publications

Modeling Metabolic Interactions in a Consortium of the Infant Gut Microbiome.

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