Information enhanced model selection for Gaussian graphical model with application to metabolomic data.

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Impact of antibiotics on off-target infant gut microbiota and resistance genes in cohort studies.

BACKGROUND: Young children are frequently exposed to antibiotics, with the potential for collateral consequences to the gut microbiome. The impact of antibiotic exposures to off-target microbes (i.e., bacteria not targeted by treatment) and antibiotic resistance genes (ARGs) is poorly understood. METHODS: We used metagenomic sequencing data from paired stool samples collected prior to antibiotic exposure and at 1 year from over 200 infants and a difference-in-differences approach to assess the relationship between subsequent exposures and the abundance or compositional diversity of microbes and ARGs while adjusting for covariates. RESULTS: By 1 year, the abundance of multiple species and ARGs differed by antibiotic exposure. Compared to infants never exposed to antibiotics, Bacteroides vulgatus relative abundance increased by 1.72% (95% CI: 0.19, 3.24) while Bacteroides fragilis decreased by 1.56% (95% CI: -4.32, 1.21). Bifidobacterium species also exhibited opposing trends. ARGs associated with exposure included class A beta-lactamase gene CfxA6. Among infants attending day care, Escherichia coli and ARG abundance were both positively associated with antibiotic use. CONCLUSION: Novel findings, including the importance of day care attendance, were identified through considering microbiome data at baseline and post-intervention. Thus, our study design and approach have important implications for future studies evaluating the unintended impacts of antibiotics. IMPACT: The impact of antibiotic exposure to off-target microbes and antibiotic resistance genes in the gut is poorly defined. We quantified these impacts in two cohort studies using a difference-in-differences approach. Novel to microbiome studies, we used pre/post-antibiotic data to emulate a randomized controlled trial. Compared to infants unexposed to antibiotics between baseline and 1 year, the relative abundance of multiple off-target species and antibiotic resistance genes was altered. Infants who attended day care and were exposed to antibiotics within the first year had a higher abundance of Escherichia coli and antibiotic resistance genes; a novel finding warranting further investigation.

The infant gut resistome is associated with E. coli and early-life exposures.

BACKGROUND: The human gut microbiome harbors a collection of bacterial antimicrobial resistance genes (ARGs) known as the resistome. The factors associated with establishment of the resistome in early life are not well understood. We investigated the early-life exposures and taxonomic signatures associated with resistome development over the first year of life in a large, prospective cohort in the United States. Shotgun metagenomic sequencing was used to profile both microbial composition and ARGs in stool samples collected at 6 weeks and 1 year of age from infants enrolled in the New Hampshire Birth Cohort Study. Negative binomial regression and statistical modeling were used to examine infant factors such as sex, delivery mode, feeding method, gestational age, antibiotic exposure, and infant gut microbiome composition in relation to the diversity and relative abundance of ARGs. RESULTS: Metagenomic sequencing was performed on paired samples from 195 full term (at least 37 weeks' gestation) and 15 late preterm (33-36 weeks' gestation) infants. 6-week samples compared to 1-year samples had 4.37 times (95% CI: 3.54-5.39) the rate of harboring ARGs. The majority of ARGs that were at a greater relative abundance at 6 weeks (chi-squared p < 0.01) worked through the mechanism of antibiotic efflux. The overall relative abundance of the resistome was strongly correlated with Proteobacteria (Spearman correlation = 78.9%) and specifically Escherichia coli (62.2%) relative abundance in the gut microbiome. Among infant characteristics, delivery mode was most strongly associated with the diversity and relative abundance of ARGs. Infants born via cesarean delivery had a trend towards a higher risk of harboring unique ARGs [relative risk = 1.12 (95% CI: 0.97-1.29)] as well as having an increased risk for overall ARG relative abundance [relative risk = 1.43 (95% CI: 1.12-1.84)] at 1 year compared to infants born vaginally. CONCLUSIONS: Our findings suggest that the developing infant gut resistome may be alterable by early-life exposures. Establishing the extent to which infant characteristics and early-life exposures impact the resistome can ultimately lead to interventions that decrease the transmission of ARGs and thus the risk of antibiotic resistant infections.

Associations between the gut microbiome and metabolome in early life.

BACKGROUND: The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life. RESULTS: Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. CONCLUSIONS: Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Bronchiolitis hospitalizations in rural New England: clues to disease prevention.

Background: An improved understanding of the clinico-epidemiology of bronchiolitis hospitalizations, a clinical surrogate of respiratory syncytial virus (RSV) disease, is critical to inform public health strategies for mitigating the in-patient burden of bronchiolitis in early life. Methods: A retrospective chart review was conducted of all bronchiolitis first admissions (N = 295) to the Children's Hospital at Dartmouth-Hitchcock, CHaD, between 1 November 2010 and 31 October 2017 using the relevant International Classification of Diseases (ICD)-9 and ICD-10 codes for this illness. Abstracted data included laboratory confirmation of RSV infection, severity of illness, duration of hospitalization, age at admission in days, weight at admission, prematurity, siblings, and relevant medical pre-existing conditions. Results: Admissions for bronchiolitis were strongly associated with age of the child, the calendar month of an infant's birth, and the presence of older children in the family. Medical risk factors associated with admission included premature birth and underlying cardiopulmonary disease. Conclusion: The very early age of hospitalization emphasizes the high penetration of RSV in the community, by implication the limited protection afforded by maternal antibody, and the complexity of protecting infants from this infection. Plain Language Summary: Although risks for respiratory syncytial virus (RSV)/bronchiolitis hospitalization are well described, few studies have examined, with precision, the age-related frequency and severity of RSV/bronchiolitis. We also explore the implications of RSV clinico-epidemiology for our understanding of the pathogenesis of the disease and development of optimal approaches to prevention.

Metagenomic analysis reveals associations between salivary microbiota and body composition in early childhood.

Several studies have shown that body mass index is strongly associated with differences in gut microbiota, but the relationship between body weight and oral microbiota is less clear especially in young children. We aimed to evaluate if there is an association between child growth and the saliva microbiome. We hypothesized that associations between growth and the saliva microbiome would be moderate, similarly to the association between growth and the gut microbiome. For 236 toddlers participating in the New Hampshire Birth Cohort Study, we characterized the association between multiple longitudinal anthropometric measures of body height, body weight and body mass. Body Mass Index (BMI) z-scores were calculated, and dual-energy x-ray absorptiometry (DXA) was used to estimate body composition. Shotgun metagenomic sequencing of saliva samples was performed to taxonomically and functionally profile the oral microbiome. We found that within-sample diversity was inversely related to body mass measurements while community composition was not associated. Although the magnitude of associations were small, some taxa were consistently associated with growth and modified by sex. Certain taxa were associated with decreased weight or growth (including Actinomyces odontolyticus and Prevotella melaninogenica) or increased growth (such as Streptococcus mitis and Corynebacterium matruchotii) across anthropometric measures. Further exploration of the functional significance of this relationship will enhance our understanding of the intersection between weight gain, microbiota, and energy metabolism and the potential role of these relationships on the onset of obesity-associated diseases in later life.

Association of Cesarean Delivery and Formula Supplementation with the Stool Metabolome of 6-Week-Old Infants.

Cesarean delivery and formula feeding have both been implicated as important factors associated with perturbations to the infant gut microbiome. To investigate the functional metabolic response of the infant gut microbial milieu to these factors, we profiled the stool metabolomes of 121 infants from a US pregnancy cohort study at approximately 6 weeks of life and evaluated associations with delivery mode and feeding method. Multivariate analysis of six-week stool metabolomic profiles indicated discrimination by both delivery mode and diet. For diet, exclusively breast-fed infants exhibited metabolomic profiles that were distinct from both exclusively formula-fed and combination-fed infants, which were relatively more similar to each other in metabolomic profile. We also identified individual metabolites that were important for differentiating delivery mode groups and feeding groups and metabolic pathways related to delivery mode and feeding type. We conclude based on previous work and this current study that the microbial communities colonizing the gastrointestinal tracts of infants are not only taxonomically, but also functionally distinct when compared according to delivery mode and feeding groups. Further, different sets of metabolites and metabolic pathways define delivery mode and diet metabotypes.

Infant Feeding Alters the Longitudinal Impact of Birth Mode on the Development of the Gut Microbiota in the First Year of Life.

Cesarean-delivered (CD) infants harbor a distinct gut microbiome from vaginally delivered (VD) infants, however, during infancy, the most important driver of infant gut microbial colonization is infant feeding. Earlier studies have shown that breastfeeding is associated with higher levels of health-promoting bacteria such and Bifidobacterium and Bacteroides via modulation of the immune system, and production of metabolites. As the infant gut matures and solid foods are introduced, it is unclear whether longer duration of breast feeding restore loss of beneficial taxa within the intestinal microbiota of operatively delivered infants. Within the New Hampshire Birth Cohort Study, we evaluated the longitudinal effect of delivery mode and infant feeding on the taxonomic composition and functional capacity of developing gut microbiota in the First year of life. Microbiota of 500 stool samples collected between 6 weeks and 12 months of age (from 229 infants) were characterized by 16S ribosomal RNA sequencing. Shotgun metagenomic sequencing was also performed on 350 samples collected at either 6 weeks or 12 months of age. Among infant participants, 28% were cesarean-delivered (CD) infants and most (95%) initiated breastfeeding within the first six months of life, with 26% exclusively breastfed and 69% mixed-fed (breast milk and formula), in addition to complementary foods by age 1. Alpha (within-sample) diversity was significantly lower in CD infants compared to vaginally delivered (VD) infants (P < 0.05) throughout the study period. Bacterial community composition clustering by both delivery mode and feeding duration at 1 year of age revealed that CD infants who were breast fed for < 6 months were more dissimilar to VD infants than CD infants who breast fed for ≥ 6 months. We observed that breastfeeding modified the longitudinal impact of delivery mode on the taxonomic composition of the microbiota by 1 year of age, with an observed increase in abundance of Bacteroides fragilis and Lactobacillus with longer duration of breastfeeding among CD infants while there was an increase in Faecalibacterium for VD infants. Our findings confirm that duration of breastfeeding plays a critical role in restoring a health-promoting microbiome, call for further investigations regarding the association between breast milk exposure and health outcomes in early life.