Sequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-predict subjects (p = 0.045). DBNs provided quantitative likelihood estimates for rare abruptions events. Iterative prediction was less accurate (p < 0.001), but showed remarkable insensitivity to initial conditions and predicted convergence to a mix of Clostridia, Gammaproteobacteria and Bacilli. DBNs were able to identify important relationships between microbiome taxa and predict future changes in microbiome composition from measured or synthetic initial conditions. DBNs also provided likelihood estimates for sudden, dramatic shifts in microbiome composition, which may be useful in guiding further analysis of those samples.

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Circulating microRNAs have shown promise as non-invasive biomarkers and predictors of disease activity. Prior asthma studies using clinical, biochemical and genomic data have not shown excellent prediction of exacerbation. We hypothesized that a panel of circulating microRNAs in a pediatric asthma cohort combined with an exacerbation clinical score might predict exacerbation better than the latter alone. Serum samples from 153 children at randomization in the Childhood Asthma Management Program were profiled for 754 microRNAs. Data dichotomized for asthma exacerbation one year after randomization to inhaled corticosteroid treatment were used for binary logistic regression with miRNA expressions and exacerbation clinical score. 12 of 125 well-detected circulating microRNAs had significant odd ratios for exacerbation with miR-206 being most significant. Each doubling of expression of the 12 microRNA corresponded to a 25–67% increase in exacerbation risk. Stepwise logistic regression yielded a 3-microRNA model (miR-146b, miR-206 and miR-720) that, combined with the exacerbation clinical score, had excellent predictive power with a 0.81 AUROC. These 3 microRNAs were involved in NF-kβ and GSK3/AKT pathways. This combined circulating microRNA-clinical score model predicted exacerbation in asthmatic subjects on inhaled corticosteroids better than each constituent feature alone. ClinicalTrials.gov Identifier: NCT00000575 .

Purpose of review: This review will catalog the many recent longitudinal studies that have investigated the relationship between asthma and lung function, or the persistence and trajectories of lung function deficits. Recent findings: Recent work has reported on 50-year follow-ups of some prominent population cohorts. A history of asthma confers a 10–30-fold risk of chronic obstructive pulmonary disease. Individuals reaching a reduced maximum growth of forced expiratory volume in 1 s in early adulthood are at risk for early or more severe chronic obstructive pulmonary disease (COPD). Summary: Taken together, there is a wealth of overlapping cohort studies of lung function, asthma and COPD. These show that asthma is associated with reduced lung function, which may start in infancy or prenatally, persists through childhood and adulthood and predisposes for early or more severe COPD.

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

Rationale: Inhaled corticosteroids (ICS) are key treatments for controlling asthma and preventing asthma attacks. However, the responsiveness to ICS varies among individuals. MicroRNAs (miRNAs) have been lauded for their prognostic utility. Objectives: We hypothesized that circulating miRNAs obtained at baseline/prerandomization in the Childhood Asthma Management Program (CAMP) could serve as biomarkers and biologic mediators of ICS clinical response over the 4-year clinical trial period. Methods: We selected baseline serum samples from 462 CAMP subjects subsequently randomized to either ICS (budesonide) or placebo. Samples underwent small RNA sequencing, and read counts were normalized and filtered by depth and coverage. Linear regression was used to associate miRNAs with change in FEV1% (prebronchodilator FEV1 as a percent predicted) over the 4-year treatment period in both main effects and interaction models. We validated the function of the top associated miRNAs by luciferase reporter assays of glucocorticoid-mediated transrepression and predicted response to ICS through logistic regression models. Measurements and Main Results: We identified 7 miRNAs significantly associated with FEV1% change (P ≤ 0.05) and 15 miRNAs with significant interaction (P ≤ 0.05) to ICS versus placebo treatments. We selected three miRNAs for functional validation, of which hsa-miR-155-5p and hsa-miR-532-5p were significantly associated with changes in dexamethasone-induced transrepression of NF-κB. Combined, these two miRNAs were predictive of ICS response over the course of the clinical trial, with an area under the receiver operating characteristic curve of 0.86. Conclusions: We identified two functional circulating miRNAs predictive of asthma ICS treatment response over time.