Anne G. Hoen

MicroRNAs (miRNAs) are a class of small non-coding RNAs, 19 to 24 nucleotides in length, that regulate a range of developmental and physiological processes by modulating gene expression through pos...

Background: Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. Methods: Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. Results: Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. Limitations: The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. Conclusions: Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories.

Background Set-based pathway analysis is a powerful tool that allows researchers to summarize complex genomic variables in the form of biologically interpretable sets. Since the microbiome is characterized by a high degree of inter-individual variability in taxonomic compositions, applying enrichment methods using functionally driven taxon sets can increase both the reproducibility and interpretability of microbiome association studies. However, there is still an open question of which knowledge base to utilize for set construction. Here, we evaluate microbial trait databases, which aggregate experimentally determined microbial phenotypes, as a potential avenue for meaningful construction of taxon sets. Method Using publicly available microbiome sequencing data sets (both 16S rRNA gene metabarcoding and whole-genome metagenomics), we assessed these trait-based sets on two criteria: first, do they cover the diversity of microbes obtained from a typical data set, and second, do they confer additional predictive power on disease prediction tasks when assessed against measured pathway abundances and PICRUSt2 prediction. Results Trait annotations are well annotated to a small number but most abundant taxa within the community, concordant with the concept of the core-peripheral microbiome. This pattern is consistent across all categories of traits and body-sites for whole genome sequencing data, but much more heterogenous and inconsistent in 16S rRNA metabarcoding data due to difficulties in assigning species-level traits to genus. However, trait-set features are well predictive of disease outcomes compared against predicted and measured pathway abundances. Most important trait-set features are more interpreable and reveal interesting insights on the relationship between microbiome, its function, and health outcomes.

Significance: To reduce the transmission of COVID-19, many retail stores use one-way aisles, while local governments enforce occupancy limits or require “safe shopping” times for vulnerable groups. To assess the value of these interventions, we formulate and analyze a mathematical model of customer flow and COVID-19 transmission. We find that the value of specific operational changes depends on how the virus is transmitted, through close contact or suspended aerosols. If transmission is primarily due to close contact, then restricting customers to one-way movement can dramatically reduce transmission. Other interventions, such as reductions in customer density, are effective at a distance but confront store operators with trade-offs between infection rates and customer flow.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet