Cytokine induced inflammatory bowel disease model using organ-on-a-chip technology.

Over 2 million people in North America suffer from inflammatory bowel disease (IBD), a chronic and idiopathic inflammatory condition. While previous research has primarily focused on studying immune cells as a cause and therapeutic target for IBD, recent findings suggest that non-immune cells may also play a crucial role in mediating cytokine and chemokine signaling, and therefore IBD symptoms. In this study, we developed an organ-on-a-chip co-culture model of Caco2 epithelial and HUVEC endothelial cells and induced inflammation using pro-inflammatory cytokines TNF-α and IFN-γ. We tested different concentration ranges and delivery orientations (apical vs. basal) to develop a consistently inducible inflammatory response model. We then measured pro-inflammatory cytokines and chemokines IL-6, IL-8, and CXCL-10, as well as epithelial barrier integrity. Our results indicate that this model 1. induces IBD-like cytokine secretion in non-immune cells and 2. decreases barrier integrity, making it a feasible and reliable model to test the direct actions of potential anti-inflammatory therapeutics on epithelial and endothelial cells.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism.

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data.

Large-scale microbiome studies investigating disease-inducing microbial roles base their findings on differences between microbial count data in contrasting environments (e.g., stool samples between cases and controls). These microbiome survey studies are often impeded by small sample sizes and database bias. Combining data from multiple survey studies often results in obvious batch effects, even when DNA preparation and sequencing methods are identical. Relatedly, predictive models trained on one microbial DNA dataset often do not generalize to outside datasets. In this study, we address these limitations by applying word embedding algorithms (GloVe) and PCA transformation to ASV data from the American Gut Project and generating translation matrices that can be applied to any 16S rRNA V4 region gut microbiome sequencing study. Because these approaches contextualize microbial occurrences in a larger dataset while reducing dimensionality of the feature space, they can improve generalization of predictive models that predict host phenotype from stool associated gut microbiota. The GMEmbeddings R package contains GloVe and PCA embedding transformation matrices at 50, 100 and 250 dimensions, each learned using ∼15,000 samples from the American Gut Project. It currently supports the alignment, matching, and matrix multiplication to allow users to transform their V4 16S rRNA data into these embedding spaces. We show how to correlate the properties in the new embedding space to KEGG functional pathways for biological interpretation of results. Lastly, we provide benchmarking on six gut microbiome datasets describing three phenotypes to demonstrate the ability of embedding-based microbiome classifiers to generalize to independent datasets. Future iterations of GMEmbeddings will include embedding transformation matrices for other biological systems. Available at: https://github.com/MaudeDavidLab/GMEmbeddings.

Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning.

A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers.

BACKGROUND: Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction. RESULTS: On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84. CONCLUSIONS: SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers .

Children with Autism and Their Typically Developing Siblings Differ in Amplicon Sequence Variants and Predicted Functions of Stool-Associated Microbes.

The existence of a link between the gut microbiome and autism spectrum disorder (ASD) is well established in mice, but in human populations, efforts to identify microbial biomarkers have been limited due to a lack of appropriately matched controls, stratification of participants within the autism spectrum, and sample size. To overcome these limitations, we crowdsourced the recruitment of families with age-matched sibling pairs between 2 and 7 years old (within 2 years of each other), where one child had a diagnosis of ASD and the other did not. Parents collected stool samples, provided a home video of their ASD child's natural social behavior, and responded online to diet and behavioral questionnaires. 16S rRNA V4 amplicon sequencing of 117 samples (60 ASD and 57 controls) identified 21 amplicon sequence variants (ASVs) that differed significantly between the two cohorts: 11 were found to be enriched in neurotypical children (six ASVs belonging to the Lachnospiraceae family), while 10 were enriched in children with ASD (including Ruminococcaceae and Bacteroidaceae families). Summarizing the expected KEGG orthologs of each predicted genome, the taxonomic biomarkers associated with children with ASD can use amino acids as precursors for butyragenic pathways, potentially altering the availability of neurotransmitters like glutamate and gamma aminobutyric acid (GABA).IMPORTANCE Autism spectrum disorder (ASD), which now affects 1 in 54 children in the United States, is known to have comorbidity with gut disorders of a variety of types; however, the link to the microbiome remains poorly characterized. Recent work has provided compelling evidence to link the gut microbiome to the autism phenotype in mouse models, but identification of specific taxa associated with autism has suffered replicability issues in humans. This has been due in part to sample size that sufficiently covers the spectrum of phenotypes known to autism (which range from subtle to severe) and a lack of appropriately matched controls. Our original study proposes to overcome these limitations by collecting stool-associated microbiome on 60 sibling pairs of children, one with autism and one neurotypically developing, both 2 to 7 years old and no more than 2 years apart in age. We use exact sequence variant analysis and both permutation and differential abundance procedures to identify 21 taxa with significant enrichment or depletion in the autism cohort compared to their matched sibling controls. Several of these 21 biomarkers have been identified in previous smaller studies; however, some are new to autism and known to be important in gut-brain interactions and/or are associated with specific fatty acid biosynthesis pathways.

Longitudinal study of stool-associated microbial taxa in sibling pairs with and without autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder influenced by both genetic and environmental factors. Recently, gut dysbiosis has emerged as a powerful contributor to ASD symptoms. In this study, we recruited over 100 age-matched sibling pairs (between 2 and 8 years old) where one had an Autism ASD diagnosis and the other was developing typically (TD) (432 samples total). We collected stool samples over four weeks, tracked over 100 lifestyle and dietary variables, and surveyed behavior measures related to ASD symptoms. We identified 117 amplicon sequencing variants (ASVs) that were significantly different in abundance between sibling pairs across all three timepoints, 11 of which were supported by at least two contrast methods. We additionally identified dietary and lifestyle variables that differ significantly between cohorts, and further linked those variables to the ASVs they statistically relate to. Overall, dietary and lifestyle features were explanatory of ASD phenotype using logistic regression, however, global compositional microbiome features were not. Leveraging our longitudinal behavior questionnaires, we additionally identified 11 ASVs associated with changes in reported anxiety over time within and across all individuals. Lastly, we find that overall microbiome composition (beta-diversity) is associated with specific ASD-related behavioral characteristics.

Correction: Decoding the language of microbiomes using word-embedding techniques, and applications in inflammatory bowel disease.

[This corrects the article DOI: 10.1371/journal.pcbi.1007859.].

Decoding the language of microbiomes using word-embedding techniques, and applications in inflammatory bowel disease.

Microbiomes are complex ecological systems that play crucial roles in understanding natural phenomena from human disease to climate change. Especially in human gut microbiome studies, where collecting clinical samples can be arduous, the number of taxa considered in any one study often exceeds the number of samples ten to one hundred-fold. This discrepancy decreases the power of studies to identify meaningful differences between samples, increases the likelihood of false positive results, and subsequently limits reproducibility. Despite the vast collections of microbiome data already available, biome-specific patterns of microbial structure are not currently leveraged to inform studies. Here, we derive microbiome-level properties by applying an embedding algorithm to quantify taxon co-occurrence patterns in over 18,000 samples from the American Gut Project (AGP) microbiome crowdsourcing effort. We then compare the predictive power of models trained using properties, normalized taxonomic count data, and another commonly used dimensionality reduction method, Principal Component Analysis in categorizing samples from individuals with inflammatory bowel disease (IBD) and healthy controls. We show that predictive models trained using property data are the most accurate, robust, and generalizable, and that property-based models can be trained on one dataset and deployed on another with positive results. Furthermore, we find that properties correlate significantly with known metabolic pathways. Using these properties, we are able to extract known and new bacterial metabolic pathways associated with inflammatory bowel disease across two completely independent studies. By providing a set of pre-trained embeddings, we allow any V4 16S amplicon study to apply the publicly informed properties to increase the statistical power, reproducibility, and generalizability of analysis.

Immersive Virtual Reality for Pediatric Pain.

Children must often endure painful procedures as part of their treatment for various medical conditions. Those with chronic pain endure frequent or constant discomfort in their daily lives, sometimes severely limiting their physical capacities. With the advent of affordable consumer-grade equipment, clinicians have access to a promising and engaging intervention for pediatric pain, both acute and chronic. In addition to providing relief from acute and procedural pain, virtual reality (VR) may also help to provide a corrective psychological and physiological environment to facilitate rehabilitation for pediatric patients suffering from chronic pain. The special qualities of VR such as presence, interactivity, customization, social interaction, and embodiment allow it to be accepted by children and adolescents and incorporated successfully into their existing medical therapies. However, the powerful and transformative nature of many VR experiences may also pose some risks and should be utilized with caution. In this paper, we review recent literature in pediatric virtual reality for procedural pain and anxiety, acute and chronic pain, and some rehabilitation applications. We also discuss the practical considerations of using VR in pediatric care, and offer specific suggestions and information for clinicians wishing to adopt these engaging therapies into their daily clinical practice.