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Intro Literature establishes conserved changes in the gut microbiome (GMB) correlated with behavior.1 Using voluntary running, we tested if GMB variability explains differences in voluntary exercise using a rat model. Methods Cohabitating male rats were randomized into running (n=8) and sedentary (n=3) groups and placed in individual cages. Running wheel access was withheld for a week. The running group received free access to the wheels for 27 days. Daily and cumulative distances were recorded with weekly fecal collection for 16s amplification and sequencing. Terminal seven‐day distances stratified rats into high (8341‐10209 m/wk) and low (1763‐5201 m/wk) groups. Analysis was done in QIIME2 and R. Results At randomization, alpha‐diversity (AD) was similar between sedentary, low, and high runners (ANOVA, F = 2.812, Pr(>F) = .065). Shannon diversity showed significant correlation with daily distance and an interaction running subgroup (high v. low, MLM, int = ‐6,778, Shannon est. = 2498±118, low running interaction = ‐2135±217). Across all samples, there was a significant correlation between AD and distance (Pearson correlation, p <0.001, corr = 0.41). Fixed slope random intercept modelling showed a significant relationship between distance and phyla abundance. Verrucomicrobia (MLM, int = 3610, est. 294925±142844, p = 0.048), and Saccharibacteria (MLM, int = 3342, est. 3830783±1245092, p = 0.005) showed significant increases in beta‐diversity. Actinobacteria (MLM, int 5634, est. =45608±13682) was negatively associated with distance. Discussion Our findings that voluntary distances run was associated with increases in microbiome richness supports the importance of ecosystem diversity to support host function. This is consistent with previous research which establishes the role of increasing AD with broad improvements in performance. Decreased Verrucomicrobia is associated with the development of metabolic diseases, which are associated with decreased running behaviors.2 Verrucomicrobia and Actinobacteria impact gut homeostasis by modulating permeability, immune and inflammatory responses at the mucosal level.3 Analysis is ongoing and may influence these findings. Further research is needed on the impact of these changes to determine their effect on the overall health of the organism. References 1. Denou, E., et al. (2016). High‐intensity exercise training increases the diversity and metabolic capacity of the mouse distal gut microbiota during diet‐induced obesity. American Journal of Physiology‐Endocrinology and Metabolism, 310(11). https://doi.org/10.1152/ajpendo.00537.2015 2. Zhang, T., et al. (2019). Akkermansia Muciniphila is a promising probiotic. Microbial Biotechnology, 12(6), 1109–1125. https://doi.org/10.1111/1751-7915.13410 3. Binda, C., et al. (2018). Actinobacteria: A relevant minority for the maintenance of gut homeostasis. Digestive and Liver Disease, 50(5), 421–428. https://doi.org/10.1016/j.dld.2018.02.012

The cost of next‐generation sequencing has significantly reduced since its arrival in 2004, allowing metagenomic research to flourish with applications in medicine, genetics, agriculture, and environment. QIIME 2 is an open‐source microbiome analysis software package that converts raw sequence data into interpretable visualizations and statistical results. Most common analyses include classifying sequences taxonomically, analyzing alpha and beta diversity, assessing phylogenetic relationship between features, and identifying features with differential abundances in various treatment groups. Third parties can contribute functionality such as longitudinal analysis and machine‐learning analysis, making it a robust microbiome tool that is widely used. However, there exist several barriers in using QIIME 2. There is a steep learning curve for users with limited technical computer skill. The high volume of user inputs increases risk for user error and inefficient reproducibility among collaborators. However, these issues with efficiency, accessibility, and consistency can be addressed through automation. Using LINUX shells, we developed a robust QIIME 2 automation pipeline that automates the core functions of QIIME 2 metagenomic analysis. The process covers raw sequence data importing, demultiplexing, and denoising. It also automates data subset filtering, taxonomic classification, and alpha and beta diversity analyses. Files created with standardized nomenclature are organized into a simple folder structure comprising of data, visualizations, and metadata. QIIME 2 parameters are saved into accessible text files for transparency. The pipeline was trialed with 3 novices to both data science and metagenomic research: one PhD principal investigator, one 2nd year medical student and one 1st year medical student. They were given our scripts and tutorial, and they recorded the time and keystrokes to reach 3 endpoints: 1) construct a PCOA plot of an entire dataset, 2) perform ADONIS test on a distance matrix subset, and 3) compare alpha diversity using the Shannon metric on a metadata subset feature table. 2 out of 3 trials were successful in meeting these endpoints within 2 weeks, requiring 7 (PhD) and 9 hours (2nd year medical student) to do so. Using the fewest possible keystrokes, this analysis would have taken 11,594 characters over roughly 40 hours if done manually. Using our QIIME 2 automation, these endpoints were completed with 228 user keystrokes. The limitations of QIIME 2 can be diminished by automation and clarification of steps at decision points within a metagenomic analysis. The goal of this project was to reduce risk of error and time spent, limiting the obstacles facing a new user using QIIME 2. With growing popularity in metagenomic research, our automation tool can help computer novices navigate through the technical barriers of metagenomic analysis.

ObjectiveThis project evaluated the relationship of gut microbiome composition of first year medical students and stress resilience over a period of 4 months. Our objective was to identify gut microbiome characteristics of individuals that showed long term stress resilience.MethodsStudents were voluntarily recruited and screened for lifestyle and environmental factors. Their degree of psychologic stress at 3 timepoints during the first semester was measured with Cohen's perceived stress scale and PHQ‐9 depression scores. Fecal samples were collected at the same time using uBiome home kits at participant convenience. Samples were processed by uBiome and amplified for the 16s V4 gene. Stress and depression scores were normalized and summed to produce a psychologic index score. These scores were used to identify what microbiome characteristics may be correlated to stress resiliency of susceptibility. Metagenomic workup was performed using QIIME, MetaComet, and R Studio.Summary of resultsAcross phyla present in 100% of samples, none were independently correlated with psychologic scores using a linear model while Bacteroidete:Firmicute ratio showed a significant negative linear correlation with the psychologic index (Pearon's, p=0.05, R=−0.24). Phylogenetic assembly of participants by microbiome relatedness found that 100% of subjects who were resilient to stress across all timepoints (n=8) were phylogenetically clustered in adjacent positions, showing a high degree of temporal stability. Of participants who were not durably resilient to stress, only 62% of participants (n=8) showed microbiomes that were phylogenetically related across the same 4 month period. We identified 2,102 Operational Taxonomic Units (OTUs) which were unique to the durable resilience group and 94 OTUs which were unique to the susceptible group. Of the 4,794 observed OTUs, 6.1% (n=294) were significantly different between groups by Kruskal‐Wallis before post‐test adjustment.ConclusionFindings of a correlation between an index accounting for stress and depression and the abundance of Bacteroidetes: Firmicutes, and a relationship between durable resiliency and microbiome stability support that the gut microbiome may play an important role in stress resilience at a time scale of 4 months. More research is indicated in this field to identify microbiome characteristics which are responsible for stress resilience. These may include the presence or absence of specific taxa or ratios between taxa, such as the Bacteroidete:Firmicute ratio. Eventually, a better understanding of the role of the gut microbiome in stress resilience may someday create opportunities to help those with conditions which are characterized by low stress resilience when faced with new challenges. We hope someday that exploration of the microbiome will produce actionable, clinically relevant opportunities.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.