QIIME is a widely used, open-source microbiome analysis software package that converts raw sequence data into interpretable visualizations and statistical results. QIIME2 has recently succeeded QIIME1, becoming the most updated platform. The protocols in this article describe our effort in automating core functions of QIIME2, using datasets available at docs.qiime2.org. While these specific examples are microbial 16S rRNA gene sequences, our automation can be easily applied to other types of QIIME2 analysis.

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.

Intro The gut microbiome is increasingly known to play a role in obesity; a major health concern globally. It has been noted that cannabis users tend to have lower body mass indexes than non‐users, and theorized that the delta(9)‐tetrahydrocannabinol (THC) in cannabis could play a role in this effect. We hypothesized that the gut microbiome may play a key role in THC induced weight loss. Methods We investigated temporal effects of oral THC supplementation on the microbiome of male and female mice made obese by the D12311 diet from Research. At day 0, the THC group (male = 10, female = 8) was supplemented with 10mg/kg of oral THC. Control (male = 6, female = 3) was supplemented with the sweetened milk vehicle. Fecal samples were collected at days 0, 2, 9, and 15, prepared for V4 16s sequencing, and analyzed with QIIME2 and R. Beta diversity by weighted UniFrac PERMANOVA. Multilevel modeling (MLM) was performed at sequentially deeper taxa to identify significant effects of THC treatment on microbiome abundance. We evaluated microbiomes of both sexes for conserved effects. Results Over the 15‐day experiment, male and female mice treated with THC demonstrated weight loss of 12.5±4.1% and 15.7±5.8% respectively, compared with 0.4±1.4% and 1.3±4.6% among controls. THC treatment had a significant effect on microbiome beta diversity in both males (PERMANOVA, F=5.32, P=0.001) and females (PERMANOVA, F=4.22, P=0.011). THC treatment increased Shannon alpha diversity from baseline in females (T‐test, p <0.01). MLM identified several taxa which were significantly up‐regulated between days 0 and 2 (T2 spikes). Here we present effects which were conserved between sexes. T2 spikes in THC treated mice were significant at a phyla level among Proteobacteria, Verrucomicrobia, Bacteroidetes, and Firmicutes. The Verrucomicrobia spike represented a single species, Akkermansia muciniphila, in both males and females which increased 9.8±6% and 7.9±9.6% respectively. Relative frequency of the A. muciniphila T2 spike was significantly negatively correlated with weight loss (GLS, est = ‐0.66, SE = 0.18, p<0.01). This effect was conserved between sexes. Few other significant effects on the microbiome were conserved between genders. Among Firmicutes, S. variabile showed a large T2 spike in males but not females and a significant longitudinal upregulation in females but not males, and Ruminococcus showed a significant upregulation in males. Discussion T2 A. muciniphila spikes occurred in THC treated mice only and occurred before THC‐induced weight loss was achieved. This suggests a possible mechanistic role of A. muciniphila. A. muciniphila has been identified by other studies as a potential therapeutic target for obesity, diabetes, and metabolic syndrome. Ruminococcaceae have been implicated in butyrate production, which is known to increase mitochondrial activity, improve insulin sensitivity, and protect against diet induced obesity. These findings support that the gut microbiome may have an integral role in THC induced weight loss, potential...