Do Antibiotics Change Your Gut Microbiome?

This project follows the Scientific Method. Review the steps before you begin.

Accessing the Data

1. Navigate to Microbiome DB. This tool houses data from microbiome studies, including the one you will be accessing for this science project, and tools to analyze the data.
   1. Optional: make an account at Microbiome DB. You can do everything as a guest, but if you are signed in the tool will automatically save your analysis as you work.
2. Navigate to the FARMM study in the Study Summaries section. This will open up the View Study Details section where you can see a link to the paper the authors published about a different aspect of this study — the effects of fiber on the gut microbiome and the metabolic products produced by the microbiome.
3. In the Browse and Subset section you will choose the participants and samples you want to use in your analysis. The left-hand side has all the ways the data can be filtered. To start, we recommend you choose:
   1. Participant -> Administrative information -> Case or control participant -> False. This will exclude one participant that did not complete the study. This selection will leave you with 30 of 31 participants.
   2. Clinical history -> Antibiotic medication related to sample collection -> choose all three options: antibiotics treatment, post antibiotics, and pre antibiotics. These are the three groups you are interested in comparing.

Image Credit: Screen shot of Microbiome DB taken by Sandra Slutz / Science Buddies
Figure 2. After completing the Browse and Subset section, the flow chart of participants and samples available for your analysis steps should look like this.

Visualizing and Analyzing the Data

1. In the Visualize section you will see many options of how to look at the data. We will lead you through some, but you should feel free to explore others. You may also find that as you explore you want to split your data differently. If this is the case, return to the Browse and Subset section to make your choices.
2. The first question you will explore is "Does exposure to oral antibiotics change the human gut microbiome?" To answer this question, look at the beta diversity of the samples.
   1. Beta diversity is a measurement of how similar or dissimilar the diversity is between samples. Watch the video about Jaccard Similarity to learn more about this measure of beta diversity.
      

      https://www.youtube.com/watch?v=YotbvhndSf4
   2. In the Beta Diversity visualization, choose:
      1. Data: Metagenomic sequencing assay > Genus
      2. Dissimilarity method: jaccard
      3. Stratification variables: Overlay -> Participant repeated measure -> Antibiotic medication related to sample collection
   3. This will give you a graph where each dot represents the diversity of bacteria in a stool sample from a person in the study. The dots are colored according to whether they were taken before antibiotics, after antibiotics, or during antibiotic treatment. The closer the dots cluster, the more similar they are in their diversity. What do you notice about the clustering of the dots?
   4. Are the samples from after antibiotics more similar to one another or are they mixed in with the pre-antibiotic samples? What about the during antibiotic samples? What can you conclude about whether or not oral antibiotics change the human gut microbiome?
3. The second question you will explore is, "Does the microbiome have the same level of diversity before and after a course of antibiotics?" To answer this question, look at the alpha diversity of the samples.
   1. Alpha diversity is a measurement of how much diversity is in each sample. Watch the video about the Simpson's diversity index to learn more about what this measure of alpha diversity means.
      

      https://www.youtube.com/watch?v=7DOuku8876I
   2. In the Alpha Diversity visualization, choose:
      1. Data: Metagenomic sequencing assays > Genus
      2. Method: Simpson
      3. After generating the alpha diversity results, choose X-axis: Participant repeated measure -> Antibiotic medication related to sample collection
   3. The resulting graph consists of three box and whisker plots. If you are unfamiliar with this type of graph, read the Understanding and using Box and Whisker Plots overview.
   4. How does the median diversity for pre-antibiotic samples compare to post-antibiotic samples?
4. The last question we will explore together is "Are the same types of bacteria present in the gut before and after antibiotics?". To answer this question, look at the ranked diversity of the samples as the study progressed.
   1. In the Ranked Abundance settings, choose:
      1. Data: Metagenomic sequencing assay > Genus
      2. Method: median
   2. After generating the ranked abundance results, choose X-axis: Sample -> Observation details -> Study timepoint.
   3. This will give you a graph with ranked abundance on the Y-axis and Days in the study on the X-axis. Each dot on the graph represents how much of a specific genus of bacteria was found in a sample relative to the other bacteria in the same sample. Only the top 10 most abundant genus of bacteria are shown.
   4. To simplify the visual, turn on smoothed mean with raw in the plot mode. This will give you a line showing the mean ranked abundance of each genus of bacteria. You can also deselect the individual genus dots using the graph's key.
   5. Look back at Figure 1 to remind yourself which study days were pre, during, and post-antibiotics. Are the same bacteria the most abundant in the gut pre and post-antibiotics? Make a list of the most abundant genera of bacteria for each part of the study (pre, post, and during antibiotics).
5. Feel free to continue to explore the data. There are suggestions in the Variations of how you can extend this analysis. What else can you discover by exploring this microbiome data?