Authors: Connor Hough
Mentors: Johnny Chaston
Insitution: Brigham Young University
Many variables can have an effect on the microbiota (microbial community associated with an organism) which can, in turn, affect the health and lifestyle of the microbiota's host organism. One such variable is geographic latitude (distance away from the earth's equator) which was the subject of a portion of a study performed by Walters et al. in 2020 and a similar study performed by Henry et al. in 2022. Walters et al. determined that latitude had a significant effect on the composition of the gut microbiota in Drosophila melanogaster (fruit flies) while Henry et al. determined that latitude did not have a significant effect. To better understand what variables may have caused a difference in the results of these two studies I plan to perform data analysis on the data provided from both Walters et al. and Henry et al. Particularly, Henry et al. provided data about the microbiota of other sample types associated with D. melanogaster across the latitudinal cline such as the fruit fly excrement, the leaves in their environment, and their diet which consisted of apples and grapes. While this data was mentioned in Henry et al.’s paper I feel that a more in depth analysis of these sample types could bring insight about how these microbiota affect one another and how latitude affects each of them separately. I will use QIIME (a bioinformatics data analysis software) data analysis methods, such as alpha and beta diversity metrics, and R to analyze the data provided and create graphs. Because each sample type in the data was recorded with unequal sample sizes I will also need to make a new taxonomic graph of the sample types when measured proportionally to each other. To do this I will use QIIME to group the original feature table metadata from Henry et al. by sample type and return a new table with an equal number of samples for each sample type. With this done I will then be able to make a new bar plot using QIIME. I will then use this new set of adjusted data to analyze the relationships between latitude and each sample type as well as the relationship between each sample type to one another. I will also perform these data analytics methods on the data provided on fruit fly gut microbiota by Walters et al. and compare the results with the fruit fly gut microbiota provided by Henry et al.. I hypothesize that further analyzing the data from these sample types will reveal new correlations between the microbiota of these sample types and with latitude. If no new information is learned through this analysis then it will solidify Henry et al.’s claim that latitude does not significantly affect the microbiota. Alternative outcomes may show that there is not a correlation between the microbiota of sample types which would indicate that the microbiota exhibits a neutral behavior and is not selective. However, if my hypothesis is correct then these correlations would show that the microbiota of separate sample types are related to the microbiota of other sample types and that latitude does have a significant effect on the microbiota of each sample type. The results of this analysis will open up more pathways for study about the reason behind these correlations or the lack thereof.