Location: Crop Improvement and Protection ResearchTitle: Clustering of soil sample microbiomes using metagenomic shotgun sequencing data
|MAMMEL, MARK - Food And Drug Administration(FDA)|
|LEONARD, SUSAN - Food And Drug Administration(FDA)|
|RICHTER, TAYLOR - Food And Drug Administration(FDA)|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2020
Publication Date: 9/1/2020
Citation: Mammel, M., Leonard, S.R., Richter, T., Simko, I., Brandl, M. 2020. Clustering of soil sample microbiomes using metagenomic shotgun sequencing data. FDA Foods Program Regulatory Science Conference, September 1, 2020, College Park, Maryland.
Technical Abstract: The safety of leafy green produce grown in the United States is of great concern due to recent outbreaks of Escherichia coli O157:H7. An understanding of the typical and changed microbiomes of the plants and the surrounding soil may help identify risk factors on farms. A survey of soil microbiomes in California lettuce farms was conducted to gather an initial index of the organisms present and to observe differences in the population changes across different farms and seasons. DNA was isolated from soil samples and DNA libraries were prepared for shotgun metagenomics using the Illumina Nextera Flex library preparation kit. The libraries were sequenced using the Illumina NextSeq Platform. Relative abundances of bacterial species were determined using an in-house k-mer database and program. Distances between sample microbiomes were calculated by Bray-Curtis dissimilarity matrixes, and visualization of the microbiome Beta-diversity was performed using Non-Metric Multidimensional Scaling using the Python scikit-learn library. PERMANOVA analysis using the Python scikit-bio library provided a measure of statistical significance for differences between groups. There was a significant difference (P < 0.01) between samples grouped by farm and by season. These methods aid in determining baseline assessments of microbial communities at specific farms. Additional monitoring of the farms would allow for detection of shifts in the microbiome, giving possible warning of areas of potential risk due to contamination or susceptibility to contamination. Wider sampling of farms in the region would allow for greater generalization in interpretation of observed microbiome changes.