DNA extraction and host depletion methods significantly impact and potentially bias bacterial detection in a biological fluid

Abstract

ABSTRACTUntargeted sequencing of nucleic acids present in food can inform the detection of food safety and origin, as well as product tampering and mislabeling issues. The application of such technologies to food analysis could reveal valuable insights that are simply unobtainable by targeted testing, leading to the efforts of applying such technologies in the food industry. However, before these approaches can be applied, it is imperative to verify that the most appropriate methods are used at every step of the process: gathering primary material, laboratory methods, data analysis, and interpretation.The focus of this study is in gathering the primary material, in this case, DNA. We used bovine milk as a model to 1) evaluate commercially available kits for their ability to extract nucleic acids from inoculated bovine milk; 2) evaluate host DNA depletion methods for use with milk, and 3) develop and evaluate a selective lysis-PMA based protocol for host DNA depletion in milk.Our results suggest that magnetic-based nucleic acid extraction methods are best for nucleic acid isolation of bovine milk. Removal of host DNA remains a challenge for untargeted sequencing of milk, highlighting that the individual matrix characteristics should always be considered in food testing. Some reported methods introduce bias against specific types of microbes, which may be particularly problematic in food safety where the detection of Gram-negative pathogens and indicators is essential. Continuous efforts are needed to develop and validate new approaches for untargeted metagenomics in samples with large amounts of DNA from a single host.ImportanceTracking the bacterial communities present in our food has the potential to inform food safety and product origin. To do so, the entire genetic material present in a sample is extracted using chemical methods or commercially available kits and sequenced using next-generation platforms to provide a snapshot of what the relative composition looks like. Because the genetic material of higher organisms present in food (e.g., cow in milk or beef, wheat in flour) is around one thousand times larger than the bacterial content, challenges exist in gathering the information of interest. Additionally, specific bacterial characteristics can make them easier or harder to detect, adding another layer of complexity to this issue. In this study, we demonstrate the impact of using different methods in the ability of detecting specific bacteria and highlight the need to ensure that the most appropriate methods are being used for each particular sample.