Optimizing tissue lysis and DNA extraction protocols to enhance bacterial diversity profiling in the Drosophila melanogaster gut microbiome

Abstract The gut microbiota is a dynamic community that influences host metabolism, immunity, and overall health. Accurate characterization of this community requires robust and reproducible DNA extraction methods; however, technical biases introduced during tissue lysis and DNA isolation remain major challenges in microbiome research, particularly in animal model systems. In this study, we compared two lysis methods (manual pestle homogenization and bead-beating) and two commercial DNA extraction kits (Qiagen and Zymo) to evaluate their impact on microbiota profiling in a microbial community standard (MCS) and Drosophila melanogaster gut samples, a tractable model for host–microbe interactions. Full-length 16S rRNA sequencing was performed using Oxford Nanopore Technologies (ONT), followed by taxonomic classification with EPI2ME and downstream diversity analyses using standard pipelines implemented through a custom script. Our data revealed that extraction and lysis methods influence microbial composition, with some protocols resulting in additional species-level assignments in MCS samples, likely reflecting limitations of sequencing technology and taxonomic misidentifications. Pestle homogenization, combined with the Qiagen kit, recovered the highest number of detected taxa and showed a more balanced representation of both Gram-positive and Gram-negative bacteria across the Drosophila gut samples. These findings highlight that extraction methodology influences microbial diversity estimates in Drosophila gut samples, although sequencing approach and taxonomic classification accuracy may also contribute to the observed differences. Our results emphasize that the choice of DNA extraction protocol should be guided by the biological question being addressed and highlight the need for standardized protocols to ensure reproducibility across microbiome studies, particularly those using model systems.