Impact of rumen microbiome sample handling and extraction procedures on downstream long-read sequencing

The rumen microbiome is a complex microbial ecosystem central to ruminant nutrition and productivity. Long-read sequencing technologies, such as Oxford Nanopore, have greatly improved resolution of strain-level diversity and enabled assembly of near-complete genomes, but these benefits depend on the integrity and purity of input DNA, which can be affected by sample preservation and extraction methods. This study evaluated multiple DNA extraction protocols to best maximize high–molecular-weight DNA recovery for long-read sequencing and whether flash freezing or freezing and thawing samples biased read length, microbial composition, or the recovery of different taxa. We also used simulated datasets to assess how read length influences taxonomic classification accuracy. Rumen fluid samples of equal volume from four lactating Holstein cows were preserved using two different methods: 1. Pelleting 1 ml of rumen fluid then flash freezing; and 2. bulk freezing 15ml aliquots, thawing, then pelleting 1 ml of rumen fluid. DNA was extracted using four modified Zymo Quick-DNA HMW MagBead protocols (0BB, 3BB, DDZ, PREC) incorporating different lysis and purification strategies, then sequenced on Oxford Nanopore PromethION flow cells. Read length, yield, and microbial composition were analyzed using nonparametric statistics, while synthetic long-read datasets (400–7600 bp) generated with Nano Sim evaluated classification accuracy with Kaiju. The precipitation method produced the highest DNA yields and maintained fragment integrity, whereas bead-based methods yielded less DNA due to high–molecular-weight DNA–selective binding. Preservation of DNA quality was identified in FF over FT samples with longer reads and higher archaeal and Gram-positive abundances. Read lengths were reduced in FT samples for both Gram-positive and Gram-negative taxa, with Gram-negative bacteria showing the greatest loss. Simulations confirmed that longer reads improve taxonomic classification, underscoring the need to optimize upstream protocols for high-resolution rumen microbiome profiling.