Comparative metataxonomic analysis of total, viable, and active rumen microbial communities under methane-mitigated conditions.

BACKGROUND

The rumen microbiota play a central role in digestion and methane production, thereby influencing ruminant productivity. Evaluating methane mitigation strategies requires consideration of both microbial viability and activity. DNA-based methods may include signals from dead or damaged cells, whereas RNA-based approaches may underrepresent viable populations with low transcriptional activity. Propidium monoazide (PMA) reduces DNA signals from membrane-compromised cells, providing an indirect, membrane-integrity-based assessment of microbial viability. This study compared taxonomic and predicted functional profiles obtained by 16S rRNA gene amplicon sequencing of total DNA (tDNA), PMA-treated DNA (vDNA), and RNA-derived cDNA (RNA) from rumen samples subjected to three in vitro methane mitigation treatments: 3-nitrooxypropanol (NOP), monensin (MON), and Asparagopsis sp. (ASP).

RESULTS

MON treatment induced the most pronounced changes in fermentation parameters and microbial composition, whereas the NOP and ASP groups exhibited similar fermentation outcomes. RNA-derived profiles exhibited the lowest alpha diversity, with vDNA profiles falling between those of RNA and tDNA. Procrustes analysis revealed metric-dependent similarities among sample types; Bray-Curtis distances showed closer alignment between tDNA and RNA, whereas Jaccard distances showed greater similarity between vDNA and RNA. Parabacteroides exhibited significantly higher relative abundance in vDNA than in tDNA or RNA. Notably, Succiniclasticum showed opposing correlations with the acetate-to-propionate ratio between tDNA and RNA under MON treatment, whereas no significant correlation was detected in vDNA. Differential analysis of PICRUSt2-predicted methanogenesis-related KEGG modules revealed sample type- and treatment-specific patterns.

CONCLUSIONS

PMA-treated DNA provided a distinct perspective on the rumen microbiota by reducing signals from membrane-compromised cells and revealing populations that were differently represented in tDNA and RNA-derived profiles, including membrane-intact populations with low RNA representation. Thus, vDNA may serve as a practical additional approach when RNA analysis is technically constrained.