DNA ‐ and RNA ‐based microbial community diversity and composition in a deep agricultural subsurface environment

Abstract Analysis of microbial community compositions in subsurface environments is essential because microbes significantly influence biogeochemical processes, including carbon storage, nutrient cycling, and groundwater quality. Many previous studies have relied on DNA‐based methods, which provide a robust taxonomic identification but can also overestimate microbial diversity and activity by including dormant or dead cells. Here, we demonstrate how combining DNA‐ and RNA‐based profiling provides a more ecologically meaningful understanding of active microbial processes in a deep aquifer impacted by long‐term agricultural activity. This study applied an RNA‐based approach (16S rRNA gene transcript‐based amplicon sequencing) along with DNA‐based profiling to identify metabolically active microbial communities across a 41‐m vertical subsurface profile from an agricultural field, divided into surface, unsaturated, groundwater‐fluctuated, and saturated zones. Microbial diversity and composition differed markedly along vertical zonation in both RNA‐ and DNA‐based analyses; however, RNA‐based alpha‐diversity indices were consistently lower (38%–74%) than DNA‐based analyses. While γ‐proteobacteria dominated DNA‐based results, their reduced presence in RNA‐based analyses indicated that fewer active cells within γ‐proteobacteria were involved at deeper zones. Conversely, RNA‐based analyses revealed higher relative abundance of active cells affiliated with α‐proteobacteria, Actinobacteriota, and Bacteroidota in the saturated zone, consistent with their potential role in denitrification under anoxic conditions. Our results emphasize that integrating RNA‐ and DNA‐based approaches, when interpreted in conjunction with geochemical data, is crucial to accurately characterizing microbial contributions to subsurface biogeochemical processes in environments influenced by anthropogenic activities.