bioRxiv · 10.64898/2026.08.25.746930
Activity-resolved microbial community profiling using rpoB gene and transcript sequencing
Abstract
Determining which members of a microbial community are metabolically active remains a central challenge in microbial ecology. Although the 16S rRNA gene is the dominant marker for bacterial community profiling, it cannot reliably distinguish active cells from dormant or dead populations. As a result, complementary phylogenetic markers whose transcript abundance more closely reflects cellular activity are needed. Here, we systematically evaluated 80 Bacterial protein-coding marker genes and identified rpoB, encoding the beta subunit of bacterial RNA polymerase, as the optimal candidate. We designed a new primer pair (1528F 2041R) from a curated database of 305,274 unique rpoB sequences and validated it for quantitative PCR and amplicon sequencing of DNA and RNA templates. The rpoB qPCR assay achieved a limit of quantification two orders of magnitude lower than the benchmark 16S rRNA assay, for which a limit of detection could not be determined because of no-template-control amplification. In soil and sediment communities, rpoB recovered community composition comparable to 16S rRNA while providing a quantitative activity signal: rpoB cDNA:DNA ratios correlated significantly with taxon-level transcript abundance (R squared between 0.22 and 0.29, p < 0.001), whereas 16S rRNA ratios did not (p > 0.5). In a biological activated carbon biofilter experiment, rpoB transcript abundance tracked the decline in dissolved organic carbon removal rates across a 72 hour time series (correlation coefficients between 0.84 and 0.99), whereas 16S rRNA transcripts were uninformative (correlation coefficients between -0.4 and 0.98). These results establish rpoB as a quantitatively robust, activity-responsive complement to 16S rRNA for linking community composition to ecosystem processes.
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Cholet, F., Sloan, W., Smith, C. J.. 2026-08-25. Activity-resolved microbial community profiling using rpoB gene and transcript sequencing. https://doi.org/10.64898/2026.08.25.746930
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