Microbial community structure and a core microbiome in biological rapid sand filters at Danish waterworks

Rapid sand filtration is a traditional and common technology for drinking water purification from groundwater. Despite its wide scale and long-term use, the diversity and characterization of microbial communities in these engineered systems have remained unexplored and their roles in removal performances yet to be discovered. In order to explore the microbial ecology of these systems, we conducted 16S rRNA gene (rDNA) based 454 pyrosequencing as a deep sequencing approach to 94 sample cores retrieved from 5 different waterworks including proper biological replication. This comprehensive sampling of replicate rapid sand filters across many waterworks together with high-throughput sequencing provides a first glimpse into the microbial communities in rapid sand filters and their potential roles in the treatment process

Underestimation of ammonia-oxidizing bacteria abundance by amplification bias in amoA-targeted qPCR

Molecular methods to investigate functional groups in microbial communities rely on the specificity and selectivity of the primer set towards the target. Here, using rapid sand filters for drinking water production as model environment, we investigated the consistency of two commonly used quantitative PCR methods to enumerate ammonia‐oxidizing bacteria (AOB): one targeting the phylogenetic gene 16S rRNA and the other, the functional gene amoA. Cloning‐sequencing with both primer sets on DNA from two waterworks revealed contrasting images of AOB diversity. The amoA‐based approach preferentially recovered sequences belonging to Nitrosomonas Cluster 7 over Cluster 6A ones, while the 16S rRNA one yielded more diverse sequences belonging to three AOB clusters, but also a few non‐AOB sequences, suggesting broader, but partly unspecific, primer coverage. This was confirmed by an in silico coverage analysis against sequences of AOB (both isolates and high‐quality environmental sequences). The difference in primer coverage significantly impacted the estimation of AOB abundance at the waterworks with high Cluster 6A prevalence, with estimates up to 50‐fold smaller for amoA than for 16S rRNA. In contrast, both approaches performed very similarly at waterworks with high Cluster 7 prevalence. Our results highlight that caution is warranted when comparing AOB abundances obtained using different qPCR primer sets