Nitrifying and Denitrifying Bacterial Communities in Advanced Nitrogen‐Removal Onsite Wastewater Treatment Systems

Abstract

Advanced N‐removal onsite wastewater treatment systems (OWTS) rely on nitrification and denitrification to remove N from wastewater. Despite their use to reduce N contamination, we know little about microbial communities controlling N removal in these systems. We used quantitative polymerase chain reaction and high‐throughput sequencing targeting nitrous oxide reductase (nosZ) and bacterial ammonia monooxygenase (amoA) to determine the size, structure, and composition of communities containing these genes. We analyzed water samples from three advanced N‐removal technologies in 38 systems in five towns in Rhode Island in August 2016, and in nine systems from one town in June, August, and October 2016. Abundance of nosZ ranged from 9.1 × 103 to 9 × 108 copies L−1 and differed among technologies and over time, whereas bacterial amoA abundance ranged from 0 to 1.9 × 107 copies L−1 and was not different among technologies or over time. Richness and diversity of nosZ—but not amoA—differed over time, with median Shannon diversity indices ranging from 2.61 in October to 4.53 in August. We observed weak community similarity patterns driven by geography and technology in nosZ. The most abundant nosZ‐ and amoA‐containing bacteria were associated with water distribution and municipal wastewater treatment plants, such as Nitrosomonas and Thauera species. Our results show that nosZ communities in N‐removal OWTS technologies differ slightly in terms of size and diversity as a function of time, but not geography, whereas amoA communities are similar across time, technology, and geography. Furthermore, community composition appears to be stable across technologies, geography, and time for amoA. Core Ideas We describe N‐cycling bacterial communities in advanced N‐removal septic systems. Geographic factors are weak drivers of community composition in nosZ. Technology design is a weak driver of community composition in nosZ. Community composition of amoA is similar across time, space, and among technologies Time drives differences in nosZ—but not amoA—diversity and abundance.

Influence of Season, Occupancy Pattern, and Technology on Structure and Composition of Nitrifying and Denitrifying Bacterial Communities in Advanced Nitrogen-Removal Onsite Wastewater Treatment Systems

“…Genomic DNA was extracted from each membrane filter using the PowerWater DNA Isolation Kit (MoBio Laboratories, Waltham, MA, USA). Gene fragments for nosZ and amoA were amplified using polymerase chain reaction (PCR) in a single, 50 µL reaction using the primer pairs nosZ 1F and nosZ 1662R, and amoA 1F and amoA 862R [12,17]. Reactions for nosZ amplification contained 1.25 µL of extracted DNA template, 25 µL of BIO-X-ACT TM Short Mix (Bioline, Taunton, MA, USA), 21.75 µL of water, and 1 µL of nosZ forward and reverse primers.…”

“…Thermocycler conditions for amoA amplification were as follows: 4 min at 94 • C, 35 amplification cycles (each 60 s at 94 • C, 60 s annealing at 58 • C, and 60 s 72 • C), and a final extension at 72 • C for 5 min. After quality checking the PCR products on an ethidium bromide agarose gel, products were sequenced using an Illumina MiSeq Next Generation Sequencer at the University of Rhode Island Genetic Sequencing Center (Kingston, RI, USA) [17]. Additional details of DNA extraction, PCR methods, and sample metadata can be found in Appendix A.…”

“…We used QIIME 2 (version 2019.10) for downstream sequence analysis, as outlined in Cox et al [17]. After demultiplexing and quality filtering the raw forward and reverse reads using the q2-demux plugin, we identified and removed the primer sequences for nosZ and amoA, so that the individual gene amplicons could be analyzed.…”

“…Differences in beta diversity as a function of technology, occupancy pattern, and season were determined based on Bray-Curtis dissimilarity values and cluster analysis using Ward's cluster method with the vegan package in R [20]. Significant clustering, as a function of technology, occupancy pattern, and/or season, was identified using nonparametric permutational multivariate ANOVAs (PERMANOVAs) on Bray-Curtis distance matrices using the Adonis function (with 999 permutations for each correlation) [17,19,20].…”

“…By narrowing the geographic range of the study systems, we expected to improve our ability to detect differences as a function of technology, use pattern, and season that may be overshadowed by geography, which Wigginton et al [17] found to partially drive community differences. The sites studied by Wigginton et al [17] were located in six towns within the Greater Narragansett Bay watershed, covering an area of 855 km 2 , nearly six-fold larger than Charlestown, which has an area of 154 km 2 .…”