Applying Molecular Tools for Monitoring Inhibition of Nitrification by Heavy Metals

The biological removal of ammonia in conventional wastewater treatment plants (WWTPs) is performed by promoting nitrification, which transforms ammonia into nitrate, which in turn is converted into nitrogen gas by denitrifying bacteria. The first step in nitrification, the oxidation of ammonia to nitrite by ammonia oxidizing bacteria (AOB), is sensitive to various inorganic contaminants such as heavy metals (e.g., Cu, Zn, Cd) that enter WWTPs via industrial discharges or stormwater runoff. Nitrification inhibition is commonly assessed by specific oxygen uptake rates (sOUR) of bacterial pure cultures (i.e., Nitrosomonas sp) exposed to several metal concentrations. Chemical methods are relatively cumbersome, time consuming, and are not amenable to automation and simultaneous processing of multiple samples. Moreover, respirometry methods alone do not always provide sufficient information for accurate assessment of nitrification rates in wastewater treatment systems and do not help us understand how the composition and physiology of the microbial community affects nitrification pathways. To circumvent these limitations we applied molecular methods to measure the degree of nitrification inhibition in wastewater treatment systems, and to study the bacterial composition and dynamics of a nitrifying bioreactor. Specifically, we extracted RNA and DNA from nitrifying enrichment samples and determined the presence and relative activity of genes coding for ammonia oxidation (amoA), hydroxylamine oxidation-reduction (hao), nitrite reduction (nirK), and nitric oxide reduction (norB) after samples were exposed to copper (Cu(II)) under starving conditions. The presence of all functional genes was confirmed via PCR. Sequencing analysis of clone libraries indicated that amoA clones were most similar to the Nitrosomonas group and that multiple AOB populations (i.e., <97% sequence identity) co-existed in the nitrifying bioreactor.The presence of different nitrifying bacteria was also detected via sequencing of hao and nirK clone libraries. Moreover, 16S rRNA gene next-generation sequencing libraries showed that N. europaea-like populations represented 80-90% of the bacterial community, while other nitrifiers represented <2%. Based on the RT-qPCR data, there was no considerable changes in amoA transcript levels (normalized against 16S rRNA) after batch cultures were exposed for 12 hours to 0.1 to 100 ppm of Cu(II), suggesting that there is no measurable effect of Cu-induced toxicity on the expression of amoA. Similarly, there was no inhibition observed as measured by sOUR assays for Cu(II) exposure, although slight inhibition (<10 %) was observed at 10 ppm. Overall, these results validate the use of molecular methods combined with conventional nitrification inhibition assays to better evaluate the response of wastewater nitrifying systems to the presence of heavy metals.