New Molecular Tools to Detect Nosz and Nrfa Genes in Soils: An Expanded View of Nitrous Oxide and Nitrite Reduction Among Diverse Bacterial Populations.

Denitrification is thought to account for the main biological process mediating the reduction of nitrate to N₂O or N₂ in soil. Dissimilatory nitrite reduction to ammonia (DNRA) provides an alternate pathway and is catalyed by a nitrite reductase encoded by nrfA. Testing the significance of DNRA in soil was hampered previously by use of ineffective PCR primers that targeted only 77% of true nrfA. Conversely, genes facilitating denitrification are well-studied, particularly nosZ, the gene encoding nitrous oxide reductase. Recent whole-genome analysis of nosZ, however, revealed homologs distinct from those of known denitrifers that are overlooked using existing molecular probes and include populations of non-denitrifiers. To improve our ability to detect a broader range of nosZ and address the significance of DNRA in soil, we designed PCR primers that effectively targeted new and diverse nosZ and nrfA genes. In two geomorphically distinct agricultural soils, we obtained clones that clustered into two main phyletic groups of nosZ: “typical” denitrifiers like Pseudomonas stutzeri and at least 27 novel “atypical” ones corresponding to diverse species like Opitutus terrae and the nitrite-ammonifier Anaeromyxobacter dehalogenans. Cloned nrfA sequences (n=95) amplified from both soils showed up to 30% divergence and were most similar to soil bacteria like Anaeromyxobacter spp. Sixteen soil isolates grown under nitrite selection included strains not traditionally studied for N-cycling like Bacillus spp., Ensifer adhaerens, and Pseudogulbenkiania subflava with nosZ and/or nrfA detected. The results show a much higher diversity of nosZ and nrfA in soil than previously known. By adding new previously undescribed nosZ and nrfA sequences to existing databases and providing new strains that can serve as references, application of next generation sequencing technologies and metagenomics-based approaches can be expanded with improved functional gene annotations and identification of more diverse populations involved in N-cycling processes.