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The Diversity of Nitrogen-Cycling Microbial Genes in a Waste Stabilization Pond Reveals Changes over Space and Time that Is Uncoupled to Changing Nitrogen Chemistry.

ABSTRACT: Nitrogen removal is an important process for wastewater ponds prior to effluent release. Bacteria and archaea can drive nitrogen removal if they possess the genes required to metabolize nitrogen. In the tropical savanna of northern Australia, we identified the previously unresolved microbial communities responsible for nitrogen cycling in a multi-pond wastewater stabilization system by measuring genomic DNA and cDNA for the following: nifH (nitrogen fixation); nosZ (denitrification); hzsA (anammox); archaeal AamoA and bacterial BamoA (ammonia oxidation); nxrB (nitrite oxidation); and nrfA (dissimilatory NO₃ reduction to NH₃). By collecting 160 DNA and 40 cDNA wastewater samples and measuring nitrogen (N)-cycling genes using a functional gene array, we found that genes from all steps of the N cycle were present and, except for nxrB, were also expressed. As expected, N-cycling communities showed daily, seasonal, and yearly shifts. However, contrary to our prediction, probes from most functional groups, excluding nosZ and AamoA, were different between ponds. Further, different genes that perform the same N-cycling role sometimes had different trends over space and time, resulting in only weak correlations between the different functional communities. Although N-cycling communities were correlated with wastewater nitrogen levels and physico-chemistry, the relationship was not strong enough to reliably predict the presence or diversity of N-cycling microbes. The complex and dynamic response of these genes to other functional groups and the changing physico-chemical environment provides insight into why altering wastewater pond conditions can result an abundance of some gene variants while others are lost.

SUBMITTER: Rose A

PROVIDER: S-EPMC8062326 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAssessments of the soil microbiome provide valuable insight to ecosystem function due to the integral role microorganisms play in biogeochemical cycling of carbon and nutrients. For example, treatment effects on nitrogen cycling functional groups are often presented alongside one another to demonstrate how agricultural management practices affect various nitrogen cycling processes. However, the functional groups commonly evaluated in nitrogen cycling microbiome studies range from phylogenetically narrow (e.g., N-fixation, nitrification) to broad [e.g., denitrification, dissimilatory nitrate reduction to ammonium (DNRA)]. The bioinformatics methods used in such studies were developed for 16S rRNA gene sequence data, and how these tools perform across functional genes of different phylogenetic diversity has not been established. For example, an OTU clustering method that can accurately characterize sequences harboring comparatively little diversity may not accurately resolve the diversity within a gene comprised of a large number of clades. This study uses two nitrogen cycling genes, nifH, a gene which segregates into only three distinct clades, and nrfA, a gene which is comprised of at least eighteen clades, to investigate differences which may arise when using heuristic OTU clustering (abundance-based greedy clustering, AGC) vs. true hierarchical OTU clustering (Matthews Correlation Coefficient optimizing algorithm, Opti-MCC). Detection of treatment differences for each gene were evaluated to demonstrate how conclusions drawn from a given dataset may differ depending on clustering method used.ResultsThe heuristic and hierarchical methods performed comparably for the more conserved gene, nifH. The hierarchical method outperformed the heuristic method for the more diverse gene, nrfA; this included both the ability to detect treatment differences using PERMANOVA, as well as higher resolution in taxonomic classification. The difference in performance between the two methods may be traced to the AGC method's preferential assignment of sequences to the most abundant OTUs: when analysis was limited to only the largest 100 OTUs, results from the AGC-assembled OTU table more closely resembled those of the Opti-MCC OTU table. Additionally, both AGC and Opti-MCC OTU tables detected comparable treatment differences using the rank-based ANOSIM test. This demonstrates that treatment differences were preserved using both clustering methods but were structured differently within the OTU tables produced using each method.ConclusionFor questions which can be answered using tests agnostic to clustering method (e.g., ANOSIM), or for genes of relatively low phylogenetic diversity (e.g., nifH), most upstream processing methods should lead to similar conclusions from downstream analyses. For studies involving more diverse genes, however, care should be exercised to choose methods that ensure accurate clustering for all genes. This will mitigate the risk of introducing Type II errors by allowing for detection of comparable treatment differences for all genes assessed, rather than disproportionately detecting treatment differences in only low-diversity genes.

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The Diversity of Nitrogen-Cycling Microbial Genes in a Waste Stabilization Pond Reveals Changes over Space and Time that Is Uncoupled to Changing Nitrogen Chemistry.

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