Temporal metagenomic characterization of microbial community structure and nitrogen modification genes within an activated sludge bioreactor system
Bibliographic record
Abstract
ABSTRACT The biological removal of nitrogen using natural microbial metabolic processes can be a valuable component of wastewater treatment that helps reduce downstream eutrophication of receiving water ecosystems. Biological nutrient removal (BNR) is a well-established component of wastewater treatment due to its recognized environmental benefits. The composition and diversity of these microbial communities are an important consideration, as disruptions to or instability in the microbial community can negatively impact N cycling and reduce treatment efficiency. To characterize the bacterial community and associated nitrogen cycling genes within a cold-acclimated BNR facility, metagenomic sequencing combined with a read-based quantification strategy and metagenomic assembled genome (MAG) generation was used on samples collected from a Canadian prairie wastewater treatment plant. Generally, this system had a high abundance of Proteobacteria and Actinobacteria throughout the year, including the genera Thiomonas, Tetrasphaera, Afipia, and Hyphomicrobium . Communities remained stable throughout the different bioreactors in this system, while diversity varied between sampling months, demonstrating seasonal effects on the population dynamics. Genes involved in the denitrification pathway were abundant and distributed widely across different MAGs, while genes involved in nitrification were absent. Additionally, these genes remained stable across all sampling months, suggesting that the efficacy and robustness of this system rely on more than the taxonomic composition of the microbial community. IMPORTANCE Wastewater treatment plays an essential role in minimizing negative impacts on downstream aquatic environments. Microbial communities are known to play a vital role in the wastewater treatment process, particularly in the removal of nitrogen and phosphorus, which can be especially damaging to aquatic ecosystems. There is limited understanding of how these microbial communities may change in response to fluctuating temperatures or how seasonality may impact their ability to participate in the treatment process. The findings of this study indicate that the microbial communities of wastewater are relatively stable both compositionally and functionally across fluctuating temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".