Combined <scp>DNA</scp> / <scp>RNA</scp> Amplicon Sequencing and Metatranscriptomics Reveals Microbial‐Driven Nutrient Transformations and Core Taxa in Agriculturally Impacted Sediments
Bibliographic record
Abstract
Chronic non-point nitrogen (N) and phosphorus (P) loads reshape sediment microbial biogeochemical cycling in headwater systems, altering ecosystem function. This study integrates DNA and RNA amplicon sequencing with metatranscriptomics to examine microbial taxonomic and functional responses to nutrient inputs in lower Great Lakes watersheds, focusing on N, P, and sulphur (S) metabolism. RNA-based taxa showed a stronger correlation with metabolic functions than DNA-based taxa, highlighting RNA-based approaches as valuable tools for assessing active microbial responses to nutrients. Site-specific analyses revealed distinct microbial metabolic profiles linked to watershed fertiliser sources and seasonal variation. Inorganic fertiliser inputs were associated with tightly coupled N reduction and sulphur oxidation, driven by differential expression of dissimilatory nitrate reduction to ammonia (DNRA) and sox genes. In contrast, a manure-amended site exhibited elevated nitrosative stress and sulphur assimilation pathways, consistent with detection of ammonia-oxidising genera. The low-impact reference site demonstrated intermediate functional turnover, enhanced nitrogen fixation, and the highest microbial diversity, suggesting greater ecosystem resilience. Seasonally, functional turnover increased in fall, with fewer shared core taxa across sites compared to summer. These findings highlight the impact of chronic nutrient enrichment on site-specific microbial adaptations and underscore the importance of temporal dynamics in assessing freshwater sediment microbial communities.
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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) | 0.000 | 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) | 0.001 | 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".