Capturing Compositional Variation in Denitrifying Communities: a Multiple-Primer Approach That Includes Epsilonproteobacteria
Bibliographic record
Abstract
ABSTRACT Denitrifying Epsilonproteobacteria may dominate nitrogen loss processes in marine habitats with intense redox gradients, but assessment of their importance is limited by the currently available primers for nitrite reductase genes. Nine new primers targeting the nirS gene of denitrifying Epsilonproteobacteria were designed and tested for use in sequencing and quantitative PCR on two microbial mat samples (vent 2 and vent 4) from the Calypso hydrothermal vent field, Bay of Plenty, New Zealand. Commonly used nirS and nirK primer sets nirS1F/nirS6R, cd3aF/R3cd, nirK1F/nirK5R, and F1aCu/R3Cu were also tested to determine what may be missed by the common single-primer approach to assessing denitrifier diversity. The relative importance of Epsilonproteobacteria in these samples was evaluated by 16S rRNA gene sequencing. Epsilonproteobacteria represented up to 75.6% of 16S rRNA libraries, but nirS genes from this group were not found with commonly used primers. Pairing of the new primer EPSnirS511F with either EPSnirS1100R or EPSnirS1105R recovered nirS sequences from members of the genera Sulfurimonas , Sulfurovum , and Nitratifractor. The new quantitative PCR primers EPSnirS103F/EPSnirS530R showed dominance of denitrifying Epsilonproteobacteria in vent 4 compared to vent 2, which had greater representation by “standard” denitrifiers measured with the cd3aF/R3cd primers. Limited results from commonly used nirK primers suggest biased amplification between primers. Future application of multiple nirS and nirK primers, including the new epsilonproteobacterial nirS primers, will improve the detection of denitrifier diversity and the capability to identify changes in dominant denitrifying communities. IMPORTANCE Estimating the potential for increasing nitrogen limitation in the changing global ocean is reliant on understanding the microbial community that removes nitrogen through the process of denitrification. This process is favored under oxygen limitation, which is a growing global-ocean phenomenon. Current methods use the nitrite reductase genes nirS and nirK to assess denitrifier diversity and abundance using primers that target only a few known denitrifiers and systematically exclude denitrifying Epsilonproteobacteria , a group known to dominate in reducing environments, such as hydrothermal vents and anoxic basins. As oxygen depletion expands in the oceans, it is important to study denitrifier community dynamics within those areas to predict future global ocean changes. This study explores the design and testing of new primers that target epsilonproteobacterial nirS and reveals the varied success of existing primers, leading to the recommendation of a multiple-primer approach to assessing denitrifier diversity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".