Hypoosmolarity inhibits archaeal ammonia oxidation
Bibliographic record
Abstract
Abstract Salinity strongly influences the physiology and distribution of nitrifying microorganisms, yet the effects of low salinity on them remain understudied. This study investigates the impact of hypoosmolarity on different groups of ammonia oxidizers in soil and lake environments, as well as in pure culture isolates. In soil microcosms amended with ammonium, at low salinity levels (∼120 μS/cm), comparable to values commonly found in pristine terrestrial and aquatic environments, the abundance of ammonia-oxidizing bacteria (AOB), dominated by Nitrosomonas oligotropha , significantly increased. In contrast, the growth of ammonia-oxidizing archaea (AOA), dominated by “ Ca. Nitrosotenuis” of the Nitrosopumilaceae family, was stimulated by high salinity (∼760 μS/cm). In ammonium-fed lake microcosms, the abundance of AOB, dominated by N. oligotropha, significantly increased under both low (∼170 μS/cm) and high salinity (∼850 μS/cm) conditions. In the presence of allylthiourea, a bacterial nitrification inhibitor, AOA were sensitive to low salinity in both soil and lake microcosms. Consistently, pure culture studies revealed marked growth inhibition of AOA, especially members of Nitrosopumilaceae , under hypoosmolarity, unlike AOB and complete ammonia oxidizer (comammox) strains. Comparative genomic analyses with AOB and comammox, along with transcriptomic studies, suggested that the sensitivity of AOA to hypoosmolarity stress was possibly due to a lack of sophisticated osmoregulatory transport systems and their S-layer cell wall structure. Overall, this study highlights hypoosmolarity as a key factor shaping the ecological niches and distribution of ammonia oxidizers, as well as nitrification activities, in terrestrial and aquatic environments that are increasingly affected by intensified water cycles due to climate change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".