Selective cell lysis pressure on rare and abundant prokaryotic taxa across a shelf-to-slope continuum in the Northern South China Sea
Bibliographic record
Abstract
ABSTRACT Microbial growth and mortality are fundamental to community assembly and drive the elemental biogeochemical cycles in the ocean. Virus-induced host lysis contributes, on average, half of prokaryotic mortality and has a substantial effect on composition and diversity of marine microbes. Nevertheless, virus-mediated taxon-specific cell lysis is few studied to date. In the present study, we investigated the taxon-specific cell lysis and estimated its contribution to the variations of community composition in rare and abundant microbial taxa. The dominant taxa Prochlorococcus , Synechococcus , SAR11, and Rhodobacteraceae displayed lower cell lysis index (CLI, the rate of extracellular to intracellular rRNA) in surface seawater. Meanwhile, Alteromonas , Pseudomonas, and Halomonas had high CLI values in the bottom seawater. Cell lysis contributed a larger percentage of variation in rare taxa (5.0%–9.4%) than in abundant taxa (0.1%–1.7%). Furthermore, linear regression analysis indicated that rare taxa were more likely to experience higher viral lysis pressure relative to abundant taxa. Our findings provide insight into the impact of virus-mediated cell lysis on prokaryotic community structure and diversity and further improve our understanding of the various abiotic and biotic factors contributing to community assembly in the ocean. IMPORTANCE Virus-induced host lysis contributes up to 40% of total prokaryotic mortality and plays crucial roles in shaping microbial composition and diversity in the ocean. Nonetheless, what taxon-specific cell lysis is caused by viruses remains to be studied. The present study, therefore, examined the taxon-specific cell lysis and estimated its contribution to the variations in the rare and abundant microbial taxa. The results demonstrate that taxon-specific mortality differed in surface and bottom of the coastal environment. In addition, active rare taxa are more susceptible to heightened lytic pressure and suggested the importance of viral lysis in regulating the microbial community composition. These results improve our understanding of bottom-up (abiotic environmental variables) and top-down (viral lysis) controls contributing to microbial community assembly in the ocean.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".