Extracellular ribosomal RNA provides a window into taxon-specific microbial lysis
Bibliographic record
Abstract
Abstract Microbes are by far the dominant biomass in the world’s oceans and drive biogeochemical cycles that are critical to life on Earth. The composition of marine microbial communities is highly dynamic spatially and temporally, with consequent effects on their functional roles. In part, these changes in composition result from viral lysis, which is taxon-specific and estimated to account for about half of marine microbial mortality. Here we determined taxon-specific cell lysis of prokaryotes in coastal seawater by sequencing extracellular and cellular ribosomal RNA (rRNA). We detected lysis in about 15% of the 16946 prokaryotic amplicon sequence variants (ASVs) identified, and lysis of up to 34% of the ASVs within a water sample. High lysis was most commonly associated with rare but typically highly productive bacteria, while relatively low lysis was more common in taxa that are often abundant, consistent with the proposed model of “kill the winner”, and the idea that less abundant taxa generally experience higher relative lysis than dominant taxa. These results provide an explanation to the long-standing conundrum of why highly productive bacteria that are readily isolated from seawater are often in very low abundance. One Sentence Summary Extracellular rRNA shows wide variation in cell lysis among prokaryotic taxa.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".