Mortality by ribosomal sequencing (MoRS) provides a window into taxon-specific cell 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 show that extracellular ribosomal RNA (rRNAext) is produced by viral lysis, and that specific lysed populations can be identified by sequencing rRNAext recovered from seawater samples. In ten seawater samples collected at five depths between the surface and 265 m during and following a phytoplankton bloom, lysis was detected in about 15% of 16,946 prokaryotic taxa, identified from amplicon sequence variants (ASVs), with lysis occurring in up to 34% of taxa within a water sample. The ratio of rRNAext to cellular rRNA (rRNAcell) was used as an index of taxon-specific lysis, and revealed that higher relative lysis was most commonly associated with copiotrophic bacteria that were in relatively low abundance, such as those in the genera Escherichia and Shigella spp., as well as members of the Bacteriodetes; whereas, relatively low lysis was more common in taxa that are often relatively abundant, such as members of the Pelagibacterales (i.e., SAR11 clade), cyanobacteria in the genus Synechococcus, and members of the phylum Thaumarchaeota (synonym, Nitrososphaerota) that comprised about 13–15% of the 16 S rRNA gene sequences below 30 m. These results provide an explanation for the long-standing conundrum of why highly productive bacteria that are readily isolated from seawater are often in very low abundance. The ability to estimate taxon-specific cell lysis will help explore the distribution and abundance of microbial populations in nature.
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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 | 0.001 |
| 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 | 0.001 |
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".