The Environment and Cyanophage Diversity: Insights From Environmental Sequencing of DNA Polymerase
Bibliographic record
Abstract
Globally distributed and abundant cyanophages in the family Myoviridae have dsDNA genomes with variable gene content, including host-derived auxiliary metabolic genes (AMGs) that potentially can facilitate viral replication. However, it is not well understood how this variation in gene content interacts with environmental variables to shape cyanomyovirus communities. This project correlated the genetic repertoire of cyanomyoviruses with their phyologeny, and investigated cyanomyovirus ecotype distribution as a function of environmental conditions across locations and seasons. Reference cyanomyovirus genomes were compared for their overlap in gene content to infer phyologenetic distances, and these distances were compared to distances calculated based on DNA polymerase (gp43) gene sequences. In turn, gp43 partial gene sequences amplified from natural cyanophage communities were used to describe cyanomyovirus community composition and to assess the relationship between environmental variables. The results showed the following: (1) DNA polymerase gene phylogeny generally correlated with the similarity in gene content among reference cyanomyoviruses, and thus can be used to describe environmental cyanomyovirus communities; (2) spatial and seasonal patterns in cyanomyovirus communities were related to environmental variables; (3) salinity and temperature, combined with nutrient concentration were predictors of cyanomyovirus richness, diversity and community composition. This study shows that environmental variables shape viral communities by drawing on a diverse seed bank of viral genotypes. From these results it is evident that that viral ecotypes with their corresponding genetic repertoires underlie selection pressures. However, the mechanisms involved in selecting for specific viral genotypes remain to be fully understood.
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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 | 0.000 | 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) | 0.000 | 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".