Newly identified proviruses in Thermotogota suggest that viruses are the vehicles on the highways of interphylum gene sharing
Bibliographic record
Abstract
Phylogenomic analyses of bacteria from the phylum Thermotogota have shown extensive lateral gene transfer with distantly related organisms, particularly with Firmicutes. One likely mechanism of such DNA transfer is viruses. However, to date, only three temperate viruses have been characterized in this phylum, all infecting bacteria from the Marinitoga genus. Here we report 17 proviruses integrated into genomes of bacteria belonging to eight Thermotogota genera and induce viral particle production from one of the proviruses. All except an incomplete provirus from Mesotoga fall into two groups based on sequence similarity, gene synteny and taxonomic classification. Proviruses of Group 1 are found in the genera Geotoga, Kosmotoga, Marinitoga, Thermosipho and Mesoaciditoga and are similar to the previously characterized Marinitoga viruses, while proviruses from Group 2 are distantly related to the Group 1 proviruses, have different genome organization and are found in Petrotoga and Defluviitoga. Genes carried by both groups are closely related to Firmicutes and Firmicutes (pro)viruses in phylogenetic analyses. Moreover, one of the groups show evidence of recent gene exchange and may be capable of infecting cells from both phyla. We hypothesize that viruses are responsible for a large portion of the observed gene flow between Firmicutes and Thermotogota.
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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".