Evidence of a robust resident bacteriophage population revealed through analysis of the human salivary virome
Bibliographic record
Abstract
Viruses are the most abundant known infectious agents on the planet and are significant drivers of diversity in a variety of ecosystems. Although there have been numerous studies of viral communities, few have focused on viruses within the indigenous human microbiota. We analyzed 2 267 695 virome reads from viral particles and compared them with 263 516 bacterial 16S rRNA gene sequences from the saliva of five healthy human subjects over a 2- to 3-month period, in order to improve our understanding of the role viruses have in the complex oral ecosystem. Our data reveal viral communities in human saliva dominated by bacteriophages whose constituents are temporally distinct. The preponderance of shared homologs between the salivary viral communities in two unrelated subjects in the same household suggests that environmental factors are determinants of community membership. When comparing salivary viromes to those from human stool and the respiratory tract, each group was distinct, further indicating that habitat is of substantial importance in shaping human viromes. Compared with coexisting bacteria, there was concordance among certain predicted host-virus pairings such as Veillonella and Streptococcus, whereas there was discordance among others such as Actinomyces. We identified 122 728 virulence factor homologs, suggesting that salivary viruses may serve as reservoirs for pathogenic gene function in the oral environment. That the vast majority of human oral viruses are bacteriophages whose putative gene function signifies some have a prominent role in lysogeny, suggests these viruses may have an important role in helping shape the microbial diversity in the human oral cavity.
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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 | 0.000 |
| 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 | 0.000 |
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".