Chemostat culture systems support diverse bacteriophage communities from human feces
Bibliographic record
Abstract
BACKGROUND: Most human microbiota studies focus on bacteria inhabiting body surfaces, but these surfaces also are home to large populations of viruses. Many are bacteriophages, and their role in driving bacterial diversity is difficult to decipher without the use of in vitro ecosystems that can reproduce human microbial communities. RESULTS: We used chemostat culture systems known to harbor diverse fecal bacteria to decipher whether these cultures also are home to phage communities. We found that there are vast viral communities inhabiting these ecosystems, with estimated concentrations similar to those found in human feces. The viral communities are composed entirely of bacteriophages and likely contain both temperate and lytic phages based on their similarities to other known phages. We examined the cultured phage communities at five separate time points over 24 days and found that they were highly individual-specific, suggesting that much of the subject-specificity found in human viromes also is captured by this culture-based system. A high proportion of the community membership is conserved over time, but the cultured communities maintain more similarity with other intra-subject cultures than they do to human feces. In four of the five subjects, estimated viral diversity between fecal and cultured communities was highly similar. CONCLUSIONS: Because the diversity of phages in these cultured fecal communities have similarities to those found in humans, we believe these communities can serve as valuable ecosystems to help uncover the role of phages in human microbial communities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".