Assessing phage-host population dynamics by reintroducing virulent viruses to synthetic microbiomes
Bibliographic record
Abstract
Microbiomes feature complex interactions between diverse bacteria and bacteriophages. Synthetic microbiomes offer a powerful way to study these interactions; however, a major challenge is obtaining a representative bacteriophage population during the bacterial isolation process. We demonstrate that colony isolation reliably excludes virulent viruses from sample sources with low virion-to-bacteria ratios such as feces, creating "virulent virus-free" controls. When the virulent dsDNA virome is reintroduced to a 73-strain synthetic gut microbiome in a bioreactor model of the human colon, virulent viruses target susceptible strains without significantly altering community structure or metabolism. In addition, we detected signals of prophage induction that associate with virulent predation. Overall, our findings indicate that dilution-based isolation methods generate synthetic gut microbiomes that are heavily depleted, if not devoid, of virulent viruses and that such viruses, if reintroduced, have a targeted effect on community assembly, metabolism, and prophage replication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".