CRISPR-resolved virus-host interactions in a municipal landfill include non-specific viruses, hyper-targeted viral populations, and interviral conflicts
Bibliographic record
Abstract
Viruses are the most abundant microbial guild on the planet, impacting microbial community structure and ecosystem services. Viruses are specifically understudied in engineered environments, including examinations of their host interactions. We examined host-virus interactions via host CRISPR spacer to viral protospacer mapping in a municipal landfill across two years. Viruses comprised ~ 4% of both the unassembled reads and assembled basepairs. A total of 458 unique virus-host connections captured hyper-targeted viral populations and host CRISPR array adaptation over time. Four viruses were predicted to infect across multiple phyla, suggesting that some viruses are far less host-specific than is currently understood. We detected 161 viral elements that encode CRISPR arrays, including one with 187 spacers, the longest virally-encoded CRISPR array described to date. Virally-encoded CRISPR arrays targeted other viral elements in interviral conflicts. CRISPR-encoding proviruses integrated into host chromosomes were latent examples of CRISPR-immunity-based superinfection exclusion. The bulk of the observed virus-host interactions fit the one-virus-one-host paradigm, but with limited geographic specificity. Our networks highlight rare and previously undescribed complex interactions influencing the ecology of this dynamic engineered system. Our observations indicate landfills, as heterogeneous contaminated sites with unique selective pressures, are key locations for atypical virus-host dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".