Bacteria defend against phages through type IV pilus glycosylation
Bibliographic record
Abstract
ABSTRACT Bacterial surface structures such as type IV pili are common receptors for phage. Strains of the opportunistic pathogen Pseudomonas aeruginosa express one of five different major type IV pilin alleles, two of which are glycosylated with either lipopolysaccharide O-antigen units or polymers of D-arabinofuranose. Here we show that both these post-translational modifications protect P. aeruginosa from a variety of pilus-specific phages. We identified a phage capable of infecting strains expressing both non-glycosylated and glycosylated pilins, and through construction of a chimeric phage, traced this ability to its unique tail proteins. Alteration of pilin sequence, or masking of binding sites by glycosylation, both block phage infection. The energy invested by prokaryotes in glycosylating thousands of pilin subunits is thus explained by the protection against phage predation provided by these common decorations. SIGNIFICANCE Post-translational modification of bacterial and archaeal surface structures such as pili and flagella is widespread, but the function of these decorations is not clear. We propose that predation by bacteriophages that use these structures as receptors selects for strains that mask potential phage binding sites using glycosylation. Phages are of significant interest as alternative treatments for antibiotic-resistant pathogens, but the ways in which phage interact with host receptors are not well understood. We show that specific phage tail proteins allow for infection of strains with glycosylated pili, providing a foundation for the creation of designer phages that can circumvent first-line bacterial defenses.
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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".