Oscillating bacterial expression states generate herd immunity to viral infection
Bibliographic record
Abstract
Abstract Hypermutable loci are widespread in bacteria as mechanisms for rapid generation of phenotypic diversity, enabling individual populations to survive fluctuating, often antagonistic, selection pressures. As observed for adaptive immunity, hypermutation may facilitate survival of multiple, spatially-separated bacterial populations. We developed an ‘oscillating prey assay’ to examine bacteriophage (phage) spread through populations of Haemophilus influenzae whose phage receptor gene, lic2A , is switched ‘ON’ and ‘OFF’ by mutations in a hypermutable tetranucleotide repeat tract. Phage extinction was frequently observed when the proportion of phage-resistant sub-populations exceeded 34%. In silico modelling indicated that phage extinction was interdependent on phage loss during transfer between populations and the frequency of resistant populations. In a fixed-area oscillating prey assay, heterogeneity in phage resistance was observed to generate vast differences in phage densities across multiple bacterial populations resulting in protective quarantining of some populations from phage attack. We conclude that phase-variable hypermutable loci produce bacterial ‘herd immunity’ with resistant intermediary-populations acting as a barricade to reduce the viral load faced by phage-sensitive sub-populations. This paradigm of meta-population protection is applicable to evolution of hypermutable loci in multiple bacteria-phage and host-pathogen interactions. Importance Herd immunity is a survival strategy wherein populations are protected against invading pathogens by resistant individuals within the population acting as a barrier to spread of the infectious agent. Although, this concept is normally only applied to higher eukaryotes, prokaryotic organisms also face invasion by infectious agents, such as bacterial viruses, bacteriophage (phage). Here we use novel experimental approaches and mathematical modelling, to show that bacteria exhibit a form of herd immunity through stochastically generated resistant variants acting as barricades to phage predation of sensitive cells. With hypermutable loci found in many prokaryotic systems, this phenomenon may be widely applicable to phage-bacteria interactions and could even impact phage-driven evolution in bacteria.
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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 | 0.000 | 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".