Identifying your enemies - could envelope stress trigger microbial immunity?
Bibliographic record
Abstract
Microbes utilize defence systems with fundamental similarities to our innate and adaptive immune responses to protect themselves from harmful invaders. One system, made up of CRISPR loci & Cas proteins, incorporates recognizable features from the genomes of viruses (bacteriophages) and plasmids into bacterial genomes, where they are later used to direct a ribonucleoprotein complex to destroy invading nucleic acids upon re-exposure. CRISPR-mediated defence against invasive nucleic acids is found in most archaea and many eubacteria. Many aspects of this newly described defence system have not been worked out, including the molecular mechanisms by which foreign nucleic acids are incorporated into microbial genomes during adaption and destroyed during interference. In this issue of Molecular Microbiology, DeLisa and colleagues provide insight into how this form of microbial immunity might be regulated in eubacteria. They demonstrate that Escherichia coli CRISPR-mediated immunity requires the presence of the BaeSR two-component system under certain conditions. Since BaeSR regulate an envelope stress response, their data imply that immunity against invading, foreign nucleic acids may be somehow linked to stresses to the bacterial membrane. These observations will help pave the way to understanding how and when CRISPR-based immunity may be important in driving evolution and adaptation in eubacteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".