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Record W3092703056 · doi:10.1016/j.celrep.2020.108259

Defending against the Type Six Secretion System: beyond Immunity Genes

2020· review· en· W3092703056 on OpenAlex
Steven J. Hersch, Kevin Manera, Tao Dong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Reports · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSecretionImmunityGeneBiologyImmune systemImmunologyGeneticsEndocrinology

Abstract

fetched live from OpenAlex

The bacterial type six secretion system (T6SS) delivers toxic effector proteins into neighboring cells, but bacteria must protect themselves against their own T6SS. Immunity genes are the best-characterized defenses, protecting against specific cognate effectors. However, the prevalence of the T6SS and the coexistence of species with heterologous T6SSs suggest evolutionary pressure selecting for additional defenses against it. Here we review defenses against the T6SS beyond self-associated immunity genes, such as diverse stress responses that can recognize T6SS-inflicted damage and coordinate induction of molecular armor, repair pathways, and overall survival. Some of these stress responses are required for full survival even in the presence of immunity genes. Finally, we propose that immunity gene-independent protection is, mechanistically, bacterial innate immunity and that such defenses and the T6SS have co-evolved and continue to shape one another in polymicrobial communities.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.301
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it