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Record W3034544216 · doi:10.1016/j.celrep.2020.107766

Differential Cellular Response to Translocated Toxic Effectors and Physical Penetration by the Type VI Secretion System

2020· article· en· W3034544216 on OpenAlexafffund
Fatima Kamal, Xiaoye Liang, Kevin Manera, Tong‐Tong Pei, Haeun Kim, Linh Lam, Alexander Pun, Steven J. Hersch, Tao Dong

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research ChairsCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsEffectorType VI secretion systemSecretionPseudomonas aeruginosaBiologyMutantCell biologyEscherichia coliVibrio choleraeMicrobiologyCell envelopeBacteriaVirulenceGeneticsBiochemistryGene

Abstract

fetched live from OpenAlex

The type VI secretion system (T6SS) is a lethal microbial weapon that injects a large needle-like structure carrying toxic effectors into recipient cells through physical penetration. How recipients respond to physical force and effectors remains elusive. Here, we use a series of effector mutants of Vibrio cholerae to determine how T6SS elicits response in Pseudomonas aeruginosa and Escherichia coli. We show that TseL, but no other effectors or physical puncture, triggers the tit-for-tat response of P. aeruginosa H1-T6SS. Although E. coli is sensitive to all periplasmically expressed effectors, P. aeruginosa is most sensitive to TseL alone. We identify a number of stress response pathways that confer protection against TseL. Physical puncture of T6SS has a moderate inhibitory effect only on envelope-impaired tolB and rseA mutants. Our data reveal that recipient cells primarily respond to effector toxicity but not to physical contact, and they rely on the stress response for immunity-independent protection.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations73
Published2020
Admission routes2
Has abstractyes

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