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Record W3096175189 · doi:10.1016/j.heliyon.2020.e05330

AU richness within the 5′ coding region of the Escherichia coli heat-stable enterotoxin b mRNA affects toxin secretion

2020· article· en· W3096175189 on OpenAlexafffund
Eyad Kinkar, Ayat Kinkar, Mazen Saleh

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsLaurentian University
FundersLaurentian University
KeywordsSecretionCoding regionMessenger RNASpecies richnessBiologyEscherichia coliSignal peptideGeneSecretory proteinEnterotoxinMolecular biologyGeneticsBiochemistryPeptide sequenceEcology

Abstract

fetched live from OpenAlex

heat-stable enterotoxin b (STb). This toxin is a well studied T2SS substrate. The mutations were generated such that AU richness within the 5' coding region (corresponding to the N-terminal signal sequence) was gradually reduced. Reduction of AU richness within the first 15 codons resulted in reduced secretion of the toxin as the AU/GC ratio was reduced from 2.13 for the WT STb to 1.65 (S-I) and subsequently to 1.30 (S-II). This reduction did not correlate with mRNA accumulation and decreased stability of the transcripts could not account for the reduced secretion observed. Reduction of AU richness beyond the first 15 codons recovered secretion efficiency of the toxin (S-III). To validate the experimental approach, a positive control was used in which a mutation involving the insertion of a positive charge within the hydrophobic domain of the N-terminal signal sequence was constructed. As expected, this mutation abolished secretion of the toxin. In conclusion, reducing AU richness within the 5'coding region in the STb mRNA reduces toxin secretion but other factors, such as formation of hairpins, must also be taken into consideration. This will have implications for both homologous and heterologous expression of STb for biological studies and for toxin production.

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 score0.008
Threshold uncertainty score0.358

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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