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Record W2896899835 · doi:10.3390/v10100553

Roles of orf60a and orf61 in Development of Bacteriophages λ and Φ24B

2018· article· en· W2896899835 on OpenAlexafffund
Aleksandra Dydecka, Bożena Nejman-Faleńczyk, Sylwia Bloch, Gracja Topka, Agnieszka Necel, Logan W. Donaldson, Grzegorz Węgrzyn, Alicja Węgrzyn

Bibliographic record

VenueViruses · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProphageORFSBiologyBacteriophageEscherichia coliGenomeLysogenic cycleOpen reading frameGeneticsLambda phageGeneLytic cycleVirusPeptide sequence

Abstract

fetched live from OpenAlex

The exo-xis region of lambdoid bacteriophage genomes contains several established and potential genes that are evolutionarily conserved, but not essential for phage propagation under laboratory conditions. Nevertheless, deletion or overexpression of either the whole exo-xis region and important regulatory elements can significantly influence the regulation of phage development. This report defines specific roles for orf60a and orf61 in bacteriophage λ and Φ24B, a specific Shiga toxin-converting phage with clinical relevance. We observed that mutant phages bearing deletions of orf60a and orf61 impaired two central aspects of phage development: the lysis-versus-lysogenization decision and prophage induction. These effects were more pronounced for phage Φ24B than for λ. Surprisingly, adsorption of phage Φ24B on Escherichia coli host cells was less efficient in the absence of either orf60a or orf61. We conclude that these open reading frames (ORFs) play important, but not essential, roles in the regulation of lambdoid phage development. Although phages can propagate without these ORFs in nutrient media, we suggest that they may be involved in the regulatory network, ensuring optimization of phage development under various environmental conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.766

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)0.0010.000

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 head0.015
GPT teacher head0.256
Teacher spread0.242 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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