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Record W3194990160 · doi:10.1128/msphere.00650-21

RNase III and RNase E Influence Posttranscriptional Regulatory Networks Involved in Virulence Factor Production, Metabolism, and Regulatory RNA Processing in Bordetella pertussis

2021· article· en· W3194990160 on OpenAlexafffund
Gyles Ifill, Travis M. Blimkie, Amy Huei‐Yi Lee, George A. Mackie, Qing Chen, Scott Stibitz, Robert E. W. Hancock, Rachel C. Fernandez

Bibliographic record

VenuemSphere · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchSimon Fraser UniversityNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsBiologyRNase PTranscriptomeBordetella pertussisVirulenceGeneRNAGeneticsRegulonGene expressionBacteria

Abstract

fetched live from OpenAlex

Noncoding, regulatory RNAs in bacterial pathogens are critical components required for rapid changes in gene expression profiles. However, little is known about the role of regulatory RNAs in the growth and pathogenesis of Bordetella pertussis. To address this, mutants separately lacking ribonucleases central to regulatory RNA processing, RNase III and RNase E, were analyzed by RNA-Seq. Here, we detail the first transcriptomic analysis of the impact of altered RNA degradation in B. pertussis. Each mutant showed approximately 1,000 differentially expressed genes, with significant changes in the expression of pathways associated with metabolism, bacterial secretion, and virulence factor production. Our analysis suggests an important role for these ribonucleases during host colonization and provides insights into the breadth of posttranscriptional regulation in B. pertussis, further informing our understanding of B. pertussis pathogenesis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.797

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 head0.011
GPT teacher head0.224
Teacher spread0.214 · 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 designObservational
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

Citations11
Published2021
Admission routes2
Has abstractyes

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