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Record W2896509234 · doi:10.1016/j.celrep.2018.09.078

Regulatory Evolution Drives Evasion of Host Inflammasomes by Salmonella Typhimurium

2018· article· en· W2896509234 on OpenAlexafffund
Bushra Ilyas, David T. Mulder, Dustin J. Little, Wael Elhenawy, María M. Banda, Deyanira Pérez‐Morales, Caressa N. Tsai, N. Y Elizabeth Chau, Vı́ctor H. Bustamante, Brian K. Coombes

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsMcMaster University
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoCanadian Institutes of Health ResearchConsejo Nacional de Ciencia y TecnologíaCanadian Association of GastroenterologyMcMaster University
KeywordsSalmonellaEvasion (ethics)Host (biology)Salmonella infectionBiologyMicrobiologyVirologyGeneticsBacteriaImmune system

Abstract

fetched live from OpenAlex

Bacterial two-component regulatory systems (TCS) couple the detection of niche-specific cues with adaptive gene expression to optimize fitness. In Salmonella Typhimurium (STM), the SsrA-SsrB TCS regulates virulence genes needed for survival within host cells, yet the impact of this TCS on regulatory evolution in this pathogen remains incompletely understood. Here, we show that SsrB alters a transcriptional network controlling bacterial motility to limit inflammasome activation during host cell infection. Using comparative RNA sequencing between STM and S. bongori (SBG) engineered to express SsrB, we show that SsrB represses flagellar gene expression in STM but activates this pathway in SBG, which has evolved in the absence of SsrB. Motility repression in STM is driven by an SsrB-binding region upstream of flhDC that appears to have evolved in STM following divergence from SBG. These data reveal a divergent regulatory circuit in non-coding DNA that reduces flagellar gene expression to evade host defenses.

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 score0.571
Threshold uncertainty score0.350

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations30
Published2018
Admission routes2
Has abstractyes

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