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Record W4389336549 · doi:10.1128/spectrum.04981-22

<i>Salmonella</i> actively modulates TFEB in murine macrophages in a growth-phase and time-dependent manner

2023· article· en· W4389336549 on OpenAlexafffund
Subothan Inpanathan, Erika Ospina-Escobar, Vanessa Cruz Li, Zainab Adamji, Tracy Lackraj, Youn Hee Cho, Natasha Porco, Christopher H. Choy, Joseph B. McPhee, Roberto J. Botelho

Bibliographic record

VenueMicrobiology Spectrum · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsToronto Metropolitan University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaToronto Metropolitan UniversityOntario Ministry of Economic Development, Job Creation and TradeCanada Foundation for InnovationCanada Research ChairsGovernment of Canada
KeywordsSalmonellaBiologyPhase (matter)Cell biologyMicrobiologyChemistryGeneticsBacteria

Abstract

fetched live from OpenAlex

ABSTRACT The transcription factor TFEB drives the expression of lysosomal, autophagic, and immune-responsive genes in response to LPS and phagocytosis. Interestingly, compounds that promote TFEB activity enhance bactericidal activity, while intracellular pathogens like Mycobacterium and Salmonella repress TFEB. However, Salmonella enterica sv. Typhimurium ( S . Typhimurium) was reported to actively stimulate TFEB, implying a benefit to Salmonella . To better understand the relationship between S . Typhimurium and TFEB, we assessed if S . Typhimurium regulated TFEB in macrophages in a manner dependent on infection conditions. We observed that macrophages that engulfed late-logarithmic grown Salmonella accumulated nuclear TFEB, comparable to macrophages that engulfed Escherichia coli . In contrast, stationary-phase S . Typhimurium infection of macrophages actively delayed TFEB nuclear mobilization. The delay in TFEB nuclear mobilization was not observed in macrophages that engulfed heat-killed stationary-phase Salmonella , or Salmonella lacking functional SPI-1 and SPI-2 type 3 secretion systems. S . Typhimurium mutated in the master virulence regulator phoP or the secreted effector genes sifA , and sopD also showed TFEB nuclear translocation. Interestingly, while E. coli survived better in tfeb −/− macrophages, S . Typhimurium growth was similar in wild-type and tfeb −/− macrophages. Moreover, Salmonella survival was not readily affected by its growth phase in wild-type or knockout macrophages, though in HeLa cells late-log Salmonella benefitted from the loss of TFEB. Priming macrophages with phagocytosis enhanced the killing of Salmonella in wild-type, but not in tfeb − /− macrophages. Collectively, S . Typhimurium orchestrate TFEB in a manner dependent on infection conditions, while disturbing this context-dependent control of TFEB may be detrimental to Salmonella survival. IMPORTANCE Activation of the host transcription factor TFEB helps mammalian cells adapt to stresses such as starvation and infection by upregulating lysosome, autophagy, and immuno-protective gene expression. Thus, TFEB is generally thought to protect host cells. However, it may also be that pathogenic bacteria like Salmonella orchestrate TFEB in a spatio-temporal manner to harness its functions to grow intracellularly. Indeed, the relationship between Salmonella and TFEB is controversial since some studies showed that Salmonella actively promotes TFEB, while others have observed that Salmonella degrades TFEB and that compounds that promote TFEB restrict bacterial growth. Our work provides a path to resolve these apparent discordant observations since we showed that stationary-grown Salmonella actively delays TFEB after infection, while late-log Salmonella is permissive of TFEB activation. Nevertheless, the exact function of this manipulation remains unclear, but conditions that erase the conditional control of TFEB by Salmonella may be detrimental to the microbe.

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 score0.024
Threshold uncertainty score0.641

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 head0.006
GPT teacher head0.240
Teacher spread0.235 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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