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SM22 is needed for actin‐rich structures formed by enteropathogenic <i>Escherichia coli</i> and <i>Listeria monocytogenes</i>

2018· article· en· W3175596780 on OpenAlexafffund
Michael Dominic Chua, Kevin Jay Hipolito, Julian Solway, Julian A. Guttman

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsListeria monocytogenesEnteropathogenic Escherichia coliActinBiologyMicrobiologyCell biologyListeriaBacteriaEscherichia coliBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

Food‐borne bacterial pathogens colonize the intestinal epithelia and cause various diseases. Enteropathogenic Escherichia coli (EPEC) remains extracellular and uses disease‐causing bacterial proteins to generate membrane‐bound actin protrusions on the host cell surface. These protrusions, called pedestals are crucial for disease and allow the bacteria to surf atop the intestinal epithelia ultimately leading to progressive changes to the tissue that cause diarrhea in the host. On the other hand, Listeria monocytogenes enters intestinal cells. Once within the cells these microbes initially form of branched networks of actin filaments around the bacterium called an actin cloud. These actin filaments then concentrate to one end of the microbe forming a comet tail that allows the bacterium to move within the host cell. L. monocytogenes can then use its comet tail to spread from cell‐to‐cell and infect various organs in the host. Through a previous mass spectrometry analysis of EPEC pedestals, we found that the actin bundling protein SM22 was concentrated through the full length of the pedestal. Because SM22 was enriched in EPEC pedestals, we then hypothesized that SM22 plays a crucial role in actin‐rich structures formed by both EPEC and other bacteria that hijack the actin filaments of the host during their disease processes. To test this, we initially used immunolocalization with L. monocytogenes infected cultured cells and found SM22 concentrated at L. monocytogenes actin clouds and comet tails. Using small interfering RNA, we depleted SM22 expression levels in host cells and found that fewer EPEC pedestals were able to form and less L. monocytogenes bacteria formed comet tails. The average comet tail length was also significantly shorter. As the reduction of SM22 diminished the abundance of actin‐rich structures generated by both EPEC and L. monocytogenes , we then determined if expressing EGFP‐tagged SM22 would increase the amount of EPEC pedestals and L. monocytogenes comet tails. Although EPEC pedestal formation was unchanged in cells overexpressing SM22, more comet tails were formed in L. monocytogenes ‐infected cells and those comet tails were longer. Taken together, EPEC pedestals and L. monocytogenes comet tails rely on SM22 for their formation and the disease process and identify SM22 as a target for regulating these infections. Support or Funding Information This study was funded through NSERC. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.021
GPT teacher head0.288
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes2
Has abstractyes

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