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<i>Klebsiella pneumoniae</i> disassembles microtubules and kills vinca alkaloid resistant lung cancer cells

2018· article· en· W3176871908 on OpenAlexafffund
Michael Dominic Chua, Alexander Constantine Bogdan, Brittany Dominique Walker, Julian A. Guttman

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsMicrotubuleCell biologyMitosisProtein subunitBiologyMicrotubule organizing centerCell divisionCell cycleCellCentrosomeGeneticsGene

Abstract

fetched live from OpenAlex

Microtubules play an important role in maintaining cell shape, protein and organellar transport, and cell division. The integrity of the microtubule network is crucial for the viability of mammalian cells. Consequently, the cell has evolved many regulatory mechanisms to ensure the proper regulation of microtubules, especially during the cell cycle. The katanin family of microtubule severing proteins maintain microtubule lengths during interphase. When mitosis occurs, these katanins are recruited to the microtubule organizing centers (MTOCs) to facilitate DNA separation. Previously, we have shown that the microtubules networks in lung epithelia were disassembled by the bacterial pathogen Klebsiella pneumoniae . Because the microtubules were targeted by disease‐causing proteins originating from K. pneumoniae , we hypothesized that K. pneumoniae has devised strategies to manipulate the katanin microtubule severing enzymes to ultimately cause cell cycle arrest. To test this hypothesis, we infected A549 lung cells with K. pneumoniae and immunolocalized the katanin proteins ‐ katanin catalytic subunit A1 protein (KATNA1), katanin catalytic subunit like protein A1 like protein (KATNAL1), katanin regulatory subunit B1 (KATNB1), and katanin regulatory subunit B1 like protein (KATNBL1). In uninfected cells, these proteins immunolocalized to the MTOCs and the cleavage furrow during cell division, but in infected dividing cells, these proteins were absent. This suggested that mitosis was halted. Thus, we examined if these infected cells were still viable and able to proliferate. Using a live/dead staining kit, we found that at this point of the infections, the cells were non‐viable and cell death has occurred. Given that host cells could be killed by the microbes and that microtubules were disassembled during the infections, we investigated whether microtubules could be destroyed in vincristine‐ and vinblastine‐resistant H69AR lung cancer epithelial cells. Microtubule disassembly occurred in these H69AR cells and taken together, we have identified a potent mechanism for destroying the microtubule cytoskeleton and ultimately killing lung cancer cells that are resistant to conventional therapeutics. Support or Funding Information This study was funded by SFU institutional funds. 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 score0.003
Threshold uncertainty score0.006

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)0.0010.000

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 head0.005
GPT teacher head0.239
Teacher spread0.234 · 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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