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Record W4401284615 · doi:10.1016/j.isci.2024.110662

Single-cell transcriptomics reveals subset-specific metabolic profiles underpinning the bronchial epithelial response to flagellin

2024· article· en· W4401284615 on OpenAlexaff
Ivan Ramirez‐Moral, Alex R. Schuurman, Christine C. A. van Linge, Joe M. Butler, Xiao Yu, Karen de Haan, Sarah van Leeuwen, Alex F. de Vos, Menno D. de Jong, Felipe A. Vieira Braga, Tom van der Poll

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsInstitute of Infection and Immunity
FundersZonMwEuropean CommissionHorizon 2020 Framework ProgrammeJoint Programming Initiative on Antimicrobial Resistance
KeywordsFlagellinTranscriptomeCell biologyBiologyImmune systemInflammationRespiratory epitheliumCellEpitheliumImmunologyGene expressionBiochemistryGene

Abstract

fetched live from OpenAlex

Airway epithelial cells represent the first line of defense against respiratory pathogens. Flagellin drives the motility of many mucosal pathogens and has been suggested as an immune enhancing adjunctive therapeutic in infections of the airways. This study leveraged single-cell RNA sequencing to determine cell-specific effects of flagellin in primary human bronchial epithelial cells growing in air-liquid interface. Seven cell clusters were identified, including ciliated cells, ionocytes, and several states of basal and secretory cells, of which only inflammatory basal cells and inflammatory secretory cells demonstrated a proportional increase in response to flagellin. Inflammatory secretory cells showed evidence of metabolic reprogramming toward aerobic glycolysis, while in inflammatory basal cells transcriptome profiles indicated enhanced oxidative phosphorylation. Inhibition of mTOR prevented the shift to glycolysis and reduced inflammatory gene transcription specifically in inflammatory secretory cells. These data demonstrate the functional heterogeneity of the human airway epithelium upon exposure to flagellin.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.808
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.074
GPT teacher head0.352
Teacher spread0.278 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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