Exploring the Role of ABCF1 in Mucosal Immunity of Human Airway Epithelial Cells
Bibliographic record
Abstract
Human airway epithelial cells (HAECs) play a pivotal role in creating a mechanical barrier to prevent environmental insults from entering deeper into the lung tissue and in facilitating host defence against pathogens and allergens by producing immune mediators and recruiting inflammatory cells. ABCF1, is a unique member of the ABC transporter family that it is highly expressed in the airway epithelium, however, its function in HAECs is currently not known.In this thesis, we explored the role of ABCF1 as a dsDNA viral sensor in HAECs. Our findings demonstrated that while ABCF1 is required for an immune response to a double-stranded DNA (dsDNA) viral mimic, VACV-70, our transcriptomic analysis suggested a role in pro-inflammatory responses downstream of toll-like receptors (TLR) 3 and 4 signalling pathways. We followed this outcome by investigating ABCF1 in mediating pro-inflammatory responses to TNF-α and Poly(I:C) through A20, NF-κB and IRF-3 regulated signalling pathways. Our study demonstrated that Poly(I:C) and TNF-α induced IL-8 are regulated by ABCF1 through pathways independent of NF-κB, and IRF-3 activation, although the exact mechanism remains unclear. The next approach was to run a hypothesis-free in silico investigation of the ABCF1 protein-protein interaction (PPI) network using publicly available databases and Gene Ontology (GO) term enrichment analysis. Following our in silico results of ABCF1 protein interactors, we validated a novel interaction of ABCF1 and SYK in human airway epithelial cells following Poly(I:C) stimulation. We have demonstrated that silencing ABCF1 under stimulation by VACV-70, TNF-α and Poly(I:C) in HAECs affects the induction of immune mediators, and a candidate protein interaction partner, SYK, is involved in immune signalling, however its exact mechanism is not defined. We propose that further insights into the functions of ABCF1 may aid in understanding how HAECs maintain mucosal immune homeostasis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".