Transcriptomics unravels molecular changes associated with cilia and COVID-19 in chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common upper respiratory tract complication where the pathogenesis is largely unknown. Herein, we investigated the transcriptome profile in nasal mucosa biopsies of CRSwNP patients and healthy individuals. We further integrated the transcriptomics data with genes located in chromosomal regions containing genome-wide significant gene variants for COVID-19. Among the most significantly upregulated genes in polyp mucosa were CCL18, CLEC4G, CCL13 and SLC9A3. Pathways involving "Ciliated epithelial cells" were the most differentially expressed molecular pathways when polyp mucosa and non-polyp mucosa from the same patient was compared. Natural killer T-cell (NKT) and viral pathways were the most statistically significant pathways in the mucosa of CRSwNP patients compared with those of healthy control individuals. Upregulated genes in polyp mucosa, located within the genome-wide associated regions of COVID-19, included LZTFL1, CCR9, SLC6A20, IFNAR1, IFNAR2 and IL10RB. Interestingly, the second most over-expressed gene in our study, CLEC4G, has been shown to bind directly to SARS-CoV-2 spike's N-terminal domain and mediate its entry and infection. Our results on altered expression of genes related to cilia and viruses point to the de-regulation of viral defenses in CRSwNP patients, and may give clues to future intervention strategies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".