Nanoscale Viscometry Reveals an Inherent Mucus Defect in Cystic Fibrosis
Bibliographic record
Abstract
The abnormally viscous and thick mucus is a hallmark of cystic fibrosis (CF). How the mutated CF gene causes abnormal mucus remains an unanswered question of paramount interest. Mucus is produced by the hydration of gel-forming mucin macromolecules that are stored in intracellular granules prior to release. Current understanding of mucin/mucus structure before and after secretion remains limited, and contradictory models exist. Here, we used a molecular viscometer and fluorescence lifetime imaging of human bronchoepithelial cells (Normal and CF) to measure nanometer-scale viscosity. We found significantly elevated intraluminal nanoviscosity in a population of CF mucin granules, indicating an intrinsic, presecretory mucin defect. Nanoviscosity influences protein conformational dynamics and function. Its elevation along the protein secretory pathway could arise from molecular overcrowding, impacting mucin's post-translational processing, hydration, and mucus rheology after release. The nanoviscosity of secreted CF mucus was elevated compared to that of non-CF. Interestingly, it was higher after release than in granules. Validation experiments indicate that reduced mobility of water hydrating mucin macromolecules may contribute to the high nanoviscosity in mucus and mucin granules. This suggests that mucins have a weakly ordered state in granules but adopt a highly ordered, nematic crystalline structure when secreted. This challenges the traditional view of mucus as a porous agarose-like gel and suggests an alternative model for mucin organization before and after secretion. Our study also indicates that endoplasmic reticulum stress due to molecular overcrowding could contribute to mucus pathogenesis in CF cells. It encourages the development of therapeutics that target presecretory mechanisms in CF and other muco-obstructive lung diseases.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".