Protein interactions, calcium, phosphorylation, and cholesterol modulate CFTR cluster formation on membranes
Bibliographic record
Abstract
ABSTRACT The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride channel whose dysfunction leads to intracellular accumulation of chloride ions, dehydration of cell surfaces, and subsequent damage to airway and ductal organs. Beyond its function as a chloride channel, interactions between CFTR, ENaC, and SLC transporter family membrane proteins and cytoplasmic proteins, including calmodulin and NHERF-1, co-regulate ion homeostasis. CFTR has also been observed to form mesoscale membrane clusters. However, the biophysical mechanisms that regulate the formation of CFTR clusters are unknown. Using a combination of computational modeling and complex biochemical reconstitution assays, we demonstrate that multivalent protein-protein interactions with CFTR binding partners, calcium, and membrane cholesterol can induce CFTR cluster formation on model membranes. Phosphorylation of the intracellular domains of CFTR also promotes cluster formation in the absence of calcium, indicating that multiple mechanisms can regulate CFTR cluster formation. Our findings reveal that coupling of multivalent protein and lipid interactions promote CFTR cluster formation consistent with membrane-associated biological phase separation. SIGNIFICANCE STATEMENT Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) membrane protein underlie cystic fibrosis. It is thought that molecular “hubs” of CFTR and its binding partners co-regulate ion homeostasis and that disruption of these clusters can result in disease. However, the physical basis for molecular hub formation is unclear. In this study, we present evidence that multivalent protein and lipid interactions drive the formation of mesoscale CFTR-containing clusters or “hubs” on model membranes in a manner consistent with biological phase separation. These data provide important insights into physical mechanisms that modulate CFTR membrane organization and offer a new lens for the development of corrective therapeutics.
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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 | 0.000 | 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) | 0.001 | 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".