Nanojunctions: Specificity of Ca <sup>2+</sup> signalling requires nano-scale architecture of intracellular membrane contact sites
Bibliographic record
Abstract
Abstract Specificity of control over virtually all essential cellular functions by Ca 2+ is based on the existence of separated, autonomic signaling modules. Spatiotemporal definition of Ca 2+ signals involves the assembly of signaling complexes within the nano-architecture of contact sites between the sarco/endoplasmic (SR/ER) reticulum and the plasma membrane (PM). While the requirement of precise spatial assembly and positioning of the junctional signaling elements is well documented, the role of the nano-scale membrane architecture itself, as an ion reflecting confinement of the signaling unit, remains as yet elusive. Utilizing the NCX1/SERCA2-mediated ER Ca 2+ refilling process as a junctional signalling paradigm, we provide here the first evidence for an indispensable cellular function of the junctional membrane architecture. Our stochastic modeling approach demonstrates that junctional ER Ca 2+ refilling operates exclusively at nano-scale membrane spacing, with a strong inverse relationship between junctional width and signaling efficiency. Our model predicts a breakdown of junctional Ca 2+ signaling with loss of reflecting membrane confinement, irrespective of the spatial positioning of the molecular signaling elements. Alterations in the molecular and nano-scale membrane organization at organelle-PM contacts are suggested as new concept in pathophysiology.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".