MOLECULAR INVESTIGATION OF THE MUTABLE COLLAGENOUS TISSUE IN THE SEA CUCUMBER HOLOTHURIA FORSKALI
Bibliographic record
Abstract
The phylum Echinodermata is characterised by the development of a unique connective tissue, capable of dynamically modifying its mechanical properties. This mutable collagenous tissue (MCT) can shift from a stiff state to a viscous soft state independently of muscle contraction. Thanks to this ability, echinoderms can minimise energy expenditures during various behaviours linked to locomotion, reproduction, or defence. Previous investigations on sea cucumbers have revealed that the tissue is controlled by the nervous systems, enabling effector proteins to be released rapidly. These proteins, called stiffeners and softeners, are involved in the formation or removal of transient cross-bridges between collagen fibrils. For the last 20 years, MCT has been recognized as an important concept generator and resource for the design of soft actuators in robotics, medicine, and tissue engineering due to its advantageous properties. Despite the genuine interest that the tissue has aroused over the years, the current model remains ambiguous as to the role played by these effectors, which hinders the improvement of MCT-inspired biomaterials. In order to fill this information gap on the MCT, proteomic and interactomic data were collected on the dermis of Holothuria forskali and compared to available knowledge. The molecular fingerprint of the tissue was measured by Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy, and complemented by tandem mass spectrometry of collagen fibrils. Analysis revealed that while type I collagen was abundant, evidence of a rarer type of collagen could be detected. The MCT interactome was predicted in silico and further explored based on the affinity of soluble proteins for collagen fibrils. Integrative databases were unable to identify MCT related clusters, nonetheless potential partners corresponding to, as of yet, uncharacterized proteins were identified and could be of important function for the MCT.
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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) | 0.000 | 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".