Aggregation of wild‐type gelsolin peptides and mutants and inhibition
Bibliographic record
Abstract
Gelsolin amyloidosis is a disease characterized by several point mutations that lead to cleavage and fibrillization of gelsolin. The D187 mutation to N or Y leads to aggregation of peptide fragments with shortest aggregating peptide identified as 182SFNNGDCFILD192. Recently, G167 has also been identified as relevant gelsolin mutation, which leads to gelsolin deposits in kidneys but its aggregation is much less understood. Hence, we systematically investigated in vitro the aggregation propensities of the following gelsolin peptides: 167GRRVV171, 161RLFQVKG167, 184NNGDCFILDL193, 188CFILDL193, 187DCFILDL193, and their respective mutants (G167K, G167R, N184K, D187Y, D187N), by using spectroscopic methods (fluorescence proteostat, Thioflavin T (ThT), turbidity assay, and Dynamic Light Scattering (DLS)), and Transmission Electron Microscopy (TEM). The (non) mutant peptides containing CFILDL sequence aggregated into fibrillar networks, while G167R mutation promoted aggregation compared to the wild‐type sequence. In the presence of inhibitors, Methylene Blue (MB) and Epigallocatechin gallate (EGCG), the gelsolin peptide aggregation was reduced. We discovered that inhibitors have dual functionality, as aggregation inhibitors and disaggregation promoters, potentially allowing for the prevention and reversal of gelsolin amyloidosis. Such therapeutic strategies may significantly improve outcomes related to other amyloidogenic diseases of the heart, brain and eye.
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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".