Gelsolin Amyloidosis: aggregation propensities of wild and mutant peptides and their inhibition
Bibliographic record
Abstract
Abstract Background Gelsolin is an actin‐binding protein responsible for the remodeling of the actin cytoskeleton. Gelsolin Amyloidosis, the formation of misassembled protein into insoluble amyloid fibril aggregates, is the result of point mutations that promote aberrant proteolytic cleavage. Amyloidosis can be instigated and spread by existing amyloid fibrils through the process of seeding, where existing fibrils promote the formation of new fibrils. Gelsolin fragments are prone to aggregation and systematic deposition into organs including peripheral/cranial nerves, skin, eyes, and kidneys. The D187Y mutation has been identified to promote cleavage of Gelsolin, although studies have demonstrated the mutant’s elusive aggregation propensity. Method Mutant Gelsolin peptide aggregation was monitored and characterized in vitro using various spectroscopic methods, Thioflavin T (ThT), turbidity, and Dynamic Light Scattering (DLS). Transmission electron microscopy (TEM) allowed for the identification and visualization of β‐sheet formation within aggregates Result We reported that CFILDL‐containing peptides were prone to aggregation, which may be inhibited by using small molecules. Of note is the unique behaviour of peptide mutants with respect to their aggregation propensities pointing to their biological relevance. Preliminary results suggest that seeding may induce peptide aggregation Conclusion Since other neurodegenerative proteinopathies (Alzheimer’s & Parkinson’s) exhibit similar prion‐like mechanisms of seeding that initiate aggregation, understanding the fundamentals of seeding may expose potential therapeutic targets for preventing the progression of amyloidosis. References 1) Ahmad, M., Esposto, J., Golec, C., Wu, C., & Martic‐Milne, S. (2021). Aggregation of gelsolin wild‐type and G167K/R, N184K, and D187N/Y mutant peptides and inhibition. Molecular and Cellular Biochemistry, 476, 2393–2408. https://doi.org/10.1007/s11010‐021‐04085‐6 2) Walker, L. C., Diamond, M. I., Duff, K. E., & Hyman, B. T. (2013). Mechanisms of protein seeding in neurodegenerative diseases. JAMA Neurology, 70, 304‐310. https://doi.org/10.1001/jamaneurol.2013.1453 3) Solomon, J. P., Page, L. J., Balch, W. E., & Kelly, J. W. (2012). Gelsolin amyloidosis: genetics, biochemistry, pathology and possible strategies for therapeutic intervention. Critical Reviews in Biochemistry and Molecular Biology, 47, 282–296. https://doi.org/10.3109/10409238.2012.661401 4) Ihne, S., Morbach, C., Sommer, C., Geier, A., Knop, S., & Störk, S. (2020). Amyloidosis‐the diagnosis and treatment of an underdiagnosed disease. Deutsches Ärzteblatt International, 117, 159–166. https://doi.org/10.3238/arztebl.2020.0159
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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".