Rifampicin Does Not Prevent Amyloid Fibril Formation by Human Islet Amyloid Polypeptide but Does Inhibit Fibril Thioflavin-T Interactions: Implications for Mechanistic Studies of β-Cell Death
Bibliographic record
Abstract
Amyloid formation has been implicated in more than 20 different human diseases, including Alzheimer's disease, Parkinson's disease, and type 2 diabetes. The development of inhibitors of amyloid is a topic of considerable interest, both because of their potential therapeutic applications and because they are useful mechanistic probes. Recent studies have highlighted the potential use of rifampicin as an inhibitor of amyloid formation by a variety of polypeptides; however, there are conflicting reports on its ability to inhibit amyloid formation by islet amyloid polypeptide (IAPP). IAPP is the cause of islet amyloid in type 2 diabetes. We show that rifampicin does not prevent amyloid formation by IAPP and does not disaggregate preformed IAPP amyloid fibrils;, instead, it interferes with standard fluorescence-based assays of amyloid formation. Rifampicin is unstable in aqueous solution and is readily oxidized. However, the effects of oxidized and reduced rifampicin are similar, in that neither prevents amyloid formation by IAPP. Furthermore, use of a novel p-cyanoPhe analogue of IAPP shows that rifampicin does not significantly affect the kinetics of IAPP amyloid formation. The implications for the development of amyloid inhibitors are discussed as are the implications for studies of the toxicity of islet amyloid. The work also demonstrates the utility of p-cyanoPhe IAPP for the screening of inhibitors. The data indicate that rifampicin cannot be used to test the relative toxicity of IAPP fibrils and prefibril aggregates of IAPP.
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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".