Evidence that the Human Innate Immune Peptide LL-37 may be a Binding Partner of Amyloid-β and Inhibitor of Fibril Assembly
Bibliographic record
Abstract
BACKGROUND: Identifying physiologically relevant binding partners of amyloid-β (Aβ) that modulate in vivo fibril formation may yield new insights into Alzheimer's disease (AD) etiology. Human cathelicidin peptide, LL-37, is an innate immune effector and modulator, ubiquitous in human tissues and expressed in myriad cell types. OBJECTIVE: We present in vitro experimental evidence and discuss findings supporting a novel hypothesis that LL-37 binds to Aβ42 and can modulate Aβ fibril formation. METHODS: Specific interactions between LL-37 and Aβ (with Aβ in different aggregation states, assessed by capillary electrophoresis) were demonstrated by surface plasmon resonance imaging (SPRi). Morphological and structural changes were investigated by transmission electron microscopy (TEM) and circular dichroism (CD) spectroscopy. Neuroinflammatory and cytotoxic effects of LL-37 alone, Aβ42 alone, and LL-37/Aβ complexes were evaluated in human microglia and neuroblastoma cell lines (SH-SY5Y). RESULTS: SPRi shows binding specificity between LL-37 and Aβ, while TEM shows that LL-37 inhibits Aβ42 fibril formation, particularly Aβ's ability to form long, straight fibrils characteristic of AD. CD reveals that LL-37 prevents Aβ42 from adopting its typical β-type secondary structure. Microglia-mediated toxicities of LL-37 and Aβ42 to neurons are greatly attenuated when the two peptides are co-incubated prior to addition. We discuss the complementary biophysical characteristics and AD-related biological activities of these two peptides. CONCLUSION: Based on this body of evidence, we propose that LL-37 and Aβ42 may be natural binding partners, which implies that balanced (or unbalanced) spatiotemporal expression of the two peptides could impact AD initiation and progression.
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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) | 0.003 | 0.001 |
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".