Temperature-Dependent Aggregation of Tau Protein Is Attenuated by Native PLGA Nanoparticles Under in vitro Conditions
Bibliographic record
Abstract
Introduction: Hyperphosphorylation and aggregation of the microtubule-associated tau protein, which plays a critical role in many neurodegenerative diseases (ie, tauopathies) including Alzheimer's disease (AD), are known to be regulated by a variety of environmental factors including temperature. In this study we evaluated the effects of FDA-approved poly (D,L-lactide-co-glycolic) acid (PLGA) nanoparticles, which can inhibit amyloid-β aggregation/toxicity in cellular/animal models of AD, on temperature-dependent aggregation of 0N4R tau isoforms in vitro. Methods: We have used a variety of biophysical (Thioflavin T kinetics, dynamic light scattering and asymmetric-flow field-flow fractionation), structural (fluorescence imaging and transmission electron microscopy) and biochemical (Filter-trap assay and detection of soluble protein) approaches, to evaluate the effects of native PLGA nanoparticles on the temperature-dependent tau aggregation. Results: Our results show that the aggregation propensity of 0N4R tau increases significantly in a dose-dependent manner with a rise in temperature from 27°C to 40°C, as measured by lag time and aggregation rate. Additionally, the aggregation of 2N4R tau increases in a dose-dependent manner. Native PLGA significantly inhibits tau aggregation at all temperatures in a concentration-dependent manner, possibly by interacting with the aggregation-prone hydrophobic hexapeptide motifs of tau. Additionally, native PLGA is able to trigger disassembly of preformed 0N4R tau aggregates as a function of temperature from 27°C to 40°C. Conclusion: These results, taken together, suggest that native PLGA nanoparticles can not only attenuate temperature-dependent tau aggregation but also promote disassembly of preformed aggregates, which increased with a rise of temperature. Given the evidence that temperature can influence tau pathology, we believe that native PLGA may have a unique potential to regulate tau abnormalities associated with AD-related pathology.
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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".