In Vitro Efficacy of PEI-Derived Lipopolymers in Silencing of Toxic Proteins in a Neuronal Model of Huntington’s Disease
Bibliographic record
Abstract
Background: Huntington’s Disease (HD) is a neurodegenerative disorder caused by an abnormal extension of a CAG repeat stretch located in the exon 1 of the HTT (IT15) gene, leading to production of a mutated and misfolded Huntingtin protein (muHTT) with an abnormally elongated polyglutamine (polyQ) region. This mutation causes muHTT to oligomerize and aggregate in the brain, particularly in the striatum and cortex, causing alterations in intracellular trafficking, caspase activation, and ganglioside metabolism, ultimately leading to neuronal damage and death and causing signs and symptoms such as chorea and cognitive dysfunction. Currently, there is no available cure for HD patients; hence, there is a strong need to look for effective therapies. Methods: This study aims to investigate the efficacy of siRNA-containing nano-engineered lipopolymers in selectively silencing the HTT expression in a neuronal model expressing a chimeric protein formed by the human mutated exon 1 of the HTT gene, tagged with GFP. Toxicity of lipopolymers was assessed using MTT assay, while efficacy of silencing was monitored using qRT-PCR, as well as Western blotting/flow cytometry. Changes in muHTT-GFP aggregation were observed using fluorescence microscopy and image analyses. Results: Here, we show that engineered lipopolymers can be used as delivery vehicles for specific siRNAs, decreasing the transcription of the mutated gene, as well as the muHTT protein production and aggregation, with Leu-Fect C being the most effective candidate amongst the assessed lipopolymers. Conclusions: Our findings have profound implications for genetic disorder therapies, highlighting the potential of nano-engineered materials for silencing mutant genes and facilitating molecular transfection across cellular barriers. This successful in vitro study paves the way for future in vivo investigations with preclinical models, offering hope for previously considered incurable diseases such as HD.
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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".