Protein misfolding toxicity and inclusion formation in cellular models of neurodegeneration
Bibliographic record
Abstract
Protein misfolding characterizes most neurodegenerative diseases. Protein misfolding is the conversion of specific proteins from their normal, often soluble, and native three-dimensional conformation into an aberrant, often insoluble, non-functional conformation. Protein inclusions and aggregates are among the major pathological hallmarks of protein misfolding associated with many neurodegenerative diseases. Yet, the role of aggregates and inclusions is not clearly defined and heavily debated. This study utilizes powerful genetic approaches in yeast and verification in mammalian neuronal cell lines to address the misfolding and toxicity of three proteins, the Rho Guanine Nucleotide Exchange Factor (RGNEF), Matrin3, which are involved in amyotrophic lateral sclerosis (ALS) and polyglutamine (polyQ) expanded huntingtin, which causes Huntington’s disease (HD).\nGenetic, biochemical, and pathological findings implicate RGNEF and Matrin3 in Amyotrophic Lateral Sclerosis (ALS). In this thesis we establish two novel humanized yeast models to study RGNEF and Matrin3. We find that RGNEF is toxic in yeast and can misfold and form inclusions. We also identify a potential new role for RGNEF as a microtubule regulator. Similarly, Matrin3 is also toxic and forms inclusions in yeast. We identify members of the Hsp90 and Hsp70 cytosolic chaperoning machinery as potent determinants of Matrin3 associated toxicity and misfolding and verify our findings in neuronal cell lines. Also, polyQ expanded repeats of the huntingtin protein are the sole known cause of Huntington’s disease (HD). We address the nexus of aging and aggregation, and toxicity of polyQ expanded repeats in a yeast model of aging. Aging is the most significant risk factor for all neurodegeneration, and we find that polyQ toxicity is exacerbated in aged cells while aggregates are lost. We also demonstrate that treating cells expressing polyQ with the steroidal lactone Withaferin A (WA) reduces toxicity while increasing aggregation. In essence, this study contributes to a deeper understanding of three misfolded proteins. Also, our results challenge the long-held postulation that aggregates cause neurodegeneration. Our findings provide further insight into the role of aggregation and inclusions that are crucial for developing effective therapeutic strategies that are not currently available for ALS and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".