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Record W4308331155 · doi:10.4017/gt.2022.21.s.808.sp3

Protein dynamics-based control of proteotoxicity in neurodegenerative diseases

2022· article· en· W4308331155 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGerontechnology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProteotoxicityDynamics (music)NeuroscienceBiologyProtein aggregationCell biologyPsychology

Abstract

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Purpose With increasing life expectancy, neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Huntington's disease (HD), have become one of the major threats to humans. Although the symptoms vary depending on the type of disease, these neurodegenerative diseases share protein toxicity as one of their key pathogenic mechanisms. Herein protein toxicity is defined as all the pathological changes that ensue from accumulation, mis-localization, and/or oligomerization of disease-associated toxic proteins such as -synuclein in PD, polyglutamine (polyQ)-containing proteins in polyQ diseases (e.g., HD), and dipeptide repeat proteins and TDP-43 in ALS. Conventional understanding of protein toxicity is that protein toxicity simply reflects the amount of accumulated toxic proteins. For this reason, our challenges done so far against protein toxicity have been based on a simple strategy of reducing the amount of toxic proteins. However, the exact nature of protein toxicity appears to be much more complex than we have conceived, and thus new paradigm for understanding protein toxicity is highly demanded. In this talk, I will present our current efforts to understand exact nature of proteotoxicity in neurodegenerative diseases and potential solutions that can effectively control proteotoxicity, named as protein dynamics-based control of proteotoxicity. I will tell you about our recent study unveiling cellular intrinsic mechanisms regulating nucleocytoplasmic transport of TDP-43 in neurons. Method We used Drosophila as a primary model to study intrinsic regulatory mechanisms underlying nucleocytoplasmic transport of TDP-43. We used various experimental techniques, such as genetic analyses, immunohistochemistry, behavioral analyses, neuronal imaging, and FRAP. Results and discussion Dysregulation of protein localization, often observed in various neurodegenerative diseases, impairs functionality of the protein, alters the pool of its interactors, or both, thereby leading to cellular toxicity. TDP-43, one of wellcharacterized disease-associated proteins in Lou Gehrig's disease, are known to translocate from the nucleus to the cytoplasm in the disease condition, which is considered as a hallmark of the disease. Thus, unveiling the regulatory mechanism of intracellular localization of TDP-43 is very important to better understand the pathogenesis of Lou Gehrig's disease. However, still our understanding on the neuronal intrinsic program regulating the intracellular localization of TDP-43 remains mostly unclear. Interestingly, we observed that the intracellular localization of TDP-43 dynamically changes even in normal condition of a specific neuronal cell type, named Drosophila classIV da sensory neurons, along development. We first identified intracellular Ca2+ level to be critical for the translocation of TDP-43 between the nucleus and the cytoplasm. Additionally, through fluorescence recovery after photobleaching (FRAP) imaging analyses, we found that the nuclear entry of TDP-43 is critically controlled by intracellular Ca2+ level. Further genetic analyses identified Calpain and Importin a3 as mediators of Ca2+-dependent control of TDP-43 translocation in classIV da sensory neurons. Finally, by modulating Ca2+-Calpain-Importing a3 pathway, we could significantly modify the locomotive phenotypes shown in animal models for Lou Gehrig's disease. Even though we know well that aberrant translocation of TDP-43 is closely associated with Lou Gehrig's disease, we have only limited knowledge to date particularly on how its aberrant translocation initiates at the early stages of the disease. Our findings provide invaluable clues for the neuronal intrinsic program regulating the intracellular localization of TDP-43, of which changes may lead to the initiation of the pathogenic translocation of TDP-43. This study enables other researchers to consider protein dynamics-based control of proteotoxicity as a novel strategy against neurodegenerative diseases, in addition to their conventional approach (quantitative control of toxic proteins).

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.193
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it