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Record W2618863843 · doi:10.3934/molsci.2017.2.219

Aging: when the ubiquitin–proteasome machinery collapses

2017· article· en· W2618863843 on OpenAlexaff
Mohamed A. Eldeeb

Bibliographic record

VenueAIMS molecular science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProteostasisProteasomeUbiquitinBiologyCell biologyProtein degradationProteomeDNA repairProtein aggregationDNA damageCell Cycle ProteinCell cycleCellBiochemistryDNA

Abstract

fetched live from OpenAlex

In mammalian cells, protein degradation is an essential and dynamic process that is crucial for survival, growth, differentiation and proliferation of cells. Tellingly, the majority of intracellular proteins are degraded via the ubiquitin–proteasome system (UPS). UPS-mediated protein degradation serves qualitative and quantitative roles within the cellular proteome. For instance, UPS specifically targets misfolded, aggregated, toxic, mutant and otherwise structurally abnormal proteins for destruction and hence prevent aggregation and accumulation of toxic proteins. Furthermore, several cellular regulatory proteins, including cell cycle regulators, transcription factors, DNA replication and DNA repair proteins are selectively targeted for degradation via UPS and thus contribute to maintaining protein homeostasis (proteostasis) and proper functional proteome. Concomitantly, the deregulation of proteostasis may lead to several pathological disorders including aging-associated pathologies. Remarkably, augmenting the proteasomal activity has been linked to longevity in model organisms and protect these organisms from symptoms linked to protein homeostasis disorders. Herein I comment briefly on the recent work revealing the pivotal role of ubiquitin–proteasome-mediated protein degradation with respect to regulating aging process in model organisms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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