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The physiological and pathological biophysics of phase separation and gelation of RNA binding proteins in amyotrophic lateral sclerosis and fronto-temporal lobar degeneration

2018· review· en· W2800004384 on OpenAlexafffund
Peter St George‐Hyslop, Julie Qiaojin Lin, Akinori Miyashita, Emma Claire Phillips, Seema Qamar, Suzanne J. Randle, GuoZhen Wang

Bibliographic record

VenueBrain Research · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchWellcome TrustFondation Brain Canada
KeywordsStress granuleFrontotemporal lobar degenerationAmyotrophic lateral sclerosisIntrinsically disordered proteinsBiophysicsChemistryIntracellularRNACell biologyRNA-binding proteinNeuroscienceBiologyTranslation (biology)BiochemistryFrontotemporal dementiaMedicinePathologyDiseaseMessenger RNA

Abstract

fetched live from OpenAlex

Many RNA binding proteins, including FUS, contain moderately repetitive, low complexity, intrinsically disordered domains. These sequence motifs have recently been found to underpin reversible liquid: liquid phase separation and gelation of these proteins, permitting them to reversibly transition from a monodispersed state to liquid droplet- or hydrogel-like states. This function allows the proteins to serve as scaffolds for the formation of reversible membraneless intracellular organelles such as nucleoli, stress granules and neuronal transport granules. Using FUS as an example, this review examines the biophysics of this physiological process, and reports on how mutations and changes in post-translational state alter phase behaviour, and lead to neurodegenerative diseases such as amyotrophic lateral sclerosis and frontotemporal lobar degeneration.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.152
GPT teacher head0.433
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations85
Published2018
Admission routes2
Has abstractyes

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