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Record W4386096788 · doi:10.1016/j.isci.2023.107707

Seizure enhances SUMOylation and zinc-finger transcriptional repression in neuronal nuclei

2023· article· en· W4386096788 on OpenAlexaff
Hui Rong Soon, Jessica Gaunt, Vibhavari Aysha Bansal, Clara Lenherr, Siu Kwan Sze, Toh Hean Ch’ng

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsBrock University
FundersNanyang Technological UniversityMinistry of Education - Singapore
KeywordsSUMO proteinZinc fingerBiologyProteomeCell biologyTranscriptomeNuclear proteinDownregulation and upregulationGene expressionKrüppelStatus epilepticusEpilepsyTranscription factorNeuroscienceGeneBioinformaticsGenetics

Abstract

fetched live from OpenAlex

can trigger the development of spontaneous recurrent seizures in a rodent model for epilepsy. The initial seizure-induced events in neuronal nuclei that lead to long-term changes in gene expression and cellular responses likely contribute toward epileptogenesis. Using a transgenic mouse model to specifically isolate excitatory neuronal nuclei, we profiled the seizure-induced nuclear proteome via tandem mass tag mass spectrometry and observed robust enrichment of nuclear proteins associated with the SUMOylation pathway. In parallel with nuclear proteome, we characterized nuclear gene expression by RNA sequencing which provided insights into seizure-driven transcriptional regulation and dynamics. Strikingly, we saw widespread downregulation of zinc-finger transcription factors, specifically proteins that harbor Krüppel-associated box (KRAB) domains. Our results provide a detailed snapshot of nuclear events induced by seizure activity and demonstrate a robust method for cell-type-specific nuclear profiling that can be applied to other cell types and models.

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)0.000
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 score0.366
Threshold uncertainty score0.199

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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes1
Has abstractyes

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