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Record W4311681292 · doi:10.22215/etd/2022-15184

Characterization of lysine demethylase KDM5 family: Substrate specificity and identification of potential novel non-histone substrates

2022· dissertation· en· W4311681292 on OpenAlexafffund
Matthew Hoekstra

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDemethylaseDemethylationHistoneHistone H3Histone methylationEpigeneticsBiologyLysineMethylationBiochemistryChemistryGene expressionGeneDNA methylationAmino acid

Abstract

fetched live from OpenAlex

A major regulatory influence over cell biology is lysine methylation and demethylation within histone proteins.The KDM5/JARID1 sub-family are 2oxoglutarate and Fe(II)-dependent lysine-specific histone demethylases that are characterized by their Jumonji catalytic domains.This enzyme family is known to facilitate the removal of tri-/di-methyl modifications from lysine 4 of histone H3 (i.e., H3-K4me3/2), a mark associated with active gene expression.As a result, studies to date have revolved around KDM5's influence on disease through their ability to regulate H3-K4me2/3.Recently, evidence has demonstrated that KDM5's may influence disease beyond H3-K4 demethylation, making it critical to further investigate KDM5 demethylation of non-histone proteins.In efforts to help identify potential non-histone substrates for the KDM5 family, we developed a library of 180 permutated peptide substrates (PPS), with sequences that are systematically altered from the WT H3-K4me3 sequence.From this library, we characterized recombinant KDM5A/B/C/D substrate preference.Subsequently we developed recognition motifs for each KDM5 demethylase and used them to predict potential substrates for KDM5A/B/C/D.Demethylation activity was then profiled to generate a list of high/medium/low-ranking substrates for further in vitro validation for each of KDM5A/B/C/D.Through this approach, we analyzed prediction success rate and identified 66 high-ranking substrates in which KDM5 demethylases displayed significant in vitro activity towards.We further shown the ability to monitor changes in cellular methylation in a handful of the 66 high ranking candidate substrates in response to KDM5 inhibition.Specifically, we iii focused validation efforts on a high-ranking KDM5A novel substrate: p53-K370me3.We demonstrated significant recombinant KDM5A1-588ΔAP and KDM5A1-801 activity towards the p53-K370me3 substrate in vitro.We then monitored KDM5A-mediated demethylation of the p53-K370me3/2 substrate in HCT 116 cells using a combination of wild-type KDM5A and inactive-mutant KDM5AH483A overexpression plasmids, along with immunoblotting, (co-) immunoprecipitation and mass spectrometry analysis.Furthermore, we have shown that KDM5A expression influences the established p53-53BP1 interaction.Finally, we identified a novel p53-TAF5 interaction dominated through the p53-K370me3 state and how KDM5A activity might affect this interaction.Ultimately, we have provided the first evidence of a KDM5 demethylase targeting a nonhistone substrate for demethylation, via the novel KDM5A demethylation of the p53-K370me3 substrate.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.269
Teacher spread0.259 · 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 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
Published2022
Admission routes2
Has abstractyes

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