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Record W2970017451 · doi:10.1186/s13041-019-0493-9

SUMOylation regulates USP5-Cav3.2 calcium channel interactions

2019· article· en· W2970017451 on OpenAlexafffund
Agustı́n Garcı́a-Caballero, Fang‐Xiong Zhang, Lina Chen, Saïd M’Dahoma, Junting Huang, Gerald W. Zamponi

Bibliographic record

VenueMolecular Brain · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersInstitute of Neurosciences, Mental Health and AddictionCanadian Institutes of Health ResearchAlberta InnovatesCanada Research Chairs
KeywordsSUMO proteinCalcium channelNeuroscienceCell biologyBiologyChemistryCalciumUbiquitinGeneticsGene

Abstract

fetched live from OpenAlex

Cav3.2 calcium channels play a key role in nociceptive signaling in the primary afferent pain pathway. We have previously reported the regulation of Cav3.2 calcium channels by the deubiquitinase USP5 and its importance for regulating peripheral transmission of pain signals. Here we describe the regulation of the Cav3.2-USP5 interaction by SUMOylation. We show that endogenous USP5 protein expressed in dorsal root ganglia undergoes SUMOylation, and the level of USP5 SUMOylation is reduced following peripheral nerve injury. SUMO prediction software identified several putative lysines that have the propensity to be targets for SUMO conjugation. A series of single lysine substitutions in an mCherry tagged USP5 construct followed by expression in tsA-201 cells identified lysine K113 as a key target for USP5 SUMO2/3 modification. Finally, Cav3.2 calcium channel immunoprecipitates revealed a stronger interaction of Cav3.2 with a SUMO2/3 resistant USP5-K113R mutant, indicating that SUMO2/3 modification of USP5 reduces its affinity for the calcium channel Cav3.2. Collectively, our data suggest that dysregulation of USP5 SUMOylation after peripheral nerve injury may contribute to the well described alteration in Cav3.2 channel activity during neuropathic pain states.

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 score0.031
Threshold uncertainty score0.717

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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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

Citations27
Published2019
Admission routes2
Has abstractyes

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