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Record W2288768984 · doi:10.1038/srep12615

Decrease of mRNA Editing after Spinal Cord Injury is Caused by Down-regulation of ADAR2 that is Triggered by Inflammatory Response

2015· article· en· W2288768984 on OpenAlexaff
Antonio Fabio Di Narzo, Alexey Kozlenkov, Yongchao Ge, Bin Zhang, Leo Sanelli, Zacnicte May, Yanqing Li, Karim Fouad, Christopher Cardozo, Eugene V. Koonin, David J. Bennett, Stella Dracheva

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsUniversity of Alberta
FundersU.S. National Library of MedicineNIH Clinical CenterNational Institutes of HealthU.S. Department of Veterans AffairsU.S. Department of Health and Human Services
KeywordsRNA editingTranscriptomeSpinal cordSpinal cord injuryMicrogliaMedicineNeuroscienceAdenosine deaminaseBiologyGene expressionBioinformaticsGeneInternal medicineAdenosineInflammationGenetics

Abstract

fetched live from OpenAlex

We recently showed that spinal cord injury (SCI) leads to a decrease in mRNA editing of serotonin receptor 2C (5-HT2CR) contributing to post-SCI spasticity. Here we study post-SCI mRNA editing and global gene expression using massively parallel sequencing. Evidence is presented that the decrease in 5-HT2CR editing is caused by down-regulation of adenosine deaminase ADAR2 and that editing of at least one other ADAR2 target, potassium channel Kv1.1, is decreased after SCI. Bayesian network analysis of genome-wide transcriptome data indicates that down-regulation of ADAR2 (1) is triggered by persistent inflammatory response to SCI that is associated with activation of microglia and (2) results in changes in neuronal gene expression that are likely to contribute both to post-SCI restoration of neuronal excitability and muscle spasms. These findings have broad implications for other diseases of the Central Nervous System and could open new avenues for developing efficacious antispastic treatments.

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 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 score0.180
Threshold uncertainty score0.554

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 studies0.0000.000
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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

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