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Record W2021132129 · doi:10.1371/journal.pone.0012922

Microarray Analysis of Gene Expression by Skeletal Muscle of Three Mouse Models of Kennedy Disease/Spinal Bulbar Muscular Atrophy

2010· article· en· W2021132129 on OpenAlexaff
Kaiguo Mo, Zak Razak, Pengcheng Rao, Zhigang Yu, Hiroaki Adachi, Masahisa Katsuno, Gen Sobue, Andrew P. Lieberman, J. Timothy Westwood, D. Ashley Monks

Bibliographic record

VenuePLoS ONE · 2010
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsSpinal muscular atrophySkeletal muscleMicroarray analysis techniquesPathologyProgressive muscular atrophyGene expressionMuscle atrophyAtrophyMedicineBiologyNeuromuscular diseaseGene expression profilingGeneAmyotrophic lateral sclerosisAnatomyDiseaseGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Emerging evidence implicates altered gene expression within skeletal muscle in the pathogenesis of Kennedy disease/spinal bulbar muscular atrophy (KD/SBMA). We therefore broadly characterized gene expression in skeletal muscle of three independently generated mouse models of this disease. The mouse models included a polyglutamine expanded (polyQ) AR knock-in model (AR113Q), a polyQ AR transgenic model (AR97Q), and a transgenic mouse that overexpresses wild type AR solely in skeletal muscle (HSA-AR). HSA-AR mice were included because they substantially reproduce the KD/SBMA phenotype despite the absence of polyQ AR. METHODOLOGY/PRINCIPAL FINDINGS: We performed microarray analysis of lower hindlimb muscles taken from these three models relative to wild type controls using high density oligonucleotide arrays. All microarray comparisons were made with at least 3 animals in each condition, and only those genes having at least 2-fold difference and whose coefficient of variance was less than 100% were considered to be differentially expressed. When considered globally, there was a similar overlap in gene changes between the 3 models: 19% between HSA-AR and AR97Q, 21% between AR97Q and AR113Q, and 17% between HSA-AR and AR113Q, with 8% shared by all models. Several patterns of gene expression relevant to the disease process were observed. Notably, patterns of gene expression typical of loss of AR function were observed in all three models, as were alterations in genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis. We additionally measured changes similar to those observed in skeletal muscle of a mouse model of Huntington's Disease, and to those common to muscle atrophy from diverse causes. CONCLUSIONS/SIGNIFICANCE: By comparing patterns of gene expression in three independent models of KD/SBMA, we have been able to identify candidate genes that might mediate the core myogenic features of KD/SBMA.

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 score0.014
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.233
Teacher spread0.198 · 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

Citations57
Published2010
Admission routes1
Has abstractyes

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