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Record W2568937089 · doi:10.1055/s-0036-1582635

Differential Gene Expression Profiles of Cells from Normal, Traumatic and Idiopathic Scoliotic Discs Identify Molecular Dysregulation in Scoliosis

2016· article· en· W2568937089 on OpenAlexaff
Sibylle Grad, Ying Zhang, Ольга Владимировна Рожнова, Elena Schelkunova, Mikhail Mikhailovsky, M. A. Sadovoy, Lisbet Haglund, Jean Ouellet, Mauro Alini

Bibliographic record

VenueGlobal Spine Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsScoliosisMedicineIntervertebral discDiscectomyIdiopathic scoliosisMicroarray analysis techniquesMicroarraySpinal fusionGene expression profilingDegenerative disc diseaseGene expressionPathologyGeneBioinformaticsLumbarSurgeryBiologyGenetics

Abstract

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Introduction The etiology of the spinal deformity in idiopathic scoliosis is unclear to date, both with respect to initiation and progression of the disease. While the influence of certain genetic factors has been established, the role of the intervertebral disc (IVD) in the development of idiopathic scoliosis has scarcely been investigated. The aim of this study was to identify molecular differences between disc cells from patients with idiopathic scoliosis in comparison with trauma patients and healthy individuals. To address this aim, cellular gene expression profiles were analyzed by microarray and quantitative RT-PCR. Material and Methods Surgical samples from IVDs of patients with idiopathic scoliosis were obtained after informed consent and approval of the local ethical commission at the time of discectomy during spinal fusion surgery. The disorder and exact curve pattern were documented for further records. Control disc samples were obtained from trauma fusion cases and from organ donors with no known disc disorders according to local and institutional ethical guidelines. Annulus fibrosus (AF) and nucleus pulposus (NP) tissues were separated and cells were isolated by enzymatic digestion within 24 hours. Total RNA was extracted from the cells and subjected to Affymetrix GeneChip® expression profiling. Genes with significant differences between scoliotic and control samples were further analyzed using real time RT-PCR. Results After exclusion of RNA samples with insufficient quality or quantity, the following numbers of samples were used for microarray profiling: 10 AF and 6 NP samples from scoliotic discs; 5 AF and 4 NP samples from traumatic discs; 4 AF and 4 NP samples from healthy discs of organ donors. Microarray data revealed that 52 genes were more highly expressed in scoliotic vs. healthy AF and 26 genes in scoliotic vs. traumatic AF, whereby 21 genes showed higher expression in scoliotic AF compared with both control groups. In addition, 116 genes were more highly expressed in scoliotic vs. healthy NP, 45 genes in scoliotic vs. traumatic NP, and 40 of those in scoliotic NP compared with both control groups. Quantitative gene expression analysis by real time RT-PCR ( n = 6 per group) confirmed significantly increased mRNA levels of S100A8, S100A12, MMP8, MMP13 and Collagen X in annulus fibrosus cells from scoliotic discs ( p < 0.05; Kruskal-Wallis test of log2 transformed data). Conclusion Results of this study reveal significant changes in the gene expression profile of IVD cells from patients with idiopathic scoliosis compared with patients with traumatic disc damage or donors with no known disc disorders. MMP8 and MMP13 are important collagenases involved in disc matrix degradation; while elevated S100 calcium binding proteins may indicate an inflammatory reaction. Interestingly, MMP13 has also been up-regulated by imbalanced loading in an IVD organ culture model. Better knowledge of the dysregulation of structural or regulatory molecules may identify underlying mechanisms of spinal deformities, which will help defining new targets for early therapeutic intervention. Acknowledgment This study is supported by AOSpine International.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.440

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 head0.285
Teacher spread0.271 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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