Association Between a CTGF Gene Polymorphism and Systemic Sclerosis in a French Population
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a life-threatening autoimmune disease characterized by chronic fibrosis of the skin and internal organs. Connective tissue growth factor (CTGF) is believed to be a primary mediator of chronic fibrosis. We assessed the possible association between 7 single-nucleotide polymorphisms (SNP) in the CTGF gene and scleroderma in a French population (registration number 2006/0182). METHODS: We conducted a case-control study with 241 scleroderma patients and 269 controls. Seven SNP were genotyped using the TaqMan system. Univariate and multivariate analyses were performed. In silico electrophoretic mobility shift assay (EMSA), and reverse transcriptase polymerase chain reaction analyses were done to assess the effect of the SNP on CTGF gene expression. RESULTS: The frequency of the rs9399005TT genotype was significantly lower in SSc patients than in controls. This association remained significant after adjustment for gender. An association was detected between the rs9399005 and the diffuse and limited cutaneous forms. Multivariate analysis between SSc patients and controls taking into account all 7 SNP and sex revealed that only sex and the rs9399005 SNP were associated with disease. DNA analysis by EMSA indicated that the T allele bound nuclear factors that were also bound by the C allele. The binding affinity was higher for the T allele. Analysis of the human database and experiments with human hepatocyte cell line indicated the existence of an alternative transcript containing the rs9399005 polymorphism in its 3'UTR region. In silico analysis indicated that this polymorphism may alter the structure of CTGF messenger RNA. CONCLUSION: These findings suggest that CTGF gene polymorphisms may contribute to susceptibility to scleroderma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".