Polymorphisms in the Interleukin 4, Interleukin 13, and Corresponding Receptor Genes Are Not Associated with Systemic Sclerosis and Do Not Influence Gene Expression
Bibliographic record
Abstract
OBJECTIVE: Polymorphisms in the genes encoding interleukin 4 (IL4), interleukin 13 (IL13), and their corresponding receptors have been associated with multiple immune-mediated diseases. Our aim was to validate these previous observations in patients with systemic sclerosis (SSc) and scrutinize the effect of the polymorphisms on gene expression in various populations of peripheral blood leukocytes. METHODS: We genotyped a cohort of 2488 patients with SSc and 2246 healthy controls from The Netherlands, Spain, United Kingdom, Italy, Germany, and France. Taqman assays were used to genotype single-nucleotide polymorphisms (SNP) in the following genes: (1) IL4 (-590C>T/rs2243250); (2) IL4 receptor alpha (IL4RA) (Q576R/rs1801275); (3) IL13 (R130Q/rs20541 and -1112C>T/rs1800925); and (4) IL13RA1 (43163G>A/rs6646259). The effect of these polymorphisms on expression of the corresponding genes was assessed using quantitative RT-PCR on RNA derived from peripheral blood B cells, T cells, plasmacytoid dendritic cells, monocytes, and myeloid dendritic cells. We investigated whether these polymorphisms influenced development of pulmonary complications over 15 years in patients with SSc. RESULTS: None of the investigated polymorphisms was associated with SSc or any SSc clinical subtype. We did not observe any effect on transcript levels in the cell subtypes or on development of pulmonary complications. CONCLUSION: Our data showed that polymorphisms in IL4, IL13, and their receptors do not play a role in SSc and do not influence the expression of their corresponding transcript in peripheral blood cells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".