Identification of dysregulated genes in lymphocytes from children with Down syndrome
Bibliographic record
Abstract
The molecular mechanisms by which trisomy of human chromosome 21 disrupts normal development are not well understood. Global transcriptome studies attempting to analyze the consequences of trisomy in Down syndrome (DS) tissues have reported conflicting results, which have led to the suggestion that the analysis of specific tissues or cell types may be more productive. In the present study, we set out to analyze global changes of gene expression in lymphocytes from children with trisomy 21 by means of the serial analysis of gene expression (SAGE) methodology. Two SAGE libraries were constructed using pooled RNA of normal and Down syndrome children. Comparison between DS and normal profiles revealed that most of the transcripts were expressed at similar levels and functional classes of abundant genes were equally represented. Among the 242 significantly differentially expressed SAGE tags, several transcripts downregulated in DS code for proteins involved in T-cell and B-cell receptor signaling (e.g., PI3Kdelta, RGS2, LY6E, FOS, TAGAP, CD46). The SAGE data and interindividual variability were validated by real-time quantitative PCR. Our results indicate that trisomy 21 induces a modest dysregulation of disomic genes that may be related to the immunological perturbations seen in DS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".