The Werner syndrome protein affects the expression of genes involved in adipogenesis and inflammation in addition to cell cycle and DNA damage responses
Bibliographic record
Abstract
Werner syndrome (WS) is characterized by the premature onset of several age-associated pathologies. The protein deficient in WS (WRN) is a RecQ-type DNA helicase involved in DNA repair, replication, telomere maintenance and transcription. However, precisely how WRN deficiency leads to the numerous WS pathologies is still unknown. Here we use short-term siRNA-based inhibition of WRN to test the direct consequences of its loss on gene expression. Importantly, this short-term knock down of WRN protein level was sufficient to trigger an expression profile resembling fibroblasts established from old donor patients. In addition, this treatment altered sets of genes involved in 14 distinct biological pathways. Besides the already known impact of WRN on DNA replication, DNA repair, the p21/p53 pathway, and cell cycle, gene set enrichment analyses of our microarray data also uncover significant impact on the MYC, E2F, cellular E2A and ETV5 transcription factor pathways as well as adipocyte differentiation, HIF1, NFkappaB and IL-6 pathways. Finally, short-term siRNA-based inhibition of mouse Wrn expression in the pre-adipocyte cell line 3T3-L1 confirmed the impact of WRN on adipogenesis. These results are consistent with the pro-inflammatory status and lipid abnormalities observed in WS patients. This approach thus identified new effectors of WRN activity that might contribute to the WS phenotype.
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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 | 0.000 | 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".