The Werner syndrome protein affects the expression of genes involved in adipogenesis and inflammation in addition to cell cycle and DNA damage responses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it