Translation-State Analysis of Gene Expression in Mouse Brain after Focal Ischemia
Bibliographic record
Abstract
Confounding any genome-scale analysis of gene expression after cerebral ischemia is massive suppression of protein synthesis. This inefficient translation questions the utility of examining profiles of total transcripts. Our approach to such postischemic gene profiling in the mouse by microarray analysis was to concentrate on those mRNAs bound to polyribosomes. In our proof-of-principle study, polysomally bound and unbound mRNAs were subjected to microarray analysis: of the 1,161 transcripts that we found to increase after ischemia, only 36% were bound to polyribosomes. In addition to the expected increases in heat-shock proteins and metallothioneins, increases in several other bound transcripts involved in the promotion of cell survival or antiinflammatory behavior were noted, such as CD63 (Lamp3), Lcn2 (lipocalin-2), Msn (moesin), and UCP2 (uncoupling protein 2), all of which showed increases in cognate protein by Western blotting. The list of heretofore nonfunctionally annotated transcripts (RIKEN clones/ESTs) that increased appeared to be novel. How some transcripts are selected in ischemic brain for translation into protein, while others are rejected, is not clear. The length of the 5'-UTR in the ischemically induced transcripts that occur in the NCBI RefSeq database did not indicate any general tendency to be more than 200 nt, nor to be longer than the 5'-UTRs of the unbound transcripts. Thus, the presence of a complex 5'-UTR region with internal ribosome entry sites (IRES) or polypyrimidine tracts (TOP) does not appear to be the basis of selection for translation in ischemic brain.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".