Impairing the production of ribosomal RNA activates mammalian target of rapamycin complex 1 signalling and downstream translation factors
Bibliographic record
Abstract
Ribosome biogenesis is a key process for maintaining protein synthetic capacity in dividing or growing cells, and requires coordinated production of ribosomal proteins and ribosomal RNA (rRNA), including the processing of the latter. Signalling through mammalian target of rapamycin complex 1 (mTORC1) activates all these processes. Here, we show that, in human cells, impaired rRNA processing, caused by expressing an interfering mutant of BOP1 or by knocking down components of the PeBoW complex elicits activation of mTORC1 signalling. This leads to enhanced phosphorylation of its substrates S6K1 and 4E-BP1, and stimulation of proteins involved in translation initiation and elongation. In particular, we observe both inactivation and downregulation of the eukaryotic elongation factor 2 kinase, which normally inhibits translation elongation. The latter effect involves decreased expression of the eEF2K mRNA. The mRNAs for ribosomal proteins, whose translation is positively regulated by mTORC1 signalling, also remain associated with ribosomes. Therefore, our data demonstrate that disrupting rRNA production activates mTORC1 signalling to enhance the efficiency of the translational machinery, likely to help compensate for impaired ribosome production.
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.
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".