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Record W2982815018 · doi:10.7554/elife.45002.029

Author response: A ribosome assembly stress response regulates transcription to maintain proteome homeostasis

2019· peer-review· en· W2982815018 on OpenAlexaff
Benjamin Albert, Isabelle C. Kos‐Braun, Anthony K. Henras, Christophe Dez, Maria Paula Rueda, Xu Zhang, Olivier Gadal, Martin Koš, David Shore

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsInstitute of Genetics
Fundersnot available
KeywordsRibosome biogenesisProteostasisHSF1RibosomeBiologyRibosomal proteinCell biologyProtein biosynthesisPromoterTranscription factorGeneticsGeneRNAGene expressionHsp70Heat shock protein

Abstract

fetched live from OpenAlex

When yeast cells are growing at top speed, they can make 2,000 new ribosomes every minute. These enormous molecular assemblies are the protein-making machines of the cell. Building new ribosomes is one of the most energy-demanding parts of cell growth and, if the process goes wrong, the results can be catastrophic. The proteins that make up the ribosomes themselves are sticky. Left unattended, they start to form toxic clumps inside the compartment that houses most of the cell’s DNA, the nucleus. A protein called Heat shock factor 1, or Hsf1 for short, plays an important role in the cell's quality control systems. It helps to manage sticky proteins by switching on genes that break down protein clumps and prevent new clumps from forming. Hsf1 levels start to rise whenever cells are struggling to keep up with protein production. If it is half-finished ribosomes that are causing the problem, cells can stop making ribosome proteins. The protein in charge of this in yeast is Ifh1. It is a transcription factor that sits at the front of the genes for ribosome proteins, switching them on. When yeast cells get stressed, Ifh1 drops away from the genes within minutes, switching them off again. Yet how this happens, and how it links to Hsf1, is a mystery. To start to provide some answers, Albert et al. disrupted the production of ribosomes in yeast cells and examined the consequences. This revealed a new rescue response, that they named the “ribosome assembly stress response”. Both Hsf1 and Ifh1 are sensitive to the build-up of unfinished ribosomes in the nucleus. As expected, Hsf1 activated when ribosome proteins started to build up, and switched on the genes needed to manage the protein clumps. The effect on Isfh1, however, was unexpected. When the unassembled ribosome proteins started to build up, it was the clumps themselves that pulled the Ifh1 proteins off the genes. The unassembled ribosomes proteins seemed to be stopping their own production. Low levels of clumped ribosome proteins in the nuclei of unstressed cells also helped to keep Hsf1 active and pull Ifh1 off the ribosome genes. It is possible that this provides continual protection against a toxic protein build-up. These findings are not only important for understanding yeast cells; cancer cells also need to produce ribosomes at a very high rate to sustain their rapid growth. They too might be prone to stresses that interrupt their ribosome assembly. As such, understanding more about this process could one day lead to new therapies to target cancer cells.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1370.090

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.

Opus teacher head0.030
GPT teacher head0.329
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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