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Record W4321605681 · doi:10.1101/2023.02.22.529618

A compact regulatory RNA element in mouse Hsp70 mRNA

2023· preprint· en· W4321605681 on OpenAlexaff
Wenshuai Wang, Fei Liu, Anna Marie Pyle

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsMcGill University
FundersNational Institutes of HealthHoward Hughes Medical Institute
KeywordsHsp70RNAMessenger RNACell biologyTranslation (biology)Heat shock proteinProtein biosynthesisBiologyChaperone (clinical)ChemistryMolecular biologyGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

Hsp70 performs molecular chaperone functions by assisting in folding newly synthesized or misfolded proteins, thereby counteracting various cell stresses and preventing multiple diseases including neurodegenerative disorders and cancer. It is well established that Hsp70 upregulation during post-heat shock stimulus is mediated by cap-dependent translation. However, the molecular mechanisms of Hsp70 expression during heat shock stimulus remains elusive, even though the 5' end of Hsp70 mRNA may form a compact structure to positively regulate protein expression in the mode of cap-independent translation. The minimal truncation which can fold to a compact structure was mapped and its secondary structure was characterized by chemical probing. The predicted model revealed a highly compact structure with multiple stems. Including the stem where the canonical start codon is located, several stems were identified to be vital for RNA folding, thereby providing solid structural basis for future investigations on the function of this RNA structure on Hsp70 translation during heat shock.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicHeat shock proteins research→French-language works237,207→