MétaCan
Menu
← Back to cohort

Autophagy deficiency activates rDNA transcription

2021· dataset· en· W4394553453 on OpenAlexaff
Yinfeng Xu, Yaosen Wu, Lei Wang, Lijiang Song, Hui Zhang, Chuying Qian, Qian Wang, Zhengfu He, Wei Wan

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAutophagyTranscription (linguistics)Transcription factorCell biologyChemistryBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Macroautophagy/autophagy, a highly conserved lysosome-dependent degradation pathway, has been intensively studied in regulating cell metabolism by degradation of intracellular components. In this study, we link autophagy to RNA metabolism by uncovering a regulatory role of autophagy in ribosomal RNA (rRNA) synthesis. Autophagy-deficient cells exhibit much higher 47S precursor rRNA level, which is caused by the accumulation of SQSTM1/p62 (sequestosome 1) but not other autophagy receptors. Mechanistically, SQSTM1 accumulation potentiates the activation of MTOR (mechanistic target of rapamycin kinase) complex 1 (MTORC1) signaling and promotes the assembly of RNA polymerase I pre-initiation complex at ribosomal DNA (rDNA) promoters, which leads to an increase of 47S rRNA transcribed from rDNA. Functionally, autophagy deficiency promotes protein synthesis, cell growth and cell proliferation, both of which are dependent on SQSTM1 accumulation. Taken together, our findings suggest that autophagy deficiency is involved in RNA metabolism by activating rDNA transcription and provide novel mechanisms for the reprogramming of cell metabolism in autophagy-related diseases including multiple types of cancers. Abbreviations: 5-FUrd: 5-fluorouridine; AMPK: AMP-activated protein kinase; ATG: autophagy related; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; ChIP: chromatin immunoprecipitation; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MAPK/ERK: mitogen-activated protein kinase; MTOR: mechanistic target of rapamycin kinase; NBR1: NBR1 autophagy cargo receptor; NFKB/NF-κB: nuclear factor kappa B; NFE2L2/NRF2: nuclear factor, erythroid 2 like 2; OPTN: optineurin; PIC: pre-initiation complex; POLR1: RNA polymerase I; POLR1A/RPA194: RNA polymerase I subunit A; POLR2A: RNA polymerase II subunit A; rDNA: ribosomal DNA; RPS6KB1/S6K1: ribosomal protein S6 kinase B1; rRNA: ribosomal RNA; RUBCN/Rubicon: rubicon autophagy regulator; SQSTM1/p62: sequestosome 1; STX17: syntaxin 17; SUnSET: surface sensing of translation; TAX1BP1: Tax1 binding protein 1; UBTF/UBF1: upstream binding transcription factor; WIPI2: WD repeat domain, phosphoinositide interacting 2; WT: wild-type.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.037
GPT teacher head0.300
Teacher spread0.263 · 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
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFigshare→Same topicAutophagy in Disease and Therapy→French-language works237,207→