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Record W2042125907 · doi:10.1083/jcb.200709044

<i>Drosophila</i> TIF-IA is required for ribosome synthesis and cell growth and is regulated by the TOR pathway

2007· article· en· W2042125907 on OpenAlexaff
Savraj Grewal, Justin R. Evans, Bruce A. Edgar

Bibliographic record

VenueThe Journal of Cell Biology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Calgary
FundersNational Institute of General Medical Sciences
KeywordsBiologyRibosome biogenesisRibosomal RNARNA polymerase IRibosomeCell biology5.8S ribosomal RNATranscription (linguistics)RNA polymerase IIIRNADrosophila melanogaster5S ribosomal RNAMolecular biologyGeneticsRNA polymeraseGene

Abstract

fetched live from OpenAlex

Synthesis of ribosomal RNA (rRNA) is a key step in ribosome biogenesis and is essential for cell growth. Few studies, however, have investigated rRNA synthesis regulation in vivo in multicellular organisms. Here, we present a genetic analysis of transcription initiation factor IA (TIF-IA), a conserved RNA polymerase I transcription factor. Drosophila melanogaster Tif-IA(-/-) mutants have reduced levels of rRNA synthesis and sustain a developmental arrest caused by a block in cellular growth. We find that the target of rapamycin (TOR) pathway regulates TIF-IA recruitment to rDNA. Furthermore, we show that the TOR pathway regulates rRNA synthesis in vivo and that TIF-IA overexpression can maintain rRNA transcription when TOR activity is reduced in developing larvae. We propose that TIF-IA acts in vivo as a downstream growth-regulatory target of the TOR pathway. Overexpression of TIF-IA also elevates levels of both 5S RNA and messenger RNAs encoding ribosomal proteins. Stimulation of rRNA synthesis by TIF-IA may therefore provide a feed-forward mechanism to coregulate the levels of other ribosome components.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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 teacher head, 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

Citations96
Published2007
Admission routes1
Has abstractyes

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