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Record W2039348210 · doi:10.1074/jbc.m006807200

Interorganellar Communication

2001· article· en· W2039348210 on OpenAlexaff
Ana Traven, Johnson M. S. Wong, Deming Xu, Mary Sopta, C. James Ingles

Bibliographic record

VenueJournal of Biological Chemistry · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyMitochondrionMitochondrial DNAGeneSaccharomyces cerevisiaeCell biologyNuclear geneGene expressionBiochemistry

Abstract

fetched live from OpenAlex

Communication between mitochondria and the nucleus is important for a variety of cellular processes such as carbohydrate and nitrogen metabolism, mating and sporulation, and cell growth and morphogenesis. It has long been known that the functional state of mitochondria can influence nuclear gene expression. For example, in yeast cells lacking the mitochondrial genome, the expression of several nuclear genes, such as CIT2 (citrate synthase), MRP13 (mitochondrial ribosomal protein), and DLD3 (d-lactate dehydrogenase) has been reported to be altered. Here we show by microarray analysis of the genome-wide transcription profile of Saccharomyces cerevisiae that yeast petite mutants lacking mitochondrial DNA induce genes coding for mitochondrial proteins, enzymes of the glycolytic pathway and of the citric acid cycle, cell wall components, membrane transporters, and genes normally induced by nutrient deprivation and a variety of stresses. Consistent with the observed induction of genes related to cell stress and those encoding membrane transporters, yeast petite cells showed increased resistance to severe heat shock and exhibited a pleiotropic drug resistance phenotype. The observed changes in nuclear gene expression in cells lacking mitochondrial DNA may have implications for the role of mitochondria in processes such as carcinogenesis and aging.

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)0.000
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 score0.068
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.293
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 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

Citations210
Published2001
Admission routes1
Has abstractyes

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