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Record W2196491500 · doi:10.1242/jcs.175232

Localized control of oxidized RNA

2015· article· en· W2196491500 on OpenAlexafffund
Yu Zhan, James Dhaliwal, Pauline Adjibade, James Uniacke, Rachid Mazrouï, William Zerges

Bibliographic record

VenueJournal of Cell Science · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalConcordia University
FundersCanadian Institutes of Health Research
KeywordsBiologyRNAChlamydomonas reinhardtiiRuBisCOChloroplastCompartmentalization (fire protection)Cell biologyCytoplasmBiochemistryMitochondrionPhotosynthesisGeneEnzyme

Abstract

fetched live from OpenAlex

The oxidation of biological molecules by reactive oxygen species (ROS) can render them inactive or toxic. This includes the oxidation of RNA, which appears to underlie the detrimental effects of oxidative stress, aging and certain neurodegenerative diseases. Here, we investigate the management of oxidized RNA in the chloroplast of the green alga Chlamydomonas reinhardtii. Our immunofluorescence microscopy results reveal that oxidized RNA (with 8-hydroxyguanine) is localized in the pyrenoid, a chloroplast microcompartment where CO2 is assimilated by the Calvin cycle enzyme Rubisco. Results of genetic analyses support a requirement for the Rubisco large subunit (RBCL), but not Rubisco, in the management of oxidized RNA. An RBCL pool that can carry out such a 'moonlighting' function is revealed by results of biochemical fractionation experiments. We also show that human (HeLa) cells localize oxidized RNA to cytoplasmic foci that are distinct from stress granules, processing bodies and mitochondria. Our results suggest that the compartmentalization of oxidized RNA management is a general phenomenon and therefore has some fundamental significance.

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 score0.059
Threshold uncertainty score0.168

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 head0.249
Teacher spread0.239 · 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

Citations34
Published2015
Admission routes2
Has abstractyes

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