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Record W4393178823 · doi:10.1016/j.jbc.2024.106346

Abstract 1408 "Nuclear lipid droplet characterization in budding yeast"

2024· article· en· W4393178823 on OpenAlexaffabout
Roxana Valdés Núñez, Maria Sosa Ponce, Vanina Zaremberg

Bibliographic record

VenueJournal of Biological Chemistry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBudding yeastYeastCharacterization (materials science)BuddingChemistryLipid dropletBiophysicsSaccharomyces cerevisiaeCell biologyMaterials scienceBiochemistryBiologyNanotechnology

Abstract

fetched live from OpenAlex

Lipid droplets (LDs) are often present in the cytoplasm of eukaryotes, but in some cell types such as hepatocytes or budding yeast, a number of LDs are found in the nucleus (nLDs) in response to stress. The physiological function of nLDs needs additional studies, as well as why they are abundant in only limited cell types. The inner nuclear membrane is involved in the formation of nLDs in both mammalian hepatocytes and budding yeast. We have previously shown that alterations of the nuclear envelope morphology by a non-metabolizable lysophosphatidylcholine (LysoPC) analogue led to drastic changes in gene expression driven by Mga2, Spt23 and Opi1 in their dual role of membrane property sensors and transcriptional regulators. Expression of the only yeast Δ9-desaturase Ole1 and phospholipid remodelling pathways were upregulated while all branches of the de novo phospholipid synthesis downstream of phosphatidic acid (PA) were repressed. This rewiring of lipid metabolism in response to LysoPC accumulation resulted in nLDs formation. Little is known about the proteome and lipidome of yeast nLDs. The biogenesis of both cLDs and nLDs depends on PA. The first reaction in the de novo synthesis of PA is catalyzed by two glycerol-3 phosphate acyltransferases (GPATs) in yeast, Sct1 and Gpt2. Gpt2 can also use DHAP as substrate. In this work we show that Gpt2 (but not Sct1) contributes to nLDs generation in response to a LysoPC burden. A differential role of Gpt2 was first suggested by RNAseq data from cells treated with the LysoPC analogue. Second, nLD biogenesis induced by the LysoPC drug was dependent on Gpt2 as seen by using deletion mutant strains sct1Δ and gpt2Δ expressing DsRed-HDEL to delineate the NE and stained with Bodipy® to detect LDs. Preliminary results from a thorough analysis of known LD-resident proteins, has identified Opi1-GFP association with nLDs. A strategy to enhance the accumulation of nLDs by treatment of yeast GPAT mutants with the LysoPC analogue combined with Opi1 pulldown is currently being tested to aid in nLD purification for proteomic and lipidomic future characterization. This work has been financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant to VZ

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 score0.004

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)0.0010.001

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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