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Record W2016355879 · doi:10.1074/jbc.m109.050617

Diacylglycerol Kinase ϵ Is Selective for Both Acyl Chains of Phosphatidic Acid or Diacylglycerol

2009· article· en· W2016355879 on OpenAlexaff
Michael Lung, Yulia V. Shulga, Pavlina T. Ivanova, David S. Myers, Stephen Milne, H. Alex Brown, Matthew K. Topham, Richard M. Epand

Bibliographic record

VenueJournal of Biological Chemistry · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsMcMaster University
FundersNational Institute of General Medical SciencesNational Cancer Institute
KeywordsDiacylglycerol kinasePhosphatidic acidSecond messenger systemPhosphatidylinositolBiochemistryChemistryEnzymePiLipid signalingInositolStereochemistryKinasePhospholipidCell biologyBiologyProtein kinase CReceptorMembrane

Abstract

fetched live from OpenAlex

The phosphatidylinositol (PI) cycle mediates many cellular events by controlling the metabolism of many lipid second messengers. Diacylglycerol kinase ϵ (DGKϵ) has an important role in this cycle. DGKϵ is the only DGK isoform to show inhibition by its product phosphatidic acid (PA) as well as substrate specificity for sn-2 arachidonoyl-diacylglycerol (DAG). Here, we show that this inhibition and substrate specificity are both determined by selectivity for a combination of the sn-1 and sn-2 acyl chains of PA or DAG, respectively, preferring the most prevalent acyl chain composition of lipids involved specifically in the PI cycle, 1-stearoyl-2-arachidonoyl. Although the difference in rate for closely related lipid species is small, there is a significant enrichment of 1-stearoyl-2-arachidonoyl PI because of the cyclical nature of PI turnover. We also show that the inhibition of DGKϵ by PA is competitive and that the deletion of the hydrophobic segment and cationic cluster of DGKϵ does not affect its selectivity for the acyl chains of PA or DAG. Thus, this active site not only recognizes the lipid headgroup but also a combination of the two acyl chains in PA or DAG. We propose a mechanism of DGKϵ regulation where its dual acyl chain selectivity is used to negatively regulate its enzymatic activity in a manner that ensures DGKϵ remains committed to the PI turnover cycle. This novel mechanism of enzyme regulation within a signaling pathway could serve as a template for the regulation of enzymes in other pathways in the cell. The phosphatidylinositol (PI) cycle mediates many cellular events by controlling the metabolism of many lipid second messengers. Diacylglycerol kinase ϵ (DGKϵ) has an important role in this cycle. DGKϵ is the only DGK isoform to show inhibition by its product phosphatidic acid (PA) as well as substrate specificity for sn-2 arachidonoyl-diacylglycerol (DAG). Here, we show that this inhibition and substrate specificity are both determined by selectivity for a combination of the sn-1 and sn-2 acyl chains of PA or DAG, respectively, preferring the most prevalent acyl chain composition of lipids involved specifically in the PI cycle, 1-stearoyl-2-arachidonoyl. Although the difference in rate for closely related lipid species is small, there is a significant enrichment of 1-stearoyl-2-arachidonoyl PI because of the cyclical nature of PI turnover. We also show that the inhibition of DGKϵ by PA is competitive and that the deletion of the hydrophobic segment and cationic cluster of DGKϵ does not affect its selectivity for the acyl chains of PA or DAG. Thus, this active site not only recognizes the lipid headgroup but also a combination of the two acyl chains in PA or DAG. We propose a mechanism of DGKϵ regulation where its dual acyl chain selectivity is used to negatively regulate its enzymatic activity in a manner that ensures DGKϵ remains committed to the PI turnover cycle. This novel mechanism of enzyme regulation within a signaling pathway could serve as a template for the regulation of enzymes in other pathways in the cell.

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)0.001
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 score0.013
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Citations68
Published2009
Admission routes1
Has abstractyes

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