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Record W2946995393 · doi:10.1101/633867

Phosphatidylinositol Cycle Disruption is Central to Atypical Hemolytic-Uremic Syndrome Caused by Diacylglycerol Kinase Epsilon Deficiency

2019· preprint· en· W2946995393 on OpenAlexfundno aff
Vincent So, Jing Wu, Alexis Traynor‐Kaplan, Christopher H. Choy, Richard M. Epand, Roberto J. Botelho, Mathieu Lemaire

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersSickkids Research InstituteCanadian Institutes of Health ResearchHospital for Sick ChildrenCanadian Child Health Clinician Scientist Program
KeywordsDiacylglycerol kinasePhosphatidylinositolLipidomicsPhospholipase CAtypical hemolytic uremic syndromeDiglycerideChemistryBiochemistryBiologyKinaseEndocrinologyInternal medicineProtein kinase CMedicineSignal transductionImmunologyEnzyme

Abstract

fetched live from OpenAlex

ABSTRACT Background Loss-of-function mutations in diacylglycerol kinase epsilon ( DGKE ) cause a rare form of atypical hemolytic-uremic syndrome (aHUS) for which there is no treatment besides kidney transplantation. Highly expressed in kidney endothelial cells, DGKE is a lipid kinase that phosphorylates diacylglycerol (DAG) to phosphatic acid (PA). Specifically, DGKE’s preferred substrate is 38:4-DAG, that is DAG containing stearic acid (18:0) and arachidonic acid (20:4). DAG is produced when phosphatidylinositol 4,5- bis phosphate (PtdIns(4,5)P 2 ) is cleaved by phospholipase C (PLC). A better understanding of how DGKE deficiency impacts the endothelial lipid landscape is critical to developing a treatment for this condition. Methods We used orthogonal methods to compare the lipid levels in two novel models of DGKE deficiency to their respective controls: an immortalized human umbilical vein endothelial cell (iHUVEC) engineered with CRISPR/Cas9 and a blood outgrowth endothelial cell (BOEC) from an affected patient. Methods included mass spectrometry lipidomics, radiolabeling of phosphoinositides with [ 3 H] myo -inositol, and live-tracking of a transfected fluorescent PtdIns(4,5)P 2 biosensor. Results Unexpectedly, mass spectrometry lipidomics data revealed that high 38:4-DAG was not observed in the two DGKE -deficient models. Instead, a reduction in 38:4-PtdIns(4,5)P 2 was the major abnormality.These results were confirmed with the other two methods in DGKE -deficient iHUVEC. Conclusion Reduced 38:4-PtdIns(4,5)P 2 —but not increased 38:4-DAG—is likely to be key to the pro-thrombotic phenotype exhibited by patients with DGKE aHUS. TRANSLATIONAL STATEMENT Mutations in DGKE cause a severe renal thrombotic microangiopathy that affects young children and leads to end-stage renal disease before adulthood. DGKE preferentially phosphorylates diacylglycerol to its corresponding phosphatidic acid (PA), which is then used to synthesize PtdIns(4,5)P 2 via the phosphatidylinositol cycle. Understanding the disease pathophysiology is necessary to develop a treatment to prevent this outcome. This paper describes how we applied mass spectrometry lipidomics to two novel models of DGKE deficiency to investigate how this defect impacts the levels of diacylglycerol, PA and related phosphoinositides in endothelia. Unexpectedly, our data show that the critical abnormality caused by DGKE deficiency is not high diacylglycerol, but rather low PtdIns(4,5)P 2 . Restoring endothelial PtdIns(4,5)P 2 homeostasis may be the cornerstone to treat these patients.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.229
Teacher spread0.219 · 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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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