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Plasmalogen Precursors Reverse Lipid Changes in a Barth Syndrome Cell Model

2019· article· en· W3176220043 on OpenAlexaffabout
José Carlos Bozelli, Junior Junior, Richard M. Epand

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcMaster University
FundersBarth Syndrome Foundation
KeywordsPlasmalogenCardiolipinLymphoblastPhospholipidChemistryBiochemistryCell cultureEndocrinologyBiologyInternal medicineGeneticsMedicineMembrane

Abstract

fetched live from OpenAlex

Barth syndrome is rare X chromosome‐linked genetic disorder that is caused by mutations in tafazzin, a phospholipid‐lysophospholipid transacylase responsible for the last step of cardiolipin (CL) biosynthesis in the mitochondria. The lipid alterations in Barth syndrome include loss in total CL content, an increase in the concentration of monolysocardiolipin (MLCL) and a greater heterogeneity of the acyl chains of CL. These changes are so characteristic of Barth syndrome that they have become the basis of diagnostic tests. Recently, one of us has demonstrated that the major change in the lipid composition of organs that have been depleted of tafazzin is the loss of plasmalogens. In several disease states where loss of plasmalogens has been reported, the condition could be reversed by the administration of plasmalogen precursors. Hence, we hypothesized that administration of plasmalogen precursors could reverse the phenotype of Barth syndrome. Here, we present the high‐resolution 31 P‐NMR phospholipidomic study of the total lipid extract of a Barth syndrome cell model, i.e ., lymphoblasts derived from Barth syndrome patients compared with controls in absence and presence of feeding plasmalogen precursors. Lymphoblasts derived from Barth patients in comparison to controls are ideal for studies of the mechanism as it is a single cell type that can be grown in culture and have all the characteristics of cells from patients with this Syndrome. In agreement with previous results we observed a marked decrease in plasmalogen in Barth lymphoblasts compared to controls together with other lipid alterations reported in those cells. Furthermore, as expected, feeding the cells with plasmalogen precursors restored plasmalogen levels. However, to our surprise the feeding also markedly increased the level of CL. Moreover, we also could show that the CL increase is not a consequence of a decrease in CL hydrolysis by a phospholipase (degradation) as evinced by the lack of MLCL accumulation in cells fed with plasmalogen precursors. In summary, we are able to reverse, at least in part, the lipid changes associated with Barth syndrome by feeding our cell model with plasmalogen precursors. We wish to extend these findings to determine how these changes in lipid composition affect the functioning of their mitochondria. This may present a simple and rapid process to bring these new strategies to the clinic since the precursors we are using are non‐toxic and have been administered to humans for other purposes. Support or Funding Information An award from the Barth Syndrome Foundation, Inc. and the Barth Syndrome Foundation of Canada. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.002

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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