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Record W2894931982

Development of an LC-MS/MS method for the analysis of Saccharomyces cerevisiae lipidome to determine the mechanism through which plant extract 21 delays aging by remodeling lipid composition

2017· dissertation· en· W2894931982 on OpenAlexfundno aff
Karamat Mohammad

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2017
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsnot available
FundersConcordia University
KeywordsSaccharomyces cerevisiaeLipidomeLipidomicsEukaryoteBiologyBiochemistryComputational biologyCell biologyChemistryGenomeYeastGene
DOInot available

Abstract

fetched live from OpenAlex

Aging is a multifactorial process that occurs in all biological organisms. The biological aspects of aging are evolutionary conserved across eukaryotes. Saccharomyces cerevisiae being a unicellular eukaryote has been used as a model organism to study aging. This unicellular eukaryote has a short lifespan, a fully sequenced genome and it is easy to manipulate genetically to make changes in metabolic pathways. Hence, it has been historically used to identify genes, metabolic pathways and chemical compounds that have influence on aging process. An extract from white willow bark (which is called PE21) was discovered in Dr. Vladimir Titorenko’s lab as the plant extract capable of extending the chronological lifespan (CLS) of Saccharomyces cerevisiae. Previous studies in the lab have shown that PE21 considerably alters the lipid composition of Saccharomyces cerevisiae. Lipids play crucial roles in many important pathways related to cellular signaling network, energy storage, membrane trafficking, membrane dynamics and apoptosis. Lipidomics is a relatively new research field that aims at characterizing lipid profiles. The characterization of lipids is a difficult process because of their diverse chemical and physical properties and due to lack of sensitive tools to study them. Only with the advent of mass spectrometry it became possible to characterize individual lipid species of various lipid classes. Herein, I have developed an optimized LC-MS/MS method to successfully identify and quantify many individual species of 10 different lipid classes of Saccharomyces cerevisiae. The developed method was used to analyze the effects of PE21 on the lipid composition of Saccharomyces cerevisiae. Our findings support previous studies, which stipulate that PE21 alters the lipid composition of yeast cells. PE21 markedly decreases the concentration of free fatty acids, thus it has been hypothesized that PE21 increases the CLS of yeast cells by decreasing the rate of liponecrosis known to be induced by free fatty acids. To further test this hypothesis, four single gene-deletion mutant strains differently affected in the metabolism of free fatty acids have been studied. More specifically, the lipid compositions and viabilities of such mutant strains were analyzed in yeast cultured in the presence or absence of PE21, and the results were compared to the results for wild-type strain.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.038
GPT teacher head0.327
Teacher spread0.290 · 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
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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