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Adaptation of Native GELFrEE for HDL Particle Size Subtype Separation and Differential Apolipoprotein Proteoform Quantification

2022· article· en· W4225319164 on OpenAlexaff
Nicholas DiStefano, Cameron Lloyd‐Jones, Henrique S. Seckler, Philip D. Compton, Allan D. Sniderman, Neil L. Kelleher, John Wilkins

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Institutes of HealthAmerican Heart Association
KeywordsApolipoprotein BChemistryProteomeCholesterolHigh-density lipoproteinParticle sizeReverse cholesterol transportLipoproteinChromatographyBiochemistry

Abstract

fetched live from OpenAlex

High‐density lipoproteins (HDL) are central to cholesterol transport, and higher concentration of HDL‐bound cholesterol (HDL‐C) is associated with lower coronary artery disease (CAD) risk. Recent reports refute a causal link between these variables, suggesting better understanding of HDL variation is necessary. HDL particles differ in size and size subtypes differ in proteome, lipidome, and functional characteristics. Apolipoprotein A‐I (ApoA‐I), a major determinant of HDL structure and biochemistry, is made up of 15 distinct proteoforms. ApoA‐I proteoforms correlate with HDL efflux, a measure of cholesterol transport efficiency, but it is unknown if ApoA‐I proteoforms vary with HDL particle size. Herein, we adapt a native separation methodology to inquire on the relationship between HDL size subtypes, their function, and apolipoprotein proteoform profiles. Pooled serum (20μl) from 30 individuals with high, medium and low HDL‐C levels, were loaded to CN‐GELFrEE, a native electrophoretic technique, to separate HDL particles by size. Mid‐resolution and high‐resolution modes were employed. Immunoassays were done to characterize ApoA‐I content and average particle size of the electrophoretic fractions. Proteoform quantification was performed on ApoA‐I‐containing fractions by top‐down LC‐MS. SIM scans were devised for higher sensitivity in ApoA‐I proteoform detection. Custom software matched, scored and quantified proteoform‐specific spectra, and calculated association to particle size. In both resolution modes, average fraction sizes were roughly linearly correlated to fraction collection time, and size‐range overlap between fractions was small. In mid‐resolution mode 3‐4 fractions contained ApoA‐I, while high‐resolution mode yielded 36 distinct HDL size fractions. Fractions roughly spanned the range between 5 to 11nm, similar to previously reported ranges for HDL particles. Proteoform quantification revealed significant variation of proteoform profiles in different size‐ranges of HDL. Fatty‐acylated ApoA‐I, a species previously correlated to higher cholesterol efflux, had higher relative abundance (~1.5x to 2x) in the pre‐beta‐1 (5‐7.1nm) and alpha‐3 (9‐11nm) size ranges of HDL, while glycated and oxidized proteoforms were significantly more abundant (~3x to 4x) in the medium size ranges (alpha‐4, alpha‐3 and alpha‐2). Interestingly, the truncated proteoform of ApoA‐I had no significant differences in abundance between different particle sizes. Our experimental data suggest that the profile of ApoA‐I proteoforms in HDL particles is size‐dependent and thus ApoA‐I proteoforms may be important markers or mediators of HDL size regulation. Notably, the differences in PTM prevalence from medium sizes to large and pre‐beta subtypes may be markers of the pathway of HDL maturation and/or different functions of each subtype.

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)0.003
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.031
GPT teacher head0.278
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 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
Published2022
Admission routes1
Has abstractyes

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