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Record W2343463190 · doi:10.1161/atvb.34.suppl_1.433

Abstract 433: Examination of Factors Affecting the Association of PCSK9 With Low-Density Lipoprotein Particles in Human Plasma

2014· article· en· W2343463190 on OpenAlexaff
Mia Golder, Samantha K. Sarkar, Tanja Kosenko, Ruth McPherson, Thomas A. Lagace

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2014
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsGolder Associates (Canada)University of Ottawa
Fundersnot available
KeywordsPCSK9LDL receptorChemistryLipoproteinLow-density lipoproteinIntermediate-density lipoproteinEndocrinologyKexinCholesterolInternal medicineVery low-density lipoproteinBiochemistryBiologyMedicine

Abstract

fetched live from OpenAlex

Rationale: We have previously shown that a substantial proportion of plasma PCSK9 (30-40%) is associated with LDL particles in normolipidemic subjects. Cellular assays show that LDL-bound PCSK9 is less active for binding to cell surface LDLRs. Therefore, the ability of circulating PCSK9 to direct LDLR degradation in liver could be regulated by plasma LDL levels. In addition, LDL subspecies may have altered abilities in binding PCSK9. We have mapped the LDL binding region to a short stretch of amino acids (aa 31-52) in the PCSK9 prodomain. It is unknown whether a common loss-of-function PCSK9 mutation (R46L) within this region affects LDL binding. Objective: To determine whether plasma PCSK9 distribution (LDL-bound versus unbound) is affected in hypercholesterolemic subjects. To further characterize the interaction of PCSK9 and LDL, we investigated the interaction of PCSK9 with two subspecies of LDL - large, buoyant LDL (LBLDL; d=1.019-1.044 g/ml) and small, dense LDL (SDLDL; d=1.044-1.063 g/ml). Additionally, we investigated the effect of the R46L PCSK9 mutation on the LDL binding affinity of PCSK9. Methods and Results: We used flotation ultracentrifugation in Optiprep density gradients to fractionate human plasma samples followed by immunoprecipitation and western blot to quantify PCSK9 distribution in LDL and non-LDL fractions. In a pilot study, the proportion of total plasma PCSK9 in the LDL fraction was increased from 38±5% to 57±3% (N=6) in hypercholesterolemic subjects (LDL>4.9mM, TG<2.3mM) versus normal controls (LDL<3 mM, TG<2.3mM). Saturation binding assays showed that SDLDL bound PCSK9 with lower affinity (Kd = 361.9 nM) than LDLDL (Kd = 263.9 nM). Competition binding assays determined that recombinant purified PCSK9-R46L secreted from HEK293 cells did not bind to isolated LDL with significantly altered affinity compared to wild-type PCSK9. Conclusion: Our preliminary results indicate that plasma PCSK9 distribution is altered in hypercholesterolemia, with an increased proportion of total PCSK9 bound to LDL particles. Our in vitro results suggest that circulating small, dense LDL may bind more poorly to PCSK9 than larger LDL subspecies.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.021
GPT teacher head0.257
Teacher spread0.237 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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