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Record W7132871604

Mechanisms of LDL Transcytosis during Basal and Inflammatory States

2024· dissertation· W7132871604 on OpenAlexafffund
Erika Lynn Jang

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicCaveolin-1 and cellular processes
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsTranscytosisScavenger receptorEndotheliumParacellular transportLDL receptorInflammationRegulatorReceptor
DOInot available

Abstract

fetched live from OpenAlex

Coronary artery disease is a leading cause of mortality worldwide. There is abundant literature concerning the late stages of its underlying cause, atherosclerosis, which is initiated by deposition of low-density lipoproteins (LDL) under the endothelium of arteries to form plaques. This process also involves the inflammatory system at most stages, from recruitment of leukocytes to expression of pro-inflammatory cytokines, such that it is now recognized that atherosclerosis is a chronic inflammatory disease. However, the route of LDL passage across the endothelium has historically been overlooked. It was previously believed that LDL crossed by paracellular transport via gaps between endothelial cells. Conversely, newer research supports the notion that LDL crosses through individual endothelial cells by an active process termed transcytosis, and that LDL transcytosis contributes to atherogenesis and is stimulated by various pro-atherogenic factors. The process initiates with uptake of LDL via receptors scavenger receptor BI (SR-BI) and activin receptor-like kinase 1 (ALK1) in caveolae. The canonical LDL receptor (LDLR) is not required. Nevertheless, the mechanisms of LDL transcytosis under basal and inflammatory states are still largely unknown. Here, I show that inflammation by IL-1β increased LDL transcytosis in vitro and in vivo through LDLR, the GTPase Rab27a, and the Rab27a effector, JFC1. This has implications for atherosclerosis since LDLR is not required for basal LDL transcytosis and Rab27a is a novel regulator of LDL transcytosis. I also show that novel mediators of LDL transcytosis can be identified by performing mass spectrometry on isolated membrane microdomains containing caveolin-1, the scaffolding protein in caveolae. Using this method, myosin heavy chain 9 (MYH9) was identified. MYH9 was found to mediate exocytosis of LDL during transcytosis in vitro. In vivo, endothelial MYH9 was involved in accumulation of LDL within the aortic endothelium of mice and also contributed to atherogenesis without affecting plasma lipid levels. MYH9 also has implications in human disease – public human RNA datasets showed greater MYH9 RNA expression in atherosclerotic tissues compared to controls. Furthermore, inflammation (TNFα)-stimulated transcytosis of LDL required MYH9. In summary, we have characterized novel pathways for basal and inflammation-induced LDL transcytosis. These findings have important implications for atherogenesis.

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)0.000
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.255
Teacher spread0.250 · 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
Published2024
Admission routes2
Has abstractyes

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