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Record W2963258710 · doi:10.1101/715565

GATA2 Expression by Intima-Infiltrating Macrophages Drives Early Atheroma Formation

2019· preprint· en· W2963258710 on OpenAlexaff
Charles Yin, Angela M. Vrieze, James Akingbasote, Emily N. Pawlak, Rajesh Abraham Jacob, Jonathan Hu, Neha Sharma, Jimmy D. Dikeakos, Lillian Barra, A. Dave Nagpal, Bryan Heit

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsGATA2Gene knockdownDownregulation and upregulationMacrophageAtheromaMacrophage polarizationTranscriptomeFoam cellCancer researchBiologyHaematopoiesisCell biologyRUNX1Transcription factorImmunologyGene expressionGeneMedicineIn vitroGeneticsInternal medicineStem cell

Abstract

fetched live from OpenAlex

Abstract Aberrant macrophage polarization is a major contributor to the onset and progression of atherosclerosis. Despite this, macrophage polarization during in early stages of human atherosclerotic disease is poorly understood. Using transcriptomic analysis of macrophages recovered from early-stage human atherosclerotic lesions, we have identified a unique gene expression profile dissimilar to that observed in later stages of disease that is characterized by upregulation of the hematopoietic transcription factor GATA2. GATA2 overexpression in vitro recapitulated defects observed in patient macrophages, including deficiencies in the uptake and processing of apoptotic cells, and in the catalysis of atherogenic protein modifications, with GATA2 knockdown abrogating these defects. Our data describe a previously unreported macrophage differentiation state present in early atheroma formation and identifies GATA2 as a driver of macrophage functional defects during the early stages of atherosclerosis in humans.

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 score0.005

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.211
Teacher spread0.202 · 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 routes1
Has abstractyes

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