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<i>RIPK1</i> Expression Associates With Inflammation in Early Atherosclerosis in Humans and Can Be Therapeutically Silenced to Reduce NF-κB Activation and Atherogenesis in Mice

2020· article· en· W3106805418 on OpenAlexaff
Denuja Karunakaran, My-Anh Nguyen, Michèle Geoffrion, Dianne Vreeken, Zachary Lister, Henry S. Cheng, Nicola Otte, Patricia Essebier, Hailey Wyatt, Joshua W. Kandiah, Richard G. Jung, Francis J. Alenghat, Ana Mompeón, Richard Lee, Calvin Pan, Emma Gordon, Adil Rasheed, Aldons J. Lusis, Peter Liu, Ljubica Matic, Ulf Hedin, Jason E. Fish, Liang Guo, Frank D. Kolodgie, Renu Virmani, Janine M. van Gils, Katey J. Rayner

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsUniversity Health NetworkUniversity of Ottawa
FundersNational Heart, Lung, and Blood Institute
KeywordsNecroptosisRIPK1InflammationCancer researchCell biologyProinflammatory cytokineKinaseProtein kinase ATumor necrosis factor alphaMedicineApoptosisProgrammed cell deathImmunologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Background: Chronic activation of the innate immune system drives inflammation and contributes directly to atherosclerosis. We previously showed that macrophages in the atherogenic plaque undergo RIPK3 (receptor-interacting serine/threonine-protein kinase 3)-MLKL (mixed lineage kinase domain-like protein)–dependent programmed necroptosis in response to sterile ligands such as oxidized low-density lipoprotein and damage-associated molecular patterns and that necroptosis is active in advanced atherosclerotic plaques. Upstream of the RIPK3-MLKL necroptotic machinery lies RIPK1 (receptor-interacting serine/threonine-protein kinase 1), which acts as a master switch that controls whether the cell undergoes NF-κB (nuclear factor κ-light-chain-enhancer of activated B cells)–dependent inflammation, caspase-dependent apoptosis, or necroptosis in response to extracellular stimuli. We therefore set out to investigate the role of RIPK1 in the development of atherosclerosis, which is driven largely by NF-κB–dependent inflammation at early stages. We hypothesize that, unlike RIPK3 and MLKL, RIPK1 primarily drives NF-κB–dependent inflammation in early atherogenic lesions, and knocking down RIPK1 will reduce inflammatory cell activation and protect against the progression of atherosclerosis. Methods: We examined expression of RIPK1 protein and mRNA in both human and mouse atherosclerotic lesions, and used loss-of-function approaches in vitro in macrophages and endothelial cells to measure inflammatory responses. We administered weekly injections of RIPK1 antisense oligonucleotides to Apoe −/− mice fed a cholesterol-rich (Western) diet for 8 weeks. Results: We find that RIPK1 expression is abundant in early-stage atherosclerotic lesions in both humans and mice. Treatment with RIPK1 antisense oligonucleotides led to a reduction in aortic sinus and en face lesion areas (47.2% or 58.8% decrease relative to control, P <0.01) and plasma inflammatory cytokines (IL-1α [interleukin 1α], IL-17A [interleukin 17A], P <0.05) in comparison with controls. RIPK1 knockdown in macrophages decreased inflammatory genes (NF-κB, TNFα [tumor necrosis factor α], IL-1α) and in vivo lipopolysaccharide- and atherogenic diet–induced NF-κB activation. In endothelial cells, knockdown of RIPK1 prevented NF-κB translocation to the nucleus in response to TNFα, where accordingly there was a reduction in gene expression of IL1B , E-selectin , and monocyte attachment. Conclusions: We identify RIPK1 as a central driver of inflammation in atherosclerosis by its ability to activate the NF-κB pathway and promote inflammatory cytokine release. Given the high levels of RIPK1 expression in human atherosclerotic lesions, our study suggests RIPK1 as a future therapeutic target to reduce residual inflammation in patients at high risk of coronary artery disease.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.229
Teacher spread0.212 · 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

Citations230
Published2020
Admission routes1
Has abstractyes

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