Atorvastatin downregulates co-inhibitory receptor expression by targeting Ras-activated mTOR signalling
Bibliographic record
Abstract
// Isobel Okoye 1 , Afshin Namdar 1 , Lai Xu 1 , Nicole Crux 1, 2 and Shokrollah Elahi 1, 2 1 Department of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, T6G 2E1 Canada 2 Department of Medical Microbiology and Immunology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, T6G 2E1 Canada Correspondence to: Shokrollah Elahi, email: elahi@ualberta.ca Keywords: inhibitory receptors; atorvastatin; immune checkpoints Received: June 30, 2017 Accepted: August 16, 2017 Published: September 18, 2017 ABSTRACT Regulation of T cell function in the steady state is mediated by co-inhibitory receptors or immune checkpoints such as PD-1, CTLA-4, TIM-3 and LAG-3. Persistent antigen stimulation, during chronic viral infections and cancer, results in sustained expression of multiple co-inhibitory receptors and subsequently poor effector T cell function. Immune checkpoint blockade using monoclonal antibodies against PD-1, PDL-1 and CTLA-4 has been implemented as an immunotherapy strategy- resulting in restoration of T cell function and reduction of viral load or tumour growth. Immunomodulatory roles of commonly used cholesterol-lowering medications, atorvastatin and other statins, are widely documented. We have previously shown that atorvastatin can inhibit HIV-1 infection and replication. Here, for the very first time we discovered that atorvastatin also regulates activated T cell function by mediating downregulation of multiple co-inhibitory receptors, which corresponded with increased IL-2 production by stimulated T cells. In addition, we found that atorvastatin treatment reduces expression of mTOR and downstream T cell effector genes. We demonstrate a novel mechanism showing that atorvastatin inhibition of Ras-activated MAPK and PI3K-Akt pathways, and subsequent mTOR signalling promotes gross downregulation of co-inhibitory receptors. Thus, our results suggest that statins may hold particular promise in reinvigorating T cell function in chronic conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".